Patent Publication Number: US-2013238778-A1

Title: Self-architecting/self-adaptive model

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation-in-part application of and claims the benefit of co-pending U.S. patent application Ser. No. 13/594,477 filed on Aug. 24, 2012 entitled “COHERENT PRESENTATION OF MULTIPLE REALITY AND INTERACTION MODELS” by Dan Reitan, having Attorney Docket No. REIN-001, and assigned to the assignee of the present application, which claims priority to and benefit of: U.S. provisional patent application Ser. No. 61/575,790, Attorney Docket Number REIN-001.PRO, entitled “AUGMENTING REALITY 3D STEROSCOPIC STEROPHONIC SOCIAL MEDIA PORTAL,” by Dan Reitan, filed Aug. 26, 2011, which is herein incorporated by reference in its entirety; claims priority to and benefit of U.S. provisional patent application Ser. No. 61/575,791, Attorney Docket Number REIN-002.PRO, entitled “ENABLING AUTOMATION OF BEHAVIORAL MODELING,” by Dan Reitan, filed Aug. 26, 2011, which is herein incorporated by reference in its entirety; claims priority to and benefit of U.S. provisional patent application Ser. No. 61/575,789, Attorney Docket Number REIN-003.PRO, entitled “BEHAVIORAL MODELING,” by Dan Reitan, filed Aug. 26, 2011, which is herein incorporated by reference in its entirety. 
     This application is related to co-pending U.S. patent application Ser. No. ______ filed on ______ entitled ______, by Dan Reitan, having Attorney Docket No. ______, and assigned to the assignee of the present application. 
    
    
     DESCRIPTION OF THE DRAWINGS 
       FIG. 1A  is a diagram of an example network for producing and delivering 360 degree immersive ultra high resolution media for smart devices in accordance with one embodiment. 
       FIGS. 1B ,  1 C, and  1 D show example lens/microphone arrays used in accordance with various embodiments. 
       FIG. 1E  shows an example virtual viewport selecting a respective portion of content in accordance with various embodiments. 
       FIG. 1F  shows an example virtual viewport selecting a respective portion of content in accordance with various embodiments. 
       FIG. 1G  is a block diagram showing components of a rendering component in accordance with at least one embodiment. 
       FIG. 1H  is a flowchart of an example method for delivering immersive media in accordance with an embodiment. 
       FIG. 2A  is a diagram of an example system for developing and running augmented reality based transmedia content in accordance with one embodiment. 
       FIG. 2B  is a flowchart of an example method for developing augmented reality based transmedia content in accordance with an embodiment. 
       FIG. 3A  is an example diagram upon which embodiments of the present invention may be implemented, according to an embodiment. 
       FIG. 3B  is an example diagram of a viewport, according to an embodiment. 
       FIG. 3C  is an example flowchart of a method communicating with at least one using augmented reality, according to an embodiment. 
       FIG. 3D  is an example flowchart of a method implemented by a system for creating an augmented reality environment, according to an embodiment. 
       FIG. 4A  is a block diagram of a system for providing recursive modularity in adaptive network processing, according to an embodiment. 
       FIG. 4B  is an example flowchart of a method for providing recursive modularity in adaptive network processing, according to an embodiment. 
       FIG. 5A  is an example system for navigating concurrently and from point-to-point through multiple reality models, according to an embodiment. 
       FIG. 5B  is an example flowchart of a method for navigating concurrently and from point-to-point through multiple reality models, according to an embodiment. 
       FIG. 5C  is an example device for enhancing a sensory perception in a field of view of a real-time source within a display screen through augmented reality, according to an embodiment. 
       FIG. 5D  is an example flowchart of a method for enhancing a sensory perception in a field of view of a real-time source within a display screen through augmented reality, according to an embodiment. 
       FIG. 6A  is an example system for interpreting a meaning of a dialogue between a plurality of agents, wherein the plurality of agents includes at least one of one or more automatons and one or more humans, according to an embodiment. 
       FIG. 6B  is an example flowchart of a method for interpreting a meaning of a dialogue between a plurality of agents, wherein the plurality of agents includes at least one of one or more automatons and one or more humans, according to an embodiment. 
       FIG. 7A  is an example system for modeling group dynamics using augmented reality simulation to facilitate multimedia communications and service to a distributed group of users, according to an embodiment. 
       FIGS. 7B and 7C  are an example flowchart of a method for modeling group dynamics using augmented reality simulation to facilitate multimedia communications and service to a distributed group of users, according to an embodiment. 
       FIG. 8  is a diagram of an example computer system used for performing a method for various embodiments disclosed herein. 
       FIG. 9A  is a block diagram of an aggregated social media delivery system, according to an embodiment. 
       FIG. 9B  is an illustration of the delivery of aggregated social media, according to one embodiment. 
       FIG. 9C  is a flowchart of a method for delivering aggregated social media in a user accessible format, according to one embodiment. 
       FIG. 9D  is a block diagram of an aggregated social media formatter, according to one embodiment. 
       FIG. 9E  is a flowchart of a method for formatting random social media data snippets into a structured media presentation, according to one embodiment. 
       FIG. 10A  is a block diagram of a multiple reality mapping correlator, according to one embodiment. 
       FIG. 10B  is a flowchart of a method for mapping correlation between multiple realities, according to one embodiment. 
       FIG. 11A  is an example diagram upon which embodiments of the present invention may be implemented, according to an embodiment. 
       FIG. 11B  is an example flowchart of a method for providing content to a user at an interactive device with a display, in accordance with an embodiment. 
       FIG. 11C  is an example flowchart of a method implemented by a system for performing a method for providing content to a user at an interactive device with a display, in accordance with an embodiment. 
       FIG. 12A  is a block diagram of a media metadata extractor, in accordance with an embodiment. 
       FIG. 12B  is a flowchart of a method for pre-producing media having extractable metadata, in accordance with an embodiment. 
       FIG. 12C  is a flowchart of a method for producing media having extractable metadata, in accordance with an embodiment. 
       FIG. 12D  is a flowchart of a method for post-production extraction of media metadata, in accordance with an embodiment. 
       FIG. 13A  is an example diagram upon which embodiments of the present invention may be implemented, in accordance with an embodiment. 
       FIG. 13B  is an example flowchart of a method for virtually placing an object in a piece of content, in accordance with an embodiment. 
       FIG. 13C  is an example flowchart of a method implemented by a system for performing a method for virtually placing an object in a piece of original content, in accordance with an embodiment. 
       FIG. 14  is an example flowchart of a method for regeneration in a network, in accordance with an embodiment. 
       FIG. 15  is an example flowchart of a method for regeneration in a network, in accordance with an embodiment. 
       FIG. 16  is an example flowchart of a method for regeneration in a network, in accordance with an embodiment. 
     The drawings referred to in this description should not be understood as being drawn to scale unless specifically noted. 
    
    
     DESCRIPTION OF EMBODIMENTS 
     Reference will now be made in detail to various embodiments, examples of which are illustrated in the accompanying drawings. While the subject matter will be described in conjunction with these embodiments, it will be understood that they are not intended to limit the subject matter to these embodiments. On the contrary, the subject matter described herein is intended to cover alternatives, modifications and equivalents, which may be included within the spirit and scope. Furthermore, in the following description, numerous specific details are set forth in order to provide a thorough understanding of the subject matter. However, some embodiments may be practiced without these specific details. In other instances, well-known structures and components have not been described in detail as not to unnecessarily obscure aspects of the subject matter. 
     Overview of Discussion 
     Herein, various embodiments of a system and method for coherent presentation of multiple reality and interaction models are described. The description begins with a general discussion of embodiments. This general discussion provides a framework of understanding for more particularized descriptions of features and concepts of operation associated with one or more of the described embodiments that follows. 
     Embodiments provide an enterprise system for enabling user interaction with various media modes, wherein the media mode may be displayed on different devices. Different media modes may present varying mixtures of different versions of reality (reality models) that may be discretely blended together and displayed on different devices to a device user such that the user may interact with the elements within the device&#39;s display, according to one or more interaction models. Some examples of reality models are: real-time image capture; geospatial models (as those used by locating tools and navigation equipment); produced television and movie content; produced video advertising; atmospheric and weather models; multi-sensor arrays; and virtual reality models. Some examples of interaction models are: passive viewing of video programming content (e.g., movies, television, documentaries); advertisements; programming applications (e.g., enterprise applications for businesses); interactive television; custom branded interactivity (aka “gamefied” advertising); games (e.g., augmented reality games); and computer applications (e.g., accounting application). 
     Essentially, embodiments correlate multiple versions of reality such that the multiple versions of reality may be displayed to the user as a single three-dimensional version of reality within which the user may interact. Thus, different forms of reality models may be combined into a single common view, and then displayed on a plurality of different devices and enable user interaction with the elements within the display. 
     In this manner, for example, advertisements may be enveloped into games, of which the user may interact with both the advertisements and the game elements. In another example, applications may be enveloped into a video format, of which the user may interact with both the applications and other elements displayed in the video. 
     Thus, in one embodiment, the present technology allows television and movie viewers to step into the action, moving freely about landscapes, choosing which aspects of recorded events to view based on viewer&#39;s interest and preferences, while interacting with characters and objects within the content, including the advertisers&#39; products. Viewers can explore the Grand Canyon while watching a travel documentary, engage in a battle reenactment during a movie about the American Civil War, or walk down the yellow brick road with the scarecrow and the tin man. 
     Other embodiments enable a family that is travelling together with friends in Rome to host an augmented reality party at the Coliseum, sharing their discovery and wonder in real time with friends and family on the other side of the world. Also, the event may be recorded in such a way that even participants who were unable to attend remotely can later attend and interact with real-time attendees who have already left through their avatar proxies. A final in depth recording can deliver a rich multimedia vacation record to the tourists, while selected highlights are automatically spliced into the nightly news feed as broadcast to extended friends, family and other viewers of interest, airing with other news about other friends, family, colleagues and persons and organizations of interest, as well as the usual national, international, and local news stories. 
     While enabling user interaction and in determining a response to be provided to a user, embodiments analyze workflow characteristics (e.g., how groups of individuals interact and the rules that guide this interaction), data within a data repository, and the user&#39;s behavior within and/or external to a virtual reality world (e.g., within the reality of television program, a movie, or a game). For example, with regard to user behavior within a virtual reality world, the user may direct one or more agents to perform various tasks or answer questions, wherein the agents serve or even represent the user within the virtual world, and by interface extension, the physical world. With regard to user behavior external to a virtual reality world, embodiments may analyze the user&#39;s dialogue and behavior (e.g., gestures) external of the device to which embodiments are attached. 
     Overall, embodiments utilize sophisticated systems and methods of analyzing a user&#39;s real-time and/or virtual behavior (e.g., an automaton behaving within a media mode) in order to facilitate satisfactory user interaction within that particular media mode. 
     These sophisticated systems and methods involve the mapping of the workflow characteristics, the data repository, and the user&#39;s behavior to each other and to a set of event triggers. Once mapped, an event (e.g., response to the user) is triggered to occur. Workflow refers at least to two different levels of interactions: 1) high level: the determination of a group of people&#39;s interaction (including data flow between them); and 2) low level: the determination of the logic that guides the standard behaviors of the group of people. The data repository and an engine attached thereto receive unstructured data from a variety of sources and the engine arranges the unstructured data into an intelligent format for use within and by embodiments. The user behavior includes the content and method of the user&#39;s communication (e.g., verbal, audio, visual, simulated physical interaction) with others, and social interaction between groups of people. 
     Additionally, while arranging the unstructured data into the intelligent format, the basis for such arrangement may change due to an adaptive learning component of embodiments. Embodiments learn from observing the user&#39;s behavior, and change its analysis of future behavior based on, in part, observed past behavior. While embodiments have a preprogrammed set of rules and guidelines for assisting in arriving at a response acceptable to the user, upon observing the user&#39;s behavior, these rules and guidelines change and evolve along with a user&#39;s involvement with embodiments as well as with the environment. Ultimately, embodiments, over time, are able to self-customize to a user&#39;s preferences based on observations of the user&#39;s behavior and the user&#39;s environment. 
     For example, in yet another embodiment, a pair of glasses containing aspects of embodiments described herein enable a user, Jack, to look through the glasses and at a building across the street, and see images beyond that building. Thus, embodiments have the effect of allowing Jack to look through solid objects. Additionally, embodiments answer any of Jack&#39;s questions regarding what he is viewing through the glasses, and display to Jack directions to various destinations. In this example, suppose Jack only took streets to his requested destinations that are paved. Embodiments will follow the user&#39;s requests and movements and ultimately tailor its directions and answers, without any further instructions from the user. In this case, and without any prompting from Jack, the view through the glasses begins displaying only paved routes to Jack&#39;s requested destination. 
     Further, embodiments allow for a very short compilation time period for the development of applications (e.g., games) that enable the user to interact with a single virtual reality model that was derived from multiple reality models. This is due to the highly sophisticated code structures and data libraries that are provided by embodiments and that allow for the rich anticipation of needs during development. 
     Various embodiments for developing and displaying multiple reality models as a single reality model, as well as providing capabilities for interaction with the single reality model are described herein in the following fourteen sections: (1) System For Producing And Delivering 360 Degree Immersive Ultra High Resolution Media For Smart Devices; (2) Rapid Application Development Platform For Augmented Reality Based Transmedia; (3) Communication Using Augmented Reality; (4) Self-Architecting Adaptive Network Solution; (5) Navigation Through Augmented Reality; (6) Enhanced Sensory Perception; (7) Dialogue And Behavior Modeling; (8) Customizable Group—Centric Transmedia Communications; and Customizable Augmented Reality Based Social Transmedia Combat Simulator; (9) Delivering Aggregated Social Media; (10) Aggregated Social Media Formatter; (11) A Multiple Reality Mapping Correlator; (12) Interactive User Interface; (13) Media Metadata Extractor; and (14) Product Placement Paired With Interactive Advertising. 
     Further, within each of the preceding listed fourteen sections are described subsets of each embodiment, as well as further related concepts. 
     Section One: System for Producing and Delivering 360 Degree Immersive Ultra High Resolution Media for Smart Devices 
     Various embodiments are directed to the rendering and display of immersive, and optionally interactive, 3-dimensional environments for devices such as, but not limited to, smart TVs, smart phones, tablet computing devices, laptops, and desktop computers. In at least one embodiment, an orientation of a virtual viewport of a playback device is received by a rendering component. Based upon this orientation, a portion of content from an input media stream is selected. The portion of content is then mapped, by virtual projection, to a virtual display surface and output to a display of a playback device. In one or more embodiments, the virtual display surface is polygonal (e.g., concave, spherical, semi-spherical, etc.) and may comprise more than one polygonal surface. Alternatively, a planar virtual display surface may be used to which the selected portion of content is mapped prior to displaying the content. Video frames are streamed as successive still images to the destination virtual display surface based on the virtual viewport orientation, either to an internally generated texture mapped virtual surface in the case of a polygonal virtual display surface, or by re-mapping pixels from the video frames to the planar virtual display surface. In at least one embodiment, the rendering component is disposed upon the playback device itself. As a user changes the virtual viewport orientation, different portions of content are selected and mapped to the virtual display surface. The selected portions of content can include audio content as well as video content. 
       FIG. 1A  is a diagram of an example network for producing and delivering 360 degree immersive ultra high resolution media for smart devices in accordance with one embodiment. It is noted that the components and configuration shown in  FIG. 1A  are for the purposes of discussion only and that various other configurations are possible in accordance with various embodiments. In  FIG. 1A , a production space  101  is equipped with a lens/microphone array  102 . As will be discussed in greater detail below, lens/microphone array  102  is used to capture video and audio signals which can be used to recreate an immersive video and audio experience for a user. In various embodiments, this includes stereophonic and stereoscopic 3-D playback of media being streamed to a playback device. 
     In  FIG. 1A , lens/microphone array  102  captures a plurality of audio and video streams (e.g., media streams  108  and  09 ) which are time synchronized and sent as content  110  to a content provider  103 . In accordance with various embodiments, content provider  103  can be a television station, website, or other source which in turn provides content  110  to a playback device  104 . It is noted that content  110  comprises a plurality of respective video and audio media streams which are captured by separate components comprising lens/microphone array  102  as will be discussed in greater detail below. 
     In various embodiments, playback device  104  comprises a smart TV, smart phone, laptop computer, desktop computer, or tablet computer, although other media playback devices such as smart glasses, heads up displays, etc. can be used as well. In one embodiment, a rendering component  105  disposed upon playback device  104  creates a virtual display surface upon which is mapped content  110 . In response to determining an orientation of a virtual viewport of playback device  104 , a portion of the content  110  which has been mapped onto the virtual display surface is selected and sent to the display of playback device  104 . 
       FIGS. 1B ,  1 C, and  1 D show example lens/microphone arrays  102  used in accordance with various embodiments. In the embodiment of  FIG. 1B , lens/microphone array  102  comprises a plurality of microphones  107 A,  107 B,  107 C, and  107 D and a plurality of lens arrays  106 A and  106  B. In various embodiments, lens arrays  106 A and  106 B are configured to capture all events which occur in production space  101 . Lens arrays  106 A and  106 B may comprise 180 degree fish-eye lenses, multiple lens arrays, steerable lenses, etc. Each of lens arrays  106 A and  106 B is coupled with a respective high definition (HD) video cameras. In the embodiment shown in  FIG. 1B , the content  110  output from lens/microphone array  102  comprises four audio media streams from microphones  107 A,  107 B,  107 C, and  107 D and two video media streams from lens arrays  106 A and  106  B. In at least one embodiment, the lens/microphone array  102  shown in  FIG. 1B  is used to capture medium resolution monoscopic video within production space  101 . It is further noted that, while the field of view of lens arrays  106 A and  106 B do not overlap, they still are sufficient to monitor the entirety of production space  101 . For example, if lens arrays  106 A and  106 B comprise 180 degree fish-eye lenses, each respective lens array is sufficient to monitor one half of production space  101 . 
     In the embodiment of  FIG. 1C , lens/microphone array  102  is generally configured as described above with reference to  FIG. 1B  with the addition of four additional lens arrays  106 C,  106 D,  106 E, and another lens array (not shown) which underlies lens array  106 E on an additional arm. Furthermore, lens/microphone array  102  comprises two additional microphones (not shown) which underlie lens array  106 E, one on the arm which supports lens array  106 E and one on an additional arm opposite to the arm supporting lens array  106 E. It is understood that lens arrays  106 C,  106 D,  106 E and the lens array underlying lens array  106 E are also configured as described above with reference to lens arrays  106 A and  106 B of  FIG. 1B  as being coupled with respective HD video cameras. In an embodiment, the lens/microphone array  102  shown in  FIG. 1C  is used to capture high resolution monoscopic video within production space  101 . In the embodiment of  FIG. 1C , the content  110  output from lens/microphone array  102  comprises six separate audio media streams and six separate video media streams. It is further noted that in the embodiment of  FIG. 1C , the field of view of lens arrays  106 A,  106 B,  106 C,  106 D, and  106 E (as well as the lens array underlying lens array  1 E) overlap to some degree. For example, if the lens arrays shown in  FIG. 1C  each comprise 180 degree fish-eye lenses, an object at a forty five degree angle to the axis of orientation of both of lens arrays  106 A and  106 D will be within the field of view of both lens arrays. 
     In the embodiment of  FIG. 1D , lens/microphone array  102  is configured to capture high resolution stereoscopic video with production space  101 . In the embodiment of  FIG. 1D , lens/microphone array  102  comprises lens arrays  106 A,  106 B,  106 C,  106 D,  106 E,  106 F,  106 G,  106 H,  106 I,  106 J, and  106 L, as additional lens arrays (not shown) disposed respectively below lens arrays  106 B,  106 D,  106 I, and  106 K. Additionally, lens/microphone array  102  comprises four microphones  107 A,  107 B,  107 , and  107 D. It is understood that lens arrays  106 A,  106 B,  106 C,  106 D,  106 E,  106 F,  106 G,  106 H,  106 I,  106 J, and  106 L, and the lens array underlying lens array  106 B,  106 D,  106 I, and  106 K, are configured as described above with reference to lens arrays  106 A and  106 B of  FIG. 1B  as being coupled with respective HD video cameras. In the embodiment of  FIG. 1D , the content  110  output from lens/microphone array  102  comprises sixteen video media streams and four audio media streams. As described above with reference to  FIG. 1C , it is noted that the field of view of the lens arrays of  FIG. 1D  overlap to some degree and that multiple lens arrays (e.g., 2 or more) are able to capture an image of any portion of production space  101 . 
     For the purpose of the following discussion, it will be assumed that the lens arrays used by lens/microphone array  102  comprise 180 degree fish-eye lenses although, as described above, various embodiments are not limited to this configuration alone. Due to their design, the lens arrays used by lens/microphone array  102  will record a time synchronized circular image that represents the entire optical input of the lens array which captured it. These circular images are sent as individual video media streams of output  110 . The optical transfer function describes how big of a part of space the circular image circumscribes and how it maps to a surface. 
     In accordance with various embodiments, rendering component  105  creates a virtual display surface that un-maps according to the same dimensions as the transfer function of the lens array(s) used to capture images within production space  101 . In at least one embodiment, the virtual display surface comprises a polygonal virtual projection surface (e.g., concave, semi-spherical, spherical, a complex polyhedron, etc.) onto which the images captured by the lens arrays of lens/microphone array  102  are mapped. For the purpose of the present discussion, it is intended that the term “mapped” also indicates that the optical transfer function is reversed in mapping the images captured by the lens arrays of lens/microphone array  102  to the virtual display surface created by rendering component  105 . Thus, when the images from a selected video media stream of output  110  are mapped to virtual display surfaces  134  and  135 , they represent a virtual display dome from which a portion of the content of that virtual display dome is selected and displayed on playback device  104 . It is noted that embodiments are not limited to media captured by a lens/microphone array  102  disposed in a production space  101  alone and that the mapping to virtual display surfaces can also be applied to “live” media such as may be captured by playback device  104  itself, movies, television, games, enterprise software, etc. Furthermore, the media can be streamed in real-time from content provider  103  to playback device  104  (e.g., TV broadcasts or accessed via the Internet or other network), or be stored media such as on a DVD or stored on an electronic data storage device such as a USB drive. Furthermore, rendering component  105  can be disposed upon playback device  105  itself, or operated by another party, such as content provider  104 , which is communicatively coupled with playback device  104 . 
     As an example,  FIG. 1E  shows an example virtual viewport selecting a respective portion of content in accordance with various embodiments. In the embodiment of  FIG. 1E , the images captured by the lens arrays shown in  FIG. 1B  are respectively mapped to virtual display surfaces by rendering component  105 . For example, the images captured by lens array  106 A are mapped to virtual display surface  134  by rendering component  105 . Similarly, the images captured by lens array  106 B are mapped to virtual display surface  135  by rendering component  105 . It is noted that while virtual display surfaces  134  and  135  are shown as hemispherical, in various embodiments, virtual display surfaces  134  and  135  can be other polygonal shapes such as, but not limited to, ellipsoid, semi-ellipsoid, parabolic, spherical, semi-spherical, concave, etc. According to various embodiments, complex polyhedron virtual display surfaces facilitate mapping images to an apparent infinity. In one embodiment, a plurality of polygonally shaped virtual display surfaces can be joined as well. In the example of  FIG. 1E , because the images captured by lens arrays  106 A and  106 B are being mapped to spherical virtual display surfaces, the optical transfer function is simplified. In cases in which the images captured by lens arrays  106 A and  106 B do not correspond as closely with the virtual display surfaces to which they are mapped, various optical transfer functions may be used such as, but not limited to, f*theta, or 2*f*sin(theta/2). It is noted that other optical transfer functions can be used in various embodiments if, for example, the images captured by lens arrays  106 A and  106 B are being mapped to virtual display surfaces having other shapes. In  FIG. 1E , boundary  133  represents the limit of the field of view of lens arrays  106 A and  106 B. As stated above, lens arrays  106 A and  106 B are 180 degree fish-eye lenses. Thus, by mounting lens arrays  106 A and  106 B back-to-back, a full spherical representation of production space  101  can be mapped to virtual display surfaces  134  and  135 . While the discussion above is directed to the lens/microphone array  102  shown in  FIG. 1B , it is noted that the lens/microphone arrays  102  shown in  FIGS. 1C and 1D , as well as other lens/microphone arrays not shown, can also be used and their content displayed in a similar manner on virtual display surfaces  134  and  135 . 
     Currently, many playback devices  104  such as smart TVs, tablet computers, etc., are configured with Graphics Processing Units (GPUs) which are capable of generating virtual display surfaces  134  and  135  in response to instructions from rendering component  105 . In various embodiments, rendering component  105  is configured to determine characteristics of playback device  104  including, but not limited to, determining the type of device used in rendering images (e.g., a GPU, CPU, multiple CPUs, etc.) as well as the characteristics of the display device used to present images to a user. Rendering component  105  will then adjust the manner in which images are mapped to the virtual display surfaces, as well as how those rendered images are then to be displayed on playback device  104 . In a case in which playback device  104  comprises a GPU, rendering component  105  will generate instructions causing the GPU to generate polygonal virtual display surfaces (e.g.,  134  and  135  of  FIG. 1E ). In an instance in which playback device  104  uses a CPU to render images, rendering component  105  will generate instructions causing the CPU to generate flat, or planar, virtual display surfaces as will be discussed in greater detail below. 
     Returning to  FIG. 1E , because the transfer function of lens arrays  106 A and  106 B are roughly parabolic and the images captured are being mapped to roughly hemispheric virtual display surfaces, there is no necessity for an extensive modeling of the optical transfer function when mapping images to virtual display surfaces  134  and  135 . In this instance, a UV coordinate map can be used to map the images captured by lens arrays  106 A and  106 B to virtual display surfaces  134  and  135  respectively. Most GPUs in use today are optimized to perform this type of operation and, as a result, can map bumpmaps and texture maps to virtual objects which are displayed on virtual display surfaces  134  and  135 . In so doing, rendering component  105  maps the video media streams comprising content  110  onto virtual display surfaces  134  and  135 . As a result, a user of playback device  104  will be presented with an immersive 3-D environment capable of presenting depth in a highly realistic manner. 
     In  FIG. 1E ,  136  refers to an imaginary position of playback device  104  within a virtual display space  138  defined by virtual display surfaces  134  and  135 . In accordance with various embodiments, a user can direct the orientation of a virtual viewport  137  which controls which portion of the content  110  will be displayed on playback device  104 . It is noted that there are a variety of methods for a user to control the position, or orientation, of virtual viewport  137  in accordance with various embodiments. For example, a keyboard, joystick, touchpad, voice control, a virtual control panel, camera-based gesture recognition, etc. In at least one embodiment, geospatial information about playback device  104  itself can be used to direct the orientation of virtual viewport  137 . For example, many smart phones and tablet computers are configured with accelerometers, electronic compasses, magnetometers, and other components which facilitate determining movement of the device relative to the surface of the Earth and the local gravitational vector. Thus, as a user moves, or moves the device including rotation in the X, Y, and Z axes, the device detects these changes in its orientation. Additionally, many of these devices are configured with Global Navigation Satellite System (GNSS) receivers and are capable of determining their geographic position as well. In accordance with various embodiments, as a user moves, or moves playback device  104 , in space, this is used by rendering component  105  to determine the orientation of virtual viewport  137 . Additionally, a user can manually determine which method of controlling the orientation of virtual viewport will be used. For example, in a crowded environment such as in an airport or riding a bus, a user may not desire to move their phone around in order to control the orientation of virtual viewport  137 . Thus, the user can instead select to have rendering component  105  use some other method for controlling the orientation of virtual viewport  1037  such as using a virtual joystick or simply by touching the display device of playback device  104 . In accordance with at least one embodiment, the virtual controls can be displayed with the images shown on playback device  104 . In addition to determining the orientation of the virtual viewport, apparent movement of position  136  through the space bounded by virtual display surfaces  134  and  135  can be derived by rendering component  105  using the geospatial movement information provided by playback device  104 . 
     In accordance with one embodiment, rendering component  105  can further determine whether playback device  104  is configured with stereoscopic display capabilities and model the 3-D space stereoscopically. For example, playback device  104  can comprise a smart TV having stereoscopic capabilities, or be a set of “smart glasses”. In such an instance, it may be necessary to capture the images comprising content  110  using a lens/microphone array  102  as shown in  FIG. 1D . In such an instance, at least two separate video media streams will be used and mapped to respective virtual display surfaces to model two separate viewports representing a user&#39;s eyes. For example, lens array  106 K can be used to capture the images representing a user&#39;s left eye while lens array  106 E is used to capture the images representing a user&#39;s right eye. Each of these separate video media streams will be mapped onto respective virtual display surfaces (e.g., respective virtual display surfaces  134 ) and the images displayed upon the respective virtual display surfaces will in turn be displayed upon respective display devices of playback device  104  to present stereoscopic images to a user. 
     As discussed above, with reference to  FIG. 1C , in some embodiments the field of view of the various lens arrays overlap. Thus, for an object that is at a 45 degree angle between lens arrays  106 A and  106 C of  FIG. 1C , both cameras will have that object within their respective fields of view. In accordance with various embodiments, rendering component  105  will select the video media stream of content  110  having the lowest normal angle from the object to the camera viewpoint vector. Thus, if there is a 35 degree angle from an object to the viewpoint vector of lens array  106 C and a 55 degree angle from that object to the viewpoint vector of lens array  106 A, rendering component  105  will select the video media stream of content  110  conveying the video images captured by lens array  106 C. As the object moves around in the field of view of lens arrays  106 A and  106 C, rendering component  105  will selectively map the images from these lens arrays onto virtual display surface  134 . It is noted that switching can occur between virtual domes, implementing GPU texture mapping, representing the lowest normal angle to camera vector viewpoint which is internal to a virtual dome driven by a GPU. In the example of  FIG. 1E , the virtual domes are mapped to virtual display surfaces  134  and  135  of  FIG. 1E . In another embodiment, the images from a selected lens array having the lowest normal angle to the virtual camera viewpoint vector are mapped to a flat virtual display surface (e.g.,  144  and  145  of  FIG. 1F ) using a GPU or a CPU. In this instance, a pixel re-map function inside the CPU is implemented rather than a built-in library of a GPU which is designed to perform 3-D shape generation. 
     Alternatively, a process called blending, in which the images from two or more video media streams are blended, can be implemented by rendering component  105 . Blending typically results in a better image than if only one camera is used because it removes transient noise and improves resolution beyond the original standards the data was recorded in. Thus, in a six-lens system (e.g., lens/microphone array  102  of  1 C), redundant data is recorded which can be used to remove seams and artifacts and push the resolution capabilities of lens/microphone array  102  beyond the resolution capabilities of the lens arrays used by the lens/microphone array. Thus, the images captured by lens arrays  106 A and  106 C can be blended and mapped to virtual display surface  134  by rendering component  105 . In one embodiment, one or more ideal virtual display domes, including a spherical or fully contained “dome” such as are mapped to virtual display surfaces  134  and  135  of  FIG. 1E ) are blended from multiple video sources (e.g., lens arrays  106 A and  106 B of  FIG. 1B ) using a GPU of playback device  104 . In another embodiment, video images from one of more video sources (e.g., lens arrays  106 A and  106 B of  FIG. 1B ) are mapped to a flat virtual display surface (e.g.,  144  and  145  of  FIG. 1F ). In at least one embodiment, alpha media stream translucency management is used to allow modeling of multiple infinity maps, or virtual display domes. In this instance, any given pixel may be derived from multiple lenses array by implementing real-time translucency blending using the GPU of playback device  104 . 
     In at least one embodiment, the images from the selected video media streams can be pre-blended and mapped to an idealized spherical virtual dome. Typically, this process is driven by the GPU of playback device  104 . This process could be performed as a post-production step (e.g., by content provider  103 ) prior to sending content  110  to playback device  104 , or can be performed on playback device  104  itself. This is advantageous in eliminating the necessity of switching or blending of the images from selected video media streams. This also reduces the number of video media streams from which to select. As an example, using a monoscopic display of playback device  104 , only one video media stream needs to be sent to playback device. In an instance in which playback device  104  uses a stereoscopic display, 6 idealized virtual spheres can be pre-blended from all of the lens arrays comprising lens/microphone array  102  (e.g., sixteen lens arrays as shown in  FIG. 1E , or even twenty four lens array) which significantly reduces the amount of data sent to playback device  104 . 
     In addition to determining the portion of the virtual display surface orientation of virtual viewpoint  137  selects, the orientation of audio playback is also determined. As an example, if rendering component  105  determines that playback device  104  is configured to recreate 3-D audio, various audio media streams comprising output  110  can be selected and mixed in real-time using the various microphones of lens/microphone array  102  to judge left and right audio media streams. For monophonic audio, rendering component  105  may select the audio media stream from one microphone of lens/microphone array  102 , or stream left and right audio media streams in phase to different ports and amplifiers and bridge the 2 signals. In other embodiments, a variety of audio algorithms are implemented to interpolate between two or more audio sources (e.g., the audio media streams comprising content  110 ). There are a variety of audio algorithms which can be implemented in embodiments including both linear and sine-wave based interpolation methods. 
       FIG. 1F  shows an example virtual viewport selecting a respective portion of content in accordance with various embodiments. In various embodiments, rendering component  105  maps the images from selected video media streams of content  110  to a flat, or planar, virtual display surface such as virtual display surfaces  144  and  145  of  FIG. 1F . As with  FIG. 1E  above, boundary  143  represents the limit of the field of view of lens arrays  106 A and  106 B and virtual viewport  147  controls which portion of the content  110  will be displayed on playback device  104  based upon a user&#39;s viewport control. In order to map pixels to a flat virtual surface, embodiments present the pixels as if a user sees images in full depth. For some types of lenses (e.g., multiple wide-angle lenses) used in lens arrays  106 A and  106 B, their optical transfer function maps orthogonally to a flat surface such as virtual display surfaces  144  and  145 . In one or more embodiments, rendering component  105  re-maps images from content  110  to virtual display surfaces  144  and  145  by converting the received images from content  110  using a software algorithm. This algorithm can also modify the mapping of pixels to virtual display surfaces  144  and  145  to give a user the impression that they were projected onto a concave surface, which, when mapped according to the optics of the recording lens, give the user the further impression that the user is viewing the original recording live while immersed within the scene. 
     In at least one embodiment, the algorithm makes use of an available GPU by use of the following steps: modeling a polygonal approximation of a concave surface using polygons (e.g. triangles) loaded into the GPUs rendering poly buffer, adding texture-mapping data (a UV map) to the GPUs texture-map buffer, setting the mapped source image to each frame in turn in the moving image sequence, and rendering the poly buffer. 
     In at least one embodiment, the algorithm uses a CPU and a lookup table populated according to the transfer function of the recording lens to locate source virtual pixels corresponding to each virtual pixel of a planar virtual display surface. 
     In at least one embodiment, a plurality of planar virtual display surfaces are used to form a cubic virtual display space which surrounds position  146  in a manner similar to virtual display space  138  surrounds position  136  in  FIG. 1E . 
     Interactive Augmented Reality 
     In accordance with various embodiments, because video images are mapped to an infinity model, or to a background virtual flat view surface, virtual reality objects can be rendered as overlays to the video stream of content  110  and/or, using alpha-media stream management, as video underlay. Because the video media is mapped to an infinity model, objects can be placed into the images that appear to a user as being closer in space than anything that was recorded and sent as an input media stream to the playback device  104 . In other words, if the recording is of a “background” image, objects can be mapped in front of that background image using rendering component  105 . For example, if the background image is of a bridge, a ship can be mapped to virtual display space  134  to appear to pass in between the bridge and the viewer&#39;s position in space. In accordance with various embodiments, each of media streams  108  and  109  further comprises meta-data which facilitates identifying the 3-D reality of the media streams which the meta-data describes. This can include, but is not limited to, luminance levels, chrominance, direction(s) of light source(s), atmospheric effects, etc. which can be used so that the object can be overlaid in a realistic manner in which the lighting of the background image and the overlaid object appears to come from the same source(s) and is subject to the same effects. In various embodiments, digital matting, using alpha channel management, is implemented to lay objects over other portions of the images mapped to virtual surfaces. By mapping images to an infinity model, the overlays appear to be embedded in, or part of, the original media stream. Additionally, alpha channel management can be implemented in various embodiments to facilitate underlays of embedded objects as well. Underlays make an embedded object appear to pass behind an object which is interpreted to be in the foreground of an image mapped to virtual display surface in various embodiments. In one embodiment, the birds are modeled, using rendering component  105 , in 3-D space within the virtual display space. As an example, an invisible 3-D object is mapped to a bird which appears to be passing between the ship and the viewer&#39;s position. Again, using alpha channel management allows under laying the ship relative to the bird so that the bird appears to pass between the position of the ship and that of the viewer. 
     In accordance with various embodiments, images can be mapped to convex surfaces as well. For example, a person&#39;s face within virtual display space  138  can be modeled as a 3-D convex object within virtual display space  138 . Images of a person speaking can then be mapped to that 3-D convex object to provide a realistic representation of the person speaking. 
     In at least one embodiment, this includes modeling movement of the person&#39;s jaw and facial features to give a more realistic impression of a person actually speaking. 
     In at least one embodiment this comprises a static facial model with texture mapped from moving video to model jaw and facial features movement. 
     In at least one embodiment this jaw movement and facial features and all movement of avatar talent is modeled by processor-directed sequencing of moving video segments onto a planar surface. 
     In at least one embodiment, the previous three techniques are used in combination to provide a realistic representation of the person speaking. 
     In other words, objects which are not part of the infinity model, and thus not part of the concave projection of images such as are created by using virtual display surfaces  134  and  135  of  FIG. 1E , can be modeled as a convex projection within virtual display space  138 . It is noted that other shaped objects can be embedded into virtual display space  138  such as, but not limited to, flat, planar, or polygonal objects and that media streams other than media streams  108  and  109  of  FIG. 1A  can be respectively mapped to those objects. In other words, while media streams  108  and  109  convey images captured by lens-microphone array  102  of production space  101 , other media streams (e.g.,  111  of  FIG. 1A ) can be mapped to objects which have been modeled into the virtual display space defined at least in part by virtual display surfaces  134  and  135 . It is noted that these objects can also be mapped into a cubic virtual display space which is defined at least in part by virtual display surfaces  144  and  145  of  FIG. 1F . 
       FIG. 1G  is a block diagram showing components of a rendering component  105  in accordance with at least one embodiment. In the example of  FIG. 1G , rendering component  105  comprises a playback device characteristic component  151  which is configured to determine characteristics of playback device  104 . As an example, playback device characteristic component  151  is configured to determine the display capabilities of playback device such as, but not limited to, whether playback device  104  is capable of 1080p display modes, or of a resolution (e.g., 800×400 pixels) of the display device used by playback device  104 . Playback device characteristic component  151  is also configured to determine whether playback device  104  comprises a GPU, or a CPU for mapping images from content  110  to a virtual display surface. As described above, if playback device  104  comprises a GPU, rendering component can use the OpenGL library of the GPU to create curved or polygonal virtual display surfaces such as  134  and  135  of  FIG. 1E  onto which is mapped the imaged from content  110 . Alternatively, if playback device  104  comprises one or more CPUs, rendering component  105  can use mapping algorithm  157  to generate instructions to that CPU causing the CPU to map pixels to a flat or planar virtual display surface such  144  and  145  of  FIG. 1F . 
     Audio algorithm  153  is used to interpolate audio media streams of content  110  to provide a user with a realistic 3-D audio representation based upon the orientation of virtual viewport  137 . As discussed above, audio algorithm  153  can comprise linear, sine-wave based, and other non-linear algorithms which can be used according to pre-determined settings, or selected by a user. Mixer  154  is used to mix, for example, left and right audio streams to provide realistic 3-D stereophonic audio, or monophonic audio to a user based upon the characteristics of the playback device  104  used. 
     Object modeler  155  is used to model realistic 3-D objects within the virtual display space created by rendering component  105 . As discussed above, this can include concave and convex objects to which images and/or respective media streams are mapped. Virtual viewport orientation input  156  if configured to determine the orientation of the virtual viewport (e.g.,  137  of  FIG. 1E ). As described above, this indication of virtual viewport orientation may result from a user manipulating a virtual control interface, a manual control component, or be based upon geospatial information received from playback device  104  itself. 
     Virtual viewport output  158  is configured to output the portion of content  110  which has been selected based upon the orientation of virtual viewport  137  relative to virtual display surface  134 . This output is sent to the display device of playback device  104  for presentation to a user. 
       FIG. 1H  is a flowchart of an example method  195  for delivering immersive media in accordance with an embodiment. In operation  196 , an image from at least one input media stream is mapped to a virtual display surface. As described above, in one embodiment lens/microphone array  102  is configured to output respective media streams from a plurality of lens arrays and microphones as a stream of content  110 . This stream of content  110  is then conveyed to playback device  104 , either as streaming content, or via data storage media such as CDs, DVDs, or removable electronic storage media such as USB drives. In one or more embodiments, rendering component  105  maps time synchronized images from video media streams to virtual display surfaces to facilitate mapping images to an apparent infinity. As a result, when images from the virtual display surface are sent to a user&#39;s display device, an immersive, 360 degree, high-definition environment is created for the user. 
     In operation  197 , an indication of a virtual viewport orientation of a playback device is received. In various embodiments, an indication of the orientation of a virtual viewport (e.g.,  137  of  FIG. 1E ) is received by rendering component  105 . As described above, this can be via user control of virtual control interfaces, manual operation of control devices, or via geospatial information received from playback device  104  itself. 
     In operation  198 , the indication of the virtual viewport orientation is used to select a portion of the image for displaying. In accordance with various embodiments, the orientation of the virtual viewport  137  indicates to rendering component  105  which portion of the image mapped to virtual display surface  134  will be displayed on playback device  104 . 
     In operation  199 , the portion of content which has been mapped to the virtual display surface is output. In one or more embodiments, the selected portion of content  110 , as indicated by virtual viewport  137 , is output by rendering component  105  to a user&#39;s display component of playback device  104 . It is noted that the operations described above can be performed by a rendering component  105  which is disposed upon the user&#39;s playback device, or which is disposed at a location apart from the user&#39;s playback device such as at content provider  103  of  FIG. 1A . 
     Embodiments for delivering immersive media for a device can be summarized as follows: 
     1. A method for delivering immersive media for a device, said method comprising: 
     mapping an image from at least one input media stream to a virtual display surface; 
     receiving an indication of a virtual viewport orientation of a playback device; 
     using said indication of said virtual viewport orientation to select a portion of said image for displaying; and 
     outputting said portion of said image which has been mapped to said virtual display surface. 
     2. The method of claim  1  further comprising: 
     determining a characteristic of the playback device; and 
     selecting a shape of said virtual display surface based upon said characteristic of the playback device. 
     3. The method of claim  2  further comprising: 
     determining that the playback device comprises a Graphics Processing Unit (GPU); 
     creating a polygonal virtual display surface using the GPU; and 
     mapping said image to said polygonal virtual display surface. 
     4. The method of claim  2  further comprising: 
     determining that the playback device does not comprise a GPU; 
     using at least one Central Processing Unit (CPU) of the playback device to create a planar virtual display surface; and 
     mapping said image to said planar virtual display surface. 
     5. The method of claim  1  further comprising: 
     selecting at least two images from two respective input media streams based upon said indication of said virtual viewport orientation; 
     mapping each of said at least two images to respective virtual display surfaces; and 
     outputting said selected portions of said at least two images which have been mapped to said respective virtual display surfaces to a three-dimensional (3-D) display device. 
     6. The method of claim  1  further comprising: 
     pre-blending at least two input media streams to create a blended input media stream; 
     mapping said blended input stream to a spherical virtual display surface; and 
     outputting said selected portion of said image which has been mapped to said spherical virtual display surface. 
     7. The method of claim  1  further comprising: 
     using said indication of said virtual viewport orientation to determine a position of the playback device relative to a virtual display space defined at least in part by said virtual display surface. 
     8. A non-transitory computer-readable storage medium comprising computer executable code for directing a processor to execute a method for delivering immersive media for a device, said method comprising: 
     mapping an image from at least one input media stream to a virtual display surface; 
     receiving an indication of a virtual viewport orientation of a playback device; 
     using said indication of said virtual viewport orientation to select a portion of said image for displaying; and 
     outputting said portion of said image which has been mapped to said virtual display surface. 
     9. The non-transitory computer-readable storage medium of claim  8  wherein said method further comprises: 
     determining a characteristic of the playback device; and 
     selecting a shape of said virtual display surface based upon said characteristic of the playback device. 
     10. The non-transitory computer-readable storage medium of claim  9  wherein said method further comprises: 
     determining that the playback device comprises a Graphics Processing Unit (GPU); 
     creating a polygonal virtual display surface using the GPU; and 
     mapping said image to said polygonal virtual display surface. 
     11. The non-transitory computer-readable storage medium of claim  9  wherein said method further comprises: 
     determining that the playback device does not comprise a GPU; 
     using at least one Central Processing Unit (CPU) of the playback device to create a planar virtual display surface; and 
     mapping said image to said planar virtual display surface. 
     12. The non-transitory computer-readable storage medium of Claim  8  wherein said method further comprises: 
     selecting at least two images from two respective input media streams based upon said indication of said virtual viewport orientation; 
     mapping each of said at least two images to respective virtual display surfaces; and 
     outputting said selected portions of said at least two images which have been mapped to said respective virtual display surfaces to a three-dimensional (3-D) display device. 
     13. The non-transitory computer-readable storage medium of Claim  8  wherein said method further comprises: 
     pre-blending at least two input media streams to create a blended input media stream; 
     mapping said blended input stream to a spherical virtual display surface; and outputting said selected portion of said image which has been mapped to said spherical virtual display surface. 
     14. The non-transitory computer-readable storage medium of Claim  8  wherein said method further comprises: 
     using said indication of said virtual viewport orientation to determine a position of the playback device relative to a virtual display space defined at least in part by said virtual display surface. 
     15. A system for delivering immersive media for a device comprising; 
     a recording device configured to capture a plurality of video data streams and a plurality of audio data streams; and 
     a rendering component configured to map an image from at least one input media stream to a virtual display surface, receive an indication of a virtual viewport orientation of a playback device, use said indication of said virtual viewport orientation to select a portion of said image for displaying, and to output said portion of said image which has been mapped to said virtual display surface. 
     16. The system of Claim  15  wherein said rendering component further comprises: 
     a playback device characteristic determination component configured to determining a characteristic of the playback device, and wherein said rendering component selects a shape of said virtual display surface based upon said characteristic of the playback device. 
     17. The system of Claim  16  wherein said rendering component is further configured to create a polygonal virtual display surface and to map said image to said polygonal virtual display surface in response to determining that the playback device comprises a Graphics Processing Unit (GPU) and to create a planar virtual display surface using at least one Central Processing Unit (CPU) of the playback device and to map said image to said planar virtual display surface in response to determining that the playback device does not comprise a GPU. 
     18. The system of Claim  16  wherein said rendering component is configured to select at least two images from two respective input media streams based upon said indication of said virtual viewport orientation, map each of said at least two images to respective virtual display surfaces, and to output said selected portions of said at least two images which have been mapped to said respective virtual display surfaces to a three-dimensional (3-D) display device. 
     19. The system of Claim  15  further comprising: 
     a pre-blending component configured to pre-blending at least two input media streams to create a blended input media stream, and wherein said rendering component is configured to map said blended input stream to a spherical virtual display surface and to output said selected portion of said image which has been mapped to said spherical virtual display surface. 
     20. The system of Claim  15  wherein said rendering component is further configured to use said indication of said virtual viewport orientation to determine a position of the playback device relative to a virtual display space defined at least in part by said virtual display surface. 
     Section Two: Rapid Application Development Platform for Augmented Reality Based Transmedia 
     Various embodiments are directed to a platform which is used to develop augmented reality based transmedia content and also acts an environment for running of that content. Although the following discussion is directed toward development and delivery of augmented-reality based content and applications, it is noted that stand-alone virtual reality content and applications can be created and delivered in accordance with various embodiments. As a running environment, various components can be run as an execution engine or as compiled libraries in a Just Enough Operating System (JeOS) configuration. As a development platform the availability of selected class library methods presented within progressive layers allow GUI-based programming of applications without extensive knowledge of syntax, object consumption without knowledge of object-based programming, and object-based programming without the knowledge of object-oriented programming. All of the components of the platform can be downloaded to a device to make a stand-alone mobile device. Alternatively, some of the components may be downloaded onto the device and the others can be accessed across a network. Various embodiments combine a self-adaptive self-learning network with a workflow engine which uses transactions to a database to define the workflow. The system can combine coded responses to events with learned behavior and use the learned behavior to generate code for applications. Additionally, the coded behaviors can be used as inputs to a self-adaptive network implemented by system  200 . These coded behaviors can include hard-coded behaviors, dynamically alterable code, or combinations of the two (e.g. an “interface” object design pattern, where the external “wrapper” is hard-coded and the internal “wrapped” behavior can be dynamically replaced). Also, the results of the self-adaptive networks and read the outputs from the hard-coded behavior and implement hard-coded responses to the self-adaptive networks. 
       FIG. 2A  is a diagram of an example system  200  for developing and running augmented reality based transmedia content in accordance with one embodiment. In  FIG. 2A , system  200  comprises a user interface  201 . In accordance with various embodiments, user interface  201  comprises a display(s) and inputs which facilitate control of system  200  by a user. In one example, user interface  201  may comprise a controller which is separate from the device on which the augmented-reality created by system  200  is displayed. For example, a TV controller, tablet computing device, or smart phone can be configured to control another device and used in various embodiments as a user interface  201 . As will be discussed in greater detail below, the basic unit of the behavior modeling library is an interactive element (e.g.,  230 ) also known as a “bot.” In various embodiments, interactive elements  230  are imbued with characteristics and are designed to interact with virtual reality and various simulation engines. These interactive elements  230  can interact with various reality mappings such as TV content, advertising, movies, real-time video from a user&#39;s device, geospatial data, enterprise applications, etc. The interactive elements  230  are also configurable to perform pre-determined actions based upon interactions with a user. Thus, in response to user input, interactive elements  230  can retrieve information from a website, access applications running on a local computing device, or interact with the virtual reality environment presented on a user&#39;s device including other interactive elements  230 . The interactive elements  230  can move around the virtual reality displayed on a user&#39;s device. The interactive elements  230  understand the reality in which they are embedded based upon the reality mapping performed by reality mapping component  204 . 
     In various embodiments, interactive elements  230  are created in a class inheritance hierarchy which can be imagined as a hierarchical tree structure. Succeeding levels of the tree structure define additional features which are enabled or restricted to better define the behavior of the interactive elements  230  within the virtual reality environment which combines data from reality mapping component  204  and model simulation component  205 . System  200  utilizes extensible inheritance which permits providing a newly created bot with a set of pre-determined characteristics which describes the class to which it belongs. Extensibility facilitates customizing the characteristics of the bot by defining additional characteristics to those inherited from a parent class. The design of system  200  also implements encapsulations to permit dynamically changing certain components of the behavior from each of the basic categories of bots in a library. As an example, an “information bot” inherits characteristics which permit it to retrieve information for a user when the user interacts with the bot. In another example, mobile bots describes a class of interactive elements  230  which are able to move around in the virtual reality environment created by system  200 . A sub-category of mobile bots are “fight bots” which are used in gaming to represent a character. The fight bots are designed to interact with the virtual reality environment in which they are embedded and are subject to, for example, the set of physical laws assigned to that version of reality and the behaviors assigned to that bot. An example of encapsulation would convert a basic definition of a fight bot to a more specific implementation such as, for example, a robot firing missiles. Utilizing these features, a developer can quickly define characteristics of interactive elements  230 , embed them into the reality being mapped, and create an augmented-reality based instance of content. As will be discussed in greater detail below, this can be performed by a developer without requiring extensive knowledge of programming code. 
     In accordance with various embodiments, interactive elements  230  can be created manually using the XML language which has the advantage of being easily read by a human. Thus a developer without an extensive programming background can easily create interactive elements  230  manually. Additionally, the use of a class inheritance hierarchy and encapsulation allows assigning behaviors and characteristics to interactive elements  230  rapidly and without the necessity of an extensive programming background. Additionally, this information can be attached using XML to a learned behavior using the self-learning described below. In at least one embodiment, the JavaScript Object Notation (JSON) data format can be used instead of XML. The JSON data format stores structured data in a package in a standard machine and human readable way. 
     System  200  further comprises a smart device engine  202 . Smart device engine  202  is configured to receive the augmented-reality environment generated by virtual reality component  206  and to manage the user&#39;s device to provide optimal performance when presenting content to the user in a manner which is compatible with the capabilities of the user&#39;s device. Smart device engine  202  provides the transmedia capability of system  200  by customizing the presentation of the augmented-reality environment to a user&#39;s device such as, but not limited to, a smart TV, smart phone, tablet computing device, laptop computing device, desktop computer, etc. In accordance with one or more embodiments, smart device engine  202  is disposed upon the user&#39;s device itself, in addition to user interface  201  and virtual control panel  203 , while some or all of the other components shown in  FIG. 2A  can be located at a device separate from the end user&#39;s device. Smart device engine  202  adapts the presentation of the received augmented-reality environment from virtual reality component  206  in order to provide a realistic, full immersive, 3-D content exhibiting real-time motion, frame synchronous full-speed video with full-speed complex rendered shapes with texture mapping. 
     System  200  further comprises a virtual control panel  203 . In accordance with various embodiments, virtual control panel  203  is a set of controls embedded used to control what portion of the 3-D augmented-reality environment is presented to a user. Virtual control panel  203  may be implemented in various configurations including, but not limited to, geospatial control of a user&#39;s device (e.g., either the user&#39;s display device itself or a controller of that device), voice control, camera-based gesture recognition, virtual buttons, virtual joysticks, cursor controllers, etc. Virtual control panel  203  allow a facilitates user interaction in the augmented-reality environment to control the presentation of content and to designate objects, such as selecting an interactive element  203 , and/or actions to be performed with the augmented-reality environment. 
     System  200  further comprises a reality mapping component  204 . In each type of media (e.g., TV programming, movies, real-time media, geospatial content, etc.) there is an underlying reality which is parsed out to derive meaning. In other words, there is a reality behind the representation shown on the media which may or may not be coherent to a machine, but which is coherent for a human. For example, a movie can be considered a form of virtual reality. In a movie, time and/or geography can be compressed from real-time into an abbreviated form to make the movie more interesting. This makes it apparently possible for a person to travel from New York City to Washington D.C. in a few seconds when, in reality, this is not possible in real-time. In a movie, the time base is a frame base time and the reality of the movie that is being mapped is dynamically changing, sometimes frame to frame. This underlying reality has to be mapped and correlated with other realities, to integrate various components into a realistic augmented-reality environment. In other words, these various realities have to be mapped into a single virtual environment having a common time base, dimension, laws of physics and geography, etc. In accordance with various embodiments, reality mapping component  204  manipulates data from one reality to the others being integrated into a single virtual reality environment. Reality mapping component  204  is configured to parse data from received media streams and utilize automated techniques to interpolate/extrapolate various components of the reality being mapped. For example, camera angles, camera movements, camera positions in space, depth within space, audio sources, and the like can be determined by reality mapping component  204  and used to map one reality space into a virtual reality environment. In some cases, system  200  does not simply map these realities into a virtual reality space, but maps these back into some other reality that is the primary user interface. Thus, if a user is watching a movie, the primary reality is the movie&#39;s reality, not the reality being created by virtual reality component  206 . Thus, the reality of the movie being watched may first be mapped into virtual reality in order to correlate the mappings from other realities being combined, but the combined realities are then pushed back into the reality of the movie. In one or more embodiments, the layout of parameters and the mapping(s) of reality by system  200  are performed using XML code. 
     System  200  further comprises a model simulation component  205 . In accordance with various embodiments, model simulation component  205  ties together the physics (e.g., gravity, acceleration, turn radius, etc.) of the virtual world being created by system  200 . Model simulation component  205  is also configured to control how time is modeled in virtual reality component  206 . Model simulation component  205  is also configured to model how objects change over time. 
     System  200  further comprises a virtual reality component  206 . In various embodiments, virtual reality component  206  is configured to bring together the inputs from reality mapping  204 , model simulation  205 , cloud engine  211 , and smart device engine  202  to create an immersive, 360 degree, 3-D augmented-reality environment. Virtual reality component  206  is configured to model shapes, and to connect those shapes seamlessly when they move. Virtual reality component  206  is also configured to determine lighting such as: how light interacts with objects, the location(s) of light source(s) within the virtual reality space being created, the chrominance and luminance of those respective light sources, how shadows and reflection are created by objects due to lighting, etc. In one or more embodiments, virtual reality component  206  is also configured to model human movement. Virtual reality component  206  is configured to use the inputs from the other components listed above and to integrate them seamlessly into a single immersive 3-D environment, including embedded objects and interactive elements, which is then passed to smart device engine  202 . 
     System  200  further comprises a dialogue modeling component  207 . Dialogue modeling component  207  is directed to the modeling of individuals and groups. It is configured to map the context and meaning of what has been parsed about, for example, a conversation based on a number of different contexts such as geospace and viewpoint. For example, where people are looking when they speak often colors the meaning of what they are saying. This is an example of context mapping to the dialogue. In another example, people and groups go through different states of dialogue while they are communicating with each other where what they say, or what they mean, changes in the context of a group or individual. In other words, the same word can have different meaning in different contexts. Dialogue modeling component  207  creates a mapping of context and meaning which can be passed to behavioral modeling component  208  because dialogue can also be a behavioral response. In at least one embodiment, an interactive element  230  can respond to a user based on what the user said, based on its understanding of what is happening, what the user is looking at, and what it thinks the user meant. 
     System  200  further comprises a behavioral modeling component  208 . Behavioral modeling component  208  is configured to model behavior of interactive elements  230 , and other elements, using extensible libraries. In other words, the behavior of an interactive element  230  prescribes what action the interactive element  230  will perform in response to another event. For example, in response to a user clicking on an interactive element  230 , the prescribed behavior may be to access an interactive advertisement via the Internet, or to access a website for additional information. As described above, behavioral modeling component  208  can receive context and meaning of conversation from dialogue modeling component  207  in determining a response. In accordance with various embodiments, behavior of interactive elements  230  can be laid out in XML manually, or use inherited behavior types using the class hierarchy described above. These behavior types manage interaction within the augmented-reality environment and can be encapsulated and dynamically changed according to context. In one or more embodiments, sets of behavior specifications are modeled as personalities of the interactive elements  230 . In one or more embodiments, the interactive elements  230  can implement self-learning into the interactive element itself. Thus, behavioral modeling component  208  defines the environment which interactive elements  230  populate and what they can do and access within that environment. For example, a search API can be attached to an interactive element  230  and the drivers for using that search API can be attached to communications component  210  and be made available to the interactive element  203 . Thus, in response to an interaction with a user, the interactive element  230  will have knowledge to use those drivers to implement using the search API for the user. 
     System  200  further comprises an adaptive engine  209 . In accordance with various embodiments, adaptive engine  209  is configured to implement a self-adaptive network functionality into system  200 . In one embodiment, adaptive engine  209  is coupled with database engine  213  via workflow engine  212 . Workflow is a way to define low level functionality of system  200  on the back end of the system. Adaptive engine  209  gives a single integration point of hard coded behavior and learned behavior and can mix the two. In various embodiments, the learned behavior can manage the hard coded behavior which may in part be based upon learned behavior. Workflow engine  212  also monitors communications as well. 
     System  200  further comprises a level of integration represented as interactive repository/aggregator  215  comprising, in one embodiment, communications component  210 , cloud engine  211 , workflow engine  212 , and database engine  213 . Communications component  211  is configured to provide communications to elements outside of system  200  including the Internet, e-mail, content providers, and other interactive repository/aggregators  215  (not shown). 
     Cloud computing networks are a term well known in the art in which the computing environment is run on an abstracted, virtualized infrastructure that share resources such as CPU, memory and storage between applications. Typically, a cloud computing environment implements a distributed computing architecture of distributed data storage and other content via software and services provided over a network or the Internet. Using a cloud computing network, access to computing power, computer infrastructure, applications, and business processes can be delivered as a service to a user on demand. In various embodiments, cloud engine  211  comprises a human or machine consumable middleware transactional processor that is stateful. Cloud engine  211  provides functionality such as generating queries, retrieve data, manipulate data, etc. Cloud engine  211  also provides a Service Oriented Architecture (SOA) that is consumed as a machine readable medium and still have workflow engine  212  attached that does transactional processing on the backend. In one or more embodiments, cloud engine  211  can display web pages that are part of self-contained web applications and maintains state even though the user&#39;s web browser does not maintain state. Cloud engine  211  can manage database access, applications, forms, and workflow. In various embodiments, cloud engine  211  can access other non-database repositories and use a regular database engine to do so and can consume SOA objects. 
     In accordance with various embodiments, workflow engine  212  monitors interactions between cloud engine  211 , database engine  213  and communications component  210 . Workflow engine  212  is also configured to monitor interactions between cloud engine  211  and other non-database repositories, other interactive repository/aggregators  215  (not shown) or the like. In accordance with various embodiments, system  200  implements matrix processing and builds schemas according to how developers want forms to relate to one another (e.g., parent/child relationship, cross reference forms, etc.) and with actual tables in a database. 
     In accordance with various embodiments, system  200  implements a form specification in which imperative Java-based declarations are converted to declarative Java-based declarations. In one embodiment, the form of the syntax controlling workflow engine  212  is architected in such a way so that the actual usage of the workflow can be formatted in this same syntactical way. This is not standard to Java in any way, but converts Java into a declarative language. In accordance with various embodiments, objects (e.g., interactive elements  203 ) are declared and class hierarchy based inheritance of behavior and characteristics are used. This provides a limited set of objects that can be manipulated by a developer to put objects on a screen. However, by converting the Java-based declarations into declarative form, characteristics of interactive elements  203  that are not intrinsically inherited can be added as further specified option that are appended as dot-declarations. This is easily parsed as something that can be performed using a GUI to generate Java code. They are mere declarations, and they are repetitive in their structure, so that they can be parsed out or symbols can be mapped to these declarations to sort them, or these declarations can be stored where Java Virtual Machine (JVM) executable Java out of a GUI front end very easily. As a result, extensive programming experience is not necessary to create interactive elements  203 . Instead, if the developer is given the knowledge of what kind of field is wanted, and in what order to query in, and in what order it shall be displayed on a screen, etc., these elements can be created quickly. 
     This process can also be applied to workflow engine  212  as well to facilitate putting regular expressions into a low-level workflow. The method described above provides a single object access point with an easy syntax and returns the same object in a form that can be recalled. In one or more embodiments, the operation of workflow engine  212  can be laid out using a GUI as well. In various embodiments, system  200  implements matrix processing and pattern recognition which is linked to a message bus (e.g., via workflow engine  212 ) to monitor workflow messaging. 
     System  200  further comprises a database engine  213 . Database engine  213  comprises a database management system (DBMS) software layer for storing, processing, and securing data stored by a computing device implementing system  200 . There are a variety of DBMS software drivers which can be used in accordance with various embodiments including, but not limited to, Oracle, MySQL, Sybase, MS SQL, Postgres, etc. 
     In various embodiments, system  200  is configured to automatically generate database schema in 4 th  normal form. In at least one embodiment, a form specification is laid out which sets forth the parameters for creating a database. These form specifications include relationships (e.g., parent/child, cross references, tables, etc.) between data elements on these forms and other parameters such as dependencies used to organize fields and tables of a relational database. The DBMS will use this information from the form specification and create the table structures within a Relational Database Management System (RDMS). Another embodiment can utilize a middleware driver that stores to a database, but does not actually access the database itself. 
     Self-Adaptive Networks 
     In one or more embodiments, a self-adaptive network can be embedded into any one of interactive elements  230 . This facilitates making interactive elements  230  being capable of being trained to perform an action and to implement self-learning so that the interactive element  230  can implement scoring criteria to improve the manner in which it responds to a given input or event until a desired standard is achieved. This can include learning how to interact and self-customize to a particular user, or to a set of users. 
     Various embodiments implement a low-level (e.g.,  212 ) engine linked to matrix processing and pattern recognition. In various embodiments, the low-level work engine can also interact as a message bus. Thus, a workflow event can be linked to adaptive engine  209  to process and return back to the workflow. In various embodiments, any transaction that happens in data that goes to or from a data repository (e.g., XML, RTDMS, etc.) can be processed on the back end. Thus, front-end adaptive behavior can be implemented by integrating self-adaptive modeling into each of the interactive elements  230  and back end adaptive behavior as well. Additionally, in one or more embodiments, adaptive behavior that is built into interactive elements  230  can communicate with cloud engine  211  to implement custom created behaviors for the interactive element  230 . In one embodiment, the adaptive behavior built into one of interactive elements  230  communicate with cloud engine  211  and have learned behavior on the back end serve out those same adaptive networks. 
     In various embodiments, the learned behavior by the interactive elements  230  is stored in the XML or the JSON data format although other data specifications can be used in accordance with various embodiments. By using the XML format, it is easier for a person to develop an application manually. In at least one embodiment, filters can be used to aggregate data, such as from the Internet. This filtered data can be used to automate the development of applications, behavior of interactive elements  230 , developing user profiles to implement customized delivery of content (e.g., automated TV programming), etc. 
     The combination of components described above provides a great deal of flexibility and facilitates rapid development of immersive, 360 degree, 3-D augmented reality content. In accordance with various embodiments, the resulting programming elements, behavior, and data-driven functional responses can be streamed along with television and advertising content. As discussed above, interactive elements  230  can be embedded into the augmented-reality environment created by system  200 . Although the discussion above has been directed to embedding objects within a mapped reality, embodiments can insert landscapes, backgrounds or the like behind objects which were provided as one or more of augmentations  220 . As an example, utilizing overlay and apparent underlays, objects and landscapes can be embedded into the original media content which allow other objects from the original media stream appear to pass in front of, or behind, the embedded objects. Embodiments can stream the programming elements (e.g., behavior, responses, etc.) along with the TV content or advertising being sent to a user&#39;s device. Thus, the code for the interactive elements  230  will be delivered along with the pixels and audio of the original media content. 
     Additionally, the programming elements, behavior, and data-driven functional responses can be delivered as separate meta-data to coincide with interactive television programming. In accordance with various embodiments, meta-data is used to describe the bounds and parameters within which the interactive elements  230  operate. This describes not only what type of interactive element it is, but what types of behavior it will exhibit. In accordance with at least one embodiment, this meta-data is parsed onto the user&#39;s device in real-time. This can be synthesized in real-time using smart device engine  202  on the user&#39;s device. Thus, the programming elements, behavior, and data-driven functional responses which includes interactive elements  230 , and the parameters of what the interactive elements  230  can do and how they do it, and even the appearance of the interactive elements themselves can be streamed along with TV content and/or advertising, or it can be delivered as separate metadata to coincide with interactive TV programming. The programming itself may not yet have arrived at the user&#39;s device, but the meta-data can have been downloaded with the knowledge that the TV programming will be played. In another embodiment, rather than streaming the programming elements, behavior, and data-driven functional responses in real-time, they can be accessed from, for example, a database or data storage device. 
     In accordance with one or more embodiments, these two methods of delivery can be combined. In one example, smart device engine  202  is executed as a media player which is implemented as a software layer operated by the user&#39;s device. In conjunction with other components of system  200  and the user&#39;s device, it becomes a media player for the user. In this case, the media being presented to the user is both the original programming content (e.g., TV programming, advertising, movies, real-time audio/video content, geospatial data, etc.) along with the meta-data describing the interactive elements  230  (e.g., the programming elements, behavior, and data-driven functional responses of interactive elements  230 ) which have been embedded into the original content. In one or more embodiments, the Just Enough Operating System (JeOS) is used which only compiles the portions of code needed to perform a specific task. In this instance, the components of system  200  shown in  FIG. 2A  can be thought of as a set of core libraries which interact and are compiled into a self-contained package and sent the user&#39;s device. In one embodiment, system  200  can also be implemented as a cloud server in which some, or all, of the components of system  200  are compiled and sent into a package and run locally on the user&#39;s device. In one embodiment, the interactive repository/aggregator  215  can be implemented as a service (e.g., a SOA) that is accessible across a network from any of the other components of system  200  which may be located on a separate device. 
     Alternatively, various embodiments download some, or all, of the components of system  200  onto the user&#39;s device. As an example, smart device engine  202 , virtual reality component  206 , and virtual control panel  203  can be compiled and loaded onto the user&#39;s device to improve performance in the rendering of the augmented-reality environment. Other components of system  200  can be paged in, or kept separate across a network. In various embodiments, system  200  can be implemented as a portal to content which can be accessed via, for example, a user&#39;s web browser. 
     In accordance with various embodiments, the programming elements, behavior, and data-driven functional responses can be automatically generated by conversion of aggregated data to automatically generate applications such as, but not limited to, automated television channels. As an example, interactive repository/aggregator  215  can derive data out of other programs operating on a user&#39;s device (e.g., Quicken, Quickbooks, etc.) to automatically generate a personal finance channel which is displayed as a television channel on the user&#39;s device. This can include interactive elements  203 , which are modeled as 3-D objects and texture mapped, to represent newscasters who deliver customized financial reports to a user based upon data on the user&#39;s device. Additionally, data can be derived based upon websites accessed by the user via the device. Thus, if the user regularly visits websites directed toward real-estate investments, the automatically generated television channel can feature real-estate reports as part of its larger reporting of financial markets. By aggregating data, system  200  can automatically generate coding and configuration layout constructs that change based upon a user&#39;s data. In various embodiments, actual code development is performed by cloud engine  211 , workflow engine  212 , and smart device engine  202  which can generate JVM readable code. Other operations are implemented as configurations of XML schema. 
     In various embodiments, system  200  is also configured to deliver stand-alone Cloud-based enterprise applications. As an example, interactive repository/aggregator  215  provides a sophisticated integration point to other systems and applications. In other words, cloud engine  211 , workflow engine  212 , database engine  213  and communications component  210  can be configured to deliver enterprise applications. By adding a virtual reality presentation on the front end and adaptive workflow, system  200  provides capabilities beyond standard enterprise applications. Furthermore adaptive engine  209  in combination with workflow engine  212  can identify transactions that happen often across an enterprise that can be a huge labor chore if done by manually, especially in a network that implements automated reporting. As an example, in an inventory system of all IP equipment of a business, a great deal of effort is used to monitor the equipment, to predict when the component will fail, etc. Additionally, the monitoring has to identify what actionable item has to happen, how to categorize that action, and how to de-duplicate, sort, and correlate what these events are so as not to send out numerous superfluous alerts in response to an event. Currently, these operations are done semi-automatically, but still require human intervention. In accordance with various embodiments, this categorization is coupled with the self-adaptive network implemented by system  200  which facilitates learning how to better categorize events so that every time an event is mis-categorized, system  200  can learn how to better categorize that event in the future. 
     In various embodiments, system  200  can be used to deliver stand-alone mobile applications as well. As an example, some components of system  200  such as smart device engine  202 , virtual control panel  203 , and virtual reality component  206 , if virtual reality is being used, can be downloaded onto a user&#39;s mobile device. This can include, but is not limited to, smart phones, tablet computers, laptops computers, or the like. Applications can be developed which either use those components as engines, or as compiled libraries. Media content, including augmented-reality applications and content, can be downloaded or streamed to the mobile device and presented to the user. It is noted that other components of system  200  can be downloaded onto the user&#39;s mobile device as well and may improve the performance of the device when run locally. Alternatively, all of the components of system  200  can be downloaded onto the user&#39;s mobile device to create a stand-alone mobile device that isn&#39;t connected to other components of system  200  and runs all the forms, the cloud engine, database, and workflow locally on the user&#39;s mobile device. 
     Thus, system  200  exposes progressively more sophisticated forms of functional approaches that allow it to deliver powerful augmented-reality based transmedia enterprise system applications with a very small number of simple lines of code, while still allowing flexibility of accessing progressively deeper layers of programming through object consumption and specification. For example, at the highest layer, a developer is not required to know how to write a program. At the next layer, a developer is not required to know how to consume objects. At the next layer, the developer is not required to know how the objects work, or how to make one. Thus, this multi-layered approach progressively exposes greater flexibility for increasingly experienced developers to customize the behavior of objects. 
     In accordance with various embodiments, the programming interface specification for system  200  abstracts the device layers to make it more portable and simpler to code than having to deal with the complexities of each operating system which may be used by various end user devices. This allows identifying default behaviors related specifically to the functionality of system  200 . In various embodiments, smart device engine  202  deals with the lower level functionality and presents some higher level intercepts which invoke a specified call in response to a defined event in order to determine how best to respond. Thus, the application developers can create asynchronous event-driven responses to events using a rich library of functions. 
     As discussed above, the components of system  200  is comprised of code library components which can stand alone as engines, or be compiled in a JeOS configuration. The programming interface specification includes a series of real-time event intercepts (presented as method overrides) that allow logical programmatic responses to events and modifications to, or replacement of, default system functionality. The programming interface also includes XML configuration and layout of 2-D screen layout. As an example, a standard Android device layout can be performed in XML in various embodiments. It is noted that other screen layouts can be performed in XML as well. 
     The programming interface specification also provides for the XML configuration and layout of interactive form specifications. Because Java declarations are being converted to declarative form, operations performed using a GUI layout builds a Java code that is parsed by a JVM. In one embodiment, if imperative Java declarations are also used, inline Java code can be placed inside the declarative Java libraries which is an imperative piece which is inheritable. In other words, there is an imperative statement inside each form specification. When the form specification is invoked, there is a corresponding imperative form that is automatically invoked that will allow a developer to bring that form specification up. Instead of filling out the imperative form specification, or interacting with the data related to the records that are joined from a database or external repository, the developer can actually query by example because the imperative form specification has the same layout. This provides a variety of options about lists that permit relating fields in a database query. In other words, embodiments facilitate creating automatic query by example by putting using in-line code and inheriting the query by class. The programming specification also provides for XML configuration and layout of 3-D augmented-reality as discussed above including virtual reality, geospatial relationships, and media reality. The programming specification also provides for XML configuration and layout of behavior and default system functionality as discussed above. 
     In various embodiments, the interface specification also provides for XML configuration and layout of declarative Java declarations and of event trigger specifications in JVM. In one embodiment, event overrides implemented by smart device engine  202  deal with events on the client device that flow through interactive repository/aggregator  215 . Interactive repository/aggregator  215  acts as a middleware layer between other components of system  200  and a database. In this middleware layer, event based events are defined by the programming interface. In various embodiments, workflow engine  212  comprises a library of functions which can be invoked based upon events that happen as data flows through interactive repository/aggregator  215 . For example, e-mail filters can be emplaced to store, classify, and respond to e-mails as they arrive. 
     In various embodiments, the interface specification of system  200  also provides class library access to interactive multimedia, virtual reality, geospace, dialogue modeling, workflow engines, matrix processing, adaptive networks, and fuzzy logic scripting. As discussed above, various embodiments implement a multi-layer programming interface in which succeeding layers of increasing complexity and power can be accessed by a developer. Thus, a less experienced developer may only access the top layer or two of the programming interface while more experienced developers may access deeper layers to allow for greater customization of applications. As an example, the top layer facilitates configuration of each of the engines of system  200 . The next layer down permits Java coding for components of system  200  such as the smart device engine. The design of the programming interface for system  200  is based upon the Paredo principle in which 80% of the work to be performed can be implemented using 20% of the coding. In various embodiments, this 20% of the coding can be placed in a wrapper and made immediately available. Thus, instead of having to break down and consume an object to get at the method that underlies it, the developer simply needs to know how to index the object so that a simple method call can be performed. The method call can be implemented as a simple line of coding that doesn&#39;t have to have knowledge of an object. Thus, the programming interface is exposing these library methods and the top layer of the programming interface can be made very flat with no depth to the object hierarchy. Instead, the developer is accessing the most common 80% of the methods that are related to the program being created. According to various embodiments, the availability of selected class library methods, presented with progressive layers, allow GUI based programming without the knowledge of syntax, object consumption without knowledge of object-based programming, and object-based programming without knowledge of object-oriented programming. 
       FIG. 2B  is a flowchart of an example method  250  for developing augmented reality based transmedia content in accordance with an embodiment. In operation  251  of  FIG. 2B , the structure of a Java-based imperative declaration is converted to create a declarative Java-based language structure. As discussed above, in one embodiment, the form of the syntax controlling workflow engine  212  is architected in such a way so that the actual usage of the workflow can be formatted in this same syntactical way. This is not standard to Java in any way, but converts Java into a declarative language. In accordance with various embodiments, objects (e.g., interactive elements  203 ) are declared and class hierarchy based inheritance of behavior and characteristics is used. This provides a limited set of objects that can be manipulated by a developer to put objects on a screen. However, by converting the Java-based declarations into declarative form, characteristics of interactive elements  203  that are not intrinsically inherited can be added as further specified options that are appended as dot-declarations. 
     In operation  252  of  FIG. 2B , the declarative Java-based language structure is used to generate a graphic user interface. As discussed above, the declarative Java-based language structure is easily parsed as something that can be performed using a GUI to generate Java code. They are mere declarations, and they are repetitive in their structure, so that they can be parsed out or symbols can be mapped to these declarations to sort them, or these declarations can be stored where Java Virtual Machine (JVM) executable Java out of a GUI front end very easily. 
     In operation  253  of  FIG. 2B , the graphic user interface is used to generate Java-based programming code of an instance of augmented-reality based transmedia. In accordance with various embodiments, the GUI can be used, for example, to define additional characteristics and behaviors for interactive elements in addition to those inherited through class hierarchy. This permits quickly customizing the interactive elements according to the particular needs of a software application. 
     In at least one embodiment, the declaration objects generate screen elements for user interaction at run-time, generate data schema construction at create-time including creation of tables and indexes within underlying RDBMS implementations, and manage interaction with databases or repositories at run-time, mapping screen interactions to underlying data structures and workflow events. 
     In various embodiments, Workflow Engine  212  includes the following interface methods (or subroutines) to assist non-programming complex multi-stage matrix processing and data filter implementations: Parse (string with regular expression); pullFields (from schema source through pre-defined data Map to destination data set row); pushFields (from dataset source through pre-defined data Map to destination schema rows); putFields (from dataset source through pre-defined data Map to scheme destination rows); replace (one text pattern with another within source text); roles (identified roles within system for a given identity—e.g. user); split (split text into substrings as delimited by a pattern); SQL (load scheme directly from DBMS using Standard Query Language—SQL); and xferFields (transfer field data from one form or dataset to another form or dataset). 
     Embodiments for development of augmented-reality based transmedia content can be summarized as follows: 
     1. A method for development of augmented-reality based transmedia content, said method comprising: 
     converting the structure of a Java-based imperative declaration to create a declarative Java-based language structure; 
     using said Java-based declarative language structure to generate a Graphic User Interface (GUI); and 
     using said graphic user interface to generate Java-based programming code of an instance of augmented-reality based transmedia content. 
     2. The method of claim  1  further comprising: 
     using the Extensible Mark-up Language (XML) to create a mapping of data derived from at least one source of spatial data. 
     3. The method of claim  2  further comprising: 
     correlating said mapping of data derived from at least one source of spatial data with a virtual reality model. 
     4. The method of claim  1  further comprising: 
     using the Extensible Mark-up Language (XML) to define an interactive element within an instance of augmented-reality based transmedia content; and 
     using the Extensible Mark-up Language (XML) to define a behavior of said interactive element in response to a defined event. 
     5. The method of claim  4  further comprising: 
     deriving data from a self-adaptive network describing said behavior; and 
     modifying said behavior based upon the derived data. 
     6. The method of claim  5  further comprising: 
     monitoring a response of said interactive element in response to said defined event; 
     categorizing said response of said interactive element; and 
     in response to said categorizing, automatically modifying said behavior and wherein said monitoring, said categorizing, and said automatically modifying are performed by said interactive element. 
     7. The method as recited in claim  1  further comprising: 
     automatically generating a database schema in fourth normal form. 
     8. A non-transitory computer-readable storage medium comprising computer executable code for directing a processor to execute a method for development of augmented-reality based transmedia content, said method comprising: 
     converting the structure of a Java-based imperative declaration to create a declarative Java-based language structure; 
     using said declarative Java-based language structure to generate a Graphic User Interface (GUI); and 
     using said graphic user interface to generate Java-based programming code of an instance of augmented-reality based transmedia content. 
     9. The non-transitory computer-readable storage medium of Claim  8  further comprising: 
     using the Extensible Mark-up Language (XML) to create a mapping of data derived from at least one source of spatial data. 
     10. The non-transitory computer-readable storage medium of Claim  9  further comprising: 
     correlating said mapping of data derived from at least one source of spatial data with a virtual reality model. 
     11. The non-transitory computer-readable storage medium of Claim  8  further comprising: 
     using the Extensible Mark-up Language (XML) to define an interactive element within said instance of augmented-reality based transmedia content; and 
     using the Extensible Mark-up Language (XML) to define a behavior of said interactive element in response to a defined event. 
     12. The non-transitory computer-readable storage medium of Claim  11  further comprising: 
     deriving data from a self-adaptive network describing said behavior; and 
     modifying said behavior based upon the derived data. 
     13. The non-transitory computer-readable storage medium of Claim  12  further comprising: 
     monitoring a response of said interactive element in response to said defined event; 
     categorizing said response of said interactive element; and 
     in response to said categorizing, automatically modifying said behavior and wherein said monitoring, said categorizing, and said automatically modifying are performed by said interactive element. 
     14. The non-transitory computer-readable storage medium as recited in Claim  8  further comprising: 
     automatically generating a database schema in fourth normal form. 
     15. A system for implementing development of augmented-reality based transmedia content, said method comprising: 
     a processor comprising a cloud engine communicatively coupled with a workflow engine and wherein said cloud engine and said workflow engine are configured to implement convert the structure of a Java-based imperative declaration to create a declarative Java-based language structure, use said declarative Java-based language structure to generate a Graphic User Interface (GUI), and to use said graphic user interface to generate Java-based programming code of an instance of augmented-reality based transmedia content. 
     16. The system of Claim  15  wherein said processor further comprises: 
     a smart device engine configured to use the Extensible Mark-up Language (XML) to create a mapping of data derived from at least one source of spatial data. 
     17. The system of Claim  16  wherein said processor further comprises: 
     a virtual reality component configured to correlate said mapping of data derived from at least one source of spatial data with a virtual reality model. 
     18. The system of Claim  15  wherein said cloud engine and said workflow engine are further configured to use the Extensible Mark-up Language (XML) to define an interactive element within said instance of augmented-reality based transmedia content and to use the Extensible Mark-up Language (XML) to define a behavior of said interactive element in response to a defined event.
 
19. The system of Claim  18  wherein said processor further comprises:
 
     an adaptive engine communicatively coupled with said workflow engine and configured to derive data describing said behavior; and 
     a smart device engine configured to modify said behavior based upon data derived from said adaptive engine. 
     20. The system of Claim  19  wherein said interactive element are configured with said adaptive engine and with said workflow engine and is configured to monitor a response of said interactive element in response to said defined event, categorize said response of said interactive element, and to automatically modify said behavior in response to said categorizing. 
     Section Three: Communication Using Augmented Reality 
     Notation and Nomenclature 
     Some portions of the description of embodiments which follow are presented in terms of procedures, logic blocks, processing and other symbolic representations of operations on data bits within a computer memory. These descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. In the present application, a procedure, logic block, process, or the like, is conceived to be a self-consistent sequence of steps or instructions leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, although not necessarily, these quantities take the form of electrical or magnetic signal capable of being stored, transferred, combined, compared, and otherwise manipulated in a computer system. 
     It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussions, it is appreciated that throughout the present discussions terms such as “providing”, “receiving”, “generating”, “embedding”, “creating”, “customizing”, or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system&#39;s registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices. 
     Furthermore, in some embodiments, methods described herein can be carried out by a computer-usable storage medium having instructions embodied therein that when executed cause a computer system to perform the methods described herein. 
     Brief Description 
     As computing power has continued to increase, augmented reality environments have become more complex. Augmented reality has the ability to place an avatar of a second user into the augmented reality environment of a first user. 
     Overview of Discussion 
     Example techniques, devices, systems, and methods for communicating with at least one using augmented reality are described herein. Discussion begins with a high level description of augmented reality. Example devices are then discussed. Discussion continues examples projecting augmented reality into the real world. Next, an example viewport  310  is discussed. Lastly, example methods of use are described. 
     High Level Description of Augmented Reality 
       FIG. 1  shows an augmented reality environment  300 . In an embodiment, a first user  301  can communicate with other users  302 ,  303 , and  304  in various augmented reality environments  300 . In one embodiment remote users  304  can be projected into the real world. In one embodiment augmented reality environment  300  comprises virtual geography. In an embodiment, virtual geography is a combination of real and non-real objects. 
     For the purposes of this disclosure, in various embodiments the term “real” refers to, but is not limited to: something tangible (e.g., desks, walls, mountains), something audible (e.g., speech, music, noise), etc. In an embodiment, a digital image created by a processor  315 , wherein the image is not in the “real world”, is not a real object  309 . In an example, the desk shown in augmented reality environment is a real object  309 . In other words, local users  301  can physically touch desk  309 . In one example, plant  307  may exist only in the augmented reality environment  300 , while in another example plant  307  may exist in the real world and the augmented reality environment  300 , while in yet a third example, plant  307  may exist in the real world and not in the augmented reality environment  300 . In one embodiment, a remote user  304  may write on a white board  308  that exists in the real world, where the writing is visible to local users  301 ,  302 , and  303  when they view the white board  308  with their input/output (I/O) devices  305 . Similarly, in some embodiments, local users  301 ,  302 , and  303  can only hear a remote user  304  when using an I/O device  305 . 
     In one example, an advertisement  306  is embedded in the augmented reality environment  300 , while the advertisement  306  does not exist in the real world. In an embodiment, advertisement  306  may be targeted to users  301 ,  302   303 , and  304 . In other words, in an embodiment, advertisement  306  is not viewable in the real world (e.g., without an I/O device), but is viewable in the augmented reality environment  300 , and shows different advertisements based at least in part on user  301 ,  302 ,  303  and  304 . For example, remote user  304  may be in Japan while accessing augmented reality environment  300  which is based on a real world conference room in California comprising local users  301 ,  302  and  303 . In this example, advertisement  306  may appear to be an advertisement  306  for a Japanese store to the remote user  304  in Japan, but appears to be an advertisement for a store in California to the local users  301 ,  302  and  303  that are located in California. 
     While the room in  FIG. 1  exists in the real world, it also exists in an augmented reality environment  300 . In an example, users  301 ,  302 , and  303  are in the real world, in a real room, surrounding a real desk. Users  301 ,  302 , and  303  use I/O devices  305  to access (e.g., interact with) an augmented reality environment  300 . In other words, I/O devices  305  provide local users  301 ,  302 , and  303  or remote users  304  to “enter” the augmented reality environment  300 . 
     In one embodiment, an augmented reality environment  300  provides automated adaptive behavioral responses. For instance, a remote user  304  may be sitting in a chair at home while interacting with the augmented reality environment  300 , wherein ideally a user  301 ,  302 ,  303 , and  304  would be standing. In this example augmented reality environment  300  is operable to make the avatar of remote user  304  stand. In one embodiment, when a first user  301  speaks a different language than a second user  303 , augmented reality environment  300  is operable to allow the first user  301  and the second user  302  to speak their respective languages and translates their speech such that the first user  301  hears speech in his designated language while the second user  302  hears speech in his designated language. In one embodiment, augmented reality environment  300  changes the clothes of a user  302 . 
     Example Devices 
     I/O devices  305  may include, but are not limited to: glasses, ear phones, a microphone, an image capturing device, a tablet computer, a smartphone, a personal digital assistant, a stereoscopic display, an interactive device, a transmedia device, a receiver, a monitor, a touchscreen display, a windshield, stereophonic speakers, a keyboard, a mouse, a joystick, a button, a depth sensor, a motion sensor, a trackball, a speaker, a Microsoft™ Kinect™ type device, an image capturing device or a Microsoft™ Kinect™ type device that can capture 360° of images and/or video, a device that performs operations similar to the cameras on the roofs of “Google™ street view cars”, etc. In some embodiments I/O device  305  may comprise a plurality of I/O devices  305 . In some embodiments I/O device  305  comprises at least one processor  315 . In one device, I/O device  305  is operable to take an image and/or video of the face of a user  301 ,  302 ,  303 , or  304 . In an embodiment, the face is shown on a remote user  304  within augmented reality environment  300  wherein the face is based on an image or video taken by I/O device  305 . 
     In an embodiment, augmented reality environments  300  are stored on a remote device comprising a processor  315  (e.g., a server, a computer, a plurality of electronic devices, etc.). Remote users  304  may “travel” to (e.g., interact with) different augmented reality environments  300  which may be constructed from real objects  309  in real time or otherwise (e.g., a real location in real time). In other words, in an embodiment, a remote user  304  may “visit” (e.g., interact with) a real location in real time. 
     In an embodiment, an augmented reality environment  300  is created based in part on data received and/or generated from an I/O device  305 . For example, an augmented reality environment  300  may be created by an I/O device  305  (e.g., a 360° stereoscopic video and depth capturing device) placed on the roof of a study room. In one embodiment augmented reality environment  300  may be created at least in part on data received by an I/O device  305  such as a camera and/or microphone comprised within a pair of glasses or a tablet computer. In some embodiments, an augmented reality environment  300  is formed based at least in part on the capabilities of I/O devices  305 . 
     In an embodiment, augmented reality environment  300  is comprised of images captured by I/O device  305  and streamed to places including, but not limited to: I/O devices  305  belonging to other users  302  or  303 , a cloud computing system, a server, a cluster of computers, etc. In some embodiments, the I/O device  305  is located in places including, but not limited to: the roof of a meeting room, office rooms, street corners, beaches, travel destinations, landmarks, class rooms, college campuses, sporting events, homes, vehicles, etc. 
     For example, in one embodiment a plurality of users  301 ,  302 ,  303 , and  304 , both remote and local, may meet at an augmented reality environment  300  that appears to be a club. In this example a first user  301 ,  302 ,  303 , and  304  may interact with a second user  301 ,  302 ,  303 , and  304  regardless of whether either user  301 ,  302 ,  303 , and  304  is a remote user  304  or a local user  301 . 
     In other embodiments, users  301 ,  302 ,  303 , and  304  may interact at locations such as a basketball court, a race track, or a farm. In one embodiment, augmented reality environment  300  is not created by real objects  309  in the real world but is instead completely virtual. In an embodiment, real objects  309  are mapped onto at least one augmented reality environment  300 . For example, real objects  309  may be digitized and mapped on an electronically created augmented reality environment  300 . In one embodiment, real objects  309  are blended with an augmented reality environment  300 . For example, real objects  309  may be digitized and embedded in an augmented reality environment  300 . In one embodiment real objects  309  are mapped and blended with at least one augmented reality environment  300 . 
     Projecting Augmented Reality into the Real World 
     While remote users  304  can view augmented reality environment  300  in real time, remote user  304  may be visible to local users  301 ,  302 , and  303 . In an embodiment, local users  301 ,  302 , and  303  may view and hear remote users  304  by using their I/O devices  305 . Remote users  304  and local users  301  may appear as avatars. In an embodiment a face is mapped to an avatar. 
     In one embodiment, local users  301 ,  302 , and  303  may view remote user  304 , and/or anything remote user  304  writes on white board  308  through their I/O devices  305 . In some embodiments remote user  304  is projected as a three-dimensional hologram or a two-dimensional image such that users  301  not using a viewing augmented reality environment  300  through a handheld I/O device  305  (e.g., glasses, a smartphone, glasses, etc.) may view remote user  304 . 
     In some embodiments, a plurality of remote users  304  may be in a same general “area” (e.g., augmented reality environment). For example, many remote users  304  may meet within an augmented reality environment  300  in front of the white house. Via a processor  315 , remote users  304  may see each other through their I/O devices  305  and local users  301 ,  302 , and  303  (e.g., users that are actually in front of the real white house) may see a plurality of remote users  304  walking in front of the white house by using I/O devices  305 . 
     Example Viewport 
       FIG. 3B  shows a viewport  310  comprising a position  313  in space and time, a direction  311 , and a viewpoint orientation  312 . In one embodiment, a viewport  310  refers to the view that a remote and/or local user  301 ,  302 ,  303 , and  304  sees. In one embodiment, a viewport  310  is a two-dimensional rectangle comprising a three dimensional scene shot provided by a virtual and/or real image capturing device. In one embodiment, a viewport  310  is based upon data received by an I/O device  305 . In an embodiment, a viewport is created by a processor  315 . 
       FIG. 3C  is a flow diagram  330  of an example method for communicating with at least one using augmented reality in accordance with embodiments of the present invention. 
     Example Methods of Use 
     In operation  331 , in one embodiment, at least one augmented reality environment  300  is provided. In an embodiment, augmented reality environment  300  comprises a virtual geography. In an embodiment a virtual geography comprises “real” objects  309  and/or “non-real” objects. In one example, real objects  309  are objects that are tangible or audible. In some embodiments real objects  309  are smellable. 
     In operation  332 , in one embodiment, the augmented reality environment  300  is combined with a stream of images of real objects  309 . For example, a stream of images captured by an I/O device  305  may be blended with an augmented reality environment  300 . As an example, a “yellow line” may be combined with a video stream of a football game. In an embodiment, the augmented reality environment  300  may appear on a television. In some embodiments, an augmented reality environment  300  may appear on an I/O device  305 . 
     In operation  333 , in one embodiment, data is received from a first user  301 ,  302 ,  303 ,  304  and a second user  301 ,  302 ,  303 ,  304 . In an embodiment, data is received from I/O devices  305 . In some embodiments an I/O device  305  provides a user  301  with access to an augmented reality environment  300 . For example, an I/O device  305  may show a user  301  and/or allow a user  301  to interact with an augmented reality environment  300  on a windshield and/or glasses. 
     In operation  334 , in one embodiment, a viewport  310  is created. In one embodiment a viewport comprises a position  313  in space and/or time, a direction  311 , and/or a viewpoint orientation  312 . In one embodiment a viewport  310  is the display a user  301  sees. In an embodiment processor  315  creates a viewport  310 . In another embodiment, augmented reality environment  300  creates viewport  310 . In one embodiment, servers and/or I/O devices  305  create viewports  310 . 
       FIG. 3D  is a flow diagram  340  of an example method implemented by a system for creating an augmented reality environment  300  in accordance with embodiments of the present invention. 
     In operation  341 , in one embodiment, at least one augmented reality environment  300  is provided. In an embodiment, augmented reality environment  300  comprises a virtual geography. In an embodiment a virtual geography comprises “real” objects  309  and/or “non-real” objects. In one example, real objects  309  are objects that are tangible or audible. In some embodiments real objects  309  are smellable. 
     In operation  342 , in one embodiment, the augmented reality environment  300  is combined with real objects  309  at a processor  315 . For example, a plurality of images captured by an I/O device  305  may be blended with an augmented reality environment  300 . As an example, a “yellow line” may be combined with a stream of images of a football game. In an embodiment, the augmented reality environment  300  may appear on a television. In some embodiments, an augmented reality environment  300  may appear on an I/O device. 
     In operation  343 , in one embodiment, data is received from a first user  301 ,  302 ,  303 ,  304  and a second user  301 ,  302 ,  303 ,  304 . In an embodiment, data is received from I/O devices  305 . In some embodiments an I/O device  305  provides a user  301  with access to an augmented reality environment  300 . For example, an I/O device  305  may show a user  301  and/or allow a user  301  to interact with an augmented reality environment  300  on a windshield and/or glasses. 
     In operation  344 , in one embodiment, a viewport  310  is created. In one embodiment a viewport comprises a position  313  in space and/or time, a direction  311 , and/or a viewpoint orientation  312 . In one embodiment a viewport  310  is the display a user  301  sees. In an embodiment processor  315  creates a viewport  310 . In another embodiment, augmented reality environment  300  creates viewport  310 . In one embodiment, servers and/or I/O devices  305  create viewports  310 . 
     Embodiments of the present technology are thus described. While the present technology has been described in particular examples, it should be appreciated that the present technology should not be construed as limited by such examples, but rather construed according to the claims. 
     Embodiments for communicating with at least one using augmented reality can be summarized as follows: 
     1. A method for communicating with at least one using augmented reality, said method comprising: 
     providing at least one augmented reality environment; 
     combining said augmented reality environment with a stream of images of real objects, wherein said real objects are mapped and blended with said at least one augmented reality environment; and 
     receiving data from a first user and a second user, wherein said data is generated by a plurality of input/output (I/O) devices, and wherein said I/O devices provide said first user and said second user with access to said at least one augmented reality environment. 
     2. The method of Claim  1 , further comprising: 
     creating a viewport, wherein a viewport comprises a position in space and time, a direction, and a viewport orientation. 
     3. The method of Claim  1 , wherein said augmented reality environment is projected onto said real objects.
 
4. The method of Claim  1 , wherein a said augmented reality comprises at least one advertisement.
 
5. The method of Claim  1 , wherein at least one user is physically located at said real objects.
 
6. The method of Claim  1 , wherein said augmented reality environment provides automated adaptive behavioral responses.
 
7. The method of Claim  1 , wherein said first user and said second user are mapped and blended with said at least one augmented reality environment.
 
8. The method of Claim  1 , wherein said augmented reality environment is formed based at least in part on the capabilities of said I/O devices.
 
9. A computer usable storage medium having instructions embodied therein that when executed cause a computer system to perform a method for creating an augmented reality environment, said method comprising:
 
     providing at least one augmented reality environment; 
     combining, at a processor, said augmented reality environment with real objects; and 
     receiving data from a first user and a second user, wherein said data is generated by a plurality of I/O devices, and wherein said I/O devices provide said first user and said second user with access to said at least one augmented reality environment. 
     10. The method of Claim  9 , further comprising:
 
creating a viewport, wherein a viewport comprises a position in space and time, a direction, and a viewport orientation.
 
11. The computer usable storage medium of Claim  9 , wherein said augmented reality environment is projected onto said real objects.
 
12. The computer usable storage medium of Claim  9 , wherein at least one user is physically located at said real objects.
 
13. The computer usable storage medium of Claim  9 , wherein said augmented reality environment provides automated adaptive behavioral responses.
 
14. The computer usable storage medium of Claim  9 , wherein said first user and said second user are mapped and blended with said at least one augmented reality environment.
 
15. The computer usable storage medium of Claim  9 , wherein a said augmented reality comprises at least one advertisement.
 
16. The computer usable storage medium of Claim  9 , wherein said augmented reality environment is formed based at least in part on the capabilities of said I/O devices.
 
17. A computer system for implementing augmented reality comprising:
 
     a plurality of I/O devices; 
     a processor, wherein said processor is operable to provide at least one augmented reality environment, combine said augmented reality environment with real objects, and receive data from a first user and a second user, wherein said real objects are mapped and blended with said at least one augmented reality environment, and wherein said I/O devices provide said first user and said second user with access to said at least one augmented reality environment. 
     18. The computer system of Claim  17 , wherein said augmented reality environment is projected onto said real objects.
 
19. The computer system of Claim  17 , further comprising a viewport, wherein a viewport comprises a position in space and time, a direction, and a viewport orientation.
 
20. The computer system of Claim  17 , wherein a said augmented reality comprises at least one advertisement.
 
     Section Four: Self-Architecting Adaptive Network Solution 
     Notation and Nomenclature 
     Some portions of the description of embodiments which follow are presented in terms of procedures, logic blocks, processing and other symbolic representations of operations on data bits within a computer memory. These descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. In the present application, a procedure, logic block, process, or the like, is conceived to be a self-consistent sequence of steps or instructions leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, although not necessarily, these quantities take the form of electrical or magnetic signal capable of being stored, transferred, combined, compared, and otherwise manipulated in a computer system. 
     It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussions, it is appreciated that throughout the present discussions terms such as “accessing”, “selecting”, “converting”, “cloning”, “adding”, “removing”, “determining”, “using”, “modifying”, “selecting”, “recombining” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system&#39;s registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices. 
     Furthermore, in some embodiments, methods described herein can be carried out by a computer-usable storage medium having instructions embodied therein that when executed cause a computer system to perform the methods described herein. 
     GLOSSARY 
     Parametric Transform: A processing component which converts zero or more inputs (when the number of inputs are =0, there is one or more implied or default inputs) into one or more resulting outputs under the direction of zero or more configurable dynamic parameters, one of which said parameters is a Transform Type. Transform Types can include: Digital Logic, Mathematical Formulas (including transfer functions), Digital Adaptive Networks, Analog Adaptive Networks, etc.) 
     Adaptive Network: a set of adaptive nodes connected by a common medium capable of communicating analog or digital information by some pattern of interconnection between nodes, including (but not limited to): ad hoc wirelessly connected processor based devices, neural networks, the internet, any selected subset of nodes on a connected network, smart sensor arrays, virtual private networks, memristor arrays, virtual or physical processors on virtual or physical networks, routers, distributed connected applications, podcast clients, smart broadcast receivers (e.g., smart TVs), etc. 
     Neuron: An adaptive network node 
     Synapse: A connection between nodes with weighting (product) 
     Network: Encapsulates one or more nodes and connections 
     Gene: An encoding of an Architectural or Adaptive characteristic 
     Allele: Encapsulates Genes, manages their recombination during genetic cycles 
     XformFunction (digital process) 
     Behavior (wraps either a Network or XformFunction 
     Organism (encapsulates Behavior): organizes interaction between other organisms, tribes, environment 
     Tribe (encapsulates one or more Organisms) 
     Ecosystem (encapsulates one or more Tribes) 
     Environment: Training environment—manages training and design cycles, feedback, etc. 
     Brief Description 
     Embodiments enable the provision of recursive modularity, thereby assisting in self-adaptive network processing. Further novel technology found herein provides for a meaningful use and management of the anticipated quantum increase in complexity of practical self-adaptive networks due to the expected quantum increase in performance of dedicated analog neural-network processing hardware afforded by titanium dioxide substrate memristor chips (or competitively disruptive solutions). Additionally, further novel technology found herein creates a bridge from silicon-based digital implementations of embedded and enterprise software solutions to hybrid forms that take full advantage of combined digital and analog processing capabilities. 
     Overview of Discussion 
     Example techniques, devices, systems, and methods for providing recursive modularity in adaptive network processing are described herein. Discussion begins with a description of embodiments within the larger system of a self-architecting adaptive network solution. The discussion continues with description of a use case scenario. An example system architecture is then described. Discussion continues with a description of example methods of use. 
     Furthermore, in some embodiments, methods described herein can be carried out by a computer-usable storage medium having instructions embodied therein that when executed cause a computer system to perform the methods described herein. 
     Self-Architecting Adaptive Network Solution 
     A self-architecting adaptive network solution system includes embodiments of the present technology. This system automates the design and training of high-complexity self-adaptive networks comprised of a neural-network processing capability, an automated training environment, multilevel cooperative and competitive models, recursive integration with other networks, digital logic elements, and various parametric transforms regulating dynamic redesign, training and feedback. 
     Specifically, the novelty about this solution approach is at least the following: (1) self-architecting, self-adapting capability; (2) recursive modularity within the context of both architecture and adaptation; (3) the approach to the reduction of local minima/maxima traps; and (4) the optional use of an adaptive model to optimize training in resource-limited environments. 
     (1) Self-Architecting, Self-Adapting Capability 
     Regarding the self-architecting/self-adapting capability, multiple network training cycles to automate both the weighting of network connections and the redesign of the network architecture itself are introduced, including a number of nodes, specific connections between nodes, node thresholds, etc. Further, a unique approach to sexual and asexual reproduction is utilized. Additionally, the parametric redesign utilizes a trained network or parametric transform. 
     Genetic Regeneration Algorithm 
     Currently, the proliferation of very complex networks that include massive node counts enables the advancement of artificial intelligence applications. The old fashioned way of managing such complex networks was to manually use mathematical functions to determine the desired weighting of a manually designed network (e.g., network comprising five to seven nodes). 
     “Weighting” involves the emphasizing of a contribution of some aspect of a phenomenon (or of a set of data) to a final effect or result, giving the recognized spect more weight in the analysis. Rather than each variable in the data contributing equally to the final result, some data are adjusted to contribute more than others. In reference to nodes of a neural network, each synapse has a weighting as to how important each synapse is compared to the totality of the network. 
     The weighted average logical representation of neurons and synapses is based on weighted averages and mathematics well known in the field. The meaning of the weights within a network of nodes actually alters the behavior of the output of that network, which is usually tied to a behavior of something. The meaning of the weights becomes well established in this network by the definition of how the network is architected, by how many nodes are within the network and what each node is connected to, and what the weighting is between those nodes. The combination of at least these factors creates a meaning for these nodes, the meaning significantly contributing to the determination of the training cycle (as is described herein below). The meaning (the weighting) is translated into a learned behavior. 
     When asexual regeneration (cloning) and sexual regeneration (recombinant) use identically architected source alleles, each&#39;s adaptation cycle uses conventional methods (simple cloning with mutation and conventional recombination of source alleles, respectively). The resultant nodes are weighted and have meaning, that of learned behavior. However, when asexual regeneration and sexual regeneration use dissimilar architecture, conventional methods have difficulty providing an accurate determination of the meaning (and hence the learned behavior) of the nodes since the organisms, the sets of nodes, and the connections between the sets of nodes are different. 
     Embodiments enable, when the architectures are dissimilar, the manipulation of the weights in order to better achieve more optimal training cycles. Embodiments also enable the automaton of the network design and the connections therein. 
     Embodiments further enable coherent meaning to be brought across subsets of nodes so that the learned behavior can be accrued and retained over generations of training and learning. 
     With regard to asexual regeneration (cloning), attention is brought to the adaptation cycle and the redesign cycle (rearchitecting cycle). The adaptation cycle of the sexual regeneration allows for the simple cloning with mutation. During the adaptation cycle of sexual regeneration (recombinant), the conventional method provides for the taking of two pairs of a successful population of nodes (two successful organisms) and sexually paring them to create offspring with new genetic codes. These offspring are combined, and a new string of coding and characteristics are generated. However, if the architectures of the two organisms are dissimilar, then the coding generated does not have a readily understandable meaning and the meaning may not be accurate. 
     The redesign cycle of the asexual regeneration (cloning), in accordance with an embodiment, not only clones with mutation (mutating the actual weights within the network), but adds or removes nodes and/or connections from the network and/or synaptic connections, point to point connections between the nodes. The redesign cycle uses a parametric transform, such as, but not limited to, randomization. For example, randomized mutations that may change the design of the node and the architecture of the network. 
     In another example, in accordance with an embodiment and with regard to the redesign cycle of the asexual regeneration, in a first operation, the entire code sequence for how nodes within a network are weighted is determined, and the code sequence that describes how the nodes are connected is determined. In the next operation, the code sequence that was determined is modified to specify a different architecture. The actual architecture (including the number of nodes and connections between the nodes, and the specification of the connections) is modified via automation through a parametric transfer function (e.g., randomization) to generate a new architecture and a new code for the prior architecture. The architecture is modified by adding and removing nodes and/or connections. Design parameters and mutation rates are inputs to the system. From these inputs, it is determined if a parametric transform is needed to be applied (in order to add and/or remove nodes). Then, the weightings are changed, in accordance with an embodiment. 
     Referring now to the adaptation cycle of differently architected source alleles during sexual regeneration, the encoding and weighting of the node meanings are compiled. The adaptation cycle and the redesign cycle occur during the learning process of the organism. Embodiments enable the mapping of meanings to weighting modifications. The adaptation cycle includes at least the following three operations described below: 
     (1) An architecture selection is made of one parent according to a parametric transform. For example, an embodiment selects which of the two parents is the core architecture. 
     (2) Nodes and connections with ancestry common to both parents are recombined. For example, nodes and connections are recombined in order to maintain a meaning. In order to create any meaning, the nodes and connections are recombined based on a sexual genetic algorithm that includes dominant and recessive, as well as other traits and ways of managing traits. 
     (3) Cloning is performed with mutations, only for weightings of elements not common to both parents, according to values from a source element. For example, the dissimilar cloned portions are weighted according to values from a source element. Only the weighted values from a first parent are copied, and a descendent of two parents is created. The similar architecture is separated from the dissimilar architecture, according to an embodiment. The dissimilar architecture is taken and copied, a fraction of which is selectively mutated, based on the parametric transform. The parametric transform may just be a random number that requires a weighting to be changed every 100 synapses, and required a changed threshold for every specified number of neurons. Some variable output is introduced to selectively mutate a fraction of the dissimilar architecture, the results of the function providing feedback. The feedback may then be used to determine making further changes to the architecture. 
     Thus embodiments, applying steps (1) through (3) immediately above, use feedback from training cycles across many events to help guide the resulting output of the networks, thereby creating parent behavior in a descendent (e.g., the puppy example described immediately below), as well as avoiding the max/min trap (as will also be described below). 
     With regard to a puppy example showing the adaptation cycle in operation, assume a puppy (virtual robot) on a television screen is performing what is hoped to be adorable tricks and trying to get the viewer of the television to look at the dogfood that is being advertised on the television. Further, in this example, it has already been determined that the viewer watching the television has a dog and buys different dog food than that advertised on the television. The advertiser wants to present to the user an enticing argument so that the viewer wants to buy the dogfood. Additionally, the advertiser does not wish to write an entire software application in which a dog is trying to persuade the viewer to buy dogfood. In embodiments, the puppy “learns” from the viewer, and responds to the viewer in ways that have been experienced to encourage the viewer to like the puppy. Thus, the puppy performs cute tricks on the television and gauges the viewer&#39;s reaction as to what action the puppy should take next. Thus, the puppy is “learning” from the viewer&#39;s reactions (which provide feedback) to the puppy&#39;s actions. The puppy can determine if the viewer is annoyed, irritated, happy, etc. The puppy changes its behavior according to the feedback received from the viewer. For example, if the viewer turns off the channel every time the puppy yips, then the puppy determines that yipping is not a good idea, won&#39;t be tolerated, and the puppy stops yipping. The puppy then starts to raise its paw to the viewer, in hopes of creating positive reactions, and possibly new customers for the dog food. has found. Thus is an example of the process for using feedback and training cycles across many numbers of events that help to guide the resulting output of those networks to create the parent behavior in the virtual robot that is the puppy. 
     Embodiments make the learning better, more effective and more efficient. Embodiments also help overcome the max/min trap (if the path to get to success involves something that moves away from success). Embodiments also enable the optimization of the design so that the correct amount of processing power can be anticipated. Once the architecture is changed (For example, the puppy is modified to perform a different action in response to continued negative feedback.), the architecture can be tested to determine if it has the correct amount of nodes (and the right number of neurons) for functioning. The method herein enables efficient use of resources by testing and retesting the architecture after it is modified. (In one example, such testing would reveal if the puppy is short of neurons.). Such continuous testing reduces waste (e.g., wasted resources). 
     The conventional art experiences the max/min problem. For example, if a puppy learns to raise its paw like it wants to shake its hand, some people react in ways that signify that the people think that the puppy is cute. However, other people may just think that the puppy&#39;s dog trick is boring. If the puppy stopped raising its paw, then the people who found this action to be cute would not be interested in the puppy anymore. Since the puppy wants people to be happy, the puppy will continue to raise its paw, because the puppy is getting positive reactions to some people, as opposed to no positive reactions in response to the puppy doing nothing. However, if the puppy actually rolled over, it would find that everyone was pleased with this action. However, the puppy will never stop raising its paw to learn how to roll over due to the fact that the puppy, in between the learned trick, would experience no positive reinforcement. Embodiments provide that the system can learn how many nodes are needed to solve a problem and what synaptic ratios are optimal for the situation, and then adjust those on the fly through the rearchitecting cycle. Embodiments enable the avoidance of the max/min problem by providing the ability to learn in more complex situations. 
     Referring now to the redesign cycle of differently architected source alleles during sexual regeneration, the architecture is cloned with mutation and nodes and/or connections are added or removed according to at least the following three rules outlined below: 
     (1) Cloning with mutation (as described above) with the addition or removal of nodes and/or connections according to the following rules: 
     (a) For each node not common to the ancestry of both of the parents, a parametric transform determines inclusion. As a review, in one embodiment, the adaptation cycle occurs (including the weighting system), and then the redesign cycle is applied. It should be appreciated that in optional embodiments, the adaptation cycle and the redesign cycle may run independently of or concurrently with each other and may not occur within the same learning process. Essentially, the adaptation cycle and the redesign cycle are the “learning” cycles, and the two cycles are not necessarily sequential. The steps within both cycles may happen in any number of frequency relationship with each other. 
     (b) The connections to nodes which map to common ancestry are sustained according to node-contributor-parent architecture. The parent contributing the node is determined. 
     (c) The initial node contributor parent architecture weightings are preset to parent values if persistent. Otherwise, the initial node contributor parent architecture weightings are preset to the weighting initialization parametric transform. The node contributor parent architecture weighting involves how many nodes there are and where the nodes are, and how the nodes are connected. The weightings are the things that determine the result values, wherein the result values determine the behavior. The weightings that are preset at the parent values, if they are mapped, are marked as persistent. The term “persistent” refers to a learned behavior that goes from one generation to the next. If, during the redesign cycle, a learned behavior takes place, this learned behavior can be marked as persistent or nonpersistent, and will or will not, respectively, be passed onto the next generation. This idea is roughly analogous to an organism having both instinct and the ability to learn. The organism has pre-learned things (already known to the organism) and there are other things that the organism has to figure out on its own. Embodiments describe a method of taking the result values and copying them from the parent networks into the newly generated networks, by copying the values marked as persistent, and not copying the values marked as nonpersistent (or not marked as persistent). 
     One embodiment provides a trained network or other method for parametric transform for parametric redesign. Instead of just simply mapping and directing these nodes to each other, parameters are given. Parametric redesign means that we can encode statistical characteristics according to parameters. For example, instead of saying, here&#39;s a new node and here are these five new connections that have been determined through the transfer function, the transfer function can actually be sophisticated enough to simply (instead of specifying an addition of a node or connections) specify statistical models that say what are the chances of creating nodes that connect in this direction and what is the likelihood of connecting to other nodes in this general direction within a certain map. For example, a trained network can be used as a part of a parametric transform. A neural network can be trained to design other neural networks through a task of that parametric transform. So, a redesign can be optimized in the redesign cycle by simply substituting, as a part of that parametric transform, a trained network that has been trained to be very good at redesigning other networks. For example, a trained network be a puppy trainer, that would be managing that part of the cycle. 
     With reference now to  FIG. 14 , a method  1400  for regeneration in a network is disclosed, in accordance with an embodiment. At operation  1405 , nodes are cloned within the network with a mutation, wherein the mutation includes a mutation of weights within the network. At operation  1410 , the following occurs: at least one of the nodes and connections are added or removed from at least one of the network and synaptic connections. At operation  1415 , a first code sequence describing how the nodes are weighted is determined; the first code sequence is sued to determine a second code frequency describing node connections; and the second code sequence is modified to specify a different architecture, using a parametric transfer function. 
     With reference now to  FIG. 15 , a method  1500  for regeneration in a network is disclosed, in accordance with an embodiment. At operation  1505 , an architecture of one parent of a pair of parents is selected according to a parametric transform. At operation  1510 , a node and connections are recombined with an ancestry common to the pair of parents. At operation  1515 , the nodes are cloned within the network with a first mutation only for weightings of elements not common to both parents of the pair of parents, wherein the first mutation includes a mutation of weights within the network. At operation  1520 , the following occurs: nodes are cloned within the network with a second mutation, wherein the second mutation includes a mutation of weights within the network; at least one of the nodes and connections from at least one of the network and synaptic connections is added or removed, wherein the cloning, the adding, and the removing are performed according to the following rules: for each node not common to the ancestry of both parents, a parametric transform determines inclusion; the connections to the nodes which map to the common ancestry are sustained according to node-contributor-parent architecture; and the initial node-contributor-parent-architecture weightings are preset to parent values if persistent. 
     With reference not to  FIG. 16 , a method  1600  for regeneration in a network is disclosed, in accordance with an embodiment. At operation  1605 , nodes are cloned within the network with a first mutation, wherein the first mutation includes a mutation of weights within the network. At operation  1610 , are cloned within the network with a second mutation, wherein the second mutation includes a mutation of weights within the network. At operation  1610 , at least one of the nodes and connections from at least one of the network and synaptic connections is added or removed, wherein the cloning, the adding, and the removing are performed according to the following rules: for each node not common to the ancestry of both parents, a parametric transform determines inclusion; the connections to the nodes which map to the common ancestry are sustained according to node-contributor-parent architecture; and the initial node-contributor-parent-architecture weightings are preset to parent values if persistent. 
     The following is a general summary of the component ideas relating to the asexual and sexual regeneration, and the cycles therein. Regarding the asexual regeneration (cloning), there are two cycles, that adaptation cycle (new weighting) and the redesign cycle (new architecture). The adaptation cycle refers to the simple cloning with mutation (transform with mutation rate as input). For example, the xform equals a random mutation. The redesign cycle refers to the cloning with mutation, as per the adaptation cycle, plus adding or removing node(s) and/or connection(s) (additional transform with design parameters and mutation rate as inputs). For example, the xform is random within design parameters. 
     Regarding the sexual regeneration (recombinant), there are two different types of alleles, identically architected source alleles and differently architected source alleles. 
     Regarding the identically architected source alleles, there are two types of cycles, the adaptation cycle (new weighting) and the redesign cycle (new architecture). The adaptation cycle for the sexual regeneration uses conventional recombination of source alleles. The redesign cycle for the sexual regeneration uses cloning with mutation (as mentioned above), plus adds or removes node(s) and/or connection(s) (additional mutation parametric transform with design parameters and mutation rate as inputs). For example, the xform is random within design parameters. 
     Regarding the differently architected source alleles, there are two types of cycles, also the adaptation cycle (new weighting) and the redesign cycle (new architecture). 
     There are at least three significant factors to describe regarding the adaptation cycle for the differently architected source alleles: (1) the architecture selection from one parent according to parametric transform; (2) the recombination of nodes and connections with ancestry common to both parents; and (3) the cloning with mutation only for weightings of elements not common to both parents according to values from source elements. 
     There are at least three significant factors to describe regarding the redesign cycle for the differently architected source alleles: (1) cloning with mutation (as mentioned above), plus adding or removing node(s) and/or connection(s) according to the following rules: (a) for each node not common to ancestry of both parents, parametric transform determines inclusion; (b) the connections to nodes which map to common ancestry are sustained according to node-contributor-parent architecture; and (c) the initial node contributor parent architecture weightings are preset to parent values if persistent (otherwise according to weighting initialization parametric transform). 
     (2) Design Modularity 
     Innovations regarding design modularity include: (a) recursive modularity of system architecture and adaptations; (2) alternation of balance between competitive and cooperative reinforcement in scoring during different phases of a training cycle; and (3) optionally: recursive integration of digital logic with analog matrix processing. 
     Example Process Using Self-Architecting/Self-Adapting Capability with Designed Modularity 
     The following list nine (A-I) steps that describe an example process for using self-architecting/self-adapting capability with designed modularity. 
     (A) Specify training environment (input and output training vector generator: implemented as hard-coded model, adaptive model, data map, record, or interactive real-world interactions), scoring criteria, other initial parameters: initial population, network complexity range, etc. 
     (B) Generate new initial system. 
     (C) Iterate through the following cycles (training, adaptive, design, regeneration, culling, environmental pressure) synchronously or asynchronously with similar or dissimilar frequencies until desired performance and design targets are met: 
     (C)(i) During training cycles, test current adaptation of each component and score according to environmental criteria (including appropriateness of outputs to inputs, network complexity targets, etc.). 
     (C)(ii) During adaptive cycles, create new adaptations (weighting matrices). 
     (C)(iii) During Design cycles, create new architecture forms. (Add and/or subtract nodes and connections.) 
     (C)(iv) During regeneration cycles, in conjunction with adaptive and design cycles, increase population according to transform based on targets using regeneration algorithm. 
     (C)(v) During culling cycles, reduce population according to transform based on targets. 
     (C)(vi) During environmental pressure cycles, change scoring criteria inputs to transform. 
     (D) Repeat steps A, B, and C for each of the desired number of low-level solutions, varying criteria as needed or until goals met or optimizations stabilize. 
     (E) Aggregate separate solutions into single multi-functional solution by fusing inputs and outputs of interfaces to other entities. 
     (F) Refine new solution (i.e. repeat steps A, C, and D as needed, or until goals met or optimizations stabilize). 
     (G) Recursively iterate above (i.e. repeat steps A-F as needed, or until goals met or optimizations stabilize). 
     (H) Above seven steps (A-G) may, by original specification, recursively embed any number of digital transforms in lieu of actual networks. If so, to run on specialized co-processing architecture (i.e. separate digital and analog processors), additional steps must be taken at some point during or after the training cycle, but before deployment to multiprocessing target: 
     (H)(i) Separate processing structures (e.g. queues, caches, FIFOs, etc.) for digital transforms and analog transforms (optimized networks). 
     (H)(ii) Deploy Cycle Synchronization Agent to production to correlate digital and analog inputs and outputs to common logical cycles between the two processing structures using load balancing, throttling, semaphores, or combined and/or other approaches. 
     Note: The above steps (A-H) can optionally be applied to an adaptive-model-based training environment, if used. 
     (I) Additional training, architecting, and refinement can commence as above once deployed to production (using real-world interactions as training vectors), but zero-downtime-tolerance and zero-defect-tolerance systems are best effected by the following steps: 
     (I)(i) Allocation of necessary processing resources to train independent adaptive model and primary adaptive system. 
     (I)(ii) Applying real-world training interaction as training vectors to adaptive model (including some hysteresis of training vectors from prior adaptation of model). 
     (I)(ii) Cloning production adaptive behavior system and moving clone to allocated off-line processing. 
     (I)(iii) Extensive generational training cycles against adaptive model, according to steps A-G. 
     (I)(iv) After Q/A, replacement of previous system with resultant system. 
     Note: overlapping the automated design and the training cycles presents special case problems for recombination of adaptive (weighting) characteristics between differently-architected networks. By definition, this does not apply to asexual regeneration (see below), as cloning involves only one architecture. 
     Reduction of Local Minima/Maxima Traps 
     The concept of the reduction of local minima/maxima traps can be divided into two ideas: (a) the intentional inconsistency in scoring, design, weighting and feedback algorithms; and (b) the automated re-architecting during or between feedback training cycles also reducing minima/maxima traps. 
     Regarding the Intentional inconsistency in the scoring, design, weighting and feedback algorithms, during the culling cycle, for example, rather than the simple removal of the lowest performing elements of the system, a parametric transform will inject intentional inconsistency into the selection process. A simple example transform which interjects inconsistency while reducing a population approximately by N % (a given rate) uses pseudo-random numbers to randomly cull elements scoring in the lower 50%: 
     
       
         
           
               
               
             
               
                   
                   
               
             
            
               
                   
                 \ cull(float rate, Set&lt;PopulationElement&gt; population) { 
               
               
                   
                 for each element in population below median index sorted by 
               
               
                   
                 element.performance { 
               
               
                   
                 if (xform(element)) { cull(element); } 
               
               
                   
                 } } 
               
               
                   
                 ///--------------------------------- 
               
               
                   
                 boolean xform(Element element) { return(random(1) &lt; 
               
               
                   
                 (element.environment.cullRate*2)) } 
               
               
                   
                   
               
            
           
         
       
     
     Regarding the automated re-architecting during or between feedback training cycles also reducing minima/maxima traps, the setting design cycle frequency of greater than 0 in environment initialization causes interleaving of architecture changes with the training, scoring, regeneration, and culling cycles. 
     The Use of an Adaptive Model to Optimize Training in Resource-Limited Environments 
     The techniques (noted above and described, overall, as the adaptive model) associated with the self-architecting/self-adaptive capability, the design modularity, and the reduction of the local minima/maxima traps, are used to optimize the learning and behavior adaptation to environments that include human interaction or other resource constraints. The following list is an outline of the general steps that are taken in using the adaptive model: (A) Break problem into component parts. One example of breaking a problem into component parts is the example scenario of a combat game automaton training. The overall problem is to survive the combat simulation with multiple combatants using maneuvers and firing solutions dictated by simulation parameters. An example component problem breakdown is as follows: (i) Firing solutions optimization: (a) recognize other combatant&#39;s maneuver patterns; (b) predict competitor&#39;s position; (c) compensate ballistic firing solution for physics simulation (i.e. muzzle velocity, windage, ballistic coefficient, gravity, etc.); and (d) balance firing rate with gun barrel temperature; (b) evasive maneuvers: high-frequency component of movement pattern generation to minimize hit-rate from enemy fire; and (c) strategic positioning: low-frequency component of movement pattern generation to maximize overall success rate. 
     A second example of breaking a problem into component parts involves the scenario of an interactive advertising agent component training example. The overall problem is to maximize advertising engagement relative to initial content viewership  9  e.g., balancing ratings vs. click-throughs). An example component problem breakdown is as follows: (a) special effects and highlighting (how to attract attention); (b) verbalizations (when to say what); (c) movement (how to position for perceived context and availability); and (d) request recognition (e.g., vocal, verbal, pointer cues). In this example, the training environment best includes progressive feedback from any of, but not limited to, the following: marketing professionals; focus groups; beta-testers; consumers; and adaptive models. The training and architecture cycles extend through production deployment and the entire product lifecycle. 
     (B) Construct training environment and scoring of component performance with competitive bias. 
     (C) Grow ecosystem of self-architected component solutions through multiple generations. 
     (D) Train until element performance stabilizes within goals. 
     (E) Switch scoring bias from competitive to cooperative. 
     (F) Train until overall optimization stabilizes within goals. 
     (G) Convert top performing aggregates to elements (fusing I/O integration points into Nodes &amp; Connections). 
     (H) Switch training environment scoring bias back to competitive. 
     (I) Clone a significant population of a variety of new elements. 
     Repeat steps A-I until solution performs according to specifications. 
     The following is a discussion regarding step G above, the converting of top performing aggregates to elements, and the recursive modularity of the system architecture and adaptations. The description assumes that steps A-F have been performed, in that the scoring bias from competitive to cooperative has been switches, and the objects have been trained such that their behavior falls within certain objectives for the objects. 
     As will be seen, the conversion process described below adds much flexibility to the overall adaptive network solution. In the following example, we use the behavior of puppies to describe the method step G. Therefore, it is assumed that a set of puppies is part of a pack of puppies and that those puppies have been trained to bark and wag in unison (or in some other acceptable pattern). There may be more than one pack of puppies, wherein the puppies in each pack have been trained to bark and wag in unison with the other puppies located within the same pack. 
     Of note, each pack itself is attached to the environment. In this example, there are 3 packs. The first pack of puppies has two puppies within it. The second pack of puppies has zero puppies within it. The third pack of puppies has four puppies in it. The first pack and the third pack of puppies are competing against each other. In this case, if the first pack of puppies barks and wages their tails better than the third pack of puppies, then the first pack wins. Thus, in embodiments, the third pack is eliminated. The best performing pack, the first pack, survives and is considered optimized. The first pack is considered to have been trained the best because the first pack meets expectations and stabilized results. As will be described below, this surviving pack, converted into a dog (e.g., puppies performing in unison) is the first resultant element. 
     Of note, during the training process (teaching the puppies to wag and bark in unison), test vectors are used to determine the training progress (how close the performance comes to meeting desired results). Test vectors are load inputs and outputs that strain to the environment to deal with stimulus and prepare a response. The inputs are paired with a predetermined set of expected outputs to define the test vector, of a set of test vectors (wherein the “set” can include one or more test vectors). In one embodiment, these test vectors are stored in a location that is accessible by embodiments. 
     Further, as the puppies within the pack are being trained, the behavior of the puppies is being shaped—the puppies&#39; behavior is changing to adapt to the training. 
     Once the puppies are trained to perform in unison, then these puppies are converted to being a dog (“dog A”) (that is attached to the environment), which is the first resultant element. 
     Eventually, after the dog A and other dogs that are attached to the environment are trained to behave in unison, those dogs that are attached to the environment but cannot perform acceptably are eliminated. This group of dogs (not including the dogs that were eliminated), once trained, is then converted into a single bigger dog, or a second resultant element. This process of conversion of smaller units into a single larger unit, and then taking singular larger units (that had been converted from smaller units) and converting these to a single larger unit, is repeated until an overall pre-define objective is met. 
     In some embodiments, in some cases, this progressive refinement does not necessarily lead to larger, more complex units, especially when the design cycle (aka self-architecting cycle) is biased to reduction-refinement in favor of lower node counts. 
     Regarding the first pack of puppies that had the two puppies within, puppy one is a network and has ten neurons in his head and puppy two has sixteen neurons in his head. The first pack has three connections to the environment. Once puppy one and puppy two have become a dog, according to an embodiment, the resultant element, the dog, will be one network and will have twenty six neurons in its head, with six connections to the environment. 
     An example reduction refinement embodiment goal-seeks in an attempt to retain the behavior while reducing neuron/node count to lowest possible value (example: perhaps 15). 
     This process repeats itself, thereby creating many levels of puppy and dog encapsulation. Of note, while in one embodiment, the network is an adaptive network, in another embodiment, the network is a neural network. The connection between nodes within a neural network is called a synapse, and what is the adaptive network node in an adaptive network is the neuron in a neural network. The network is the puppy brain. The genes and alleles relate to how the genetic algorithm is or is not recombined. 
     As will be described below, the supervisory element  410  coordinates the interaction between the packs and the dogs and their continuous learning (e.g., training and adapting). 
     Thus, the embodiments enable the conversion of a super structure into a substructure, the parts of which are integrated with other substructures of other superstructures, to arrive at a fully trained (optimized) structure including some or all of the now trained super structure. 
     Example aspects of the substructures and superstructures that are subject to re-architecting element by element, unless dictated by system parametric transform, are, but are not limited to being, the following: connection rate; connection geometry; mutation rate; trait dominance; adaptive persistence (replication of weights during adaptive response cycle); node count; connection ratio; environmental performance; and competitive vs. cooperative. 
     Network training cycles can be synchronous, harmonic (nested), or entirely asynchronous. An example of a harmonic network training cycle is when a training and adaptive cycle is nested within a design cycle. Network training cycles include the following: training (feed inputs to nodes and record and score outputs); adaptive (primary adaptive learning cycle-modifies weights of connections [products of sums]; design (including changes to number of nodes, specific connections between nodes, node thresholds, damping etc.); regeneration (can be modulated with culling cycle by environmental pressure cycle to introduce population expansion/contraction dynamics); culling (can be modulated with regeneration cycle by environmental pressure cycle to introduce population expansion/contraction dynamics); and environmental pressure (manage oscillations between criteria variation: collaborative vs. competitive pressures, expansion vs. contraction, etc.). 
     Design modularity may be implemented in at least the following ways: recursive modularity of system architecture and adaptations; solutions to problems relevant to one level of detail can be automatically combined to provide higher level solutions to multiple problems with a virtually unlimited number of recursively modular levels; alternation of balance between competitive and cooperative reinforcement in scoring during different phases of training cycle; and optionally, recursive integration of digital logic with analog matrix processing. 
     Example System Architecture 
       FIG. 4A  shows a device  400  for providing recursive modularity in adaptive network processing, in accordance with an embodiment. Device  400  includes, coupled with a processor: an element aggregation accessor  404 ; an aggregation element selector  412 ; and an aggregation element converter  414 . Optionally, various embodiments include: a supervisory element  410 ; a first resultant element accessor  416 ; a first resultant element selector  418 ; a first resultant element converter  420 ; a second resultant element accessor  422 ; a second resultant element selector  424 ; and a second resultant element converter  426 . 
     In one embodiment, the element aggregation accessor  404  accesses at least one trained aggregation of elements  402  that is coupled with an environment  439 , wherein each trained aggregation of elements of the at least one trained aggregation of elements  402  includes a set of trained elements and is stabilized within a set of objectives. As described above, the set of trained elements are the result of steps A through G, within the process of using an adaptive model to optimize training in resource-limited environments. Of note, the “set” of the set of trained elements may be one or more trained elements. The set of objectives are the expectations desired to be fulfilled for a set of elements. Once the expectations for the set of elements are met, then the set of elements are considered to be trained, and thus “optimized”. Of note, the “set” of the set of objectives may be one or more objectives. 
     Thus, in reference to the example given above regarding the puppies, the at least one trained aggregation of elements are the two puppies in the first pack. The two puppies are trained and are stabilized with a set of objectives. For example, the two trained puppies are wagging and barking in unison (the objective) and are thus stabilized after meeting the set of objectives. 
     In various embodiments, the element aggregation accessor  404  includes: a trained adaptive network accessor  406 ; and a logic component accessor  408 . The trained adaptive network accessor  406  accesses at least one trained adaptive network. The logic component accessor  408  accesses at least one logic component. 
     The aggregation element selector  412  selects at least one of the at least one trained aggregation of elements that meets a first performance threshold. The first performance threshold is a predetermined value that is met or exceeded by the one or more of the at least one trained aggregation of elements  402 . A predetermined value refers to quantified behavior. In one embodiment, the behavior of just one of the trained aggregation of elements exceeds the predetermined quantified behavior. However, in another embodiment, the quantified behavior of more than one of the trained aggregation of elements exceed the predetermined quantified behavior. Thus, the aggregation element selector  412  selects the aggregation(s) of elements that, according to a predetermined rule, statistically tends to better meet and/or exceed the predetermined quantified behavior, as per a pre-specified parametric transform (e.g. randomization agent). With reference to the puppy example scenario described above, the first performance threshold is the barking and the tail wagging in unison. Those aggregations of elements, the puppies, which back and wag their tail in unison within a certain range of error (the first performance threshold) are then selected. 
     The aggregation element converter  414  converts the selected at least one trained aggregation of elements to an element status to achieve a converted at least one trained aggregation of elements, such that each of the converted at least one trained aggregation of elements becomes a first resultant element  436  that competes with other first resultant elements  438 . The element status is a determination of the converted trained aggregation of elements, whether it is first resultant element  436 , a second resultant element, a third resultant element, and so on. Thus, and with reference to the puppy scenario described above, the element status of the at least one trained aggregation of puppies (the two puppies) is that of a resultant element. This first resultant element  436  will then compete with other first resultant elements. The other first resultant elements  436  refer to other trained aggregation of elements that have also met a first performance threshold and have been converted to being an element status equal to the first resultant element  436 . 
     The supervisory element  410  continuously coordinates interactions associated with learning between at least one of the at least one trained aggregation of elements  402  and an external interface to the environment  439 . 
     The first resultant element accessor  416  accesses at least one trained first resultant element  436  that is coupled with the environment  439 . Each trained first resultant element of the at least one trained first resultant element  436  includes a set of trained aggregation of elements and is stabilized within a second set of objectives. In other words, the first resultant element accessor  416  is repeating much of the functioning of the element aggregation accessor  404 , with a few exceptions. The first resultant element accessor  416  is accessing the combined result—the resultant element—of the functioning of the element aggregation accessor  404 , the aggregation element selector  412 , and the aggregation element converter  414 . The second set of objectives is just a set of objectives that is separate from the first set of objectives. In one embodiment the first and the second set of objectives are the same, while in another embodiment, the first and the second set of objectives are different. With reference to the puppy scenario described herein, the first resultant element accessor  416  accesses the at least one trained first resultant element  436 , the first pack with the two trained puppies (the first resultant element) or any of the other trained first resultant elements that had been selected and converted by the aggregation element selector  412  and the aggregation element converter  414 . In this scenario, there are only two packs of puppies left, as the second pack was eliminated from the selection process in the first round because it did not meet the first performance threshold. Thus, the first and the third pack (having four puppies) are accessed. 
     The first resultant element selector  418  selects at least one of the at least one trained first resultant elements  436  that meets a second performance threshold. The second performance threshold is just a performance threshold that is separate from the first performance threshold. In one embodiment, the second performance threshold is the same as the first performance threshold. In another embodiment, the second performance threshold is different from the first performance threshold. With reference to the puppy scenario, both the first pack and the third pack (both resultant elements) meet and/or exceed the second performance threshold. For example, both packs are sitting upon command and in unison, which is required to exceed the second performance threshold. 
     The first resultant element converter  420  converts the selected at least one trained first resultant element to a second element status to achieve a converted one or more trained first resultant element, such that the converted at least one trained first resultant element becomes a second resultant element  430  that competes with other second resultant elements  428 . Thus, with reference to the puppy scenario, the combination of the first pack and the third pack become the second resultant element  430 . 
     The second resultant element accessor  422  functions in a manner similar to that of the first resultant element accessor  416 . The second resultant element accessor  422  accesses at least one trained second resultant element that is coupled with the environment  439 , wherein each trained second resultant element of said at least one trained second resultant element includes a set of trained first resultant elements and is stabilized within a third set of objectives. Of note, the “set” of the set of trained first resultant elements may be one or more of the trained first resultant elements. Further, the third set of objectives is just objectives that are separate from the first and second set of objectives. The third set of objectives may be the same or different than the first set and/or the second set of objectives. 
     The second resultant element selector  424  functions in a manner similar to that of the first resultant element selector  418 . The second resultant element selector  424  selects at least one of the at least one trained second resultant element  430  that meets a third performance threshold. The third performance threshold is just a performance threshold that is separate from the first and the second performance thresholds. However, in various embodiments, the third performance threshold may be the same or different from either the first and the second performance threshold. 
     The second resultant element converter  426  functions in a manner similar to that of the first resultant element converter  420 . The second resultant element converter  426  converts the selected at least one trained second resultant element to a third element status to achieve a converted at least one trained second resultant element, such that the converted at least one trained second resultant element becomes a third resultant element  434  that competes with other third resultant elements  432 . 
     Example Methods of Use 
       FIG. 4B  is a flow diagram  440  of an example method for providing recursive modularity in adaptive network processing. 
     In operation  442 , in one embodiment and as described herein, at least one trained aggregation of elements  402  that is coupled with an environment  439  is accessed, wherein each trained aggregation of elements of the at least one trained aggregation of elements  402  includes a set of trained elements and is stabilized within a set of objectives. In various embodiments, the accessing of operation  442  includes the accessing of at least one trained adaptive network and the accessing of at least one logic component. In one embodiment, the accessing of the at least one logic component includes the accessing of at least one digital logic component and/or the accessing of at least one analogue logic component. In one embodiment, the accessing of at least one logic component includes accessing at least one logic component that is dynamically alterable. 
     In one embodiment, the accessing of operation  442  includes, accessing at least one trained aggregation of elements  402  that is coupled with the environment  439 , wherein each trained aggregation of elements of said at least one trained aggregation of elements  402  includes a set of trained elements and is stabilized within a set of objectives, wherein the first resultant element includes a supervisory element  410  configured for continuously coordinating interactions associated with learning between at least one of the at least one trained aggregation of elements  402  and the at least one trained aggregation of elements  402  and an external interface to the environment  439 . 
     In operation  444 , in one embodiment and as described herein, at least one of the at least one trained aggregation of elements  402  that meets a first performance threshold is selected. 
     In operation  446 , in one embodiment and as described herein, the selected at least one trained aggregation of elements is converted to an element status to achieve a converted at least one trained aggregation of elements  436 , such that each of the converted at least one trained aggregation of elements  436  becomes a first resultant element that competes with other first resultant elements  438 . 
     In operation  448 , in one embodiment and as described herein, at least one trained second resultant element that is coupled with the environment  439 , wherein each trained second resultant element of the at least one trained second resultant element includes a set of trained resultant elements and is stabilized within a third set of objectives. At least one of the at least one trained second resultant element that meets a third performance threshold is selected. The selected at least one trained second resultant element is converted to a third element status to achieve a converted at least one trained second resultant element, such that the converted at least one trained second resultant elements becomes a third resultant element that competes with other third resultant elements. 
     Embodiments for providing recursive modularity in adaptive network processing are thus described. While the present technology has been described in particular examples, it should be appreciated that the present technology should not be construed as limited by such examples, but rather construed according to the claims. 
     Various embodiments include the recursive use of the described aggregation conversion algorithm in problem solving in combination with some or all of the following approaches: 
     Multiple network refinement cycles, which can be synchronous, harmonic (aka “nested”), or asynchronous, comprised of one or more of the following: training cycles (where nodes are fed inputs and outputs scored against goal criteria); adaptive cycles (where weights of connections are modified to improve prospect of future scoring); design cycles (where different network architectures are generated to improve the prospect of more efficient adaptations as measured by adaptive cycle response, including changes to network node counts and connection counts and ratios, in addition to the map of specific connections); regeneration cycles (where elements are replicated according to one or more regeneration algorithms to provide an improved quality of diversity, as measured by scoring against cooperative or competitive goals); culling cycles (where element count is reduced according to a statistical model to restrain runaway complexity); environmental cycles (manages oscillations between criteria variation (e.g. collaborative vs. competitive scoring bias, element population expansion vs. contraction bias, relative design scoring between element node complexity vs. other scoring factors, etc.). 
     The regeneration and culling cycles can be modulated to introduce population expansion and contraction dynamics into the competitive and cooperative scoring approach, which can accelerate adaptation. Specific regeneration and culling activities can be governed by one or more parametric transforms, according to the algorithms used. A simple example of a useful parametric transform for culling is a random (or pseudo-random) function within a range of values to introduce population reduction based on statistical probability. The following pseudo code represents logic that introduces some variation in performing an element population reduction by a given cull rate: 
     
       
         
           
               
               
             
               
                   
                   
               
             
            
               
                   
                 cull(float rate, Set&lt;PopulationElement&gt; population) { 
               
               
                   
                 for each element in population below median index sorted by 
               
               
                   
                 element.performance { 
               
               
                   
                 if (xform(element)) { cull(element); } 
               
               
                   
                 } } 
               
               
                   
                 // --------------------------------- 
               
               
                   
                 boolean xform(Element element) { return(random(1) &lt; 
               
               
                   
                 (element.environment.getCullRate( )*2)) } 
               
               
                   
                   
               
            
           
         
       
     
     Such an approach helps to minimize local minima/maxima traps. 
     Various embodiments address the issue of recombinant regeneration (aka sexual reproduction) between dissimilar architectures during the regeneration cycle by the following method: 1) Cloning with mutation (aka asexual reproduction) as indicated by statistical parametric transform (e.g. pseudorandom go/no go based on mutation rate); and 2) Mutation process adds or removes nodes and or connections according to the following rules: for each node not common to ancestry of both parents, an additional parametric transform determines inclusion of node; connections to nodes which map to common ancestry are sustained according to node-contributor-parent architecture; initial node contributor parent architecture weightings are then preset to parent values if persistent (persistence can itself be an inheritable trait); if not persistent, weightings are set according to a weighting initialization parametric transform. 
     Various embodiments address the issue of recombinant regeneration (aka sexual reproduction) between dissimilar architectures during the adaptation cycle by the following method: Architecture selection from one parent according to a selection parametric transform; Recombination of nodes and connections with ancestry common to both parents; Cloning with mutation only (aka asexual reproduction) for determination of weightings of elements not common to both parents according to values from source ancestor element. 
     Various embodiments further organize the recursively embedded logic elements and network elements into separate distributed processing structures (e.g. queue, cache, etc.) based on the target processor for each element&#39;s response processing (during some combination of the various cycles), and manage the processing structures with a synchronization agent, to ensure that like cycle&#39;s interfaces match each to the other using one or more of the following approaches: load balancing, throttling, semaphores, other methods. 
     At least one embodiment uses this approach to efficiently couple a dedicated titanium dioxide based analog coprocessor to a traditional digital Von Neuman silicon dioxide based processor. 
     At least one embodiment uses the synchronization agent management of recursively embedded logic elements and network elements to distribute processing across a wide network of connected devices (such as a smart-device sensor array, or a population of concurrent mobile device app users) to partition and concurrently solve problems across all device nodes. 
     Various embodiments simulate neural network analog processing on digital processor based devices. 
     Various embodiments include at least one of the following characteristics as part of the genetic code sequence for regeneration: connection rate (the rate at which an individual node tends to connect to other nodes); connection geometry; mutation rate; trait dominance; adaptive persistence (the reuse of connection weightings on regeneration cycles); node count (the number of nodes); connection ratio (aka synaptic ratio, the overall ratio of connections to nodes); environmental performance; node thresholds; and competitive vs. cooperative bias (used in conjunction with similar bias from environment). 
     Various embodiments use one or more of the following approaches: managing environmental feedback and dynamic parameters supplied to parametric transforms with trained adaptive networks; Replacing the parametric transforms with direct output from trained adaptive networks. The result of combinations of these approaches is to train adaptive networks to train adaptive networks. 
     Various embodiments use adaptive models (instead of static test vectors or real-world interactions) for continuation training. Such an approach is particularly useful when considerable adaptation is desired based on relatively little real-world data interaction (e.g. training against a single consumer&#39;s response to a limited set of stimuli, vs. against an entire audience with multiple instantiations). 
     Various embodiments iterate through one or more of the following problem-solving steps (sometimes recursively), using fully-automated or semi-automated interactive tools: Problem Decomposition; Training Environment Specification; System Initialization; Cycle Iteration; Training Goal(s) Stabilization Analysis; Scoring Bias Adjustment; Element Aggregation; Refinement; Processing Structure Separation; Deployment; Real-World Training (production); Off-line Training Cycles (“sleep cycles”, once deployed). 
     Embodiments for providing recursive modularity in adaptive network processing can be summarized as follows:
     1. A computer usable storage medium having instructions embodied therein that when executed cause a computer system to perform a method for providing recursive modularity in adaptive network processing, said method comprising:
       accessing, by a processor, at least one trained aggregation of elements that is coupled with an environment, wherein each trained aggregation of elements of said at least one trained aggregation of elements comprises a set of trained elements and is stabilized within a set of objectives;   selecting, by said processor, at least one of said at least one trained aggregation of elements that meets a first performance threshold;   converting, by said processor, selected at least one trained aggregation of elements to an element status to achieve a converted at least one trained aggregation of elements, such that each of said converted at least one trained aggregation of elements becomes a first resultant element that competes with other first resultant elements.   
       2. The computer usable storage medium of claim  1 , wherein said accessing at least one trained aggregation of elements comprises:
       accessing at least one trained adaptive network.   
       3. The computer usable storage medium of claim  1 , wherein said accessing at least one trained aggregation of elements comprises:
       accessing at least one logic component.   
       4. The computer usable storage medium of claim  3 , wherein said accessing at least one trained aggregation of elements comprises:
       accessing at least one digital logic component.   
       5. The computer usable storage medium of claim  3 , wherein said accessing at least one trained aggregation of elements comprises:
       accessing at least one analogue logic component.   
       6. The computer usable storage medium of claim  1 , wherein said accessing at least one trained aggregation of elements comprises:
       accessing at least one logic component, wherein said at least one logic component is dynamically alterable.   
       7. The computer usable storage medium of claim  1 , wherein said accessing at least one trained aggregation of elements that is coupled with an environment comprises:
       accessing at least one trained aggregation of elements that is coupled with an environment, wherein each trained aggregation of elements of said at least one trained aggregation of elements comprises a set of trained elements and is stabilized within a set of objectives, wherein said first resultant element comprises a supervisory element configured for continuously coordinating interactions associated with learning between at least one of said at least one trained aggregation of elements and said at least one trained aggregation of elements and an external interface to said environment.   
       8. The computer usable storage medium of claim  1 , further comprising:
       accessing, by said processor, at least one trained first resultant element that is coupled with said environment, wherein each trained first resultant element of said at least one trained first resultant element comprises a set of trained aggregation of elements and is stabilized within a second set of objectives;   selecting, by said processor, at least one of said at least one trained first resultant elements that meet a second performance threshold;   converting, by said processor, selected at least one trained first resultant elements to a second element status to achieve a converted one or more trained first resultant element, such that said converted at least one trained first resultant element becomes a second resultant element that competes with other second resultant elements.   
       9. The method of claim  8 , further comprising:
       accessing, by said processor, at least one trained second resultant element that is coupled with said environment, wherein each trained second resultant element of said at least one trained second resultant element comprises a set of trained resultant elements and is stabilized within a third set of objectives;   selecting, by said processor, at least one of said at least one trained second resultant element that meets a third performance threshold;   converting, by said processor, selected at least one trained second resultant element to a third element status to achieve a converted at least one trained second resultant element, such that said converted at least one trained second resultant elements becomes a third resultant element that competes with other third resultant elements.   
       10. A device for providing recursive modularity in adaptive network processing, said device comprising:
       an element aggregation accessor coupled with a processor, said element aggregation accessor configured for accessing at least one trained aggregation of elements that is coupled with an environment, wherein each trained aggregation of elements of said at least one trained aggregation of elements comprises a set of trained elements and is stabilized within a set of objectives;   an aggregation element selector coupled with said processor, said aggregation element selector configured for selecting at least one of said at least one trained aggregation of elements that meets a first performance threshold;   an aggregation element converter coupled with said processor, said aggregation element converter configured for converting selected at least one trained aggregation of elements to an element status to achieve a converted at least one trained aggregation of elements, such that each of said converted at least one trained aggregation of elements becomes a first resultant element that competes with other first resultant elements.   
       11. The device of claim  10 , wherein said element aggregation accessor comprises:
       a trained adaptive network accessor configured for accessing at least one trained adaptive network.   
       12. The device of claim  10 , wherein said element aggregation accessor comprises:
       a logic component accessor configured for accessing at least one logic component.   
       13. The device of claim  10 , further comprising:
       a supervisory element coupled with said processor, said supervisory element configured for continuously coordinating interactions associated with learning between at least one of said at least one trained aggregation of elements and at said at least one trained aggregation of elements and an external interface to said environment.   
       14. The device of claim  10 , further comprising:
       a first resultant element accessor coupled with said processor, said first resultant element accessor configured for accessing at least one trained first resultant element that is coupled with said environment, wherein each trained first resultant element of said at least one trained first resultant element comprises a set of trained aggregation of elements and is stabilized within a second set of objectives;   a first resultant element selector coupled with said processor, said first resultant element selector configured for selecting at least one of said at least one trained first resultant elements that meets a second performance threshold;   a first resultant element converter coupled with said processor, said first resultant element converter configured for converting selected at least one trained first resultant elements to a second element status to achieve a converted one or more trained first resultant element, such that said converted at least one trained first resultant element becomes a second resultant element that competes with other second resultant elements.   
       15. The device of claim  14 , further comprising:
       a second resultant element accessor coupled with said processor, said second resultant element accessor configured for accessing at least one trained second resultant element that is coupled with said environment, wherein each trained second resultant element of said at least one trained second resultant element comprises a set of trained first resultant elements and is stabilized within a third set of objectives;   a second resultant element selector coupled with said processor, said second resultant element selector configured for selecting at least one of said at least one trained second resultant element that meets a third performance threshold;   a second resultant element converter coupled with said processor, said second resultant element converter configured for converting selected at least one trained second resultant element to a third element status to achieve a converted at least one trained second resultant element, such that said converted at least one trained second resultant element becomes a third resultant element that competes with other third resultant elements.   
       16. A method for providing recursive modularity in adaptive network processing, said method comprising:
       accessing at least one trained aggregation of elements that is coupled with an environment, wherein each trained aggregation of elements of said at least one trained aggregation of elements comprises a set of trained elements and is stabilized within a set of objectives;   selecting at least one of said at least one trained aggregation of elements that meets a first performance threshold;   converting selected at least one trained aggregation of elements to an element status to achieve a converted at least one trained aggregation of elements, such that each of said converted at least one trained aggregation of elements becomes a first resultant element that competes with other first resultant elements.   
       17. The method of claim  16 , wherein said accessing at least one trained aggregation of elements comprises:
       accessing at least one logic component, wherein said at least one logic component is dynamically alterable.   
       18. The method of claim  16 , wherein said accessing at least one trained aggregation of elements that is coupled with an environment comprises:
       accessing at least one trained aggregation of elements that is coupled with an environment, wherein each trained aggregation of elements of said at least one trained aggregation of elements comprises a set of trained elements and is stabilized within a set of objectives, wherein said first resultant element comprises a supervisory element configured for continuously coordinating interactions associated with learning between at least one of said at least one trained aggregation of elements and said at least one trained aggregation of elements and an external interface to said environment.   
       19. The method of claim  16 , further comprising:
       accessing at least one trained first resultant element that is coupled with said environment, wherein each trained first resultant element of said at least one trained first resultant element comprises a set of trained aggregation of elements and is stabilized within a second set of objectives;   selecting at least one of said at least one trained first resultant elements that meet a second performance threshold;   converting selected at least one trained first resultant elements to a second element status to achieve a converted one or more trained first resultant element, such that said converted at least one trained first resultant element becomes a second resultant element that competes with other second resultant elements.   
       20. The method of claim  19 , further comprising:
       accessing at least one trained second resultant element that is coupled with said environment, wherein each trained second resultant element of said at least one trained second resultant element comprises a set of trained resultant elements and is stabilized within a third set of objectives;   selecting at least one of said at least one trained second resultant element that meets a third performance threshold;   converting selected at least one trained second resultant element to a third element status to achieve a converted at least one trained second resultant element, such that said converted at least one trained second resultant elements becomes a third resultant element that competes with other third resultant elements.   
       

     Section Five: Navigation Through Augmented Reality 
     Notation and Nomenclature 
     Some portions of the description of embodiments which follow are presented in terms of procedures, logic blocks, processing and other symbolic representations of operations on data bits within a computer memory. These descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. In the present application, a procedure, logic block, process, or the like, is conceived to be a self-consistent sequence of steps or instructions leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, although not necessarily, these quantities take the form of electrical or magnetic signal capable of being stored, transferred, combined, compared, and otherwise manipulated in a computer system. 
     It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussions, it is appreciated that throughout the present discussions terms such as “generating”, “receiving”, “comparing”, “advancing”, “using”, “enabling”, “providing”, “locating”, or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system&#39;s registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices. 
     Furthermore, in some embodiments, methods described herein can be carried out by a computer-usable storage medium having instructions embodied therein that when executed cause a computer system to perform the methods described herein. 
     Brief Description 
     Embodiments enable the navigation through concurrent models of reality, in conjunction with viewpoint, orientation through space and time, and other factors, in order to represent the meaning and context of user interaction with others and presentations. 
     Overview of Discussion 
     Example techniques, devices, systems, and methods for navigating concurrently and from point-to-point through multiple reality models are described herein. Discussion begins with example use case scenarios. An example system architecture is then described. Discussion continues with a description of example methods of use. 
     Use Case Scenarios 
       FIG. 5A  shows an example system  500  for navigating concurrently and from point-to-point through multiple reality models, in accordance with an embodiment. In various embodiments, models of reality are, but are not limited to being, based upon any of the following items: geospatial sensors; real-time image capture; produced video, television, movies, and advertisements; real-time audio capture; perceived reality through lens or heads-up display; geospatial database (e.g., geodetic models); GPS signals; mathematically derived ideal models (e.g., ellipsoidal earth model); virtual reality (any internally consistent model of space and time (can include intentionally distorted, unnatural, and non-historical models of reality); recorded audio; and recorded video. 
     In an example first use case scenario, person A is holding a smart-phone and is sitting on a sidewalk bench in a busy and unfamiliar shopping district. The smart-phone is equipped with various components, an image capture device, a GPS, a processor, a magnetometer, an accelerometer, etc. Person A has arranged to meet his friends at a restaurant down the street. Person A wonders what establishments are located further down the block and then to the right (out of person A&#39;s line of sight). Person A points the smart-phone in the direction of interest (down the block and to the right) and either zooms (e.g., by magnifying the screen image) the smart-phone in towards the direction of interest or physically moves in this direction of interest until the virtual location shown on the display screen of the smart-phone matches person A&#39;s location of interest. 
     Once the virtual location shown on the display screen matches the location of interest, a virtual viewing point is created, from which person A may look around and virtually view on the display screen what is within a short walking distance from that virtual viewing point. In this scenario, person A spots a familiar neighborhood coffee shop that is located two blocks to the left of the virtual viewing point. 
     While still viewing the coffee shop (which is out of person A&#39;s line of sight in the physical world) in the display screen, person A contacts his friends and suggests meeting at this coffee shop instead of the original meeting destination. Of note, in this example scenario, person A has not moved from his original physical location, sitting on the side-walk bench. After making this new meeting arrangement, person A directs his smart-phone (which includes system  500 ) to virtually return to person A&#39;s physical location (the sidewalk bench). In response to this request to return home, person A&#39;s virtual position is reconciled with his physical position, such that person A&#39;s new virtual viewing point is the bench upon which he is sitting. Person A is now able to look at the screen of his smart-phone and virtually view his surroundings. Additionally, person A is also able to virtually view the new meeting destination, the coffee shop (which is out of person A&#39;s line of sight), which concurrently virtually viewing his surroundings in the smart-phone&#39;s display screen. 
     Person A decides that he wants to scan the horizon, from the virtual viewing point of the sidewalk bench, through buildings, trees, earth and other obstructions. This virtual viewing may be in normal sight in real-time, or through non-real-time stored images. For example, person A may see the park on the other side of the building situated in front of him and see children playing in the park playground. In another embodiment, person A may see the park, but also see a stored image of the park that was captured twenty years ago; thus, person A would be viewing the park in non-real-time. 
     Person A then directs system  500  to show the physical positions of the avatars of his friends, as well as the shops in the area of the avatars, in order to make sure that his friends are all converging at the correct destination point, the coffee shop. Since person A sees that his friends are still about ten minutes away from the coffee shop, person A decides that he is hungry and would like to eat some donuts while walking to the coffee shop. Person A directs his smart phone to find the donut shop, which is several blocks away. Also, several buildings exist between person A and the donut shop. System  500  then causes the augmented donut shop to be virtually displayed in the smart-phone&#39;s display screen. Looking at the augmented donut shop, person A then requests route guidance and an estimated time of arrival at the donut shop. Further, person A asks his friends for donut orders. 
     Thus, as can be seen, the system  500  enables person A to concurrently navigate from a first point (his sidewalk bench) to a second point (the coffee shop, the donut shop, etc.) within multiple reality models, such as a virtual reality models in real time and non-real time. 
     While the smart-phone in the example scenario above was used as a pointing device to instruct a direction of interest, in various embodiments other pointing devices may, but are not limited to including any of the following: a mouse; eyeballs; a digitizing Tablet; a trackball; a touchscreen; a lightpen; a motion in real-world space; an orientation of a display frame; and virtual controls. 
     In three dimensional reality models, the virtual views shown on the display screen, or other device, that are navigatable by a user, are, but are not limited to being, defined by viewports including any of the following: a visual; a positional (three dimensional vector relative to a frame of reference which resolves to a coordinate position point); a view direction (a three dimensional vector or normal vector indicating direction of view from the position point); a view frame orientation (a three dimensional vector or normal vector indicating orientation of a view frame); a time (a scalar value relative to a timeframe reference); an audio; a left direction; a right direction; a sensitivity; and an audio subsection. 
     In embodiments, there are two types of viewports, a virtual viewport and a physical viewport. The virtual viewport is derived virtually or from physical sensors. A stateful model of a virtual viewport is derived from, but is not limited to be derived from, any of the following: a physical orientation relative to the Earth; a physical orientation relative to other objects; and a virtual orientation from a user&#39;s voice, pointing device, etc. 
     The physical viewport (e.g., a heads-up display) includes, but is not limited to including, any of the following: a mapping of other reality models to perceived reality from a direct vision (and hearing) (e.g., heads-up displays); a viewpoint of a display (e.g., car, helmet, glasses, etc.); a viewpoint of user eyeballs; and characteristics of a user&#39;s eyeballs such as a focal length, resolution, optical transfer, etc. 
     In a second use case scenario, person B is driving his family while on vacation in San Francisco in a car that is fitted with system  500 . System  500  is fitted within a heads-up-display, through which person B is able to look while driving. While person B is driving along the Embarcadero, he notices a building that interests him. Person B looks at the building of interest (a non-virtual location), which is the location of interest, and asks the system  500  about the building. The system  500  replies with the name and the address of the building. 
     Person B then requests information about the history of the building of interest, but person B is no longer looking at the building. Person B is looking at another object. The system  500 , in response to the history question, responds that in 1851 the vigilance committee used the building as a fortress while fighting mobsters and the police. Further, system  500  informs person B that the fortress had previously been located at a less defensible Portsmouth square, which is the site of earlier hangings (and currently within Chinatown). 
     Hungry now for Chinese food, person B requests directions of system  500  to a Chinese restaurant in Portsmouth square. In response to the request, the system  500  generates a virtual vehicle that appears on the road ahead of person B. This virtual vehicle guides person B to available parking that is closest to the Chinese restaurant (the second location of interest). 
     Next, person B observes a location (Union Square) en route to the Chinese restaurant. Person B asks if this location is Portsmouth Square. The system  500  responds by stating, “No, it is Union Square”. The virtual vehicle continues to drive ahead of person B&#39;s vehicle, until person B is parked in a parking spot. 
     In a third use case scenario, person C is working at a desk and wearing glasses with system  500  attached thereto. Also coupled with the glasses and the system  500  is an image capture device and a digital storage medium. Person C looks through, the glasses and a pile of virtual papers. The virtual papers are mapped positionally to the real desk. Person C is able to look at a specific pile of virtual papers (a first location of interest) that represent a set of documents. Person C requests that the system  500  search through the set of documents and find a particular document based on a keyword and/or subject matter and instructs system  500  what to do once locating the requested the requested document. 
     The system  500  performs such a search, locates the appropriate virtual paper, picks it up from the physical desk, places it on a virtual bulletin board, and reads it, all according to person C&#39;s requests and instructions. 
     Next, person C looks at a pile of physical business cards (a second location of interest), and requests that system  500  search the virtual business cards for a name. The system  500  then accesses OCR and a geospatially indexed digital storage of the business cards&#39; placement. The system  500  is then able to locate the appropriate virtual card based on its placement and the search results. Person C is also able to file the virtual business card in an electronic file system by looking at the virtual file cabinet (third location of interest) and giving the system  500  the instruction, “save”. In response to this instruction, the system  500  files the virtual business card within the virtual file cabinet. 
     In a fourth use case scenario, Person D is watching on a smart-TV a training video about an assembly line. Person D begins to wonder about the function of a specific station device (location of interest) within the training video. System  500  enables Person D to virtually enter the training video, via various methods (e.g., pointing, looking in the direction of interest [point within the training video], etc.]. Once virtually within the training video, Person D walks over to the other side of the station device in question to gain a perspective (e.g., get a clearer view of the station device, lets the system  500  know that the station device is the location of interest). 
     Person D then asks the system  500  how the station device works. In response to Person D&#39;s question, the system  500  shows Person D a working model animation and explains the functionality and the specification regarding the station device. 
     Example System Architecture 
     According to embodiments and with reference still to  FIG. 5A , the system  500  includes: a first navigatable virtual view generator  502  coupled with a processor (e.g., processor  1700 ); and a second navigatable virtual view generator  504  coupled with the first navigatable virtual view generator  502  and the processor. 
     Optionally, the system  500  includes any of the following coupled with the processor: a third navigatable virtual view generator  566 ; a first virtual position information request receiver  524 ; a first virtual position information request comparor  528 ; a response generator  532 ; an advancement instruction receiver  534 ; an advancer  548 ; an advancement information receiver  540 . 
     The first navigatable virtual view generator  502  generates a first navigatable virtual view  508  of a first location of interest  506 , wherein the first location of interest  506  is a virtual location  520  and/or a non-virtual location. The term navigatable refers to, at least, the capability for moving around in the subject area (e.g., virtual view  508 , virtual view  510 ). The second navigatable virtual view generator  504 , concurrently with the generating of the first navigatable virtual view generator  502 , generates a second navigatable virtual view  510  corresponding to a current physical location  516  of an object  514  that is coupled with the system  500 . Real-time sight at the current physical position  516  is enabled within the second navigatable virtual view  510 . In one embodiment, the second navigatable virtual view includes a virtual vehicle, as that described above in the use case scenario two. The virtual vehicle remains within a predetermined distance from the object  514  as the object  514  moves. 
     The first location of interest  506  is that location to which the system  500  is instructed to address and to which the user of the system  500  is interested. The first location of interest  506  is a virtual location  520  or a non-virtual location  522 . The virtual location  520  may be, for example, the first virtual set of documents  518 , as described above in use case scenario three. The non-virtual location  522  may be, for example, a real physical location such as the coffee shop described above in use case scenario one. 
     The virtual view of the first navigatable virtual view  508  and the second navigatable virtual view  510  refers to a view that is displayed on a screen. The term navigatable, in the context of the virtual view, refers to the ability of the virtual view shown in the display screen to be explored (moving from one point to another within the virtual scene shown by the virtual view) by a user. For example, the virtual view may be that of a street three blocks away and that is out of user&#39;s line of sight. The user may navigate within that virtual scene, starting at the street that is three blocks away, and continue to a street that is six blocks away and still out of the user&#39;s line of site. In some embodiments, the new virtual view may be that of the street that is six blocks away. In other embodiments, the new virtual view may show both the street that is three blocks away and the street that is six blocks away. Various virtual scenes may be shown in the virtual view at the display screen, and these virtual scenes may change to other virtual scenes, depending upon the user&#39;s given navigation directions. 
     The system  500  is coupled with an object  514 . The object  514  may be anything to which the system  500  may be coupled. For example, the object  514  may be a human, a pair of glasses, a watch, a phone, a T.V., etc. The current physical location  516  of the object  514  refers to the real-time location of the object  514  as it finds itself on Earth. 
     Real-time sight  512  at the current physical location  516  refers to being able to view what is happening at the current physical location  516  as it is occurring. In one embodiment, the real-time sight  512  includes real-time virtual sight  562 . In one embodiment, non-real-time stored imaging associated with the current physical location  516  is further enabled. Non-real-time stored imaging may be, in one embodiment, images stored of the current physical location  516  and its surrounding area of a time period different from the real-time period. 
     Thus, as described above, for example, in use case scenario one, the first location of interest  506  is the position that is down the block and to the right. The first navigatable virtual view generator  502  generates the first navigatable virtual view  508  of the area that is down the block and to the right of the object  514  (e.g., the user in this case, to whom the system  500  is attached). In this use case scenario, the first location of interest  506  (down the block and to the right) is a non-virtual location  522 . Additionally, and as applied to the use case scenario one, the second navigatable virtual view generator  504  also generates the virtual view from person A&#39;s home position, that is the position that person A is while coupled with the device  500 . Thus, person A is able to also virtually view his surroundings as seen from his current physical location  516 . Person A is also able to navigate in real time within the second navigatable virtual view  510  (via scanning the horizon through buildings, trees, earth, etc.) to determine his surroundings. 
     The third navigatable virtual view generator  566 , concurrently with the generating the first navigatable virtual view  508  of the first location of interest  506 , generates a third navigatable virtual view  568  of a second location of interest  544 , wherein the second location of interest  544  is one of a second virtual location  546  and a second non-virtual location  548 . For example, in use case scenario one, the second location of interest  544  is the donut shop. Of note, in one embodiment, the first virtual location  520  and the second virtual location  546  are the same. In another embodiment, the first virtual location  520  and the second virtual location  546  are different. Likewise, in one embodiment, the first non-virtual location  522  and the second non-virtual location  548  are the same, whereas in another embodiment, the first non-virtual location  522  and the second non-virtual location  548  are different. 
     The first virtual position information request receiver  524  receives a first virtual position information request  526  associated with the first location of interest  506 . For example, the first virtual position information request  526  may be, in one instance, a request from a user of the system  500  to provide a virtual view of a specific physical location (first location of interest  506 ), such as the position down the block and to the right, yet out of the user&#39;s line of sight, as is described above in use case scenario one. In another instance, the first virtual position information request  526  may be a request from a user of the system  500  to provide a virtual view of a specific virtual location (first location of interest  506 ), such as the first virtual set of documents  518  described above in use case scenario three. In another embodiment, the first virtual position information request  526  may be a request for information about something that is within the virtual view and/or about the first location of interest  506  and/or the second location of interest  544 . For example, the first virtual position information request  526  may be question about the history of an interesting looking building (first location of interest  506 ), as is described above in the use case scenario two. 
     The first virtual position information request comparor  528  compares the first virtual position information request  526  with a store of location position information  530 . The store of location position information  530 , in one embodiment, is internal to the system  500 . In another embodiment, the store of location position information  530  is located external to the system  500 . Further, it should be appreciated that the store of location position information  530  may be any place in which information is kept (e.g., database, WEB, etc.) and that is accessible by the system  500 , via wire or wirelessly. By comparing, it is meant that a determination is made if the subject of the first virtual position information request  526  is addressed and/or answered at the store of location position information  530 . 
     The response generator  532 , based on the comparing, generates a response  560  to the first virtual position information request  526 . The information residing at the store of location position information  530  that is able to satisfy the first virtual position information request  526  is, via the response  560 : 1) provided via the system  500 , either via audio and/or visual techniques well known in the art; and/or 2) used to accommodate the first virtual position information request  526  (e.g., displaying a virtual view of the first location of interest  506 ). 
     The advancement instruction receiver  534  receives an advancement instruction  536  to virtually advance towards the first location of interest  506  until virtual position information of the first virtual position information request  526  matches the first location of interest  506 . For example and as described above in use case scenario one, person A requests of the system  500  to move closer to the position virtually shown in the display screen, the position down the block and to the right (first location of interest  506 ). This is an advancement instruction  536 . The advancer  538 , in response to receiving the advancement instruction  536 , then virtually advances towards the position down the block and to the right. The point at which the virtual advancement reaches in response to the advancement instruction  536 , is referred to herein as the virtual viewing position  564 . 
     In another embodiment, the system  500  includes the advancement information receiver  540  that receives advancement information that signifies that a physical advancement towards the first location of interest  506  has occurred, wherein the virtual position information matches the first location of interest  506  and the advancement information includes the virtual viewing position  564  of the first location of interest  506 . In other words, in one embodiment, the system  500  is informed that the object  514  with which it is coupled, has been physically moved towards the first location of interest such that the virtual position information matches the first location of interest (e.g., the object  514  has arrived at the first location of interest  506 ) and the virtual viewing position  564  has been established. 
     Example Methods of Use 
       FIG. 5B  is a flow diagram  570  of an example method for navigating concurrently and from point-to-point through multiple reality models. In operation  571 , in one embodiment and as described herein, a first navigatable virtual view of a first location of interest is generated, wherein the first location of interest is one of a virtual location and a non-virtual location. In operation  572 , in one embodiment and as described herein, concurrently with the generating the first navigatable virtual view of the first location of interest in operation  571 , a second navigatable virtual view corresponding to a current physical position of an object is generated, such that real-time sight at the current physical position is enabled within the second navigatable virtual view. 
     In operation  573 , in one embodiment and as described herein, concurrently with the generating the first navigatable virtual view of the first location of interest, generating a third navigatable virtual view of a second location of interest, wherein the second location of interest is one of the virtual location and the non-virtual location. 
     In operation  574 , in one embodiment and as described herein, a first virtual position information request associated with the first location of interest is received. The first virtual position information request is compared with a store of location position information. Then, based on the comparing, a response to the first virtual position information request is generated. 
     In operation  575 , in one embodiment and as described herein, at least one of the following is received: an advancement instruction to virtually advance towards the first location of interest until virtual position information of the first virtual position information request matches the first location of interest; and advancement information signifying that a physical advancement towards the first location of interest has occurred, wherein the virtual position information matches the first location of interest and the advancement information includes a virtual viewing position of the first location of interest. In response to a received advancement instruction, an advancement towards the first location of interest occurs, thereby achieving the virtual viewing position. 
     In operation  576 , in one embodiment and as described herein, non-real-time stored imaging associated with the current physical position is used. 
     In operation  577 , in one embodiment and as described herein, a second virtual position information request associated with the second navigatable virtual view is received. The second virtual position information request is compared with a store of location position information. Based on the comparing, a response to the second virtual position information request is generated. 
     In operation  578 , in one embodiment and as described herein, a second navigatable view of a second virtual set of documents at the second location of interest is generated. 
     In operation  579 , in one embodiment and as described herein, a search request object is located within the first virtual set of documents. 
     Various embodiments include multi-stage clipping (aka culling) algorithms (e.g. monoscopic/stereoscopic/monophonic/stereophonic) for managing lists of potentially significant data for “visualization”. Some of these embodiments include hysterisis, neuromorphic, geospatial and other optimizations. One such embodiment includes weighting relative significance of interest-mapping, relative distance to idealized viewpoint, relative distance to idealized focal point, and relative distance from each location vector to the idealized viewpoint line of sight. 
     Lexicon: Clipping=clipping or culling of data outside of area of interest—normal art distinguishes between clipping (removal of elements of an object—e.g. individual polygons from a displayed object) vs. culling (removal of the entire object). For the purposes of discussing multi-staging clipping (culling), the two terms are considered synonymous. 
     Embodiments for navigating concurrently and from point-to-point through multiple reality models are thus described. While the present technology has been described in particular examples, it should be appreciated that the present technology should not be construed as limited by such examples, but rather construed according to the claims. 
     Embodiments for navigating concurrently and from point-to-point through multiple reality models can be summarized as follows: 
     1. A computer usable storage medium having instructions embodied therein that when executed cause a computer system to perform a method for navigating concurrently and from point-to-point through multiple reality models, said method comprising: 
     generating, at a processor, a first navigatable virtual view of a first location of interest, wherein said first location of interest is one of a first virtual location and a first non-virtual location; and 
     concurrently with said generating said first navigatable virtual view of said first location of interest, generating, at said processor, a second navigatable virtual view corresponding to a current physical position of an object, such that real-time sight at said current physical position is enabled within said second navigatable virtual view. 
     2. The non-transitory computer-readable storage medium of claim  1 , wherein the method further comprises: 
     concurrently with said generating said first navigatable virtual view of said first location of interest, generating a third navigatable virtual view of a second location of interest, wherein said second location of interest is one of a second virtual location and a second non-virtual location. 
     3. The non-transitory computer-readable storage medium of claim  1 , wherein the method further comprises: 
     receiving a first virtual position information request associated with said first location of interest; 
     comparing said first virtual position information request with a store of location position information; and 
     based on said comparing, generating a response to said first virtual position information request. 
     4. The non-transitory computer-readable storage medium of claim  3 , wherein the method further comprises: 
     receiving at least one of:
         an advancement instruction to virtually advance towards said first location of interest until virtual position information of said first virtual position information request matches said first location of interest; and   advancement information signifying that a physical advancement towards said first location of interest has occurred, wherein said virtual position information matches said first location of interest and said advancement information includes a virtual viewing position of said first location of interest; and       

     in response to a received advancement instruction, advancing towards said first location of interest, thereby achieving said virtual viewing position. 
     5. The non-transitory computer-readable storage medium of claim  1 , wherein the method further comprises: 
     using non-real-time stored imaging associated with said current physical position. 
     6. The non-transitory computer-readable storage medium of claim  1 , wherein the method further comprises, wherein enabling said real-time sight at said current physical position comprises: 
     enabling real-time virtual sight. 
     7. The non-transitory computer-readable storage medium of claim  1 , wherein the method further comprises: 
     receiving a second virtual position information request associated with said second navigatable virtual view; 
     comparing said second virtual position information request with a store of location position information; and 
     based on said comparing, generating a response to said second virtual position information request. 
     8. The non-transitory computer-readable storage medium of claim  1 , wherein the method further comprises, wherein said providing a second navigatable virtual view comprises: 
     providing a virtual vehicle within said second navigatable virtual view, wherein said virtual vehicle remains within a predetermined distance from said object as said object moves. 
     9. The non-transitory computer-readable storage medium of claim  1 , wherein the method further comprises, wherein said generating a first navigatable virtual view of a first location of interest comprises: 
     generating said first navigatable view of a first virtual set of documents as said first location of interest. 
     10. The non-transitory computer-readable storage medium of claim  9 , wherein the method further comprises, further comprising: 
     generating a second navigatable view of a second virtual set of documents at said second location of interest. 
     11. The non-transitory computer-readable storage medium of claim  9 , wherein the method further comprises, further comprising: 
     locating a search request object within said first virtual set of documents. 
     12. The non-transitory computer-readable storage medium of claim  9 , wherein the method further comprises, wherein said generating a first navigatable virtual view of a first location of interest comprises: 
     generating said first navigatable virtual view of a video. 
     13. A system for navigating concurrently and from point-to-point through multiple reality models, said system comprising: 
     a first navigatable virtual view generator coupled with a processor, said first navigatable virtual view generator for generating a first navigatable virtual view of a first location of interest, wherein said first location of interest is one of a first virtual location and a first non-virtual location; and 
     a second navigatable virtual view generator coupled with said processor, said second navigatable virtual view generator for, concurrently with said generating said first navigatable virtual view, generating a second navigatable virtual view corresponding to a current physical position of an object coupled with said system, such that real-time sight at said current physical position is enabled within said second navigatable virtual view. 
     14. The system of claim  13 , further comprising: 
     a third navigatable virtual view generator coupled with said processor, said third navigatable virtual view generator for, concurrently with said generating said first navigatable virtual view of said first location of interest, generating a third navigatable virtual view of a second location of interest, wherein said second location of interest is one of a second virtual location and a second non-virtual location. 
     15. The system of claim  13 , further comprising: 
     a first virtual position information request receiver coupled with said processor, said first virtual position information request receiver configured for receiving a first virtual position information request associated with said first location of interest; 
     a first virtual position information request comparor coupled with said processor, said first virtual position information request comparor configured for comparing said first virtual position information request with a store of location position information; and 
     a response generator coupled with said processor, said response generator configured for, based on said comparing, generating a response to said first virtual position information request. 
     16. The method of claim  15 , further comprising: 
     an advancement instruction receiver coupled with said processor, said advancement instruction receiver configured for receiving an advancement instruction to virtually advance towards said first location of interest until virtual position information of said first virtual position information request matches said first location of interest; 
     an advancer coupled with said processor, said advancer configured for virtually advancing towards said first location of interest, thereby achieving a virtual viewing position; and 
     an advancement information receiver coupled with said processor, said advancement information receiver configured for receiving advancement information signifying that a physical advancement towards said first location of interest has occurred, wherein said virtual position information matches said first location of interest and said advancement information includes said virtual viewing position of said first location of interest. 
     17. The system of claim  13 , wherein non-real-time stored imaging associated with said current physical location is further enabled.
 
18. The system of claim  13 , wherein said real-time sight comprises:
 
     real-time virtual sight. 
     19. The system of claim  13 , wherein said second navigatable virtual view comprises: 
     a virtual vehicle that remains within a predetermined distance from said object as said object moves. 
     20. The system of claim  13 , wherein said first location of interest comprises: 
     a first virtual set of documents. 
     Section Six: Enhanced Sensory Perception 
     Notation and Nomenclature 
     Some portions of the description of embodiments which follow are presented in terms of procedures, logic blocks, processing and other symbolic representations of operations on data bits within a computer memory. These descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. In the present application, a procedure, logic block, process, or the like, is conceived to be a self-consistent sequence of steps or instructions leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, although not necessarily, these quantities take the form of electrical or magnetic signal capable of being stored, transferred, combined, compared, and otherwise manipulated in a computer system. 
     It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussions, it is appreciated that throughout the present discussions terms such as “receiving”, “rendering”, “generating”, “utilizing”, or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system&#39;s registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices. 
     Furthermore, in some embodiments, methods described herein can be carried out by a computer-usable storage medium having instructions embodied therein that when executed cause a computer system to perform the methods described herein. 
     Brief Description 
     Embodiments improve a user&#39;s sensory and extra-sensory perception of the world through augmented reality. Embodiments enable the user to see real-time composite visible, radar, infrared, ultraviolet, or sonar still images or video, or locally cached or remote database stored images from a similar variety of sources blended in virtually any combination with the real-time sources to add understanding of the world around the user. Embodiments may be used within, among other devices, heads-up-display devices, including wearable devices and vehicular (windshield), and windows, along with geospatial sensors coupled therewith. 
     Overview of Discussion 
     Example techniques, devices, systems, and methods for enhancing a sensory perception in a field of view of a real-time source within a display screen through augmented reality are described herein. Discussion begins with example use case scenarios. An example system architecture is then described. Discussion continues with a description of example methods of use. 
     Use Case Scenarios 
       FIG. 5D  shows an example device  580  for enhancing a sensory perception in a field of view of a real-time source within a display screen through augmented reality, in accordance with an embodiment. The field of view is the view displayed within the display screen. 
     In an example first use case scenario, after a red-eye flight to San Francisco for a business convention, Person A wakes up in a hotel room in a city he has never before visited. Person A puts on his wearable supervision smart-glasses that contain the device  580 . While still dressing in his hotel room, Person A uses his smart-glasses to look through the hotel walls to the hotel restaurant. Person A is able to look at the breakfast menu with the smart-glasses having device  580 . Person A decides that the hotel&#39;s breakfast menu is too high priced and does not find the food appealing. 
     While leaving the hotel room, Person A looks around the nearby city streets (through hotel walls and other buildings) for a local diner. Person A finds a diner nearby and then looks at the diner&#39;s menu while riding down the hotel&#39;s elevator to the street level. Person A then requests of the device  580  for the quickest route. The device  580  is guided out the front door of the hotel, at which point the user notices a floral garden in the hotel&#39;s front lawn. Person A remembers a documentary about flower patterns being adaptive for ultraviolet light. Person A then states, “ultraviolet”. In response to hearing the request, “ultraviolet” regarding the floral garden (the first location of interest  506 ), the device  580  generates an augmented floral garden, in which the flowers are down converted to visible color/saturation coded visible augmented translucent image overlay to actual flowers. In other words, the floral garden was made to look more spectacular by creating eye popping colors for Person A to see. Objects are placed in front and behind the field of view within the display screen of the glasses such that flowers appear to Person A in a three dimensional format, and appear to be brighter, more colorful, and more real. 
     On route to the diner, Person A recognizes business competitors standing across the street, engaging in a heated debate. Curious as to what the animated discussion is about, Person A requests of device  580  to listen more closely to the debate (the first location of interest  506 ), and the device  580  illuminates the conversation (with the assistance of directional microphones and/or amplifiers) such that Person A can hear. Person A finds the conversation boring, as they are arguing about where to eat breakfast. 
     Next, Person A calls an old college friend who lives in San Francisco. The friend convinces Person A to skip the first day of the business convention and go fishing instead. Person A checks the convention schedule, decides that he can skip one day, and calls a taxi to get to the marina. While in the taxi, Person A tours the virtual convention with his glasses that are equipped with device  580  to assuage his guilt. 
     Person A arrives at the marina before his friend and looks at the sky, wondering about his decision to skip his business convention. Person A then says, “weather”. Through the glasses coupled with device  580 , Person A looks around and sees color-coded imaging with satellite cloud image overlays with sighted clouds through lenses. Person A zooms in via the advancement instruction  536 , and flies through the weather pattern, which looks like a small squall. Person A then says, “from space”, from which he receives a stereoscopic GOES west/GOES east satellite image from 10 minutes ago with composite radar overlay. Person A zooms in to his physical location, and sees clear skies behind the squall line. Person A smiles because his fishing trip does not have to worry about the weather during his fishing excursion. 
     Person A then goes fishing with his friend. On the water, Person A says, “Hydra”. Person A, through his smart-glasses, can see the topography of the lake bottom as they boat to their destination. Person A says to the friend, “Is that the latest fish-finder 5000 mounted on your transom?” The friend responds with, “Why yes it is! Why do you ask?” Person A then states, “Do me a favor and hit the ‘find blue tooth device’ button on your fish-finder.” The boat slows as they arrive near the fishing spot. Person A sees a large school of fish swim under the boat. The friend gets excited, but the user says, “It&#39;s only a school of Iowa-walleye.” Then person A remembers that he is now in Iowa, and says, “Er, uh, Carp, I mean.” 
     Thus, the system  580  enables the user to enjoy heightened perceptions of reality, based on various interactions between the device  580  and the user/wearer of the device  580 , between different perceptions or combinations of perceptions of reality, based on a number of sources. 
     Example System Architecture 
     According to embodiments and with reference still to  FIG. 5C , the system  580  includes: a sensory perception enhancement request receiver  582 ; and a three dimensional graphical image rendering module  583  that includes a virtual object generator  584 . 
     In one embodiment, the sensor perception enhancement request receiver receives a sensory perception enhancement request  581  associated with the first location of interest  506 . The three dimensional graphical image rendering module  583  renders a three dimensional graphical image  586  and includes the virtual object generator  584 . The virtual object generator  584  generates a first virtual object  587  in the forefront of the field of view and a second virtual object  588  behind the field of view. The first virtual object  584  and the second virtual object  588  are displayed within the user&#39;s perceived depth of normal vision. The first virtual object  584  and second virtual object  588  may be anything that is visible to the human eye. In some embodiments, these objects are a simulation of real objects, whereas in other embodiments, these objects are created to represent ideas and/or real objects. Thus, three dimensional virtual-reality modeled alpha-channel management and real-time object recognition and other video metadata mining allows three dimensional graphical image rendering to effectively overlay and underlay human sight on such displays, as well as all of the above imaging sources in any combination. In other words, the user sees virtual reality modeled objects navigating in front of and behind objects near and far in their field of view, and imaging from a variety of sources are displayed within the perceived depth of normal vision. 
     In one embodiment, the device  580  optionally includes the system  500  coupled therewith, and incorporates the features/functions of the system  500  as already described above and herein. Thus, device  580 , in some embodiments includes: a first navigatable virtual view generator  502  that generates a first navigatable virtual view  508  of the first location of interest  506 , wherein the first location of interest  506  is one of a first virtual location  520  and a first non-virtual location  522 ; and a second navigatable virtual view generator  504  that, concurrently with said generating said first navigatable virtual view  508 , generates a second navigatable virtual view  510  corresponding to a current physical position  516  of an object  514  coupled with the system  500 , such that real-time sight at the current physical position  516  is enabled within the second navigatable virtual view  510 . 
     Various embodiments optionally include the following components that are well known in the art: an infrared image capture device  589 ; an ultraviolet image capture device  590 ; a radar image capture device  591 ; a sonar image capture device  592 ; at least one of a direction microphone  593  and an amplifier  594 ; and a visible spectrum image capture device  595 . 
     Example Methods of Use 
       FIG. 5D  is a flow diagram  596  of an example method for enhancing a sensory perception in a field of view of a real-time source within a display screen  585  through augmented reality. In operation  597 , in one embodiment and as described herein, a sensory perception enhancement request associated with a location of interest is received. 
     In operation  598 , in one embodiment and as described herein, in response to the receiving in operation  597 , a three dimensional graphical image is rendered. The rendering includes generating at least one of a first virtual object in a forefront of the field of view and a second virtual object behind the field of view, wherein the first virtual object and the second virtual object are displayed within a perceived depth of normal vision. 
     In operation  599 , in one embodiment and as described herein, a first navigatable virtual view of the first location of interest is generated, wherein the first location of interest is one of a virtual location and a non-virtual location. Further, and concurrently with the generating of the first navigatable virtual view of the first location of interest, a second navigatable virtual view corresponding to a current physical position of an object is generated, such that real-time sight at the current physical position is enabled within the second navigatable virtual view. In various embodiments and as described herein, the generating in operation  599  includes utilizing any of the following to assist in the rendering: an infrared image capture device; an ultraviolet image capture device; a radar image capture device; a sonar image capture device; at least one of directional microphones and amplifiers; a visible spectrum image capture device; a stereophonic audio capability; and an eyeball direction detector. 
     Various embodiments use translucency management to assist the user in differentiating between simultaneously displayed sensor input. Frequency shifts for audio sources, and chrominance shifts, saturation and luminance blending ratios, individual color-space component blending (e.g. RGB, CLS, etc.) and other filters are used to allow differentiable simultaneous displays (visual and audio, etc.) from differently-abled sensors and sensor arrays. 
     Embodiments for enhancing a sensory perception in a field of view of a real-time source within a display screen  585  through augmented reality are thus described. While the present technology has been described in particular examples, it should be appreciated that the present technology should not be construed as limited by such examples, but rather construed according to the claims. 
     Embodiments for enhancing a sensory perception in a field of view of a real-time source within a display screen  585  through augmented reality can be summarized as follows: 
     1. A computer usable storage medium having instructions embodied therein that when executed cause a computer system to perform a method for enhancing a sensory perception in a field of view of a real-time source within a display screen through augmented reality, said method comprising: 
     receiving, at a processor, a sensory perception enhancement request associated with a location of interest; 
     in response to said receiving, rendering, by said processor, a three dimensional graphical image, wherein said rendering comprises:
         generating at least one of a first virtual object in a forefront of said field of view and a second virtual object behind said field of view, wherein said first virtual object and said second virtual object are displayed within a perceived depth of normal vision.
 
2. The computer usable storage medium of claim  1 , wherein said method further comprises:
       

     generating, at said processor, a first navigatable virtual view of said first location of interest, wherein said first location of interest is one of a virtual location and a non-virtual location; and 
     concurrently with said generating said first navigatable virtual view of said first location of interest, generating, at said processor, a second navigatable virtual view corresponding to a current physical position of an object, such that real-time sight at said current physical position is enabled within said second navigatable virtual view. 
     3. The computer usable storage medium of claim  1 , wherein said generating comprises: 
     utilizing an infrared image capture device to assist in said rendering. 
     4. The computer usable storage medium of claim  1 , wherein said generating comprises: 
     utilizing an ultraviolet image capture device to assist in said rendering. 
     5. The computer usable storage medium of claim  1 , wherein said generating comprises: 
     utilizing a radar image capture device to assist in said rendering. 
     6. The computer usable storage medium of claim  1 , wherein said generating comprises: 
     utilizing a sonar image capture device to assist in said rendering. 
     7. The computer usable storage medium of claim  1 , wherein said generating comprises: 
     utilizing at least one of directional microphones and amplifiers to assist in said rendering. 
     8. The computer usable storage medium of claim  1 , wherein said generating comprises: 
     utilizing a visible spectrum image capture device to assist in said rendering. 
     9. The computer usable storage medium of claim  1 , wherein said generating comprises: 
     utilizing a stereophonic audio capability to assist in said rendering. 
     10. The computer usable storage medium of claim  1 , wherein said generating comprises: 
     utilizing an eyeball direction detector to assist in said rendering. 
     11. A device for enhancing a sensory perception in a field of view of a real-time source within a display screen through augmented reality, said device comprising: 
     a sensory perception enhancement request receiver coupled with a processor, said sensory perception enhancement request receiver configured for receiving a sensory perception enhancement request associated with a location of interest; and 
     a three dimensional graphical image rendering module coupled with said processor, said three dimensional graphical image rendering module configured for rendering a three dimensional graphical image and comprises:
         a virtual object generator configured for generating at least one of a first virtual object in a forefront of said field of view and a second virtual object behind said field of view, wherein said first virtual object and said second virtual object are displayed within a perceived depth of normal vision.
 
12. The device of claim  11 , further comprising:
       

     a first navigatable virtual view generator coupled with said processor, said first navigatable virtual view generator for generating a first navigatable virtual view of said first location of interest, wherein said first location of interest is one of a first virtual location and a first non-virtual location; and 
     a second navigatable virtual view generator coupled with said processor, said second navigatable virtual view generator for, concurrently with said generating said first navigatable virtual view, generating a second navigatable virtual view corresponding to a current physical position of an object coupled with said system, such that real-time sight at said current physical position is enabled within said second navigatable virtual view. 
     13. The device of claim  11 , further comprising: 
     an infrared image capture device coupled with said processor and configured for assisting in said rendering. 
     14. The device of claim  11 , further comprising: 
     an ultraviolet image capture device coupled with said processor and configured for assisting in said rendering. 
     15. The device of claim  11 , further comprising: 
     a radar image capture device coupled with said processor and configured for assisting in said rendering. 
     16. The device of claim  11 , further comprising: 
     a sonar image capture device coupled with said processor and configured for assisting in said rendering. 
     17. The device of claim  11 , further comprising: 
     at least one of directional microphones and amplifiers coupled with said processor and configured for assisting in said rendering. 
     18. The device of claim  11 , further comprising: 
     a visible spectrum image capture device coupled with said process and configured for assisting in said rendering. 
     19. A method for enhancing a sensory perception in a field of view of a real-time source within a display screen through augmented reality, said method comprising: 
     receiving, at a processor, a sensory perception enhancement request associated with a location of interest; 
     in response to said receiving, rendering, by said processor, a three dimensional graphical image, wherein said rendering comprises:
         generating at least one of a first virtual object in a forefront of said field of view and a second virtual object behind said field of view, wherein said first virtual object and said second virtual object are displayed within a perceived depth of normal vision.
 
20. The method of claim  19 , further comprising:
       

     generating, at said processor, a first navigatable virtual view of said first location of interest, wherein said first location of interest is one of a virtual location and a non-virtual location; and 
     concurrently with said generating said first navigatable virtual view of said first location of interest, generating, at said processor, a second navigatable virtual view corresponding to a current physical position of an object, such that real-time sight at said current physical position is enabled within said second navigatable virtual view. 
     Section Seven: Dialogue and Behavior Modeling 
     Notation and Nomenclature 
     Some portions of the description of embodiments which follow are presented in terms of procedures, logic blocks, processing and other symbolic representations of operations on data bits within a computer memory. These descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. In the present application, a procedure, logic block, process, or the like, is conceived to be a self-consistent sequence of steps or instructions leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, although not necessarily, these quantities take the form of electrical or magnetic signal capable of being stored, transferred, combined, compared, and otherwise manipulated in a computer system. 
     It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussions, it is appreciated that throughout the present discussions terms such as “accessing”, “comparing”, “determining”, “generating”, or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system&#39;s registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices. 
     Furthermore, in some embodiments, methods described herein can be carried out by a computer-usable storage medium having instructions embodied therein that when executed cause a computer system to perform the methods described herein. 
     Immediately below is provided a definition for the following terms used herein: 
     An automaton is a virtual autonomous agent and a bot. 
     Scripting is a structured behavioral metadata that drives interpretation and response. 
     Fixed scripting is a direct 1:1 relationship specification between an input set (including context) and outputs. 
     Fuzzy scripting is an associative array (or complex relational structure or transfer function reduced to an associative array [e.g., multiple sqi join]) that determines a scored set of potential outputs from an input set, and a behavioral transfer function that can introduce randomization from other sources, including pseudo-random number generation. 
     Parametric scripting is when parameters dictate the boundaries that indicate the successful output selection from a behavioral transfer function. 
     A behavioral transfer function is a combination of one or more of the following processes to resolve outputs from inputs: Boolean algebra; a logical algorithm; a matrix processing; an adaptive network response; a database query; an external API; an Internet search; and other mathematical, logical or data forms. 
     Brief Description 
     Embodiments interpret the meaning of a dialogue between a plurality of agents, wherein the plurality of agents includes one or more automatons and/or one or more humans (e.g., one or more users). Thus, multilayer state-machine modeling of individual and group interactions (including dialogue) between automatons and users are combined to interpret a meaning of a dialogue. 
     Various embodiments parse meaning according to several categories: What (based on Regular Expression extraction, Event Trigger, Search Results, Interaction, etc.); Who (Person, User, Personality, Self); When (time of day, time of year, time of month, State Machine State, Conversation Thread, etc.); Where (viewpoint, geospatial position, navigation, virtual reference, screen location, etc.). 
     Various embodiments organize the relationship between components of parsed meaning of dialogue and observed behaviors by mapping relationships between the following aspects of context and meaning: Personality; Dialogue; Vocabulary (aka lexicon); Association; Trigger; Dialogue Personality (cross-reference between Dialogue and Personality entries); Association (cross-reference between Dialogue and Vocabulary entries); Speech; Listener; Scripts; Response; Command; Action; Choice; Criteria; Voice and Sequence. 
     Overview of Discussion 
     Example techniques, devices, systems, and methods for interpret the meaning of a dialogue between a plurality of agents are described herein. Discussion begins with example use case scenarios. An example system architecture is then described. Discussion continues with a description of example methods of use. 
     Use Case Scenarios 
       FIG. 6A  shows an example device  600  for interpreting the meaning of a dialogue  642  between a plurality of agents  634 , in accordance with an embodiment. In various embodiments, the plurality of agents  634  is one or more automatons  636  and/or one or more humans  640 . In various embodiments, the dialogue  642  is, optionally one or more of the following: an audio communication  644  between the plurality of agents  634 ; and an action  646  communicated between the plurality of agents  634 . 
     In an example use case scenario, the device  600  is coupled with a global positioning system (GPS) that is itself coupled with a vehicle. The device  600  observes the behavior of a driver while the driver is driving his vehicle and interacting with the GPS. Without the device  600 , the GPS would inform the driver to make a U-turn, repeatedly, which may cause irritation to the driver. However, with the implementation of the device  600  coupled with the GPS, the device  600  observes the driver&#39;s behavior and response to its guidance, and interacts/adapts its behavior with/to the driver to be more user friendly and interactive. For example, if the driver does not make a U-turn in response to the GPS instruction to, “make a U-turn”, instead of the GPS repeatedly stating, “make a U-turn”, the GPS will instead pose a more user friendly interactive question to the user/driver, such as, “Why did you turn left?” The driver may then respond to the GPS by stating, “I&#39;m taking the scenic route”. Then, the GPS follows up with the driver by asking, “OK, should I guide you along the river?” Thus, in comparison to current technology, the GPS and the attached device  600  take a more interactive, social, and intelligent approach to instructing the driver, thus creating a friendlier environment for the driver. The device  600  observes the audio communication between the driver (a human) and the GPS system (an automaton). The audio communication includes details such as the tone and type of statement (imperative vs. declarative vs. interrogative vs. exclamatory and/or a command and/or conversational) which the driver displays to the GPS system. Further, the driver may make gestures to other vehicles, other drivers, or display gestures representing emotion, such as despair and/or confusion. Recognition of audio and visual aspects of a human is performed by systems and devices known to those in the art and are therefore not described herein. 
     Further, multilayer state machines of the device  600  may indicate a conversational exclamatory tone and type of statement as a response to the environment, but the combined context of a detected sharper tone of voice and an indication through viewpoint data vector thresholds that the user is “looking directly at” a subject can change the states of the machines to recognize a command imperative statement (instead of a conversational exclamatory statement). Similarly, a key-phrase (such as “Command Mode”) made by the user/driver can change the state machines according to a transition logic or scripting stored either at the device  600  and/or external to the device  600 . Of note, the above example context modifiers (e.g., “Command Mode”) can also be fed directly into adaptive networks coupled with device  600  for more sophisticated learned behavior. The above techniques can also be used in conjunction with a more standardized voice-recognition approach to score weighted permutations of potential word-recognitions to form candidate sentences against a lexical parsing score. 
     In a second use case scenario, a smart T.V. with the system  600  coupled therewith enables voice interactivity via the T.V. user interface between one or more viewers of the T.V. and characters within the program being viewed on the T.V. A viewer of the T.V. program may speak with a character(s) within the T.V. program, while the context and meaning of the viewer&#39;s words and actions to the character(s) are interpreted via system  600 . 
     In a third use case scenario, system  600  provides for a more highly interactive, realistic and entertaining application interface structure for games by interpreting the context and meaning of the users words and actions. For example, a user may wave his arms frantically while fairly calmly stating “Get away.” While the system  600  is hearing the words, “Get away.” Spoken in a fairly calm manner, the user&#39;s gestures provide more meaning to the user&#39;s words. The combination of the user&#39;s words and user&#39;s gestures lead the system  600  to interpret the user&#39;s words to be strong command made in desperation, and responds to these words accordingly within the game structure (e.g., providing an interpretation that is used in causing instructions to an agent within the game to withdraw immediately and quickly from the viewer&#39;s agent represented in the game). 
     In a fourth use case scenario, a smart vehicle coupled with the system  600  may be managed to provide meaning to the words spoken and actions performed by one or more users of the vehicle, using the vehicle/device  600  at separate times or concurrently. For example, a driver and two passengers set out on the car trip to visit a local sightseeing attraction, a quant amusement park. One of the passengers gets into an argument with the driver over the best route to take to the amusement park. Both the driver and the passenger are using obscene language and making violent gestures. The system  600  interprets the meaning of this language and gesturing to be that of a fight, and provides this interpretation such that the following request is caused to be posed in firmly stated manner to the car&#39;s inhabitants, “Pull over to the side of the road until this issue is resolved”. 
     Thus, the device  600  is able to interpret the context and meaning of the user&#39;s wording and/or gestures and cause a response to the user to occur. This response can either be in the form of words given to the user and/or actions presented to the user&#39;s agents by other agents with whom the user&#39;s agent is interacting, such as is shown in the car management scenario and the application interface scenario presented above. 
     Example System Architecture 
     As is illustrated herein, embodiments provide a device for modeling the behavior and interaction of automatons and users as they interact spatially, temporally, and through dialogue and other stimuli. The other stimuli includes: a fixed class hierarchy of behavior types; dynamically encapsulated behavior modules; context mapped to multiple reality environments; multilayer state machines modeling multiple aspects of individual and group interaction states; context mapped to multiple state-machines; Ack/Nack as feedback to dynamic behavior (including adaptive networks); integration with adaptive networks; and fixe, fuzzy, and parametric scripting. 
     Embodiments combine multilayer state-machine modeling of individual and group interactions (including dialogue) between users and automatons. Further, embodiments dynamically map behaviors with behavior capabilities with reality models through independent agents coordinated by structured behavioral metadata (scripting). Additionally, embodiments dynamically map augmented reality to meaning as a context for interpretation. Embodiments also enable: an integrated adaptive behavior with hard-coded and fuzzy logic that allows for hybrid behavioral forms; a coherent many to many interaction between multiple automatons and users; the utilization of a meaning bus; and the modeling of context as a set of characteristics to be filtered to assist in selecting an interpretation of a behavior. 
     According to embodiments and with reference still to  FIG. 6A , the device  600  includes, coupled with a processor: a dialogue accessor  608 ; an input receiver  610 ; an input comparor  612 ; and a meaning determiner  622 . In various embodiments, the device  600  further and optionally includes a response instruction generator  626 . 
     The dialogue accessor  608  accesses a dialogue  642  between the plurality of agents  634 . In various embodiments, the dialogue  642  is at least one of the following: an audio communication  644  between the plurality of agents  634 ; and an action  646  communicated between the plurality of agents  634 . 
     The input accessor  610  accesses input associated with the behavior of the plurality of agents  634  and an interaction between the plurality of agents  634 . As described above, in one example, the gestures of the plurality of agents  634  are observed (accessed), while in another example, language and gestures between the plurality of agents  634  is observed. 
     The input comparor  612  compares the accessed input  602  to a script type  614 . In various embodiments, this script type  614  optionally includes the following: a fixed script  616 ; a fuzzy scripting  618 ; a parametric scripting  620 ; and a hybrid scripting including portions of scripting from at least two of a fixed script  616 , a fuzzy scripting  618 , and a parametric scripting  620 . Of note, the script type  616  may be located internally and/or externally to the device  600 . The script type  616  may be accessed via wire and/or wirelessly. 
     The meaning determiner  622  determines a meaning of the dialogue  642  based on the comparing at the input comparor  612 . As described above, the determined meaning may be stateful, in that previous input may be taken into account in determining the context of behavior. Taking into account the previous input (stored internal and/or external to the device  600 ), as well as the real-time input, the interpretation of the meaning of the language and gestures of a user may cause a change in state of the state machine coupled with the device  600  (e.g. the input  602  is accessed as a conversational exclamatory, but changed to a command imperative meaning based on the comparing that is performed by the input comparor as well, in this case, previously stored input). 
     The response instruction generator  626  generates a response instruction  628  based on the determining of the meaning performed by the meaning determiner  622 . In various embodiments, the response instruction  628  may optionally be any of the following: an instruction for a verbal response  630 ; and an instruction for a non-verbal response  632 . By instruction for, it is meant that the response instruction generator  626  generates a response instruction that is used by either another component within the device  600  or a component coupled with the device  600 , which causes the instructed response to occur. For example, coupled with the device  600  is an audio component having audio capabilities. The device generates a response instruction for the following words to be spoken, “Turn right.” In this example, the audio component receives the response instruction, via wire and/or wirelessly, from the response instruction generator of device  600 , and proceeds to cause the words, “Turn right.” to be heard. Similarly, other components having the capabilities to cause a plurality of agents to make specific gestures are coupled with the system  600 . These other components enable the gestures that are the subject of the response instruction to be performed by the plurality of agents (e.g., within an interactive AI of a game). 
     Example Methods of Use 
       FIG. 6B  is a flow diagram  650  of an example method for interpreting meaning of a dialogue between a plurality of agents, wherein the plurality of agents comprises at least one of one or more automatons and one or more humans. In operation  652 , in one embodiment and as described herein, a dialogue between said plurality of agents is accessed. As described herein, this dialogue may optionally include one or more of: an audio communication between the plurality of agents; and an action communicated between the plurality of agents. 
     In operation  654 , in one embodiment and as described herein, input associated with the behavior of the plurality of agents and an interaction between the plurality of agents is accessed. As stated herein, this input may be stateful. 
     In operation  656 , in one embodiment and as described herein, the received input of operation  654  is compared to a script type. As described herein, in various embodiments, the received input is optionally compared to any of the following: a fixed script; a fuzzy scripting; a parametric scripting; and a hybrid scripting. 
     In operation  658 , in one embodiment and as described herein, the meaning of the dialogue is determined. In operation  660 , in one embodiment and as described herein, a response instruction is generated based on the meaning determined in operation  658 . In various embodiments and as described herein, the response instruction that is generated instructs any of the following: a verbal response; and a non-verbal response. 
     At least one embodiment includes a specific state machine design comprising the following states: COMMAND; ACK; and NACK. 
     At least one embodiment includes a specific state machine design comprising the following states: WAIT; LISTEN; and REPLY. 
     At least one embodiment includes a specific state machine design comprising the following states: IMPERATIVE; DECLARATIVE; INTERROGATIVE; and EXCLAMATORY. 
     Various embodiments include specific state machine designs comprising the following states: STANDBY; HAIL; ACK; NACK; NACK-ACK; CANCEL; EXECUTE, wherein next-state transitions are governed by state transition logic based on contextual parsing of dialogue and behavior such that the states represent meaning assigned to individual and/or group expression providing context to parsing of dialogue and other interactions. An example transition goes as follows: STANDBY/Silence; HAIL/“Car”; ACK/“Yes”; NACK-ACK/“Not You”; CANCEL/“OK. Sorry”; and STANDBY/Silence. 
     Various embodiments include specific state machine designs comprising the following states: STANDBY; HAIL; ACK; NACK; REQUEST; COMPLETED; ROGER; and EXECUTE, wherein next-state transitions are governed by state transition logic based on contextual parsing of dialogue and behavior such that the states represent meaning assigned to individual and/or group expression providing context to parsing of dialogue and other interactions. At least one such embodiment maps next-state transitions from: STANDBY to HAIL; ACK to NACK; NACK to STANDBY; ACK to REQUEST; REQUEST to ROGER; ROGER to EXECUTE; EXECUTE to COMPLETED; EXECUTE to DONE; 
     Various embodiments include specific state machine designs comprising the following states: IDLE, SLEEP, HAIL, ACK, NACK, NON-NACK, STANDBY, ROGER, OVER, EXECUTE wherein next-state transitions are governed by state transition logic based on contextual parsing of dialogue and behavior such that the states represent meaning assigned to individual and/or group expression providing context to parsing of dialogue and other interactions. At least one such embodiment maps next-state transitions from: IDLE to HAIL; HAIL to ACK; ACK to NACK; ACK to NON-NACK; NON-NACK to STANDBY; STANDBY to ROGER; ROGER to EXECUTE; EXECUTE to STANDBY (via !Singleton &amp; clone); and EXECUTE to IDLE. 
     Various embodiments include specific state machine designs comprising the following states: COMMAND, TEACH, CONVERSE, OBEY, SNIPE, MODERATE wherein next-state transitions are governed by state transition logic based on contextual parsing of dialogue and behavior such that the states represent meaning assigned to individual and/or group expression providing context to parsing of dialogue and other interactions. 
     Various embodiments include specific state machine designs comprising the following states: PSEUDO-COMMUNITY, CHAOS, EMPTINESS, COMMUNITY wherein next-state transitions are governed by state transition logic based on contextual parsing of dialogue and behavior such that the states represent meaning assigned to individual and/or group expression providing context to parsing of dialogue and other interactions. At least one such embodiment maps next-state transitions from PSEUDO-COMMUNITY to CHAOS, CHAOS to EMPTINESS, EMPTINESS to COMMUNITY, CHAOS to PSEUDO-COMMUNITY, EMPTINESS to PSEUDO-COMMUNITY, COMMUNITY to PSEUDO-COMMUNITY. 
     Various embodiments include specific state machine designs comprising the following states: FORMING, STORMING, NORMING and PERFORMING, wherein next-state transitions are governed by state transition logic based on contextual parsing of dialogue and behavior such that the states represent meaning assigned to individual and/or group expression providing context to parsing of dialogue and other interactions. At least one such embodiment maps next-state transitions from FORMING to STORMING, STORMING to NORMING, NORMING to PERFORMING, and PERFORMING to FORMING. 
     Various embodiments include specific state machine designs comprising the following states: FALSE ACTUALIZATION, CHAOS, MOB, BUREAUCRACY, LEADERSHIP, ACTUALIZATION wherein next-state transitions are governed by state transition logic based on contextual parsing of dialogue and behavior such that the states represent meaning assigned to individual and/or group expression providing context to parsing of dialogue and other interactions. At least one such embodiment maps next-state transitions from: FALSE ACTUALIZATION to CHAOS; CHAOS to FALSE ACTUALIZATION; CHAOS to MOB; MOB to CHAOS; CHAOS to BUREAUCRACY; BUREAUCRACY to CHAOS; CHAOS to LEADERSHIP; LEADERSHIP to ACTUALIZATION; LEADERSHIP to FALSE ACTUALIZATION; and ACTUALIZATION to FALSE ACTUALIZATION. 
     Various embodiments include specific state machine designs comprising the following states: DENIAL, ANGER, BARGAINING, DEPRESSION, ACCEPTANCE wherein next-state transitions are governed by state transition logic based on contextual parsing of dialogue and behavior such that the states represent meaning assigned to individual and/or group expression providing context to parsing of dialogue and other interactions. At least one such embodiment maps next-state transitions from DENIAL to ANGER, DENIAL to BARGAINING, ANGER to DENIAL, BARGAINING to DENIAL, ANGER to DEPRESSION, BARGAINING to DEPRESSION, DEPRESSION to ACCEPTANCE, and ACCEPTANCE to DENIAL. 
     One or more embodiments combine synchronous and asynchronous state machines, using the following Boolean formulas to determine next-state transitions: COMPLETED=((ASYNCHRONOUS AND STARTED) OR (SYNCHRONOUS AND FINISHED)); DONE=COMPLETED OR CANCELLED; 
     Embodiments of the present technology are thus described. While the present technology has been described in particular examples, it should be appreciated that the present technology should not be construed as limited by such examples, but rather construed according to the claims. 
     Embodiments for interpreting meaning of a dialogue between a plurality of agents, wherein said plurality of agents comprises at least one of one or more automatons and one or more humans can be summarized as follows: 
     1. A computer usable storage medium having instructions embodied therein that when executed cause a computer system to perform a method for interpreting meaning of a dialogue between a plurality of agents, wherein said plurality of agents comprises at least one of one or more automatons and one or more humans, said method comprising:
         accessing, by a processor, a dialogue between said plurality of agents;   accessing, by said processor, input associated with a behavior of said plurality of agents and an interaction between said plurality of agents;   comparing, by said processor, received input to a script type; and   based on said comparing, determining, by said processor, a meaning of said dialogue.
 
2. The computer usable storage medium of claim  1 , wherein said method further comprises:
   based on said determining said meaning, generating, at said processor, a response instruction.
 
3. The computer usable storage medium of claim  2 , wherein said generating a response instruction comprises:
   generating a response instruction that instructs a verbal response.
 
4. The computer usable storage medium of claim  2 , wherein said generating a response comprises:
   generating a response instruction that instructs a non-verbal response.
 
5. The computer usable storage medium of claim  1 , wherein said accessing a dialogue between said plurality of agents comprises:
   accessing an audio communication between said plurality of agents.
 
6. The computer usable storage medium of claim  1 , wherein said accessing a dialogue between said plurality of agents comprises:
   accessing an action communicated between said plurality of agents.
 
7. The computer usable storage medium of claim  1 , wherein said comparing received input to a script type comprises:
   comparing received input to a fixed script.
 
8. The computer usable storage medium of claim  1 , wherein said comparing received input to a script type comprises:
   comparing received input to a fuzzy scripting.
 
9. The computer usable storage medium of claim  1  wherein said comparing received input to a script type comprises:
   comparing received input to a parametric scripting.
 
10. The computer usable storage medium of claim  1 , wherein said comparing received input to a script type comprises:
   comparing received input to a hybrid scripting comprising scripting aspects from at least one of a fixed script, a fuzzy scripting, and a parametric scripting.
 
11. A device for interpreting meaning of a dialogue between a plurality of agents, wherein said plurality of agents comprises at least one of one or more automatons and one or more humans, said device comprising:
   a dialogue accessor coupled with a processor, said dialogue accessor configured for accessing a dialogue between said plurality of agents;   an input accessor coupled with said processor, said input accessor configured for accessing input associated with a behavior of said plurality of agents and an interaction between said plurality of agents;   an input comparor coupled with said processor, said input comparor configured for comparing accessed input to a script type; and   a meaning determiner coupled with said processor, said meaning determiner configured for determining a meaning of said dialogue based on said comparing.
 
12. The device of claim  11 , further comprising:
   a response instruction generator coupled with said processor, said response generator configured for, based on said determining said meaning, generating a response instruction.
 
13. The device of claim  12 , wherein said response instruction comprises:
 
an instruction for a verbal response.
 
14. The device of claim  12 , wherein said response instruction comprises:
 
an instruction for a non-verbal response.
 
15. The device of claim  11 , wherein said dialogue comprises:
 
an audio communication between said plurality of agents.
 
16. The device of claim  11 , wherein said dialogue comprises:
 
an action communicated between said plurality of agents.
 
17. The device of claim  11 , wherein said script type comprises:
 
a fixed script.
 
18. The device of claim  11 , wherein said script type comprises:
 
a fuzzy scripting.
 
19. The device of claim  11 , wherein said script type comprises:
   a parametric scripting.
 
20. The device of claim  11 , wherein said script type comprises:
   a hybrid scripting comprising portions of scripting from at least two of a fixed script, a fuzzy scripting, and a parametric scripting.       

     Section Eight: Customizable Group—Centric Transmedia Communications; and Customizable Augmented Reality Based Social Transmedia Combat Simulator 
     Notation and Nomenclature 
     Some portions of the description of embodiments which follow are presented in terms of procedures, logic blocks, processing and other symbolic representations of operations on data bits within a computer memory. These descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. In the present application, a procedure, logic block, process, or the like, is conceived to be a self-consistent sequence of steps or instructions leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, although not necessarily, these quantities take the form of electrical or magnetic signal capable of being stored, transferred, combined, compared, and otherwise manipulated in a computer system. 
     It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussions, it is appreciated that throughout the present discussions terms such as “generating”, “accessing”, “comparing”, “determining”, “receiving”, “advancing”, “using”, “enabling”, “receiving”, “comparing”, “generating”, “providing”, “locating”, or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system&#39;s registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices. 
     GLOSSARY 
     Customization: variation of application or game that requires minimal code change within structures that were designed for managing such change. 
     Skin: sets of simulation, visualizations, behavior and other configuration parameters that allow an apparently different application or game to be presented to the end-user without code changes. 
     Furthermore, in some embodiments, methods described herein can be carried out by a computer-usable storage medium having instructions embodied therein that when executed cause a computer system to perform the methods described herein. 
     Customizable Group—Centric Transmedia Communications 
     Brief Description 
     Embodiments provide models of group interaction and simulations of group activities to coordinate presentations to and interaction with users. Embodiments can be customized to fit the needs of different types of groups according to the communication and service delivery needs of each type of group. Individual groups can further change the functionality of the system through configuring group and personal preferences. Thus, embodiments provide a method for facilitating multimedia communications and service to a distributed group of users using augmented reality simulation and modeling of group dynamics. 
     Overview of Discussion 
     Example techniques, devices, systems, and methods modeling group dynamics using augmented reality simulation to facilitate multimedia communications and service to a distributed group of users are described herein. Discussion begins with example use case scenarios. An example system architecture is then described. Discussion continues with a description of example methods of use. 
     Use Case Scenarios 
       FIG. 7A  shows an example system  700 , in one embodiment, for modeling group dynamics using augmented reality simulation to facilitate multimedia communications and service to a distributed group of users, in accordance with an embodiment. In embodiments, the system  700  includes the system  500  of  FIG. 5A  coupled with the device  600  of  FIG. 6A . The system  700  is configurable such that customized applications may be built according to preferences, such as the club rules. 
     In an example first use case scenario, the system  700  enables the behavior of yachts in the water to be modeled in a simulation. This simulation includes the optimization of performance within weather and water conditions. The simulation further includes the significance of movement and position of yachts in the water relative to each other and to a defined course, including calculating the handicap adjustments and determining a winner in real time. 
     For example, using embodiments, a particular yacht configures the system  700  according to the yacht club&#39;s preferences or club rules, including what handicap method to use, and even whether or not to handicap the race at all. 
     Also configurable are what actions (verbal and nonverbal responses,  630  and  632 , respectively) will be taken upon the occurrence of a type(s) of events and the significance of the events. For example, boats crossing a finish line can trigger automatic content-capture events (can be both verbal and nonverbal responses,  630  and  632 , respectively), which are then woven into automated content generation. These configurations described above, in some cases, need only be done once per year per yacht club, or as the rules and/or preferences change. 
     Real-time automated multimedia content generation, in the form of (automated content generation) interactive automated augmented reality transmedia breaking news/live event coverage, is streamed back to the yacht club and/or remotely to participants and/or to other users. The event coverage that is shown as a breaking news/live event coverage, in this instance, is the first navigatable virtual view of a first location of interest (the yacht race). Within this event coverage, a dialogue and input have already been accessed, compared with a script type, and a meaning of the dialogue determined. 
     Of note, this streaming occurs according to model simulation or race and configuration parameters set by the yacht club and by individual members and their device capabilities. 
     If a given configuration option is enabled, users can enter virtual boats in the race and operate them remotely (including from the yacht club). Another configuration option governs whether or not a right-of-way is granted to virtual boats. Virtual boats become visible to on-the-water sailors through augmented reality viewport devices (a navigatable virtual view). Of note, this capability is particularly useful for training youth prior to giving them a chance to crash the family yacht. 
     A user may also initiate a content capture (a third navigatable virtual view of a second location of interest), which can then assist race rules governance (greatly streamlining protest committee activities). Further, this content capture adds an entertaining on-the-water feel to content being streamed back to people ashore who have volunteered for shore-based service or are gathering for the after party. 
     At the end of the event, an automated interactive augmented reality transmedia news documentary television program is created (in response to a first virtual position information request associated with the first location of interest) and distributed to all club members. The documentary includes the stories of the overall event, and the individual stories of all of the participants to the event. 
     The following second use case scenario example is similar to the first use case scenario, except that the application in this use case scenario is tailored for groups of people taking a cruise vacation together. For example the simulation and group dynamic mapping significance to events and content capture center around, but not limited to, the following aspects: the ship itinerary, a group itinerary, individual itineraries and movement of individuals through the ship and movement of the ship to ports of call (as opposed to the on-the-water yacht performance models discussed above). 
     Additional customization uses near-field-communications (NFC) (either as embedded NFC component, or as component added to 802.11, blue tooth, or other wireless communication capability) to establish a point-to-point alternate communications network between passenger devices. Used in combination with successive approximation, numerical methods, or trained adaptive network, this network also models location of individuals below decks (and out of reach of GPS signals). 
     Passengers are able to view automated news and entertainment television programming content generated, similarly to the above example, on the ship&#39;s smart-TV based CATV or other device. Passengers are given reminders and navigation assistance to events for which they are signed up, as well as automated RSVP, ETA, and other communications assists. 
     At the end of the cruise, the cruise line delivers customized interactive augmented reality transmedia automated television programming that summarizes the passenger&#39;s experience, and the highlights associated with friends, family, etc. 
     The following third use case scenario is similar to the first and second use case scenario except that the modeling revolves around a prognosis, a stage of disease, roles of friends and family relative to the patient and the illness, and individual and group transition through Kubler-Ross and other models (best practice Kubler-Ross model is a non-linear state machine). 
     If the prognosis is for recovery (e.g. broken leg), then the social hub becomes analogous to a high-tech remote multimedia get-well card/recovery party that can be participated in remotely. If the prognosis leads to hospice care and death, meaningful communications connect people in direct contact and remotely and capture content and expressions that are communicated back to other members of the patient support group, but are also retained for inclusion in persistent virtual transmedia memorial. 
     The following fourth use case scenario involves the operations management of a restaurant. Using a combination of heads-up-display devices (or other viewport-oriented mobile devices) for roving server help and management staff, with stationary monitors/television devices for kitchen and other non-mobile staff, with cloud-based workflow and augmented reality based transmedia presentation, different roles within the organization can have virtual presentations of necessary service-related info presented as overlay to perceived reality of environment. For example, a waitress can see color-coded virtual plates overlaying actual customer plates and/or service stations to see how long individual customers have been waiting for their meal; A maître d can see what areas new customers should be seated in next (by color, luminance, or other code). A manager can see, at a glance, visualizations of wait times for each area covered by service staff. Chefs and other kitchen staff can see order times, back-orders, priorities, etc. A whole delivery service sector can integrate with mobile devices to coordinate kitchen readiness and food delivery with customer demand and navigation route optimization. 
     Customizable Augmented Reality Based Social Transmedia Combat Simulator 
       FIG. 7A  shows an example system  700 , in one embodiment, for enabling at least one user to interact with each other and/or with at least one non-user characters (automatons, or Bots) within an immersed 360 degree augmented reality simulation of combat. As stated herein, the system  700  includes the system  500  of  FIG. 5A  and the device  600  of  FIG. 6A . The system  700  is configurable such that customized applications may be built according to preferences to allow variation in interaction and capability. 
     Embodiments provide a simulation of “combat” (including hunting, spear-fishing, etc.) using augmented reality immersion that combines information from geospatial sensors, geospatial models and virtual reality models to achieve simulated movement, aiming, viewing, directional cues (e.g., sounds) and other interactions. Additionally, embodiments utilize network capability to model multiple users real-time interaction across complex networks. Embodiments are capable of being utilized by many different device types (e.g., smart phones, tablets, stereoscopic and monoscopic, stereophonic an monophonic, smart-televisions, laptops, etc.). 
     Embodiments also provide for different selectable modes, such as different roles and interactions based in part on media capabilities of the device, as well as circumstances. For example, when the user finds himself constricted in a public space, he may choose the mode setting, mobile geospatially-aware for non-geospatial input. 
     While the system is customizable to allow for variation in interaction and capability, each customization is configurable to have different “skins” that determine appearance, simulation parameters and artwork. Each skin can have one or more historical or non-historical “battles” which is a simple specification of assets, domains, and conditions (e.g., how many ships were placed where, with, what weather conditions in the battle of Trafalgar). 
     In an example third use case scenario, a land battle (e.g., paintball), the system  700  is designed to be a multiplayer augmented reality game to be played out of doors by people using heads-up-display glasses/helmets/goggles, and optionally, using specialized electronic smart-device weapons (e.g., smart gun). The electronic smart-device weapons have processors, geosensors, NFC/Bluetooth/802.11 or other communications capability. The virtual field of battle for the multiplayer augmented reality game is mapped to actual fields and woods where teams can attempt to achieve strategic objectives. Other devices, besides the heads-up-display glasses/helmets/goggles can support user interaction with the multiplayer augmented reality game, including any smart device capable of viewport display and virtual reality modeling in real-time. 
     A nearly endless list of virtual weapons can be simulated and brought into real world skirmish simulations/games such as paintball and laser tag guns (obsoleting weapons), historical and non-historical weapons (science fiction and fantasy) such as rifles, shotguns, pistols, swords, chainsaws, darts, cannonry, artillery, catapults, bazookas (rpgs), missiles, mortar, bows and arrows, spears, bomb, landmines, etc. 
     Virtual tanks, aircraft, and other vehicles and combatants can engage remotely from users/players not in the field (e.g. airstrikes can be called in with a WWII version, to be carried out by automatons or by other combatants (e.g, who are playing on a computer or smart-TV at home). 
     Different skins or sets of simulation and visualization parameters allow for many different historical and non-historical contexts. The following is a non-exhaustive list of land battle skins: (1) WWII skin: includes rifles, machine guns, tanks, propeller warplanes, landmines, grenades, RPGs, etc.; (2) WWI skin: including machine guns, rifles, artillery, crude aircraft, and chemical weapons; (3) Civil War skin: includes muskets and rifles, pistols, artillery, horse arty, cavalry; (4) 1812 skin: includes smooth bore cannonry, cavalry, muskets; and (5) stone age skin: includes slings, spears, axes, bows, and arrows. 
     In an example fourth use case scenario, a naval battle, the system  700  is designed to be a multiplayer augmented reality game. The following is a non-exhaustive list of naval battle skins: (1) Golden Age of Sail skin: a) wooden ships with cannons are mounted primarily broadside and sailing characteristics matching relative sailing characteristics of involved real vessels, and b) automated derivation of wind vectors on water from observed boat behavior (sideslip, performance against polars from low-pass filter applied to VMG, etc.) coupled with external wind indicators or models can help accuracy of artillery simulation and virtual reality boats; (2) Trireme skin: ideal for use with real canoes, kayaks, rowboats, and slower motor boats, virtual dimensions extending well beyond real boat dimensions allows safe AR naval combat simulation based on ancient ramming warships; (3) WWII skin: a) motor boats or rowboats/canoes; and b) remote virtual mode players can work virtual submarines that attack real boats; and 4) monitor vs. Virginia: slow motor boat vs. sailboat (or canoe vs. dinghy) plus simulation of historical weapon effectiveness provide entertaining experiential education. 
     In an example fifth use case scenario, a hunting game, the system  700  is designed to be a multiplayer augmented reality game. Hunting simulators based on previous technology have been able to provide an analog experience to “swing shooting” and “lead a shooting” techniques, but a true “snap shooting” hunting simulation requires immersive augmented reality to capture the subtle interplay between stereophonic audio cues to initial target direct, identification, and movement and the transition to three dimensional visual cues for a firing solution (and potential additional transition to “lead shooting” or “swing shooting” modes). 
     Adaptive network behavior simulated upland birds learn behaviors to avoid getting shot, similar to real-world populations in areas of hunting pressure (raising skill level with statistical distribution of learned behavior models), providing for more realistic behaviors. 
     In an example sixth use case scenario, in an immersed augmented reality transmedia game, the system  700  is designed to be a multiplay augmented reality game. The following is a non-exhaustive list of skins utilized for this type of game: (1) snowballs skin: animated snowmen throwing snowballs (iceballs, etc.) at each other while users and automatons are manifested as snowmen/snowwomen avatars; (2) Clash of the Titans skin: based loose on Greek mythology (variants based on other mythologies), giant avatars (relative to the size of earth as modeled within the game); (3) Mars skin: similar to the Clash of the Titans skin and using public-domain Martian landscape topography; (4) Moon skin: similar to the Mars skin, and using public-domain Moonscape topography and images; (5) space skin: a) immersed 360 degree space ship-to-ship combat simulation; and b) accurate view from solar system for navigation/orientation within the game; and 6) tanks skin: a) topographic AR tank battle simulation; and b) historical and non-historical contexts. 
     Example System Architecture 
     According to embodiments and with reference still to  FIG. 7A , the system  700  includes the system  500  coupled with the device  600 , as are described above. 
     Example Methods of Use 
       FIGS. 7B and 7C  are a flow diagram of method  702  for modeling group dynamics using augmented reality simulation to facilitate multimedia communications and service to a distributed group of users, in accordance with an embodiment. 
     In operation  704 , in one embodiment and as described herein, a first navigatable virtual view of a first location of interest (e.g., yachting area described above) is generated, wherein the first location of interest is one of a first virtual location (e.g., a virtual yachting race at a virtual ocean) and a first non-virtual location (e.g., the actual area in which the yachting race is to be held). In one embodiment, the first location of interest is a first set of documents. While in another embodiment, the first location of interest is of a video. 
     In operation  706 , in one embodiment and as described herein, concurrently with the generating the first navigatable virtual view of the first location of interest, a second navigatable virtual view corresponding to a current physical position of an object is generated, such that real-time sight at the current physical position is enabled within the second navigatable virtual view. In one embodiment, the real-time sight is virtual. In one embodiment, the second navigatable virtual view includes a virtual vehicle that remains within a predetermined distance from the object as the object moves. 
     In operation  708 , in one embodiment and as described herein, a dialogue between the plurality of agents is accessed. In various embodiments, the dialogue that is accessed is an action communicated between the plurality of agents and/or an audio communication between the plurality of agents. 
     In operation  710 , in one embodiment and as described herein, concurrently with the generating the first navigatable virtual view of the first location of interest, a second navigatable virtual view corresponding to a current physical position of an object is generated, such that real-time sight at the current physical position is enabled within the second navigatable virtual view. 
     In operation  712 , input associated with a behavior of a plurality of agents and an interaction between said plurality of agents is accessed, wherein the plurality of agents comprises at least one of one or more automatons and one or more humans. 
     In operation  714 , in one embodiment and as described herein, received input is compared to a script type. In various embodiments, the received input is compared to a fixed script, fuzzy scripting, a parametric scripting, and a hybrid scripting. In operation  716 , in one embodiment and as described herein, based on the comparing, determining, a meaning of the dialogue. In operation  718 , in one embodiment and as described herein, concurrently with the generating of operation  704  of the first navigatable virtual view of said first location of interest, generating a third navigatable virtual view of a second location of interest, wherein the second location of interest is one of a second virtual location and a second non-virtual location. 
     In operation  720 , in one embodiment and as described herein, a first virtual position information request associated with said first location of interest is received, the first virtual position information request is compared with a store of location position information, and based on the comparing, a response to the first virtual position information request is generated. 
     In operation  722 , in one embodiment and as described herein, at least one of following is received: an advancement instruction to virtually advance towards the first location of interest until virtual position information of the first virtual position information request matches the first location of interest; and advancement information signifying that a physical advancement towards the first location of interest has occurred, wherein the virtual position information matches the first location of interest and the advancement information includes a virtual viewing position of the first location of interest; and in response to a received advancement instruction, an advancement is made towards the first location of interest, thereby achieving the virtual viewing position. 
     In operation  724 , in one embodiment and as described herein, a non-real-time stored imaging associated with the current physical position is used. In operation  726 , in one embodiment and as described herein, a second virtual position information request associated with the second navigatable virtual view is received, the second virtual position information request is compared with a store of location position information, and based on the comparing, a response to the second virtual position information request is generated. 
     In operation  728 , in one embodiment and as described herein, a second navigatable view of a second virtual set of documents at the second location of interest is generated. In operation  730 , in one embodiment and as described herein, a search request object within the first virtual set of documents is located. In operation  731 , in one embodiment and as described herein, the first navigatable virtual view of a video is generated. In operation  732 , in one embodiment and as described herein, based on the determining the meaning, a response instruction is generated. In various embodiments, the response instruction is a verbal response and/or a non-verbal response. 
     Embodiments of the present technology are thus described. While the present technology has been described in particular examples, it should be appreciated that the present technology should not be construed as limited by such examples, but rather construed according to the claims. 
     Embodiments for modeling group dynamics using augmented reality simulation to facilitate multimedia communications and service to a distributed group of users can be summarized as follows: 
     1. A computer usable storage medium having instructions embodied therein that when executed cause a computer system to perform a method for modeling group dynamics using augmented reality simulation to facilitate multimedia communications and service to a distributed group of users, said method comprising: 
     generating, at a processor, a first navigatable virtual view of a first location of interest, wherein said first location of interest is one of a first virtual location and a first non-virtual location; 
     concurrently with said generating said first navigatable virtual view of said first location of interest, generating, at said processor, a second navigatable virtual view corresponding to a current physical position of an object, such that real-time sight at said current physical position is enabled within said second navigatable virtual view; 
     accessing, by said processor, a dialogue between said plurality of agents; 
     accessing, by said processor, input associated with a behavior of a plurality of agents and an interaction between said plurality of agents, wherein said plurality of agents comprises at least one of one or more automatons and one or more humans; 
     comparing, by said processor, received input to a script type; and 
     based on said comparing, determining, by said processor, a meaning of said dialogue. 
     2. The computer usable storage medium of claim  1 , further comprising: 
     concurrently with said generating, by said processor, said first navigatable virtual view of said first location of interest, generating, by said processor, a third navigatable virtual view of a second location of interest, wherein said second location of interest is one of a second virtual location and a second non-virtual location. 
     3. The computer usable storage medium of claim  1 , further comprising: 
     receiving, at said processor, a first virtual position information request associated with said first location of interest; comparing said first virtual position information request with a store of location position information; and based on said comparing, generating a response to said first virtual position information request. 
     4. The computer usable storage medium of claim  3 , further comprising: 
     receiving, at said processor, at least one of:
         an advancement instruction to virtually advance towards said first location of interest until virtual position information of said first virtual position information request matches said first location of interest; and   advancement information signifying that a physical advancement towards said first location of interest has occurred, wherein said virtual position information matches said first location of interest and said advancement information includes a virtual viewing position of said first location of interest; and       

     in response to a received advancement instruction, advancing towards said first location of interest, thereby achieving said virtual viewing position. 
     5. The computer usable storage medium of claim  1 , further comprising: 
     using, by said processor, non-real-time stored imaging associated with said current physical position. 
     6. The computer usable storage medium of claim  1 , wherein enabling said real-time sight at said current physical position comprises: 
     enabling real-time virtual sight. 
     7. The computer usable storage medium of claim  1 , further comprising: 
     receiving, at said processor, a second virtual position information request associated with said second navigatable virtual view; 
     comparing, by said processor, said second virtual position information request with a store of location position information; and 
     based on said comparing, generating, by said processor, a response to said second virtual position information request. 
     8. The computer usable storage medium of claim  1 , wherein said providing a second navigatable virtual view comprises: 
     providing a virtual vehicle within said second navigatable virtual view, wherein said virtual vehicle remains within a predetermined distance from said object as said object moves. 
     9. The computer usable storage medium of claim  1 , wherein said generating a first navigatable virtual view of a first location of interest comprises: 
     generating said first navigatable view of a first virtual set of documents as said first location of interest. 
     10. The computer usable storage medium of claim  1 , further comprising: 
     generating, at said processor, a second navigatable view of a second virtual set of documents at said second location of interest. 
     11. The computer usable storage medium of claim  1 , further comprising: 
     locating, by said processor, a search request object within said first virtual set of documents. 
     12. The computer usable storage medium of claim  1 , wherein said generating a first navigatable virtual view of a first location of interest comprises: 
     generating said first navigatable virtual view of a video. 
     13. The computer usable storage medium of claim  1 , wherein said method further comprises: 
     based on said determining said meaning, generating, at said processor, a response instruction. 
     14. The computer usable storage medium of claim  13 , wherein said generating a response instruction comprises: 
     generating a response instruction that instructs a verbal response. 
     15. The computer usable storage medium of claim  13 , wherein said generating a response comprises: 
     generating a response instruction that instructs a non-verbal response. 
     16. The computer usable storage medium of claim  1 , wherein said accessing a dialogue between said plurality of agents comprises: 
     accessing an audio communication between said plurality of agents. 
     17. The computer usable storage medium of claim  1 , wherein said accessing a dialogue between said plurality of agents comprises: 
     accessing an action. 
     18. The computer usable storage medium of claim  1 , wherein said comparing received input to a script type comprises: 
     comparing received input to a fixed script. 
     19. The computer usable storage medium of claim  1 , wherein said comparing received input to a script type comprises: 
     comparing received input to a fuzzy scripting. 
     20. The computer usable storage medium of claim  1 , wherein said comparing received input to a script type comprises: 
     comparing received input to a parametric scripting. 
     21. The computer usable storage medium of claim  1 , wherein said comparing received input to a script type comprises: 
     comparing received input to a hybrid scripting comprising scripting aspects from at least one of a fixed script, a fuzzy scripting, and a parametric scripting. 
     22. A system for modeling group dynamics using augmented reality simulation to facilitate multimedia communications and service to a distributed group of users, said system comprising: 
     a first navigatable virtual view generator coupled with a processor, said first navigatable virtual view generator for generating a first navigatable virtual view of a first location of interest, wherein said first location of interest is one of a first virtual location and a first non-virtual location; 
     a second navigatable virtual view generator coupled with said processor, said second navigatable virtual view generator for, concurrently with said generating said first navigatable virtual view, generating a second navigatable virtual view corresponding to a current physical position of an object coupled with said system, such that real-time sight at said current physical position is enabled within said second navigatable virtual view; 
     a dialogue accessor coupled with said processor, said dialogue accessor configured for accessing a dialogue between a plurality of agents, wherein said plurality of agents comprises at least one of one or more automatons and one or more humans; 
     an input accessor coupled with said processor, said input accessor configured for accessing input associated with a behavior of said plurality of agents and an interaction between said plurality of agents; 
     an input comparor coupled with said processor, said input comparor configured for comparing accessed input to a script type; and 
     a meaning determiner coupled with said processor, said meaning determiner configured for determining a meaning of said dialogue based on said comparing. 
     23. The system of claim  22 , further comprising: 
     a third navigatable virtual view generator coupled with said processor, said third navigatable virtual view generator for, concurrently with said generating said first navigatable virtual view of said first location of interest, generating a third navigatable virtual view of a second location of interest, wherein said second location of interest is one of a second virtual location and a second non-virtual location. 
     24. The system of claim  22 , further comprising: 
     a first virtual position information request receiver coupled with said processor, said first virtual position information request receiver configured for receiving a first virtual position information request associated with said first location of interest; 
     a first virtual position information request comparor coupled with said processor, said first virtual position information request comparor configured for comparing said first virtual position information request with a store of location position information; and 
     a response generator coupled with said processor, said response generator configured for, based on said comparing, generating a response to said first virtual position information request. 
     25. The method of claim  24 , further comprising: 
     an advancement instruction receiver coupled with said processor, said advancement instruction receiver configured for receiving an advancement instruction to virtually advance towards said first location of interest until virtual position information of said first virtual position information request matches said first location of interest; 
     an advancer coupled with said processor, said advancer configured for virtually advancing towards said first location of interest, thereby achieving a virtual viewing position; and 
     an advancement information receiver coupled with said processor, said advancement information receiver configured for receiving advancement information signifying that a physical advancement towards said first location of interest has occurred, wherein said virtual position information matches said first location of interest and said advancement information includes said virtual viewing position of said first location of interest. 
     26. The system of claim  22 , wherein non-real-time stored imaging associated with said current physical location is further enabled.
 
27. The system of claim  22 , wherein said real-time sight comprises:
 
     real-time virtual sight. 
     28. The system of claim  22 , wherein said second navigatable virtual view comprises:
 
a virtual vehicle that remains within a predetermined distance from said object as said object moves.
 
29. The system of claim  22 , wherein said first location of interest comprises:
 
a first virtual set of documents.
 
30. The device of claim  22 , further comprising:
 
     a response instruction generator coupled with said processor, said response generator configured for, based on said determining said meaning, generating a response instruction. 
     31. The device of claim  30 , wherein said response instruction comprises: 
     an instruction for a verbal response. 
     32. The device of claim  30 , wherein said response instruction comprises: 
     an instruction for a non-verbal response. 
     33. The device of claim  22 , wherein said dialogue comprises: 
     an audio communication between said plurality of agents. 
     34. The device of claim  22 , wherein said dialogue comprises: 
     an action communicated between said plurality of agents. 
     35. The device of claim  22 , wherein said script type comprises: 
     a fixed script. 
     36. The device of claim  22 , wherein said script type comprises: 
     a fuzzy scripting. 
     37. The device of claim  22 , wherein said script type comprises: 
     a parametric scripting. 
     38. The device of claim  22 , wherein said script type comprises: 
     a hybrid scripting comprising portions of scripting from at least two of a fixed script, a fuzzy scripting, and a parametric scripting. 
     Computer System Description 
       FIG. 8  is a block diagram of an example of a computer system  800 , in accordance with an embodiment. With reference now to  FIG. 8 , portions of the technology for the coherent presentation of multiple reality and interaction models are composed of computer-readable and computer-executable instructions that reside, for example, in computer-readable storage media of a computer system. That is,  FIG. 8  illustrates one example of a type of computer that can be used to implement embodiments, which are discussed below, of the present technology. 
     It is appreciated that system  800  of  FIG. 8  is an example only and that the present technology can operate on or within a number of different computer systems including general purpose networked computer systems, embedded computer systems, routers, switches, server devices, user devices, various intermediate devices/artifacts, standalone computer systems, and the like. As shown in  FIG. 8 , computer system  800  of  FIG. 8  is well adapted to having peripheral computer readable media  802  such as, for example, a floppy disk, a compact disc, and the like coupled thereto. 
     System  800  of  FIG. 8  includes an address/data bus  804  for communicating information, and a processor  806 A coupled to bus  804  for processing information and instructions. As depicted in  FIG. 8 , system  800  is also well suited to a multi-processor environment in which a plurality of processors  806 A,  806 B, and  806 C are present. Conversely, system  800  is also well suited to having a single processor such as, for example, processor  806 A. Processors  806 A,  806 B, and  806 C may be any of various types of microprocessors. System  800  also includes data storage features such as a computer usable volatile memory  808 , e.g. random access memory (RAM), coupled to bus  804  for storing information and instructions for processors  806 A,  806 B, and  806 C. 
     System  800  also includes computer usable non-volatile memory  810 , e.g. read only memory (ROM), coupled to bus  804  for storing static information and instructions for processors  806 A,  806 B, and  806 C. Also present in system  800  is a data storage unit  812  (e.g., a magnetic or optical disk and disk drive) coupled to bus  804  for storing information and instructions. System  800  also includes an optional alphanumeric input device  814  including alphanumeric and function keys coupled to bus  804  for communicating information and command selections to processor  806 A or processors  806 A,  806 B, and  806 C. System  080  also includes an optional cursor control device  816  coupled to bus  804  for communicating user input information and command selections to processor  806 A or processors  806 A,  806 B, and  806 C. System  800  of the present embodiment also includes an optional display device  818  coupled to bus  804  for displaying information. 
     Referring still to  FIG. 8 , optional display device  818  of  FIG. 8  may be a liquid crystal device, cathode ray tube, plasma display device or other display device suitable for creating graphic images and alphanumeric characters recognizable to a user. Optional cursor control device  816  allows the computer user to dynamically signal the movement of a visible symbol (cursor) on a display screen of display device  818 . Many implementations of cursor control device  816  are known in the art including a trackball, mouse, touch pad, joystick or special keys on alpha-numeric input device  814  capable of signaling movement of a given direction or manner of displacement. Alternatively, it will be appreciated that a cursor can be directed and/or activated via input from alpha-numeric input device  814  using special keys and key sequence commands. 
     System  800  is also well suited to having a cursor directed by other means such as, for example, voice commands. System  800  also includes an I/O device  820  for coupling system  800  with external entities. For example, in one embodiment, I/O device  820  is a modem for enabling wired or wireless communications between system  800  and an external network such as, but not limited to, the Internet. A more detailed discussion of the present technology is found below. 
     Referring still to  FIG. 8 , various other components are depicted for system  800 . Specifically, when present, an operating system  822 , applications  824 , modules  826 , and data  828  are shown as typically residing in one or some combination of computer usable volatile memory  808 , e.g. random access memory (RAM), and data storage unit  812 . However, it is appreciated that in some embodiments, operating system  822  may be stored in other locations such as on a network or on a flash drive; and that further, operating system  822  may be accessed from a remote location via, for example, a coupling to the internet. In one embodiment, the present technology, for example, is stored as an application  824  or module  826  in memory locations within RAM  808  and memory areas within data storage unit  812 . The present technology may be applied to one or more elements of described system  800 . For example, a method for identifying a device associated with a transfer of content may be applied to operating system  822 , applications  824 , modules  826 , and/or data  828 . 
     The computing system  800  is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the present technology. Neither should the computing environment  800  be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the example computing system  800 . 
     Section Nine: Delivering Aggregated Social Media 
     Overview 
     Embodiments described herein provide aggregated media programming from a plurality of media types including real-time and non-real-time video and audio elements. Example media types may include, but are not limited to, social media information such as text information, photographs, and videos that are posted to the Internet, information selected to be followed by a user, sent to a user&#39;s mobile device, emailed to a user, generated by a user, broadcast for radio or television, and the like. The media types are aggregated into a customized media content that can be delivered in a single coherent broadcast. The broadcast may be viewed on a television, a computer, a mobile device, listened to over the radio, provided in the form of a podcast, and the like. 
     In other words, instead of requiring interaction with a computer program to access social media or other specific user interests, each user or group of users is able to initially select the type of media that they would like to access and the media will be presented as a passive information broadcast that allows the viewer to “opt-in” to interaction at any time. 
     In one embodiment, the content can be created from scratch for each viewer or group of viewers. However, in another embodiment, the broadcast may combine elements common to broad viewership interests with elements of personalized viewership interests. For example, the social media data stream broadcast may include portions of national and international evening news shows interspersed with a personal news channel incorporating information from friends, family, work, industry, colleagues, and the like; social media friend updates; emailed information; and the like. 
     In other words, by using, pre-produced elements and layout and behavior modeling, in conjunction with data received from a variety of unstructured or differently structured sources, a passively viewable optionally interactive cohesive social media data stream can be dynamically generated. In so doing, the present technology goes beyond simple combined displays of information by relating structure between various social media portals, and restructuring the data sources of each resulting in a cohesive social media data stream. 
     With reference now to  FIG. 9A  a block diagram of an aggregated social media delivery system  900  is shown in accordance with one embodiment of the present technology. In general, social media delivery system  900  receives social media data snippets from cloud  905  and combines the data snippets into a coherent customized media presentation  918 . 
     In general, the social media data snippets may be collected from across a network cloud including, but not limited to, the Internet. The media presentation  918  may be a broadcast such as a radio or television broadcast. That is, the media presentation  918  may be an audio presentation, an audio visual presentation, or the like. 
     In one embodiment, the social media data snippets include text  901 , audio  902 , video  903 , audio/video  904  and other  90   n . For example, the social media data stream broadcast may include portions of national and international evening news shows; information from friends, family, work, industry, colleagues, and the like; social media friend updates; emailed information; and the like. 
     In one embodiment, social media delivery system  900  includes a social media collector  910 , a media aggregator  912  and a social media formatter  914 . In one embodiment, social media collector  910  includes a user customizable configuration allowing a user to personalize the type of media data snippets received from cloud  905 . In addition, in one embodiment social media collector  910  may store the data snippets in a repository such as database  911 . 
     Media aggregator  912  merges at least two social media data snippets from the repository into a coherent social media data stream. In one embodiment, a user input module  913  may be optionally coupled with media aggregator  912 . User input module  913  allows a user to optionally add additional content and direction to the media presentation  918 . In general, user direction may include source provider information as well as viewer side information. 
     Social media formatter  914  provides the coherent media data stream in a user accessible format. In a further embodiment, social media formatter  914  may access optional canned data  915  to supplement and/or provide formatting information to the media presentation  918 . For example, canned data  915  may include canned scripts and metadata structures developed to provide flexible structures to guide generation of media presentation  918  in formats specific to social media sources. 
     In one embodiment, media presentation  918  may be provided upon user access. For example, if media presentation  918  is a television broadcast, media presentation  918  may begin when a user turns on a television and selects the appropriate channel. Upon selecting the channel, the social media delivery system  900  will begin media presentation  918 . 
     In another embodiment, media presentation  918  may be a continuously provided data stream. In other words, media presentation  918  would be available even if the media playing device was not activated, similar to any broadcast that occurs regardless of whether the broadcast is actually being watched. As such, a user would be able to activate the presentation device and tune into the in-progress media presentation  918 . In one embodiment, media presentation  918  may be a loop that is updated at a pre-defined interval, updated when a threshold of new or modified information is achieved, updated when a user defined change occurs, or the like. For example, if a user were following the football season, media presentation  918  may be updated after a game has ended, whenever a score changes, if news is provided about a favorite team, etc. 
     Referring now to  FIG. 9B , an illustration of the delivery of aggregated social media is shown in accordance with one embodiment of the present technology. In one embodiment,  FIG. 9B  includes a space  920 , a media device  921 , media presentation  918  and a user  922 . In general, space  920  may be a room, a hall, a public square, or the like, wherein a media presentation  918  may be presented. 
     Media device  921  is any device capable of presenting media presentation  918 . For example, media device  921  may be, but is not limited to, a radio, a television, a computer, a portable device, a mobile phone, a laptop computer, and the like. User  922  may represent a person or a group of people to whom the media presentation  918  has been customized. 
     With reference now to  FIG. 9C , a flowchart  925  of a method for delivering aggregated social media in a user accessible format is shown in accordance with one embodiment of the present technology. 
     Referring now to  930  of  FIG. 9C  and  FIG. 9A , one embodiment collects a plurality of social media data snippets. As shown in  FIG. 9A , the plurality of social media data snippets are selected from the group of videos, audio files, images, and text. In addition, the social media data snippets may be one or more of real-time, near-real-time and evergreen media data snippets. In general, evergreen refers to data that is not time specific. 
     For example, if a friend had been climbing Mt. Everest, the days of climbing to the peak may be near-real time information, while it would be important to have the actual achieving of the summit in real-time. In contrast, evergreen media data may be background information such as information about Mt. Everest, the friend&#39;s previous successful climbs, backstory about the friend, backstory about other climbers in the friend&#39;s group, historical weather information, and the like. 
     With reference now to  932  of  FIG. 9C  and  FIG. 9A , one embodiment stores the plurality of social media data snippets in a media data repository. 
     Referring now to  934  of  FIG. 9C  and  FIG. 9A , one embodiment aggregates at least two of the plurality of social media data snippets into a cohesive social media data stream. In other words, media aggregator  912  organizes the plurality of social media data snippets into a pre-defined order. For example, the order may be based on a timeline. Similarly, the pre-defined order may include a metric to adjust the order of social media data snippets based on the level of intensity of the information, e.g., information about a birth or death may be placed ahead of information about a friends outfit. 
     The pre-defined order metric may also adjust the order of social media data snippets based on relevancy of the information. For example, location data that includes information about a traffic accident on the route the user is presently traveling would be placed ahead of a social media data snippet about a friend&#39;s night out. In another embodiment, the pre-defined order metric may be user driven such that the social media data snippets are organized by media aggregator  912  based on user defined criteria. 
     With reference now to  936  of  FIG. 9C  and  FIG. 9A , one embodiment formats the cohesive social media data stream into a coherent social media data stream. In one embodiment, user input may be used to selectively modify the media presentation  918 . 
     For example, in one embodiment, social media formatter  914  metadata may utilize metadata such as scripting and logic filters to guide a structured content programming format based on real-time synthesis of the cohesive social media data stream. In general, the metadata may include pre-produced video and audio captured sequences from photographic/video/multimedia recordings. In one embodiment, the video and audio may be edited for use similarly to wave-table synthesis with random-access to frame and subframe samples. 
     For example, social media formatter  914  metadata may include customized segments such as, but not limited to: upcoming social events, synthesized on-air talent announcing birthdays, graduations, parties, trips, visitors, and other events in the coming month. Audio and talking-head video sequences related to announcing dates, duration, and basic event types are structured enough to be highly realistic in their real-time synthesis by “kerning” together audio and video segments (reducing bad edit-spots and unnatural speech gaps). Common given names (and some surnames) are also limited enough in scope to allow for natural pre-produced pronunciation “wave-table-synthesis” of video and audio segments to be stitched together to provide content. 
     In one embodiment, social media formatter  914  metadata may utilize Avatars (e.g., texture maps to shape models including recognizable faces) to simulate or illustrate social interaction. In addition, the avatars may be combined with speech synthesis to deliver less structured data (including less common names for the above example). 
     Social media formatter  914  metadata may additionally utilize phoneme-based speech synthesis and/or interactive simulations depicting model representations of events that can be augmented by voice-over or simulation audio. For example: social media formatter  914  may utilize a time-accelerated augmented reality fly-through sequence of day trip through Paris, bump-shots from walk-through of virtual model of trade convention, surfing simulation with real-time conditions at Waikiki (forecast, current, or at date of past event), or the like. 
     Social media formatter  914  metadata may additionally utilize calendar graphics, charts, and the like to depict statistical and time-based information; For example, a month in review calendar graphic, a workload, networking group results, and the like. 
     In another embodiment, social media formatter  914  metadata may include traditional multimedia segments (video, audio, photos, slideshows, etc.) uploaded into portals. For example: videos of niece waterskiing, photos of friends at the Coliseum, etc. 
     Social media formatter  914  metadata may include pre-produced augmented reality based interactive transmedia segments. In other words, segments that can cross-link to presented content and allow greater interactivity between passively viewed programming content and more interaction with in-depth content, or full interactivity with underlying posts. 
     In another embodiment, social media formatter  914  metadata may include highlighted text filtered from raw social media data snippets presented as summaries of longer messages or information. For example, Business-slide-like text presentations of business connection tweet highlights, news-font-graphic-like presentations of personal events or wall posts, and the like. 
     Referring now to  938  of  FIG. 9C  and  FIGS. 9A and 9B , one embodiment provides the coherent social media data stream in a user accessible format. In one embodiment, a user  922  may select additional social media data snippets to be added to the media presentation  918 . Similarly, a user  922  may select social media data snippets to be removed from the media presentation  918 . 
     A summary of embodiments for directing a processor to execute a method for delivering aggregated social media is as follows: 
     1. An aggregated social media delivery system comprising: 
     a social media collector to collect a plurality of social media data snippets in a social media data repository;
         a media aggregator for merging at least two social media data snippets from the repository into a coherent social media data stream; and   a media formatter to provide the coherent media data stream in a user accessible format.
 
2. The user configurable social media delivery system of Claim  1  wherein the plurality of social media data snippets are selected from the group of videos, audio files, images, and text.
 
3. The user configurable social media delivery system of Claim  1  wherein the coherent media data stream is an audio visual format.
 
4. The user configurable social media delivery system of Claim  1  wherein the coherent media data stream is an audio format.
 
5. The user configurable social media delivery system of Claim  1  wherein the media aggregator combines real-time, near-real-time and evergreen media data snippets.
 
6. The user configurable social media delivery system of Claim  1  further comprising:
       

     a user selectable module which modifies the coherent media data stream based on user provided input. 
     7. The user configurable social media delivery system of Claim  6  wherein the user provided input is selected from the group comprising: adding additional social media data content and selecting social media data content to be removed.
 
8. The user configurable social media delivery system of Claim  1  further comprising:
 
     a canned data module to provide canned data to the media formatter to modify the coherent media data stream. 
     9. The user configurable social media delivery system of Claim  1  wherein the coherent media data stream is provided upon access.
 
10. The user configurable social media delivery system of Claim  1  wherein the coherent media data stream is a continuously provided data stream.
 
11. The user configurable social media delivery system of Claim  1  wherein the coherent media data stream is updated at a pre-defined interval.
 
12. A non-transitory computer-readable storage medium comprising computer executable code for directing a processor to execute a method for delivering aggregated social media, said method comprising:
 
     collecting a plurality of social media data snippets; 
     storing the plurality of social media data snippets in a media data repository; 
     aggregating at least two of the plurality of social media data snippets into a cohesive social media data stream; and 
     formatting the social media data stream into a coherent social media data stream; and 
     providing the coherent social media data stream in a user accessible format. 
     13. The non-transitory computer-readable storage medium recited of Claim  12  wherein the plurality of social media data snippets are selected from the group of videos, audio files, images, and text.
 
14. The non-transitory computer-readable storage medium recited of Claim  12  wherein the social media data snippets are selected from the group consisting of: real-time, near-real-time and evergreen media data snippets.
 
15. The non-transitory computer-readable storage medium recited of Claim  12  further comprising:
 
     receiving user input to selectively modify the coherent social media data stream. 
     16. The non-transitory computer-readable storage medium recited of Claim  15  further comprising: 
     selecting additional social media data snippets to be added; and 
     selecting social media data snippets to be removed. 
     17. The non-transitory computer-readable storage medium recited of Claim  12  further comprising: 
     utilizing at least one canned data snippet to adjust the formatting of the cohesive social media data stream into the coherent social media data stream. 
     18. The non-transitory computer-readable storage medium recited of Claim  12  wherein the coherent social media data stream is provided from the group consisting of: upon an access; in a continuous format and at a pre-defined time interval.
 
19. A social media delivery system comprising:
 
     a social media collector to collect a plurality of social media data snippets in a social media data repository, wherein the plurality of social media data snippets are selected from the group of videos, audio files, images, and text; 
     a media aggregator for combining at least two social media data snippets from the repository into a social media data stream, wherein the media aggregator combines real-time, near-real-time and evergreen media data snippets; 
     a canned data module to provide canned data; and 
     a media formatter to modify the social media data stream in conjunction with the canned data to generate a coherent social media data stream in a user accessible format. 
     20. The social media delivery system of Claim  19  further comprising: 
     a user selectable module which modifies the coherent media data stream based on user provided input, wherein the user provided input is selected from the group comprising: adding additional social media data content and selecting social media data content to be removed. 
     Section Ten: Aggregated Social Media Formatter 
     Overview 
     Embodiments described herein provide aggregated media programming from a plurality of media types including real-time and non-real-time video and audio elements. Example media types may include, but are not limited to, social media information such as text information, photographs, and videos that are posted to the Internet, information selected to be followed by a user, sent to a user&#39;s mobile device, emailed to a user, generated by a user, broadcast for radio or television, and the like. 
     In one embodiment, the content can be created from scratch for each viewer or group of viewers. However, in another embodiment, the broadcast may combine elements common to broad viewership interests with elements of personalized viewership interests. For example, the social media data stream broadcast may include portions of national and international evening news shows interspersed with a personal news channel incorporating information from friends, family, work, industry, colleagues, and the like; social media friend updates; emailed information; and the like. 
     In other words, by using, pre-produced elements and layout and behavior modeling, in conjunction with data received from a variety of unstructured or differently structured sources, a passively viewable optionally interactive cohesive social media data stream can be dynamically generated. In so doing, the present technology goes beyond simple combined displays of information by relating structure between various social media portals, and restructuring the data sources of each resulting in a cohesive social media data stream. 
     With reference now to  FIG. 9D  a block diagram of a social media formatter  914  is shown in accordance with one embodiment of the present technology. In general, social media formatter  914  receives a social media data stream  952  and transforms the social media data stream  952  into a formatted customized media presentation  918 . 
     In general, social media data stream  952  consists of social media data snippets that may be collected from across a network cloud, such as, but not limited to, the Internet. The media presentation  918  may be a broadcast such as a radio or television broadcast. That is, the media presentation  918  may be an audio presentation, an audio visual presentation, or the like. 
     In one embodiment, the social media data stream  952  includes text, audio, video, audio/video and the like. For example, the social media data stream  952  may include portions of national and international evening news shows; information from friends, family, work, industry, colleagues, and the like; social media friend updates; emailed information; and the like. 
     Social media formatter  914  includes a social media data stream receiver  955 , media presentation guide  957 , virtual reality module  959  and media outputter  961 . In addition, social media formatter  914  may include significance metric module  958 . 
     Social media data stream receiver  955  receives a plurality of social media data snippets organized into a coherent social media data stream. In one embodiment, the plurality of social media data snippets is selected from the group of videos, audio files, images, and text. 
     Media presentation guide  957  formats the coherent social media data stream into a structured media presentation. For example, media presentation guide  957  may utilize a pre-produced video captured sequencer, a pre-produced audio captured sequencer, a natural pre-produced pronunciation wave-table-synthesizer of video and audio segments, and the like. In addition, in one embodiment, media presentation guide  957  may also utilize a text filter to provide a summary of a text based social media data snippet. 
     In one embodiment, media presentation guide  957  utilizes a significance metric to format the coherent social media data stream into a structured media presentation. For example, significance metric module  958  may include metrics based on one or more of: a timeline, an intensity level, a relevancy, a user selectable criterion and the like. 
     Virtual reality module  959  adds virtual reality aspects into the structured media presentation. In one embodiment, virtual reality module  959  includes an Avatar generator to simulate social interaction and a phoneme-based speech synthesizer to provide voice-over or simulation audio for the Avatar. In another embodiment, virtual reality module  959  includes a virtual reality augmenter to provide augmented reality visualizations of real-world models. 
     Media outputter  961  provides the structured media data stream in a user accessible format. In one embodiment, media presentation  918  may be provided upon user access. For example, if media presentation  918  is a television broadcast, media presentation  918  may begin when a user turns on a television and selects the appropriate channel. Upon selecting the channel, the social media delivery system  900  will begin media presentation  918 . 
     In another embodiment, media presentation  918  may be a continuously provided data stream. In other words, media presentation  918  would be available even if the media playing device was not activated, similar to any broadcast that occurs regardless of whether the broadcast is actually being watched. As such, a user would be able to activate the presentation device and tune into the in-progress media presentation  918 . In one embodiment, media presentation  918  may be a loop that is updated at a pre-defined interval, updated when a threshold of new or modified information is achieved, updated when a user defined change occurs, or the like. For example, if a user were following the football season, media presentation  918  may be updated after a game has ended, whenever a score changes, if news is provided about a favorite team, etc. 
     In general, media presentation  918  may be formatted for any device capable of presenting media. For example, but not limited to, a radio, a television, a computer, a portable device, a mobile phone, a laptop computer, and the like. 
     With reference now to  FIG. 9E , a flowchart  975  of a method for formatting random social media data snippets into a structured media presentation is shown in accordance with one embodiment of the present technology. 
     Referring now to  980  of  FIG. 9E  and  FIG. 9D , one embodiment receives a plurality of social media data snippets organized into a coherent social media data stream. As shown in  FIG. 9A , the plurality of social media data snippets are selected from the group of videos, audio files, images, and text. In addition, the social media data snippets may be one or more of real-time, near-real-time and evergreen media data snippets. In general, evergreen refers to data that is not time specific. 
     For example, if a friend had been climbing Mt. Everest, the days of climbing to the peak may be near-real time information, while it would be important to have the actual achieving of the summit in real-time. In contrast, evergreen media data may be background information such as information about Mt. Everest, the friend&#39;s previous successful climbs, backstory about the friend, backstory about other climbers in the friend&#39;s group, historical weather information, and the like. 
     With reference now to  982  of  FIG. 9E  and  FIG. 9D , one embodiment formats the coherent social media data stream into a structured media presentation. In one embodiment, the formatting includes utilizing a significance metric module  958  to organize the social media data stream  952  into a pre-defined order. For example, the order may be based on a timeline or the level of intensity of the information, e.g., information about a birth or death may be placed ahead of information about a friends outfit. 
     Additionally, significance metric module  958  may also adjust the order of social media data stream  952  based on relevancy of the information. For example, location data that includes information about a traffic accident on the route the user is presently traveling would be placed ahead of a social media data about a friend&#39;s night out. In another embodiment, significance metric module  958  may be user driven such that the social media data is organized based on user defined criteria. 
     With reference still to  982  of  FIG. 9E  and  FIG. 9D , in one embodiment, social media formatter  914  may utilize metadata such as scripting and logic filters to guide a structured content programming format based on real-time synthesis of the cohesive social media data stream. In general, the metadata may include pre-produced video and audio captured sequences from photographic/video/multimedia recordings. In one embodiment, the video and audio may be edited for use similarly to wave-table synthesis with random-access to frame and subframe samples. 
     For example, social media formatter  914  metadata may include customized segments such as, but not limited to: upcoming social events, synthesized on-air talent announcing birthdays, graduations, parties, trips, visitors, and other events in the coming month. Audio and talking-head video sequences related to announcing dates, duration, and basic event types are structured enough to be highly realistic in their real-time synthesis by “kerning” together audio and video segments (reducing bad edit-spots and unnatural speech gaps). Common given names (and some surnames) are also limited enough in scope to allow for natural pre-produced pronunciation “wave-table-synthesis” of video and audio segments to be stitched together to provide content. 
     With reference now to  984  of  FIG. 9E  and  FIG. 9D , one embodiment adds virtual reality characteristics into the structured media presentation. For example, social media formatter  914  metadata may utilize Avatars (e.g., texture maps to shape models including recognizable faces) to simulate or illustrate social interaction. In addition, the avatars may be combined with speech synthesis to deliver less structured data (including less common names for the above example). 
     Social media formatter  914  metadata may additionally utilize phoneme-based speech synthesis and/or interactive simulations depicting model representations of events that can be augmented by voice-over or simulation audio. 
     Additionally, social media formatter  914  metadata may include augmented reality visualizations of real-world models. For example: social media formatter  914  may utilize a time-accelerated augmented reality fly-through sequence of day trip through Paris, bump-shots from walk-through of virtual model of trade convention, surfing simulation with real-time conditions at Waikiki (forecast, current, or at date of past event), or the like. 
     Social media formatter  914  metadata may additionally utilize calendar graphics, charts, and the like to depict statistical and time-based information; For example, a month in review calendar graphic, a workload, networking group results, and the like. 
     In another embodiment, social media formatter  914  metadata may include traditional multimedia segments (video, audio, photos, slideshows, etc.) uploaded into portals. For example: videos of niece waterskiing, photos of friends at the Coliseum, etc. 
     Social media formatter  914  metadata may include pre-produced augmented reality based interactive transmedia segments. In other words, segments that can cross-link to presented content and allow greater interactivity between passively viewed programming content and more interaction with in-depth content, or full interactivity with underlying posts. 
     In another embodiment, social media formatter  914  metadata may include highlighted text filtered from raw social media data snippets presented as summaries of longer messages or information. For example, Business-slide-like text presentations of business connection tweet highlights, news-font-graphic-like presentations of personal events or wall posts, and the like. 
     Referring now to  986  of  FIG. 9E  and  FIG. 9D , one embodiment provides the structured media data stream in a user accessible format. The media presentation  918  may be a broadcast such as a radio or television broadcast. That is, the media presentation  918  may be an audio presentation, an audio visual presentation, or the like. 
     In one embodiment, the social media data stream  952  includes text, audio, video, audio/video and the like. For example, the social media data stream  952  may include portions of national and international evening news shows; information from friends, family, work, industry, colleagues, and the like; social media friend updates; emailed information; and the like. 
     Embodiments for formatting random social media data snippets into a structured media presentation can be summarized as follows: 
     1. A media formatter comprising: 
     a social media data stream receiver to receive a plurality of social media data snippets organized into a coherent social media data stream; 
     a media presentation guide to format the coherent social media data stream into a structured media presentation; 
     a virtual reality module to add virtual reality aspects into the structured media presentation; and 
     a media outputter to provide the structured media data stream in a user accessible format. 
     2. The user configurable social media delivery system of Claim  1  wherein the plurality of social media data snippets are selected from the group of videos, audio files, images, and text.
 
3. The user configurable social media delivery system of Claim  1  wherein the media presentation guide utilizes a significance metric to format the coherent social media data stream into a structured media presentation.
 
4. The user configurable social media delivery system of Claim  3  wherein the significance metric is based on a timeline.
 
5. The user configurable social media delivery system of Claim  3  wherein the significance metric organizes is based on an intensity level of the social media data snippets.
 
6. The user configurable social media delivery system of Claim  3  wherein the significance metric is based on a relevancy of the social media data snippets.
 
7. The user configurable social media delivery system of Claim  3  wherein the significance metric is based on a user selectable criterion.
 
8. The user configurable social media delivery system of Claim  1  wherein the media presentation guide comprises:
 
     at least one pre-produced video captured sequencer; 
     at least one pre-produced audio captured sequencer; and 
     a natural pre-produced pronunciation wave-table-synthesizer of video and audio segments. 
     9. The user configurable social media delivery system of Claim  1  wherein the media presentation guide comprises: 
     a text filter to provide a summary of a text based social media data snippet. 
     10. The user configurable social media delivery system of Claim  1  wherein the virtual reality module comprises: 
     an Avatar generator to simulate social interaction; and 
     a phoneme-based speech synthesizer to provide voice-over or simulation audio for the Avatar. 
     11. The user configurable social media delivery system of Claim  1  wherein the virtual reality module comprises: 
     a virtual reality augmenter to provide augmented reality visualizations of real-world models. 
     12. A non-transitory computer-readable storage medium comprising computer executable code for directing a processor to execute a method for formatting random social media data snippets into a structured media presentation, said method comprising: 
     receiving a plurality of social media data snippets organized into a coherent social media data stream; 
     formatting the coherent social media data stream into a structured media presentation; 
     adding virtual reality characteristics into the structured media presentation; and 
     providing the structured media data stream in a user accessible format. 
     13. The non-transitory computer-readable storage medium recited of Claim  12  wherein the plurality of social media data snippets are selected from the group of videos, audio files, images, and text.
 
14. The non-transitory computer-readable storage medium recited of Claim  12  further comprising:
 
     utilizing a significance metric to format the coherent social media data stream into a structured media presentation. 
     15. The non-transitory computer-readable storage medium recited of Claim  14  wherein the significance metric is selected from the group consisting of: a timeline, an intensity level, a relevancy and a user selectable criterion.
 
16. The non-transitory computer-readable storage medium recited of Claim  12  wherein formatting the coherent social media data stream into a structured media presentation comprises:
 
     utilizing at least one pre-produced video captured sequencer; 
     utilizing at least one pre-produced audio captured sequencer; and 
     utilizing a natural pre-produced pronunciation wave-table-synthesizer of video and audio segments to format the coherent social media data stream into a structured media presentation. 
     17. The non-transitory computer-readable storage medium recited of Claim  12  wherein adding virtual reality characteristics into the structured media presentation comprises: 
     generating an Avatar to simulate social interaction; and 
     utilizing a phoneme-based speech synthesizer to provide simulation audio for the Avatar. 
     18. The non-transitory computer-readable storage medium recited of Claim  12  wherein adding virtual reality characteristics into the structured media presentation comprises: 
     providing augmented reality visualizations of real-world models. 
     19. A social media formatter comprising: 
     a social media data stream receiver to receive a plurality of social media data snippets organized into a coherent social media data stream; 
     a media presentation guide comprising: 
     a significance metric to format the coherent social media data stream into a structured media presentation; 
     a virtual reality module to add virtual reality aspects into the structured media presentation; and 
     a media transmitter to provide the structured media data stream in a user accessible format. 
     20. The user configurable social media delivery system of Claim  19  wherein the significance metric is selected from the group consisting of: a timeline, an intensity level, a relevancy and a user selectable criterion.
 
21. The user configurable social media delivery system of Claim  19  wherein the virtual reality module comprises:
 
     an Avatar generator to simulate social interaction; 
     a phoneme-based speech synthesizer to provide voice-over or simulation audio for the Avatar; and 
     a virtual reality augmenter to provide augmented reality visualizations of real-world models. 
     Section Eleven: a Multiple Reality Mapping Correlator 
     Overview 
     Embodiments described herein provide multiple reality mapping correlation. In other words, embodiments described herein reconcile different models of realities into an apparently seamless augmented reality model. 
     For example, a given location may have a number of different reality models associated therewith. In general, reality models include live television, canned television, movies, chat, texting, personal directional camera video and stills, photographs, through-lens heads up viewing, geospace sensor data, database time-shifted real-world model data, virtual models, and the like. In addition, each reality model includes underlying characteristics or metadata information such as visual space, audio space and time domains. 
     Thus, if a person wanted to view a city block of San Francisco, the user may choose to access one or more reality models to obtain the view. However, each different reality model that a user viewed would have different underlying metadata information. These underlying differences may range from minute differences to significant deviation depending upon which reality models are selected. 
     For example, a web cam mounted within the city block would provide a reality model that included fixed location and normal time domain metadata information. In contrast, a television show filmed within the same city block may include a plurality of different locations as well as non-linear time domain metadata information. 
     In one embodiment, by defining a single reality model as the base reality model and then adjusting the underlying metadata structures of any other reality model to correlate with the underlying metadata structures of the base reality model, a plurality of reality models can be combined into a seamless augmented reality model. 
     Further, in at least one embodiment, multiple viewports from multiple devices super-impose multiple sets of blended multiple realities, one upon the other. For example: a viewer is wearing heads-up display eyeglasses and is watching augmented reality based transmedia content on a Smart TV monitor with additional augmentation from his heads-up glasses, such that not only is the viewed interactive automated television programming content unique to the Smart TV device among primary transmedia display devices, but the content being viewed (and optionally interacted with) is unique to the said viewer among all viewers of the same primary display device (in this case, a Smart TV monitor). 
     Metadata Information 
     Metadata information can additionally include: frame time, camera position, camera orientation vector, camera frame orientation vector (up indicator), camera frustum (camera lens: zoom/perspective), camera aperture, camera focus, light source positions, light source intensity, light source chrominance, flying mobility boundaries, floating mobility boundaries, hard surface mobility boundaries, video object positions, ghost bot positions (“invisible” functional interactive potential video reality objects), video object depth (used for matting approach to hidden object removal and stereoscopy), video object shape models (used for 3D model approach to hidden object removal and stereoscopy), ghost bot identity (action) mapping, video clarity (visibility), video resolution, video luminance, video chrominance, audio source positions, audio range, dialogue, dialogue to audio source mapping, infinity mapping, effective distance, interpolation, extrapolation, behavioral cues, proximity, periodicity, dialogue, value of user interaction, significance (relative weighting of value), and the like. 
     With reference now to  FIG. 10A  a block diagram of a multiple reality correlator  1000  is shown in accordance with one embodiment of the present technology. In general, multiple reality correlator  1000  includes a reality data receiver  1005 , an underlying reality model definer  1007 , a multiple reality model combiner  1009  and a media outputter  1011 . 
     Reality data receiver  1005  receives a plurality of different reality models  1002 . Different reality model examples include: live television, canned television, movies, chat, texting, personal directional camera video and stills, photographs, through-lens heads up viewing, geospace sensor data, database time-shifted real-world model data, and the like. In one embodiment, reality data receiver  1005  identifies metadata structures for each of the plurality of different reality models. 
     Underlying reality model definer  1007  defines a base reality model. In one embodiment, the underlying reality model definer  1007  selects the base reality model from one of the plurality of different reality models. However, in another embodiment, the base reality model is a virtual reality model that is distinct from the plurality of different reality models. 
     Multiple reality model combiner  1009  maps each of the plurality of different reality models to the base reality model to form an augmented reality model  1015 . In one embodiment, multiple reality model combiner  1009  utilizes a time indices of the base reality model as the time indices for the augmented reality model; and the time indices of each of the plurality of different reality models is adjusted to correlate to the time indices of the augmented reality model. 
     In one embodiment, multiple reality model combiner  1009  utilizes a geospatial indices of the base reality model to define a geospatial layout for the augmented reality model; and the geospatial indices of each of the plurality of different reality models is adjusted to correlate with the geospatial layout of the augmented reality model. In one embodiment, multiple reality model combiner  1009  also asynchronously renders a virtual reality object; and maps the virtual reality object to the augmented reality model. 
     Referring now to  FIG. 10B  is a flowchart  1050  of a method for mapping correlation between multiple realities is shown in accordance with one embodiment of the present technology. 
     With reference now to  1052  of  FIG. 10B , one embodiment accesses at least two different reality models. In one embodiment, the different reality models are accessed in the stream of reality data  1002 . In general, different reality models include real world reality models, virtual reality models, movie reality models, television reality models, real-time video reality models, audio reality models, heads up reality models, geospatial sensor reality models and the like. 
     Referring now to  1054  of  FIG. 10B , one embodiment selects a base reality model from the at least two different actual reality models. In one embodiment, the base reality model is a computer generated virtual reality model. 
     With reference now to  1056  of  FIG. 10B , one embodiment identifying a metadata structure for each of the at least two different reality models. For example, if a reality model is a movie reality model, cinema type metadata structures may be identified. In general, the cinema type metadata structures may include, but are not limited to, information for indicating camera position and movement, object positions, locations of walls and furniture and the like. For purposes of clarity, a description of metadata structures for reality models is provided herein. 
     In general, conventional video sources such as television and movies blend metadata structures derived from real world reality with other information intended to alter the user&#39;s perception of the real world reality. Examples of the metadata structures include the framing of the subject, the choice of which scenes to shoot and when, the lighting chosen or created, camera focus (soft, hard, focal length, etc.). 
     Additionally, metadata information found in highly realistic formats such as documentaries, news, and the like, usually define a reality model that includes some subtle variations. However, metadata information from formats such as “realistic” movies and TV shows may include reality models that have significant distortions, such as, but not limited to, geographical “adjustments”, non-linear timelines, and even modifications of the laws of physics. Science fiction and fantasy genres may include reality models with distortions taken to even further levels of the abstract. 
     With reference now to  1058  of  FIG. 10B  and  FIG. 10A , one embodiment correlates the at least two different reality models to generate an augmented reality model  1015 . In one embodiment, the correlating includes comparing the metadata structure of the at least two different reality models, and resolving a metadata structure discrepancy by deferring to the base reality model metadata structure. 
     In other words, to form the augmented reality model  1015  from two or more different virtual realities, metadata for each different reality model is compared to the metadata of the base reality model. 
     If the metadata from each different reality model is congruous with the metadata of the base reality model; then the different reality model can be mapped directly into the base reality model to generate the augmented reality model  1015 . 
     However, if the metadata from the different reality model is incongruous with the metadata of the base reality model; then the incongruous different reality model metadata structure is modified to correlate with the base reality model metadata structure. Then, the different reality model can be mapped directly into the base reality model to generate the augmented reality model  1015 . 
     For example, assume a virtual representation of the city block is used as the base reality model and a movie scene reality model that included the city block were to be combined to form the augmented reality model  1015 . The metadata structures of both the virtual representation of the city block and movie reality model would be identified along the data stream. While combining the two reality models, the underlying metadata structures of the movie scene reality model would be compared to the metadata structures of the base reality model. In one embodiment any divergence in metadata structure would be resolved by modifying the movie scene reality model metadata structure. In another embodiment, any divergence in metadata structure would be resolved by overriding the movie scene reality model metadata structure with the base reality model metadata structure. 
     In so doing, the augmented reality model will have a depth that is greater than any one of the original reality models. Moreover, additional reality models may be added throughout the life of the augmented reality model. For example, additional reality models such as, web cams, traffic cams, Internet advertisements, news footage and the like may also be mapped and correlated with the virtual representation of the city block to further define the augmented reality model. 
     In one embodiment, the additional reality models may be added via user interaction with the augmented reality model. For example, a user may modify the augmented reality model by either adding or removing different reality models. In another embodiment, different reality models may be added or removed automatically. 
     In one embodiment, only specified metadata structures are compared. For example, in one embodiment, only one or more of time domain, audio space, visual space and geospatial metadata structures are compared. 
     In general, time domain metadata refers to the flow of time for the reality model. For example, a streaming video would present time in real-time. In contrast, a television show may include time domains of increased rate (e.g., a week is covered in a few minutes), normal rate (e.g., a conversation between actors at a café) and slowed rate (e.g., a slow-motion sequence, two concurring events shown at different times in the show, etc.) 
     Audio space metadata refers to audio characteristics of the reality model such as actual or virtual locations of the recording device, the audio generator, the shape of the space or area at which the audio is being generated, recorded or heard and the like. Similarly, visual space metadata refers visual characteristics of the reality model such as actual or virtual locations of the recording device, the shape of the space or area at which the video is being generated, recorded or watched and the like. 
     For example, metadata indicating source, positions and movement of individual instruments from marching band parade are mapped to virtual reality objects which, on render, remix stereo audio tracks in real-time based on listener&#39;s virtual head position and actual head orientation to achieve the effect of actually being at an event. 
     Geospatial metadata refers to the location, orientation, frame orientation and the like. For example, sensors embedded in mobile smart-devices allow indirect derivation of location, orientation, and frame orientation. In non-mobile smart devices actual location is also modeled, while orientation and frame orientation can be virtualized. In any smart-device, location, orientation and frame orientation can also be virtualized. 
     In one embodiment, geospatial metadata may include mobility boundaries which identify the range of potential motion for virtual objects. For instance, geospatial metadata embedded into video allows automated behavior so that embedded objects can respond to data streams, including user interface data to provide a user-interactive and situational-interactive experience. 
     In another embodiment, geospatial sensors attached to the frame of heads-up-display devices (e.g. glasses, cars, helmets, etc.) can provide information including camera position, camera orientation, camera frame orientation and the like. In addition, the geospatial metadata can include camera orientation information such as forward and back facing. 
     Embodiments for directing a processor to execute a method for mapping correlation between multiple realities can be summarized as follows: 
     1. A multiple reality mapping correlator comprising: 
     a reality data receiver to receive a plurality of different reality models; 
     an underlying reality model definer to select a base reality model from the plurality of different reality models; 
     a multiple reality model combiner to map each of the plurality of different reality models to the base reality model to form an augmented reality model; and 
     a media outputter to provide the augmented reality model in a user accessible format. 
     2. The multiple reality mapping correlator of Claim  1  wherein the reality data receiver identifies metadata structures for each of the plurality of different reality models.
 
3. The multiple reality mapping correlator of Claim  1  wherein the multiple reality model combiner correlates a time indices of each of the plurality of different reality models to a time indices of the base reality model to form the augmented reality model.
 
4. The multiple reality mapping correlator of Claim  1  wherein the multiple reality model combiner correlates a geospatial indices of each of the plurality of different reality models to a geospatial indices of the base reality model to form the augmented reality model.
 
5. The multiple reality mapping correlator of Claim  1  wherein the multiple reality model combiner correlates an audio space indices of each of the plurality of different reality models to an audio space indices of the base reality model to form the augmented reality model.
 
6. The multiple reality mapping correlator of Claim  1  wherein the multiple reality model combiner correlates a visual space indices of each of the plurality of different reality models to a visual space indices of the base reality model to form the augmented reality model.
 
7. The multiple reality mapping correlator of Claim  1  wherein the plurality of different reality models are selected from the group consisting of:
 
     a real world reality, a virtual reality, a movie reality, a television reality, a real-time video reality, an audio reality, a heads up reality, a geospatial sensor. 
     8. The multiple reality mapping correlator of Claim  1  wherein the underlying reality model definer asynchronously renders a virtual reality object; and maps the virtual reality object to the augmented reality model.
 
9. A non-transitory computer-readable storage medium comprising computer executable code for directing a processor to execute a method for mapping correlation between multiple realities, the method comprising:
 
     accessing at least two different reality models; 
     selecting a base reality model from the at least two different reality models; 
     identifying a metadata structure for each of the at least two different reality models; and 
     correlating the at least two different reality models to generate an augmented reality model, wherein the correlating comprises:
         comparing the metadata structure of the at least two different reality models; and   resolving a metadata structure discrepancy by deferring to the base reality model metadata structure.
 
10. The non-transitory computer-readable storage medium recited of Claim  9  further comprising:
       

     comparing a time indices metadata structure of the at least two different reality models. 
     11. The non-transitory computer-readable storage medium recited of Claim  9  further comprising: 
     comparing a geospatial indices metadata structure of the at least two different reality models. 
     12. The non-transitory computer-readable storage medium recited of Claim  9  further comprising: 
     comparing an audio space indices metadata structure of the at least two different reality models. 
     13. The non-transitory computer-readable storage medium recited of Claim  9  further comprising: 
     comparing a visual space indices metadata structure of the at least two different reality models. 
     14. The non-transitory computer-readable storage medium recited of Claim  9  further comprising: 
     displaying the augmented reality model in a user accessible format. 
     15. The non-transitory computer-readable storage medium recited of Claim  9  wherein the at least two different reality models are selected from the realities consisting of: a real world reality, a virtual reality, a movie reality, a television reality, a real-time video reality, an audio reality, a heads up reality, a geospatial sensor.
 
16. The non-transitory computer-readable storage medium recited of Claim  9  further comprising:
 
     asynchronously rendering virtual reality objects; and 
     mapping the virtual reality objects to the augmented reality model. 
     17. A multiple reality mapping correlator comprising: 
     a reality data receiver to receive a plurality of different reality models and identify metadata structures for each of the plurality of different reality models; 
     an underlying reality model definer to define a base reality model; 
     a multiple reality model combiner to map each of the plurality of different reality models to the base reality model to form an augmented reality model; and 
     a media outputter to provide the augmented reality model in a user accessible format. 
     18. The multiple reality mapping correlator of Claim  17  wherein the underlying reality model definer selects the base reality model from one of the plurality of different reality models.
 
19. The multiple reality mapping correlator of Claim  17  wherein the metadata structure comprises a time indices and the multiple reality model combiner synchronizes a time indices for each of the plurality of different reality models with a time indices of the base reality model to form the augmented reality model.
 
20. The multiple reality mapping correlator of Claim  17  wherein the metadata structure comprises a geospatial indices and the multiple reality model combiner synchronizes a geospatial indices for each of the plurality of different reality models with a geospatial indices of the base reality model to form the augmented reality model.
 
     Section Twelve: Interactive User Interface 
     Notation and Nomenclature 
     Some portions of the description of embodiments which follow are presented in terms of procedures, logic blocks, processing and other symbolic representations of operations on data bits within a computer memory. These descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. In the present application, a procedure, logic block, process, or the like, is conceived to be a self-consistent sequence of steps or instructions leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, although not necessarily, these quantities take the form of electrical or magnetic signal capable of being stored, transferred, combined, compared, and otherwise manipulated in a computer system. 
     It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussions, it is appreciated that throughout the present discussions terms such as “providing”, “receiving”, “generating”, “embedding”, “creating”, “customizing”, or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system&#39;s registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices. 
     Furthermore, in some embodiments, methods described herein can be carried out by a computer-usable storage medium having instructions embodied therein that when executed cause a computer system to perform the methods described herein. 
     Brief Description 
     Customized internet news feeds that aggregate information have become popular as social media has grown. Further, today&#39;s customers often request interactivity and customization in numerous electronic devices. The novel embodiments below describe an interactive device comprising a user interface in which content, and the way in which that content is presented, is customized for at least one user. 
     Overview of Discussion 
     Example techniques, devices, systems, and methods for providing content to a user at an interactive device is described herein. Discussion begins with a high level description of interactive devices. Example presentation layers are then described. Discussion continues examples of data-driven interactive content. Next, an example avatar is discussed. Lastly, example methods of use are described. 
     High Level Description of Interactive Devices 
       FIG. 11A  shows an example interactive device  1110 . Users  1112 ,  1113  and  1114  are shown watching the display  1111  of interactive device  1110 . The display  1111  shows an example presentation layer (e.g., a layer displaying content  1105 , interactive elements  1106 , scroll bar  1107 , and avatar  1101 ). Note that the term “presentation layer” as used herein does not refer to layer six of the open systems interconnection (OSI) model. Presentation layers come in various formats, as will be discussed in more detail below. Interactive device may include, but is not limited to: computers, televisions, radios, interactive televisions, video game consoles, mobile devices, smart phones, smart televisions, automobile consoles, windshields, laptops, personal digital assistants, tablet computers, etc. 
     In various embodiments, users  1112 ,  1113 , and  1114  interact with interactive device  1110  via input/output (I/O) device  1116 . I/O device  1116  comprises, but is not limited to: a receiver, a touchscreen display  1111 , a keyboard, a mouse, a joystick, a button, a depth sensor, a motion sensor, a microphone, a trackball, a speaker, a Microsoft™ Kinect™ type device, etc. In some embodiments interactive device  1110  comprises a plurality of I/O devices  1116 . In one embodiment, an I/O device  1116  may receive signals from a mobile I/O device  1108 . Mobile I/O device  1108  may include, but is not limited to: a remote control, a tablet computer, a smart phone, a microphone, a personal digital assistant, etc. In an embodiment Mobile I/O device  1108  may be coupled to interactive device  1110 . In one embodiment Mobile I/O device  1108  may be communicatively coupled to interactive device  1110 . 
     In an embodiment, interactive device  1110  comprises a processor  1117  operable to perform various operations. In one embodiment, processor  1117  may comprise a graphics processing unit or a central processing unit. Further, interactive device  1110  may comprise a plurality of processors  1117  that may perform all, some, or none of the operations discussed herein. 
     In one embodiment processor  1117  is not located in device  1110 . In an embodiment the processing described herein is performed at a location remote from interactive device  1110 . For example, content  1105  may be placed within a presentation layer prior to the content  1105  reaching interactive device  1110 . 
     In various embodiments interactive device  1110  comprises a display  1111 . Displays are known in the art so a detailed discussion is not necessary. While in some embodiments display  1111  is flat, in various embodiments display  1111  is concave or convex. In one embodiment interactive device  1110  comprises a stereoscopic display  1111 . 
     Presentation Layers 
     For the purposes of this discussion, in an embodiment, presentation layers dictate the way in which a user  1112  views and/or interacts with content  1105  interactive elements  1106 , avatar  1101 , and other items shown on display  1111 . In an embodiment presentation layers are written in a scripting language, although it should be understood that presentation layers may be written in any programming language. In an embodiment a presentation layer is customizable. 
     In an embodiment, a presentation layer may be customized to at least one interest of a user  1112 . In an embodiment, the presentation layer creates a custom “show” comprising content  1105  for a user  1112  to passively, or interactively, watch. Note that the term “show”, as discussed herein is meant to refer to an interactive device  1110  providing at least one piece of content  1105  to a user with or without an avatar  1101 . In various embodiments, shows comprise various tempos. In an embodiment a show may comprise a news-television-show-type format where pieces of content  1105  are shown sequentially and quickly (e.g., relative to a documentary). In an embodiment a show may comprise dynamic content  1105  that changes on a display in real time or close to real time (e.g., news videos, sports scores, etc.), or evergreen content  1105  which does not change (e.g., movies or shows stored within or remote from interactive device  1110 ). In one embodiment, a highlight reel of the news or sports is shown. In an embodiment a show may be shown in a documentary type format, wherein pieces of content  1105  are longer than in a news type format. In one embodiment, a show may be shown in a breaking news type format. In some embodiments, a presentation layer interrupts what a user  1112  is watching to show breaking news. In one embodiment, a presentation layer prompts a user  1112  to watch breaking news. In one embodiment, the background of a news type program is mapped and/or rendered based on data associated with a presentation layer or content  1105 . 
     In various embodiments, presentation layers perform functions including, but not limited to: determining where to retrieve content  1105  from, determining the amount of time a particular piece of content  1105  is shown on the display  1111 , determining the type of “show”, providing a user with access to a computer program, determining the sequence of pieces of content  1105  to be shown, determining the size of the content  1105  to be shown relative to the display  1111 , determining whether an avatar  1101  is shown, determining whether to use a computer program, creating visualizations out of content  1105 , determining what elements  1106  shown on a display  1111  are interactive, creating segues between pieces of content  1105 , providing more information about the subject matter of a piece of content  1105 , piecing together content  1105  and other images and/or avatars  1101  if necessary to create the impression of a live newscast, determining and updating the preferences of a particular user  1112 , determining whether multiple items of content  1105  should be shown simultaneously, determining whether a scroll bar  1107  should be shown, providing a user  1112  with the ability to interact with content  1105 , providing a user  1112  with the ability to call or video conference with at least a second user  1113 , create visualizations based on data, etc. 
     Data-Driven Content 
     The content  1105  provided to a user  1112  during a “show” may include, but is not limited to: audio, video, a web-page, a computer program, a cable television signal, a broadcast signal, a radio signal, a satellite signal, a satellite radio signal, a television show, a web service, a Resource description framework Site Summary (RSS) feed, a Twitter™ feed, a Facebook™ feed, enterprise software, world news, news about a particular high school soccer game taken from a web page or local news broadcast, a calendar, email, local news, flight schedules, evergreen segments, data taken via xml, service oriented architecture services, meta-data sources, etc. In an embodiment interactive device  1110  receives external data in the form of content  1105  or external data to create content  1105 . In an embodiment content  1105  is located on memory within interactive device  1110 . In some embodiments content  1105  can be manipulated, restructured, reformatted and/or modified by a user. In an embodiment content  1105  comprises a computer program that provides a user  1112  with the ability to modify and/or manipulate data. 
     In an embodiment a presentation layer formats content  1105  as a visualization. In other words, in an embodiment, a presentation layer is operable to create a visual representation of data received from content  1105 . This visual representation may include video and/or audio. For example, a presentation layer may create a three dimensional (3D) graph for a user  1112  given data received from Quicken™, a finance television program, or a webpage. As another example, a presentation layer may create a user interface to show an information technologist user  1112  whether her servers at work are operating correctly. In some embodiments, these visualizations are combined with other content  1105  (including interactive content  1106 ) such as a video of national news, local news, and the local weather. In one embodiment a presentation layer provides an avatar  1101  that “reads” an RSS feed (or any content  1105 ) by blending and/or synthesizing audio and video (e.g., using wave table synthesis). In an embodiment, a wave table is created. In an embodiment sub-syllable audio and/or fragments are processed for efficiency. 
     As an example, the presentation layer may provide a user  1112  with a customized interactive show comprising content  1105 , wherein the customized interactive show: (1) plays ten minutes of video of world news; (2) plays five minutes of video of local high school sports; (3) streams video from a financial news station; (4) allows a user  1112  to interact with (e.g., click or make a gesture) on a stock symbol shown on the financial news station that user  1112  is interested in; (5) display a Yahoo™ Finance web page in response to the gesture made by user  1112 ; (6) open Quicken™ in response to another gesture by user  1112  such that user  1112  may see how the financial news affected her 401(k) account; (7) return a user  1112  to a main screen; (8) allow a user  1112  to read a Facebook™ news feed; (9) allow a user  1112  to activate an avatar  1101  to “read” a Twitter™ feed; (10) allow a user to virtually control a remote machine; and (11) show the Late Show™. In various embodiments a user  1112  may skip a segment, add a segment, or stop currently playing content  1105 . 
     In some embodiments, the customized show is shown without user  1112  interaction. In other words, in an embodiment, a user  1112  may passively watch a show created by a presentation layer. In various embodiments user  1112  may interact with interactive elements  1106  via I/O device  1116 . For example, an interactive element  1106  may include, but is not limited to: a stock symbol on the screen during a television show, the weather in a the local neighborhood of a user  1112 , a hyper-link, buttons and scroll bars in a program, a text box, a highlighted object (e.g., clothes or an athlete), etc. 
     Avatars 
     In some embodiments, the presentation layer provides an avatar  1101 . In an embodiment a user  1112  may interact with an avatar  1101 . Avatar  1101  may appear in various forms. For example, avatar  1101  may appear to be a celebrity including, but not limited to: Walter Cronkite, Brian Williams, Johnny Carson, James Earl Jones, etc. In an embodiment, an avatar  1101  is chosen based at least in part upon which user  1112 ,  1113 , and  1114  is using the interactive device  1110 . For example, a microphone may determine that a child is using the interactive device  1110  by the voice of the child and cause an avatar  1101  to appear wherein the avatar is a cartoon character. In an embodiment a microphone (e.g., by the number of voices) or a camera (e.g., by the number of bodies) may determine that a plurality of users  1112 ,  1113  and  1114  are using the interactive device  1110  and play content  1105  or choose an avatar  1101  in response to the particular users  1112 ,  1113 , and  1114  that are present. In one embodiment, a plurality of avatars  1101  is shown concurrently. 
     In various embodiments, avatars  1101  are capable of appearing as though they are a news anchor providing the news after receiving data from content  1105 . For example, content  1105  may include the website of a local newspaper that comprises local events occurring on a holiday weekend from a website, then avatar  1101  may appear as a news anchor (e.g., a visualization) and tell a viewer about the local events based on the data from the local newspaper website. 
     In an embodiment, an avatar  1101  is created by blending audio and/or video. In one embodiment this is done in real time, while in other embodiments it is produced prior to being shown. In one embodiment, a skin of a person or character is mapped onto a generic avatar  1101 . In one embodiment, an avatar  1101  is created by combining a plurality of video clips. Similarly, in an embodiment, an avatar  1101  may appear as though it is speaking by combining a plurality of audio clips. By combining clips avatars  1101  appear very realistic to viewers such that avatars  1101  appear to be real people, computer generated people, animals, or cartoon characters, etc. 
     Example Methods of Use 
       FIG. 11B  is a flow diagram  1120  of an example method for providing content  1105  to a user  1112  at an interactive device  1110  with a display  1111  in accordance with embodiments of the present invention. 
     In operation  1121 , in one embodiment, a presentation layer is provided for the content  1105 . A presentation layer receives content  1105  in a variety of formats and presents that content  1105  in an interactive format based at least in part on the type of content  1105  shown. For example, a presentation layer may receive a Facebook™ feed and provide an avatar  1101  that appears to read the Facebook™ feed. 
     In operation  1122 , in one embodiment, data is received at the interactive device  1110 . Data may include, but is not limited to: content  1105 , updates for interactive device  1110 , etc. For example, interactive device  1110  may receive data associated with an interactive calendar belonging to a user  1112 . 
     In operation  1123 , in one embodiment, content is displayed. In an embodiment, content  1105  is formatted by a presentation layer and shown to a user  1112 . The content  1105  is based at least in part on the data received by interactive device  1110 . 
     In operation  1124 , in one embodiment, a user is provided with the ability to interact with the elements  1106 . In an embodiment, interactive elements  1106  may be embedded in content  1105 . In an embodiment, a presentation layer places interactive elements  1106  on the display  1111 . In an embodiment, interactive elements  1106  are operable to cause interactive device  1110  to perform an operation (e.g., open a web page, play a video, change from one television station to another, etc.). 
     In operation  1125 , in one embodiment, the content  1105  is customized to at least one interest of the user  1112 . In various embodiments content  1105  is shown based at least in part upon the user  1112  using interactive device  1110 . For example, the microphone may determine which user  1112  is watching a smart television, and based on which viewer is watching the smart television play a particular “show” or piece of content  1105 . 
     In operation  1126 , in one embodiment, a presentation layer is generated with a plurality of customizable instructions. In an embodiment, a presentation layer is code that when executed causes a processor to perform functions including, but not limited to: facilitate user interaction with elements  1106 , format content  1105 , create at least one avatar  1101 , recognize a user  1112 , etc. 
       FIG. 11C  is a flow diagram  1130  of an example method implemented by a system for performing a method for virtually placing an object in a piece of original content in accordance with embodiments of the present invention. 
     In operation  1131 , in one embodiment, presentation layer is provided for the content  1105 . A presentation layer receives content  1105  in a variety of formats and presents that content  1105  in an interactive format based at least in part on the type of content  1105  shown. For example, a presentation layer may receive a Facebook™ feed and provide an avatar  1101  that appears to read the Facebook™ feed. 
     In operation  1132 , in one embodiment, data is received at the interactive device. Data may include, but is not limited to: content  1105 , updates for interactive device  1110 , etc. For example, interactive device  1110  may receive information associated with a calendar belonging to a user  1112 . 
     In operation  1133 , in one embodiment, content is displayed. In an embodiment, content  1105  is formatted by a presentation layer and shown to a user  1112 . The content  1105  is based at least in part on the data received by interactive device  1110 . 
     In operation  1134 , in one embodiment, a user is provided with the ability to interact with the elements. In an embodiment, interactive elements  1106  may be embedded in content  1105 . In an embodiment, a presentation layer places interactive elements  1106  on the display  1111 . In an embodiment, interactive elements  1106  are operable to cause interactive device  1110  to perform an operation (e.g., open a web page, play a video, change from one television station to another, etc.). 
     In operation  1135 , in one embodiment, the content  1105  is customized to at least one interest of the user. In various embodiments content  1105  is shown based at least in part upon the viewer  1112  using interactive device  1110 . For example, the microphone may determine which user  1112  is watching a smart television, and based on which viewer is watching the smart television play a particular “show” or piece of content  1105 . 
     In operation  1136 , in one embodiment, a presentation layer is generated with a plurality of customizable instructions. In an embodiment, a presentation layer is code that when executed causes a processor to perform functions including, but not limited to: facilitate user interaction with elements  1106 , format content  1105 , create an avatar  1101 , recognize a user  1112 , etc. 
     Embodiments of the present technology are thus described. While the present technology has been described in particular examples, it should be appreciated that the present technology should not be construed as limited by such examples, but rather construed according to the claims. 
     Embodiments for providing content to a user at an interactive device with a display can be summarized as follows: 
     1. A method for providing content to a user at an interactive device with a display, said method comprising:
         providing a presentation layer for said content, wherein said presentation layer is operable to embed interactive elements that appear on said display;   receiving, at said interactive device, data;   displaying said content, wherein said content is based at least in part on said data; and   providing said user with the ability to interact with said elements.       

     2. The method of Claim  1 , wherein said presentation layer creates audio content based at least in part by blending a plurality of audio content. 
     3. The method of Claim  1 , wherein said presentation layer creates video content based at least in part by blending a plurality of video content. 
     4. The method of Claim  3 , wherein said presentation layer is operable to execute a program. 
     5. The method of Claim  1 , further comprising:
         customizing said content to at least one interest of said user.       

     6. The method of Claim  1 , further comprising: 
     generating said presentation layer with a plurality of customizable instructions. 
     7. The method of Claim  1 , wherein said presentation layer and said content is generated at said interactive device. 
     8. The method of Claim  1 , wherein said presentation layer provides an avatar, wherein said user is able to interact with said avatar. 
     9. The method of Claim  1 , wherein said interactive device is operable to differentiate between a plurality of voices, wherein said interactive device is operable to associate said plurality of voices with a plurality of users, and wherein said interactive device is operable to change content that is currently playing based at least in part on said plurality of users. 
     10. A computer usable storage medium having instructions embodied therein that when executed cause a computer system to perform a method for providing content to a user at an interactive device with a display, said method comprising:
         providing a presentation layer for said content, wherein said presentation layer is operable to embed interactive elements that appear on said display;   receiving, at said interactive device, data;   displaying said content, wherein said content is based at least in part on said data; and   providing said user with an ability to interact with said elements.       

     11. The computer usable storage medium of Claim  10 , wherein said presentation layer creates video content based at least in part by blending a plurality of video content. 
     12. The computer usable storage medium of Claim  10 , wherein said presentation layer creates video content based at least in part by blending a plurality of video content. 
     13. The computer usable storage medium of Claim  10 , further comprising:
         customizing said content to at least one interest of said user.       

     14. The computer usable storage medium of Claim  10 , further comprising: 
     generating said presentation layer with a plurality of customizable instructions. 
     15. The computer usable storage medium of Claim  10 , wherein said layer and said content is generated at said interactive device. 
     16. The computer usable storage medium of Claim  10 , wherein said content comprises an avatar, and wherein said user is able to interact with said avatar. 
     17. The computer usable storage medium of Claim  10 , wherein said computer is operable to differentiate between a plurality of voices, wherein said computer is operable to associate said plurality of voices with a plurality of users, and wherein said interactive device is operable to change content that is currently playing based at least in part on said plurality of users. 
     18. An interactive device comprising:
         a display;   a processor, wherein said processor is operable to receive data, display said content to a user, provide said user with access to a computer program, embed interactive elements into said content, and provide a user with an ability to interact with said elements, and wherein said content is based at least in part on said data;   an input device to capture user input, wherein said user input is operable to interact with said interactive elements; and   wherein said computer program provides said user with the ability to modify data.       

     19. The processor of Claim  18 , wherein said processor is operable to customize said content to at least one interest of said user. 
     20. The processor of Claim  18 , wherein said interactive device is operable to differentiate between a plurality of voices, and wherein said interactive device is operable to associate said plurality of voices with a plurality of users. 
     Section Thirteen: Media Metadata Extractor 
     Overview 
     Embodiments described herein utilize varying combinations of Pre-production technologies, real-time devices and techniques used during production, and post-production automated processing steps to extract, interpolate, and extrapolate metadata from media with adequate accuracy to facilitate the integration of alternate and richer machine-readable models of reality (e.g. virtual reality). 
     In general, the media may be audio, video, text or a combination thereof. Moreover, the media may be live or canned. Live media refers to media that is being recorded real-time or near real time. For example, a concert, a sporting event, a news broadcast, live television, live radio, and the like. 
     In contrast, canned media refers to media that was previously recorded. For example, a television show, a rerun, a movie and the like. 
     One embodiment of post processing includes utilizing an augmented reality transmedia (ART) Editor to coordinate the application of semi-automated post-processing and interactive data entry. In another embodiment, an ART-Director is used to coordinate the integration of real-time augmenting additions to video for live events. 
     Metadata Information 
     Metadata information can include: frame time, camera position, camera orientation vector, camera frame orientation vector (up indicator), camera frustum (camera lens: zoom/perspective), camera aperture, camera focus, light source positions, light source intensity, light source chrominance, flying mobility boundaries, floating mobility boundaries, hard surface mobility boundaries, video object positions, ghost bot positions (“invisible” functional interactive potential video reality objects), video object depth (used for matting approach to hidden object removal and stereoscopy), video object shape models (used for 3D model approach to hidden object removal and stereoscopy), ghost bot identity (action) mapping, video clarity (visibility), video resolution, video luminance, video chrominance, audio source positions, audio range, dialogue, dialogue to audio source mapping, infinity mapping, effective distance, interpolation, extrapolation, behavioral cues, proximity, periodicity, dialogue, value of user interaction, significance (relative weighting of value), and the like. 
     With reference now to  FIG. 12A  a block diagram of a media metadata extractor  1200  is shown in accordance with one embodiment of the present technology. In general, media metadata extractor  1200  generates a media stream  1208  and determines media metadata  1215  therefrom. In one embodiment, media metadata extractor  1200  includes a pre-production module  1205 , a production module  1207 , and a post-production module  1209 . In one embodiment, media metadata extractor  1200  also includes an optional user interactive module  1210 . 
     In one embodiment, pre-production module  1205  determines a geospatial location of a media recording device. In one embodiment, pre-production module  1205  also determines a geospatial location of an immobile object. For example, the immobile object may be a landmark, a geographical feature, a structure, and the like. 
     In another embodiment, pre-production module  1205  additionally establishes a geospatial location tag (or sensor) on a mobile object. For example, the geospatial sensor may be a global positioning system, a distance sensor, a proximity beacon, a directional beacon, a magnetometer, an accelerometer, a gyroscope, a machine readable visual marker, a radio frequency identifier tag and the like. 
     In general, production module  1207  collects time-stamped geospatial location information from the media data produced by the media recording device. In one embodiment, the production module  1207  keys the media data with a timestamp. In one embodiment, the production module  1207  also collects time-stamped geospatial location information from the tagged mobile object. 
     In one embodiment, post-production module  1209  extracts the time-stamped geospatial location information from the media data. In addition, post-production module  1209  is able to map the extracted time-stamped geospatial location information to a reality model. 
     Optional user interactive module  1210  provides coordinated integration of an augmentation addition to the media data. In the present discussion, an augmentation addition is an object or action that is added to the media data. For example, if the media data is a live concert, when the media data is collaboratively combined with other similar media data, enough information will be available to develop an accurate reality model of the concert. The integration of the augmentation addition, would allow a user to add an alien ship landing to the reality model of the concert. 
     Geospatial information refers to the location, orientation, frame orientation and the like. For example, sensors embedded in mobile smart-devices allow indirect derivation of location, orientation, and frame orientation. In non-mobile smart devices actual location is also modeled, while orientation and frame orientation can be virtualized. In any smart-device, location, orientation and frame orientation can also be virtualized. 
     In one embodiment, geospatial metadata may include mobility boundaries which identify the range of potential motion for virtual objects. For instance, geospatial metadata embedded into video allows automated behavior so that embedded objects can respond to data streams, including user interface data to provide a user-interactive and situational-interactive experience. 
     In another embodiment, geospatial sensors attached to the frame of heads-up-display devices (e.g. glasses, cars, helmets, etc.) can provide information including camera position, camera orientation, camera frame orientation and the like. In addition, the geospatial metadata can include camera orientation information such as forward and back facing. 
     Referring now to  FIG. 12B  a flowchart  1230  of a method for pre-producing media having extractable metadata is shown, according to one embodiment of the present technology. 
     With reference now to  1231  of  FIG. 12B , one embodiment scripts a scene to be recorded. For example, scripting of significant characteristics of the scene(s) to be shot. Significant characteristics may include mobility zones, such as traversable land, navigable water, etc. 
     Referring now to  1232  of  FIG. 12B , one embodiment identifies a significant object. Significant objects are selected from the group consisting of: landmarks, vehicles, persons, and geographical features. 
     With reference now to  1233  of  FIG. 12B , one embodiment determines geospatial data of immobile objects within a set, a landscape, a false background and the like. 
     Referring now to  1234  of  FIG. 12B , one embodiment attaches geospatial sensors to animate subjects. In general, geospatial sensors include, but are not limited to, global positioning systems, distance sensors, proximity and directional beacons, magnetometers, accelerometers, gyroscopes, machine readable visual markers, radio frequency identifier tags and the like. Animate subjects refer to mobile objects, people, animals and the like. 
     With reference now to  1235  of  FIG. 12B , one embodiment calibrates the data sources using data redundancy. 
     Referring now to  FIG. 12C , a flowchart  1240  of a method for producing media having extractable metadata is shown, according to one embodiment of the present technology. 
     With reference now to  1241  of  FIG. 12C , one embodiment collects real-time geospatial data from the image capture devices. At  1242 , one embodiment collects real-time geospatial data from the previously tagged subjects. 
     Referring now to  1243  of  FIG. 12C , one embodiment captures precise time information for frames shot and all geospatial data. At  1244 , one embodiment keys the data by timestamp. At  1245 , similar to  1235  of  FIG. 12B , one embodiment periodic benchmarks or recalibrates the geospatial devices. For example, offline cameras on a multi-cam shoot. 
     With reference now to  1246  of  FIG. 12C , one embodiment utilizes one or more user-operated Director-assist systems for coordination of real-time integration of augmenting additions to the media data. 
     Referring now to  FIG. 12D , a flowchart  1250  of a method for post-production extraction of media metadata is shown, according to one embodiment of the present technology. In the following discussion  1251 - 1254  are utilized for canned media while only  1251 - 1252  are utilized for live media. 
     With reference now to  1251  of  FIG. 12D , one embodiment extracts the characteristics of previously recorded media stream. For example, a scene, location, landscape and the like. At  1252  of  FIG. 12D , one embodiment maps the extracted characteristics to a reality model. In the case of live media, post processing is a small window due to the processing occurring in real-time or near real-time. In other words, a viewer watching a live program would not want anything more than a few seconds delay in the broadcast or presentation. As such, the post-processing time window is small. 
     Some foundational processing techniques that may be used on live or canned media includes edge detection (such as convolve image filters); object detection which includes edge detection plus logic plus luminance and chrominance thresholding as well as recognized frequency domain patterns; near-horizontal line detection and near-vertical line detection which use edge detection plus logic. 
     Automated derivation of characteristics examples include: 
     1. Camera Frustum &amp; Camera Location deltas based on apparent change in image scale 
     Four camera maneuvers generally affect apparent image scale:
         i. Zoom-in (a narrowing of field of view characterized by diminished perspective approaching orthographic projection as Zoom increases)   ii. Zoom-out (a widening field of view characterized by increased perspective which exaggerates convergence of objects near the center of field relative to)   iii. Dolly-in (camera location change toward the direction of view characterized by static perspective)   iv. Dolly-out
           By monitoring changes in scale (objects moving onto frame or off frame roughly radially from center field), and comparing the relative movement of near-center-field and far-afield recognized objects we can derive camera location deltas parallel to the orientation of the camera, as well as changes to the camera frustum.   
               

     2. Light source position(s), chrominance and intensity
         a. By comparing relative luminance and chrominance on all visible portions of recognized objects which have been located in 3 space within the field of view, a model for light source position(s), chrominance and intensity can be derived.       

     3. Chrominance of film video or scene in its entirety or subframe, can be derived by a transfer function from chrominance information of a plurality of pixels and or frames. 
     Luminance bias of film, video or scene can be derived by a transfer function from chrominance information of a plurality of pixels and or frames. 
     Referring now to  1253  of  FIG. 12D , one embodiment edits the characteristics interactively. For example the characteristics may be edited using ART Editor. 
     In general, ART editor is a user interactive system capable of changing time scale of video from greater than normal speed down to frame accurate; allowing a user to switch between video source, real-world model, and virtual reality model views; pointing devices and other controls to allow specification of objects; functions that relate user interaction and input to automated extraction; allowing a user to determine highest productivity frame rate of data entry (e.g., sub full-motion); data entry capability for estimates; database access to assist common items (e.g., known landmarks, etc.); defining mobility boundaries for embedded mobile objects and the like. 
     In one embodiment defining mobility boundaries for embedded mobile objects is specified by: relative positional vectors &amp;/or abstract polyhedron, nurb or formula pinned to any of: infinity (skydomes, skycubes, etc.); placed objects (stationary or mobile); identified objects; points, including origin and the like. 
     With reference now to  1254  of  FIG. 12D , one embodiment coordinates real-time integration of an augmenting addition to the media stream. For example, in one embodiment, one or more user-operated ART Director-assist systems may be used. In general, ART director assist is a user interactive system capable of controlling movements and behavior of augmented reality objects. 
     A summary of embodiments for directing a processor to execute a method for pre-producing media having extractable metadata is the following: 
     1. A live media metadata extractor comprising: 
     a pre-production module to determine a geospatial location of a media recording device; 
     a production module to collect a time-stamped geospatial location information from a media data produced by the media recording device; and 
     a post-production module to extract the time-stamped geospatial location information from the media data. 
     2. The live video metadata extractor of Claim  1  further comprising: 
     a user interactive module to provide coordinated integration of an augmentation addition to the media data. 
     3. The live video metadata extractor of Claim  1  wherein the pre-production module determines a geospatial location of an immobile object.
 
4. The live video metadata extractor of Claim  1  wherein the pre-production module establishes a geospatial location tag on a mobile object.
 
5. The live video metadata extractor of Claim  4  wherein the production module collects a time-stamped geospatial location information from the mobile object.
 
6. The live video metadata extractor of Claim  1  wherein the production module keys the media data with a timestamp.
 
7. The live video metadata extractor of Claim  1  wherein the post-production module maps the extracted time-stamped geospatial location information to a reality model.
 
8. The live video metadata extractor of Claim  7  wherein the post-production module integrates an augmentation addition to the reality model.
 
9. A non-transitory computer-readable storage medium comprising computer executable code for directing a processor to execute a method for pre-producing media having extractable metadata, the method comprising:
 
     scripting a scene to be recorded; 
     identifying significant objects within the scene; 
     determining geospatial data for at least one immobile object within the scene; and 
     attaching a geospatial sensor to an animate subject in the scene. 
     10. The non-transitory computer-readable storage medium recited of Claim  9  wherein the significant objects are selected from the group consisting of: landmarks, vehicles, persons, and geographical features.
 
11. The non-transitory computer-readable storage medium recited of Claim  9  wherein the geospatial sensor is selected from the group consisting of: a global positioning system, a distance sensor, a proximity beacon, a directional beacon, a magnetometer, an accelerometer, a gyroscope, a machine readable visual marker, and a radio frequency identifier tag.
 
12. The non-transitory computer-readable storage medium recited of Claim  9  wherein the animate subject is selected from the group consisting of: a mobile object, a person and an animal.
 
13. The non-transitory computer-readable storage medium recited of Claim  9  further comprising:
 
     calibrating the geospatial sensor using data redundancy. 
     14. A non-transitory computer-readable storage medium comprising computer executable code for directing a processor to execute a method for producing media having extractable metadata, the method comprising: 
     collecting real-time media data from a media recording device; 
     collecting real-time geospatial data from the media recording device; 
     collecting real-time geospatial data from an animate subject having a geospatial sensor attached thereto; 
     capturing precise time information for frames shot and all geospatial data; and 
     keying all media data with a timestamp. 
     15. The non-transitory computer-readable storage medium recited of Claim  14  wherein the geospatial sensor is selected from the group consisting of: a global positioning system, a distance sensor, a proximity beacon, a directional beacon, a magnetometer, an accelerometer, a gyroscope, a machine readable visual marker, and a radio frequency identifier tag.
 
16. The non-transitory computer-readable storage medium recited of Claim  14  wherein the animate subject is selected from the group consisting of: a mobile object, a person and an animal.
 
17. The non-transitory computer-readable storage medium recited of Claim  14  further comprising:
 
     periodically calibrating the geospatial sensor using data redundancy. 
     18. The non-transitory computer-readable storage medium recited of Claim  14  further comprising: 
     utilizing a user-operated Director-assist system to coordinate real-time integration of augmenting additions to the media data. 
     19. A non-transitory computer-readable storage medium comprising computer executable code for directing a processor to execute a method for post-producing media having extractable metadata, the method comprising: 
     extracting a characteristic of a previously recorded media stream; and 
     mapping the characteristics to a reality model. 
     20. The non-transitory computer-readable storage medium recited of Claim  19  further comprising: 
     editing the characteristics interactively with an augmented reality transmedia editor. 
     21. The non-transitory computer-readable storage medium recited of Claim  19  further comprising: 
     coordinating real-time integration of an augmenting addition to the media stream. 
     Section Fourteen: Product Placement Paired with Interactive Advertising 
     Notation and Nomenclature 
     Some portions of the description of embodiments which follow are presented in terms of procedures, logic blocks, processing and other symbolic representations of operations on data bits within a computer memory. These descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. In the present application, a procedure, logic block, process, or the like, is conceived to be a self-consistent sequence of steps or instructions leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, although not necessarily, these quantities take the form of electrical or magnetic signal capable of being stored, transferred, combined, compared, and otherwise manipulated in a computer system. 
     It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussions, it is appreciated that throughout the present discussions terms such as “determining”, “placing”, “receiving”, or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system&#39;s registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices. 
     Furthermore, in some embodiments, methods described herein can be carried out by a computer-usable storage medium having instructions embodied therein that when executed cause a computer system to perform the methods described herein. 
     Brief Description 
     Product placement in television shows, films, and video games has become increasingly popular over the years. In addition, as display devices become increasingly interactive, advertisements are interactive as well. 
     Overview of Discussion 
     Example techniques, devices, systems, and methods for placing an object in a piece of content are described herein. Discussion begins with a description of product placement. Example interactive devices and their capabilities are then described. Discussion continues with a description of interactive advertising. Next, example product placement paired with interactive advertising is discussed. Lastly, example methods of use are described. 
     High Level Description of Product Placement 
       FIG. 13A  shows an example interactive device  1310 . Viewers  1312 ,  1314  and  1315  are able to watch content on the display  1311  of interactive device  1310 . In various embodiments, content includes still video, still images, and/or audio. The content in  FIG. 13A  shows an office where one person is sitting at a desk and another person is sitting in a chair. 
     Since the advent of digital video recorders, such as TiVo™, people have been able to fast-forward through commercials with ease. This, along with other factors, has increased the amount of product placement in television shows, movies, etc. For example, object  1301  in  FIG. 13A  is a soda can. When a viewer  1312  sees the soda can he may be more likely to buy that type of soda the next time he buys soda. Object  1301  may be any type of object (or portion thereof). For example, object  1301  may include, but is not limited: food, drinks, furniture, clothing, a logo, a sign, a vehicle, a billboard, a building, athletic equipment, an electronic device, a painting, a person, an animal, scenery, etc. In various embodiments object  1301  is three dimensional (3D). In some embodiments object  1301  is two dimensional (2D). Also, an object  1301  may be opaque, transparent, or translucent. 
     In some systems, objects  1301  are placed into pieces of content during production. For example, when preparing to film a show, the object  1301  may be placed on the desk before filming starts. 
     In one embodiment, computers and virtual reality allows advertisers to place objects  1301  into content (e.g., movies, slide shows, television programs, and video games) after the content is created with a high degree of realism. This is also known as retro-active product placement. For example, a system can place objects  1301  into a scene after it has been filmed. In some embodiments, a processor  1317  is operable to place objects  1301  into content that was recorded years ago. 
     Example Interactive Devices and their Capabilities 
     As discussed above,  FIG. 13A  shows an example interactive device  1310 . While a television is shown as an example, in various embodiments interactive device  1310  may include, but is not limited to: a mobile device with a display  1311 , a smart phone, a tablet computer, a laptop, a personal digital assistant, a smart television, a radio, a computer, a server, etc. 
     In some embodiments, interactive device  1310  comprises I/O device  1316 , processor  1317 , and display  1311 . 
     In one embodiment, I/O device  1316  comprises, but is not limited to: a receiver, a touchscreen, a keyboard, a mouse, a joystick, a button, a depth sensor, a motion sensor, a microphone, a speaker, a Microsoft™ Kinect™ type device, etc. In some embodiments interactive device  1310  comprises a plurality of I/O devices  1316 . In one embodiment, an I/O device  1316  may receive signals from a mobile I/O device  1308 . Mobile I/O device  1308  may include, but is not limited to: a remote control, a tablet computer, a smart phone, a microphone, a personal digital assistant, etc. In an embodiment Mobile I/O device  1308  may be coupled to interactive device  1310 . In one embodiment Mobile I/O device  1308  may be communicatively coupled to interactive device  1310 . 
     In an embodiment, interactive device  1310  comprises a processor  1317  operable to perform various operations. Processor  1317  is operable to determine available locations  1302 ,  1303  and  1319  and times within a piece of content to place an object  1301 . For example, processor  1317  may determine that the scene shown in  FIG. 13A  has available locations  1302 ,  1303 , and  1319  to place an object  1301 . Processor  1317  may also determine that this particular scene is shown for a particular amount of time (e.g., the conversation in the scene lasts two minutes, and begins at a particular time in the show). Processor  1317  may determine to place an object  1301  at location  1319 . Once a determination to place an object  1301  has been made, a processor  1317  may place object  1301  at location  1302 ,  1303 , and/or  1319 . In an embodiment object  1301  is rendered and positioned to appear as if it is part of original content (e.g., previously produced content). In some embodiments, rendering can adjust the focal length, position, and/or orientation of an object  1301 . In some embodiments rendering is performed automatically, while in other embodiments rendering is performed at least in part by a person. In some embodiments a transmedia editor is operable to perform the rendering of objects  1301  within content (e.g., original or other). It should be noted that  FIG. 13A  is not drawing to scale, including locations  1302 ,  1303  and  1319  and object  1301 . In some embodiments operations performed by processor  1317  occur in real time or near-real time. 
     In one embodiment, processor  1317  may be a graphics processing unit or a central processing unit. Further, interactive device  1310  may comprise a plurality of processors  1317  that may perform all, some, or none of the operations discussed herein. 
     In one embodiment processor  1317  is not located in device  1310 . In an embodiment the processing described herein is performed at a location remote from interactive device  1310 . For example, objects  1301  may be placed in content prior to the content reaching interactive device  1310 . In some embodiments placing an object  1301  in a piece of content occurs at a computer remote from the device on which a viewer  1312  receives the piece of content. 
     In various embodiments interactive device  1310  comprises a display  1311 . Displays are known in the art so a detailed discussion is not necessary. While in some embodiments display  1311  is flat, in various embodiments display  1311  is concave or convex. 
     Interactive Advertising 
     In an embodiment interactive device  1310  is operable to provide a viewer  1312  with additional content  1305  comprising interactive advertising. In an embodiment additional content  1305  comprises at least one advertisement  1306  and/or at least one game  1307  and/or at least one reward. In some embodiments additional content  1305  covers a portion of display  1311 , while in other embodiments additional content  1305  covers all of display  1311  (e.g., the additional content  1305  uses the entire display  1311 ). 
     As an example, interactive advertising may allow viewer  1312  to interact with an advertisement via I/O device  1316 . In an embodiment viewer  1312  can control a cursor to click on various portions/buttons of an advertisement  1306 . In an embodiment interactive advertising is prepared and sent to interactive device  1310 . In one embodiment an advertisement  1306  is a commercial. In one embodiment additional content  1305  is a webpage. 
     In addition to being additional content  1305 , in an embodiment, an interactive advertisement  1306  may be a game  1307 . For example, game  1307  may be a shooting game where a viewer/user  1312  shoots flying soda cans. Game  1307  may be any type of game including, but not limited to: a word game, an adventure game, a trivia game, a card game, a casino game, etc. 
     In an embodiment, additional content is a reward. For example, a reward may include, but is not limited to: a coupon, a discount, additional content associated with the show or movie, etc. 
     In one embodiment, targeted advertising is utilized. For example, candidate objects may be selected as object  1301 . In an embodiment, a processor  1317  may choose a candidate object from a database of objects (e.g., soda, iced tea, potato chips, yogurt, etc.). A candidate object may be selected in part on a plurality of viewer  1312  information including, but not limited to: demographic information, age, race, gender, socio-economic status, previous preferences, previous preferences within interactive device  1310 , past purchases, food preference, furniture preference, vehicle preference, whether a user typically selects one object  1301  over another object  1301 , etc. This information may be based at least in part on previous interactions with objects  1301  or from another source (e.g., information extracted from the email or a web browser belonging to viewer  1312 ). In an example, beer is chosen over soda, out of the group of candidate objects, when viewer  1312  is over 21 years of age. In one embodiment, if a type of object  1301  has not been shown as much as desired in a particular geographic area, for example, processor  1317  may determine the location of interactive device  1310  and whether it should insert more objects  1301  of that type. In an embodiment, selection of a candidate object may be selected based at least in part on a clickthrough rate (CTR). In an embodiment, a company (e.g., Proctor and Gamble™) may place various objects  1301  associated with its products (e.g., toothpaste, detergent, etc.) throughout a piece of content. 
     In one embodiment an interactive advertisement  1306  may provide a viewer  1312  with a menu. This menu may provide options to a viewer  1312  including, but not limited to: watching a commercial, playing a game  1307 , listening to a song, downloading/showing a web page, etc. In an embodiment interacting with an advertisement  1306  may cause interactive device  1310  to display a webpage that sells a product. 
     Example Product Placement Paired with Interactive Advertising 
     In one embodiment, a viewer  1312  can interact with the object  1301  wherein the interaction causes a processor  1317  to send additional content  1305  to a viewer  1312 . In some embodiments, the viewer  1312  can move and/or manipulate an object  1301  using I/O device  1316 . For example, viewer  1312  may click on an object  1301  by making gestures (e.g., pointing at an object and pretending to shoot it) recognized by a motion sensor. As another example a viewer  1312  may use a mouse to click on object  1301 . Other examples of interacting with object  1301  include, but are not limited to: making a throwing or kicking motion, speaking in a microphone, talking with other viewers  1314  and  1315 , clicking on a mobile I/O device  1308 , having a dialogue with other users  1314  and  1315 , clapping, etc. In one embodiment, clicking on an object  1301  will provide a viewer  1312  with additional content  1305 . In an embodiment a processor  1317  is operable to capture voices of a plurality of viewers  1312 ,  1314 , and  1315 . 
     As discussed above, in an embodiment, an object  1301  is rendered such that it appears to be part of the original content (e.g., the object  1301  looks like it belongs in the scene). In some embodiments, an object  1301  or content is rendered such that an indication is made to viewer  1312  that viewer  1312  can interact with object  1301 . For example, in some embodiments object  1301  is highlighted (e.g., made prominent or emphasized). Highlighting may include, but is not limited to: making an object  1301  shake or move, adding a shimmer or other special effect to an object  1301 , adding a glow to an object  1301 , producing a sound, making an object  1301  change color, etc. This list is not meant to be exhaustive. Rather, it is meant to illustrate example ways to indicate to a viewer  1312  that an object  1301 , or a portion thereof, is interactive. 
     In one embodiment, object  1301  is transparent. In other words, in one embodiment, an object  1301  is mapped to an area of a screen that corresponds to an element within content. For example, an advertiser may want to advertise the watch (i.e., element) that the person in the chair in  FIG. 13A  is wearing. An invisible object  1304  may be placed over the watch (i.e., mapped) since the watch was in the original content (e.g., the actor was wearing the watch during the filming of a show). In an embodiment the transparent object  1304  (in this case a watch) is highlighted as discussed above. As with other objects  1301 , a transparent object  1304  may be an object including, but not limited to: a painting, a dress, shoes, food, furniture, a vehicle, etc. 
     In an embodiment, an object  1301  is an interactive gateway to advertisements  1306 . In other words, in some embodiments, viewer  1312  receives additional content  1305  by interacting with object  1301 . For example, in some embodiments, when viewer  1312  interacts with object  1301  a commercial will play, a game  1307  associated with the object  1301  will appear, a website will open, a menu will appear, etc. 
     In one embodiment, I/O device  1316  may receive dialogue from a plurality of users  1312 ,  1314 , and  1315 . Dialogue may comprise any speech, for example a discussion about a piece of clothing a woman is wearing. In an embodiment, when a discussion about an object  1301  is received from viewers  1312 ,  1314  and  1315  a processor  1317  may perform an operation (e.g., provide viewers  1312 ,  1314 , and  1315  with additional content  1305 ). In an embodiment, a processor  1317  performs an operation based at least in part on the dialogue. For example, a processor  1317  may be operable to distinguish between different viewers  1312 ,  1314 , and  1315 . In an embodiment, a processor  1317  may only be responsive to one of the plurality of viewers  1312 ,  1314 , and  1315 . 
     Example Methods of Use 
       FIG. 13B  is a flow diagram  1320  of an example method for virtually placing an object  1301  in a piece of content in accordance with embodiments of the present invention. 
     In operation  1321 , in one embodiment, a processor  1317  determines available locations  1302 ,  1303 , and  1319  and times within a piece of content to place an object  1301 . In an embodiment processor  1317  determines when and/or where to place an object  1301  based at least in part on an available location  1302 ,  1303  and  1319  and/or time within a piece of content. 
     In operation  1322 , in one embodiment, a processor  1317  determines whether to place an object at at least one of the available locations  1302 ,  1303 , and  1319 . In some embodiments, an object  1301  is not placed in an available location  1302 ,  1303 , and  1319 . In an embodiment, the amount of objects  1301  placed in content is based in part upon an agreement between a content provider and a service provider, and/or another type of provider. 
     In operation  1323 , in one embodiment, an object  1301  is placed in a piece of content provided that a determination has been made to place the object  1301  into the content. In an embodiment, the object  1301  may be rendered to appear as if it were a part of the original content. In another embodiment, the object  1301  is placed into the scene prior to the scene being filmed, recorded, assembled, etc. 
     In operation  1324 , in one embodiment, a processor or provider determines a candidate object to use as an object  1301 . For example, object  1301  may be selected from a database of candidate objects. As discussed herein, in an embodiment, object  1301  may be chosen based in part on information including, but not limited to: demographic information, age, race, gender, sexual orientation, previous purchases, geography, a sponsor of the object  1301 , preferences scraped from a computer belonging to a viewer  1312 , etc. In various embodiments, these operations may be performed in real time or near real time. 
     In operation  1325 , in one embodiment, the interactive device  1310  receives user interaction with an object  1301 . As discussed herein, user interaction may include, but it not limited to: initiating interaction with an I/O device  1316 , speaking, gesturing, waving a hand, pointing, using a mouse, using a key board, using a mobile I/O device  1318 , clapping, having a dialogue with another viewer  1314 ,  1315 , clicking a button (e.g., on a remote control), etc. 
       FIG. 13C  is a flow diagram  1330  of an example method implemented by a system for performing a method for virtually placing an object in a piece of original content in accordance with embodiments of the present invention. 
     In operation  1331 , in one embodiment, available locations  1302 ,  1303 , and  1319  are determined within a piece of original content (e.g., content that has already been produced) to place an object  1301 . In an embodiment processor  1317  determines when and/or where to place an object  1301  based at least in part on an available location  1302 ,  1303  and  1319  and/or time within a piece of content. 
     In operation  1332 , in one embodiment, interactive device  1310 /processor  1317  determines whether to place the object at at least one of the available locations  1302 ,  1303 , and  1319 . In an embodiment the processing is performed remote from the interactive device  1310 . In some embodiments, an object  1301  is not placed in an available location  1302 ,  1303 , and  1319 . In an embodiment, the amount of objects  1301  placed in content is based in part upon an agreement between a content provider and a service provider, and/or another type of provider. 
     In operation  1333 , in one embodiment, an object  1301  is placed in a piece of original content provided a determination has been made to place the object  1301  into the original content. In an embodiment, the object  1301  may be rendered to appear as if it were a part of the original content. In an embodiment object  1301  is made prominent such that a viewer  1312  knows that object  1301  is interactive. As discussed above, object  1301  may be highlighted such that a viewer  1312  knows that object  1301  is interactive. 
     Embodiments of the present technology are thus described. While the present technology has been described in particular examples, it should be appreciated that the present technology should not be construed as limited by such examples, but rather construed according to the claims. 
     Embodiments for virtually placing an object in a piece of content can be summarized as follows: 
     1. A method for virtually placing an object in a piece of content, said method comprising:
         determining, at a processor, available locations and times within said piece of content to place said object;   determining, at said processor, whether to place said object at at least one of said available locations; and   provided a determination has been made to place said object, placing said object in said piece of content.       

     2. The method of Claim  1 , wherein said object is placed in said piece of content after said piece of content has been created. 
     3. The method of Claim  1 , wherein said object is an interactive gateway to advertisements. 
     4. The method of Claim  1 , further comprising:
         determining a candidate object to use as said object.       

     5. The method of Claim  1 , further comprising:
         receiving user interaction with said object, wherein said interaction causes said processor to send additional content to said user.       

     6. The additional content of Claim  5 , wherein said additional content is a reward. 
     7. The additional content of Claim  5 , wherein said additional content is a game. 
     8. The object of Claim  1 , wherein said object is transparent such it may be mapped to an area of a screen that corresponds to an element within said content. 
     9. The object of Claim  1 , wherein said object is highlighted. 
     10. The method of Claim  1 , wherein said processor is operable to capture voices of a plurality of users. 
     11. The method of Claim  1 , wherein said processor is operable to receive dialogue between viewers, and wherein said processor performs an operation on an object based at least in part on said dialogue. 
     12. A computer usable storage medium having instructions embodied therein that when executed cause a computer system to perform a method for virtually placing an object in a piece of original content, said method comprising:
         determining available locations within said piece of original content to place said object, wherein said object is placed in said piece of original content after said piece of original content has been created;   determining whether to place said object at at least one of said available locations; and   provided a determination has been made to place said object, placing said object in said piece of original content.       

     13. The computer usable storage medium of Claim  12 , wherein said determining available locations occurs in real time. 
     14. The computer usable storage medium of Claim  12 , wherein said object is an interactive advertisement. 
     15. The computer usable storage medium of Claim  12 , wherein said method further comprises:
         receiving user interaction with said object, wherein said interaction causes a processor to send additional content to said user.       

     16. The computer usable storage medium of Claim  12 , wherein said object is transparent such that it may be mapped to an area of a screen that corresponds to an element within said piece of original content. 
     17. An interactive device comprising:
         a display;   a processor, wherein said processor is operable to virtually place an object in a piece of original content to be displayed on said display, wherein said object is placed in said piece of original content after said piece of original content has been created, and wherein said object is an advertisement; and   an input device to capture user input, wherein said user input is operable to interact with said object.       

     18. The object of Claim  17 , wherein said object is transparent such that said object may be mapped to an area of said display that corresponds to an element of content, including objects previously placed in said piece of original content. 
     19. The device of Claim  17 , wherein said input device is operable to capture and distinguish a plurality of voices. 
     20. The object of Claim  17 , wherein said object is highlighted.