Patent Publication Number: US-9894415-B2

Title: System and method for media experience data

Description:
CROSS REFERENCE TO RELATED DOCUMENTS 
     This application claims benefit of provisional application Ser. 62/183,605 filed Jun. 23, 2015 entitled “Media Experience Data System And Method.” 
     RELATED U.S. PATENT DOCUMENTS 
     application Ser. No. 62/183,605 filed Jun. 2015. 
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     TECHNICAL FIELD 
     The present disclosure generally relates to electronic media content search methods. More specifically, the disclosure relates to analyzing behavioral responses collected with wearable device and camera sensors, including physical activity and physiological data, with contextual data associated with media content and experiential information associated with a media presentation. 
     BACKGROUND 
     Everyday, millions browse for media content online or searchable databases by inputting general or very specific terms that articulate or convey a subject&#39;s tastes and preferences for media content. Passive and subconscious responses to media experiences that are spontaneous, non-verbal or involuntary can also be reliable indicators of a subject&#39;s tastes and preferences. Conscious or subconscious response and reaction behaviors such as blushing, laughing, elevated heart rate, blood pressure changes and the like can be identified and measured with wearable and facial recognition technologies. 
     Captured behavioral data may provide reference points such that evaluation, estimates and predictions of a subject&#39;s taste and preference can be measured and articulated. Ongoing collection of experiential data may offer greater statistical reliability and accuracy in determining a subject&#39;s tastes and preferences or their “connectedness” to media content varieties. Such a method could support machine learning systems for media content browsing and advanced search functions that successfully interpret behavioral and biometric data. 
     SUMMARY 
     Collecting and identifying physiological data, facial expression data, and physical activity data in correlation with media experiences can uniquely reveal a subject&#39;s tastes and preferences or “connectedness” to media content. Additionally, analysis of behavioral response data can be enhanced when associated with contextual data embedded in electronic media files as well as experiential data derived from the subject&#39;s lifestyle and media viewing habits (e.g., location, time of day, device type, etc.). Given the volume of content and sources of distribution for electronic media, passive collection of media experience data can dramatically improve efficiencies in the content search process. Capturing this information with wearable and camera technologies can provide real time data that is accurate, measurable, and create efficiencies in interpreting media preferences and executing media search applications. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       The following detailed description of certain embodiments may be understood by reference, and not by way of limitation, to the following figures wherein: 
         FIG. 1  is a schematic representation of a system for collecting and presenting media experience data according to an illustrative embodiment of this disclosure. 
         FIG. 2 -A is a block diagram of system for collecting and analyzing media event data. 
         FIG. 2 -B is a block diagram of system for obtaining media event data from various media content sources. 
         FIG. 2 -C is a block diagram illustrating a menu interface according to one embodiment. 
         FIG. 3  shows a generalized embodiment of data associated with a subject&#39;s user profile including attributes associated with a system for managing media experience data, 
         FIG. 4 -A is a graphical depiction of a system for capturing and analysis of facial expressions, physical movement, and speech audio. 
         FIG. 4 -B is a block diagram that schematically shows the system  420  for capturing and processing facial expressions, hand and body movements that indicate media connectedness. 
         FIG. 5 -A Is a graphical depiction of a system  500  for capturing behavioral data, including physical and physiological data, associated with media connectedness values. 
         FIG. 5 -B is a block diagram of a presentation device used in a system for collecting, analyzing and sharing media connectedness data. 
         FIG. 5 -C is a block diagram of a wearable system for collecting physical and physiological behavioral data that indicates media connectedness values. 
         FIG. 6 -A is a graphical depiction of an embodiment for capturing experiential data. 
         FIG. 6 -B is an illustration of conditions, elements, attributes and circumstances that include experiential data that indicates media connectedness values. 
         FIG. 7 -A is a flowchart of a method for processing and analyzing media event data that may be used to evaluate and measure media connectedness values. 
         FIG. 7 -B is a method for assigning media connectedness data to a user profile. 
         FIG. 8 -A is a model of dependencies which may be used to determine, infer, and/or interpret connectedness values between a subject and presented media using media experience data. 
         FIG. 8 -B is a flow diagram of the media connectedness value analysis  820   
         FIG. 9 -A is an illustration of a system for remote access management of media experience data over a communications channel. 
         FIG. 9 -B is a graphic depiction of a process for managing and presenting media connectedness data on a computing device. 
         FIG. 10  illustrates an example implementation of a system for capturing and analysis of media experience data in a group or audience setting. 
     
    
    
     DETAILED DESCRIPTION 
     Searching for electronic media is a lifestyle experience for millions of users with devices connected to online and other networked sources. Identifying desired media can involve search terms that are general or very specific, requiring some form of cognitive input that reflects the subject&#39;s tastes and preferences. For the unsophisticated user, navigating peripheral devices and networks can be daunting, and the content search experience may be limited by the capacity to operated devices or browsing applications. Considerable time may be consumed in the search query process that delivers the desired content. And, for the technically challenged user, given the complexity of hardware interfaces and networks, there may exist little ability or opportunity to access and enjoy media that reflects their unique tastes and preferences. For this reason, a seamless experience that passively acquires media preference data and delivers media content is highly desirable. 
     Techniques disclosed herein describe how a system may passively acquire and measure data that measures media connectedness values between a subject and the media they experience using behavioral data, media contextual data and experiential data. It is also desirable to use this information to guide machine learning system searches for media consistent with the subject&#39;s media connectedness with increasing accuracy to provide more efficient and satisfying enjoyment of media content. 
     In this document, the term “connectedness” refers to the interpretations of collected media related data that indicate, in any amount, the existence of a connection (or lack thereof) between the subject and the media being experienced or that may be experienced in the future. The system may use a variety of quantitative, qualitative and machine learning processes to measure media event data and determine what media connection aspects are meaningful to the subject based primarily on non-verbal, passive, and spontaneous behavioral data. This information is correlated with contextual data that identifies the media selection and experiential data collected from media event, respectively. 
     In this document, the term “media experience data” refers to the total information, including behavioral, contextual and experiential data that is collected, assigned, or correlated with a subject&#39;s electronic user profile and the presented media or media of similar type or category. This information is obtained before, during and after their exposure (reading, watching, observing, listening, etc.) and response to various forms of presented media content, which may also be referred to, collectively, as a “media event.” 
     In this document, the term “behavioral data” refers to information collected by a camera or wearable device that measures, records, or tracks the subject&#39;s changes in physiological or physical activity. Behavioral data may include a subject&#39;s blood pressure, heart rate, skin temperature, eye movements, facial expressions, hand or body movements, and the like. 
     In this document, the term “media contextual data” refers to any information that identifies or defines a media selection. In one embodiment, media contextual data may be a visual representation of an idea or physical matter not limited to image, photo, graphic, or words. In another embodiment, media contextual data may be embedded electronically in a media file or associated with media content that identifies a media selection by using attributes that can be indexed for search term purposes such as program name, title, category, genre, commentaries, and the like. In many embodiments, this type of information is typically found electronically embedded in media files using meta tags, cookies, and other electronic identifiers and may be obtained from the distribution source, a web service, the Internet or a database. 
     In this document, the term “experiential data” identifies electronically measurable information that improves a system&#39;s and a user&#39;s ability to interpret meaning regarding connectedness values, from the media contextual data, the subject&#39;s collected behavioral data and/or the overall media event. For example, time of day, location of subject, time stamp of behavior response, device type, recording of the subject&#39;s spontaneous utterances and other relevant information may elevate the ability to interpret a subject&#39;s media event. Media event contextual data may be obtained from various components in the system. 
     In this document, the terms “media,” “content,” or “media content” refer to types of media including text, images, photos, music, audio, videos, web pages, streaming video and the like. 
     In this document, the term “communication device” refers to an electronic device with firmware, software and hardware, or a combination thereof that is capable of network connectivity, media playback, data storage, and video telephony. A communication device may be fixed or mounted, on a desktop, portable and/or handheld. Typical components of a communication device may include but are not limited to a processor, operating system, RAM, ROM, flash memory, a camera, display, microphone, a cellular antenna, and wired and/or wireless transmission and receiving means&#39; including but not limited to Wi-Fi, WiMax, USB, cellular data networks, Bluetooth, NFC, ANT and RFID. In this document, the term “presentation device” refers to a communication device that is equipped with a camera coupled to software for capturing facial expressions and means for wireless connectivity to a wearable device. In some examples, the described techniques may be implemented as a computer program or application (hereafter “applications”) or as a plug-in, module, or sub-component of another application. The described techniques may be implemented as software, hardware, firmware, circuitry, or a combination thereof. If implemented as software, the described techniques may be implemented using various types of programming, development, scripting, or formatting languages, frameworks, syntax, applications, protocols, objects, or techniques, including ASP, ASP.net, .Net framework, Ruby, Ruby on Rails, C, Objective C, C++, C#, Adobe® Integrated Runtime™ (Adobe® AIR™), ActionScript™, Flex™, Lingo™, Java™, Javascript™, Ajax, Pert, Python, COBOL, Fortran, ADA, XML, MXML, HTML, DHTML, XHTML, HTTP, XMPP, PHP, and others. The described techniques may be varied and are not limited to the embodiments, examples or descriptions provided. 
     In this document, the term “social network” refers to a collective network of devices, individual users, web services, web sites, program applications, and media aggregation sources associated with a subject&#39;s user profile. The association may be created by automated means or by physical input from a user of the system. Information and data regarding social network activities may be transferred and communicated within the social network of the system to improve analysis and interpretation of media experience data. Analyzed media experience data may be shared to assist the social network efficiencies in locating, comparing, and presenting desirable media content to the subject. 
     In this document, the term “wearable device” refers to a portable device that is worn about the body, and equipped and with sensors attached to the skin for tracking, monitoring and recording biometrics and physical activity, collectively referred to previously as “behavioral data.” Examples of wearable devices include but are not limited to a wristband, watch, arm band, pendant, headband, earpiece, and the like. Sensors may capture biometric data including but not limited to physiological and physical activity such as blood pressure, pulse rate, skin temperature, head and body movements, and hand gestures. 
     In this document, the term “synchronize” or “sync”, “analyze”, or “compare” refers to associating behavioral data, media contextual data, and/or experiential data with a specific media event. Synchronization may include a process where a subject&#39;s spontaneous behavioral responses are recorded and tracked in real time during the media event. This information is associated with media contextual data previously collected. Lastly, experiential data is also collected and combined with the above data to further increase accurate and consistency in measurements, estimates, inferences, and conclusions regarding media connectedness data values. Synchronization, sync, analysis, or comparison may refer to software, firmware, hardware, or other component that can be used to effectuate a purpose. Software instructions may be stored in a memory of system devices and program instructions are executed with a processor that manages and controls various components. 
     The present disclosure provides a description of various methods and systems associated with collecting and sharing media experience data that may be used to interpret various aspects of connectedness values between a subject and presented media before, during, and after the media experience or media event. 
       FIG. 1  is a schematically illustrated system  100  for collecting and sharing media experience data  122 . The system components may include a communication device  106 , a network  110 , a presentation device  112  equipped with a camera  114 , and a wearable sensor device  120 . The network  110  may include a combination of computers, servers, internet, and cloud based computing and storage systems. Any number of communication devices  106  may have access to the network  110 . The communication device  106  may send a media selection  102  and associated data  108 , hereinafter referred to as “media contextual” data  108 , to the presentation device  112  via the network  110 . The presentation device  112  is equipped with audio visual means for presenting the media selection  102 . Presenting media may involve an electronic display, broadcast, or playback of the media content, and may include any combination of watching, reading, listening to, and/or observing the media selection  102  which may include any one or more media forms including text, graphics, video, photos, music, voice, audio, and the like. 
     The presentation device  112  is equipped with a camera  114  that identifies, tracks measures and records audio, facial expressions and body movement during the media presentation. The camera  114  may be equipped with a microphone for capturing audio sounds. The camera  114  may measure movement, gestures or changes to the subject&#39;s head, face, eyes, and/or mouth of a subject  116 . In one embodiment, the camera  114  may be operated with computer application algorithms that use mathematical and matricial techniques to convert images into digital format for submission to processing and comparison routines. In one embodiment, the facial recognition components may use popular facial recognition techniques such as geometric, three-dimensional face recognition, photometric, Facial Action Coding System, or Principal Component Analysis (PCA) with Eigen faces derived from the covariance matrix of the probability distribution over the high-dimensional vector space of face images, Linear Discriminate Analysis, Elastic Bunch Graph Matching fisher face, the Hidden Markov model, and the neuronal motivated dynamic link matching, and the like. The camera  114  may incorporate one or a combination of the aforementioned techniques to identify a subject&#39;s behavioral data including facial expressions, vocal expressions and bodily posture. The presentation device  112  may identify experiential data  118  that reveal the environmental conditions and circumstances of the subject&#39;s  116  exposure to the media selection  102 . Experiential data  118  involves electronically measurable information that may include but not be limited to plotting locations, time of day, type of device, a timestamp during a media presentation, and the like. The presentation device  112  is connected wirelessly to a device worn by the body of the subject  116 , hereinafter referred to as a “wearable” device  120 . The wearable device  120  is equipped with sensors that capture physiological and physical activity data before, during and/or after the media presentation. 
     Individually, media contextual data  108 , data from the camera  114 , experiential data  118 , and data from the wearable device  120  may be identified or tagged by the presentation device  112  with electronic markers. A marker may be identified using a software program or a radio frequency sensor. Collectively, this group may be tagged as a unique data set and will hereinafter be referred to as media experience data  122 . Media experience data  122  may be the resulting collective information obtained from pre-existing media selection data compiled with data collected from a subject  116  while exposed to said media selection in various capacities and settings. Exposure may include one or more of the totality of audio, visual, and sensory experiences manifested by reading, watching, observing, listening, etc. to various forms of media content. Examples of a media event in which media experience data  122  is generated may be reading an e-book, observing a web page, looking at family photos, watching a movie, hearing a song, or seeing streaming video. The system  100  may analyze the collected media experience data  122  and render a connectedness data value  124 . 
       FIG. 2 -A is a diagram of an example system  200  for collecting, analyzing and sharing media experience data associated with a media selection  202  and media events  211 . The system  200  may include an application program interface (API)  210 , data manager  212 , data analysis  226 , and data aggregation  228 . The API  210  may be downloaded and installed from a web service  229  on a portable or fixed communication device  201  to establish protocols for software components and network connection  232  between the communication device  201  and the system  200 . The API  210  may access the computerized non-volatile or flash memory of the communication device  201  to select media processed by the system  200 . The API  210  may access browsing and search functions of the communication device  201  to search for content via a network  232  online and media managed by web services  229  and media aggregation sources  230 . The API  210  may allow the user to send and receive information to various components and other users of the system  200 . The API  210  may enable a user to log in and operate security or encryption functions available on the communication device  201 . The API  210  may provide a means for a user to request the system  200  to assign, store, analyze, retrieve and query data associated with an electronic user profile  224 , presentation device  209  and other devices in the system  200 . 
     The API  210  may direct media selections and media event data to the data manager  212 . The data manager  212  may provide control for indexing  213 , storing  214 , and querying  215 . The data manager  212  may store and retrieve data from a computerized non-volatile or flash storage memory  220 . The data manager  212  may index, store, or query data in accordance with parameters set by an electronic user profile  224 . Parameters that direct the data manager  212  and associated data management applications may determine qualitative and quantitative aspects of search queries, preference filters, data capture, and the like. The data manager  212  may analyze a media selection  202  to index  213  and store  214  the media contextual data  204 , prior to a request for the system  200  to send the media selection to the presentation device  209 . The data manager  212  may access the data aggregation block  228  to locate indices related to media selections  202  from a web service  229 , an electronic program guide (EPG)  225  for television media, media aggregation sources  230 , and the like. The data manager  212  may analyze and collect media experience information including behavioral data, media contextual data, and experiential data associated with a single media event or multiple media events. 
     The data manager  212  may control and/or define indexing  213  based on an automated process or prompt for human input. Indexing  213  may be performed in accordance with parameters set by an electronic user profile  224  or by an automated computerized program. Parameters for indexing  213  media selections  202  may include the associated contextual data  204  which includes any electronic information embedded in the electronic file processed by the system to determined connectedness values and measurements. For example, if a search query presents a media selection with embedded contextual data  204  that identifies, describes, clarifies, delineates, and/or distinguishes the media selection for the purposes of determining connectedness between the subject and the content, then that information is added to existing indices or a new index is created in the system. In one embodiment, the subject&#39;s user profile preferences may define specific descriptive information (e.g., named title, named artist, named genre, format, etc.) the system may use to narrow queries and create more efficient search results. The data manager  212  may identify data with a single index or combination of indices including but not limited to program name, program title, program length, category, artist(s), author, genre, origin, file size, file type, date created, date modified, publication date, distribution, meta data information and commentary. 
     Behavioral data from a camera  203  and wearable data  206  may be indexed based on facial expression, physical and physiological changes that indicate a range of favorable or unfavorable responses to media selections. One or more behavioral responses may indicate a subject&#39;s preference or lack thereof for a specific media selection. For example, in response to a photo, a frown may indicate displeasure or lack of satisfaction. In another example, in response to a news article, an intense stare without head movement may indicate a definite affinity or interest. In yet another example, in response to a video, a smile, elevated pulse rate, and hand clapping may indicate strong connectedness. 
     Experiential data  205  may be indexed based on environmental conditions and circumstances that may influence connectedness values and measurements. One or more experiential data  205  values may indicate a subject&#39;s preference or lack thereof for a specific media selection. For example, in the morning hours a subject may have a strong preference to read daily news websites compared to entertainment web sites during other hours of the day. In another example, for movie watching, the subject may prefer to watch on a specific presentation device such as a smart TV compared to other smaller or portable devices on the system. In yet another example, the speed of response to an alert indicating a new media selection is available may indicate the best time of day to interact with the subject. In one embodiment, experiential data  205  may include a timestamp that associates a particular behavioral reaction or response from the subject with a specific time during the playback or presentation of media content. 
     The API  210  may direct media selections and media event data to a data analysis block  226 . The data analysis block  226  may include artificial intelligence (AI) or machine learning-grade algorithmic programming and instructions based on known techniques such as pattern recognition, classifiers, fuzzy systems, Bayesian networks, behavior based AI, decision trees, and the like. The data analysis block  226  components may include program code, non-volatile or flash memory  220 , and a single processor  222  or multiple processors or a networked group of processors connected to a single or networked group of computerized components. The data analysis block  226  may provide analysis results for media selections  202 , media data  204 , camera data  203 , experiential data  205 , wearable data  206 , and media event data  211  relating to measuring connectedness value between the subject and the media selection  202  being analyzed. The data analysis block  226  may communicate with various components of the system using the API  210 . The data analysis block  226  may operate in conjunction with the data aggregation block  228 , data stored in available memory  220 , a web service  229 , and a media aggregator  230  to provide analysis results. 
     In one embodiment the data analysis block  226  may provide analysis of media event data  211  that is streaming in real time. In another embodiment the data analysis block  226  pre-screens media before it is sent to the presentation device based on user profile parameters, settings, and content filters. In yet another embodiment the data analysis block  226  may perform analysis of a single data set or multiple data sets to determine connectedness value or measurements. In yet a further embodiment the data analysis block  226  may perform analysis of a single or multiple media events to determine connectedness values or measurements. The data analysis block  226  may receive media selections  202  from the API  210  that were sent from a computer automated media search system managed by a web service  229 , an EPG  225  or media aggregator  230 . For example, if a search query presents a media selection  202  for presentation that has only a few indices or a small amount of contextual data  204 , the data analysis block  226  may operate in conjunction with the data aggregation block  228  to search available sources such as a web service  229  or media aggregator  230  and identify and index additional contextual data for use by the system  200 . In another example, media event data  211  renders a particular data set outcome, which may be used as a threshold or benchmark to determine connectedness. This benchmarked media event data set  211  may be analyzed in comparison to past and future media events for reference. 
       FIG. 2 -B is a diagram for a system  240  for using media experience data  259  to identify desired media content from various electronic media content sources  243 . The system may interface with media sources including web services such as web sites and search engines  244 , an electronic program guides (EPG)  246  from services such as Time Warner Cable, Comcast, Direct TV, Dish Network, media aggregation sources  248  such as YouTube and Pinterest, media libraries located on remote and local servers  250 , networked computers  252 , social networks  253  such as Facebook, and mobile communication devices  254 . The internet or a computerized network  258  may be used for communication between the various devices. Media content may be identified in the system  240  by contextual data  266  including but not limited to program name, program title, program length, category, artist(s), author, genre, origin, file size, file type, date created, date modified, publication date, distribution, meta data information and commentaries. Media content sources  243  may also present contextual data in media catalogs, indices, media libraries, program menus, and program schedules and the like. 
     In one embodiment, media event data  211  or media experience data  259  may be used, based on thresholds for media connectedness values, to initiate and complete the purchase and delivery of a physical product or download of media content  242  to the presentation device  209  from a media content source  243  with a payment system application and/or an electronic commerce account  284  associated with the user profile  280 . For example, if a physical product is identified with contextual data by a web page, video or the like, and the media experience results in media event data  211  or media experience data  259  at or above a specific level, then that product may be automatically purchased via electronic account  284  and delivered to a physical location. Likewise, if a song is presented that results in media event data  211  or media experience data  259  at or above a specific level, then that song may be automatically purchased via electronic account  284  and downloaded to the presentation device  209 . 
       482  electronic program guide EPG  290   2902002  and related systems  400  and  500  below  290  The appropriate source web service  244 , media aggregator  248 , zip code GPS The menu  291  may be comprised of fields for searchable content information  292  that identifies programming and programming characteristics including but not limited to titles, artists, genres, live broadcasts, metadata descriptors, subject matter, program summaries, and the like. The menu  290  may be synchronized with a scheduler/calendar  293  that associates the available EPG  246  programming with the data rending queue  278  for presentation of a programming playlist of scheduler/calendar may synchronize with the to  200  ( 240 ).  290  visual preferences based on user preferences for specific content, time of day, or genre. For example, the A/V preferences  294  may provide for queued content of a specific nature to play at certain hours of the day at a specific volume level. The A/V preferences  294  may be modified according to audio settings  294  for audible levels, measurements, earphone and hearing-aid compatibility and video settings  296  for visual integrity including aspect ratio, high definition/fidelity, brightness, contrast, colors and the like.  290297  analysis module  270 , media experience data  259 , ranked contextual data  268 , activity queue  278 , more preferred and less preferred EPG programming viewing and preference analytics  299  The EPG media intelligence  297  settings for identifying, measuring, and reporting data may be configured using biometric settings  298  and EPG analytics  299 . 
     one search  292  and, with compiled recommendations made available to users of the system  200  via the API  260  and data rendering queue  278 . experience based  200  ( ) the rendering queue  278   292  may the menu user interface  291  ( 422 ,  536 ,  568 ), anautomated computer, 
     The system  240  may be managed with an application programming interface (API)  260  that provides protocols for software components to interface with the devices on the system that transfer and exchange data. The API  260  may download or access instructional data from a media content source  243  to aid in media search processes, data transfers and exchanges. The system  240  may generate media experience data  259  that indicates connectedness values between a subject and presented media content  242  by analyzing  270  and associating experiential data  262 , behavioral data  264 , including physical and physiological information, with contextual data  266  embedded in electronic media files that have been presented to a subject. The system  240  may analyze media experience data  259  in an electronic user profile account  280  to establish norms and baselines for measuring, interpreting, comparing and the like. The system  240  may use these data norms and baseline data sets to identify and rank the contextual data  268  in accordance with media content search instructions input by human means or an automated means managed by the API  260 . 
     The API  260  may use an analysis module  270  to perform a comparative analysis of the identified and/or ranked contextual data  268  to contextual data  266  that identifies and describes media content  242  located on media sources  243 . The API  260  may use the analysis module  270  to perform a comparative analysis of media event  211  data sets for reference, as well as individually compiled data points and subsets of the specific media events including camera data  203 , wearable data  206  and experiential data  205 . For example, if a series of five similar images are viewed and logged as separate media events, the system may compare only the collected experiential data, excluding camera and wearable data, to better establish norms and baselines that may allow the system  240  to better calibrate to an individual&#39;s tastes and preferences and develop statistic profiles. 
     The analysis module  270  may include one or more processors  272 , a memory module  274  to store instructions, and network communications module  276  to interface with devices on the system  240 . The analysis module  270  may include a computer program application embodied in a non-transitory computer readable medium for media contextual data comparative analysis. The computer program application may include code for collecting media contextual data, code for comparative analysis of media contextual data, and code for rending comparative analysis results. The analysis module  270  and API  260  may sync, download, or work in conjunction with electronic search programming by automated means or human input. The analysis module  270  and API  260  may render  278  media content search results in a variety of forms such as a list, a ranking, a percentage, a graph, an image, alphanumeric text, or the like. The rendered analysis results may also be stored in an electronic user profile account  280 . In one embodiment the API  260  and analysis module  270  may interface with an electronic program guide (EPG)  225  or media source  243  that includes a program schedule with contextual data  266  that includes broadcast dates, air time, show times, descriptions, artists, commentaries, and the like. The system  240  may use the program schedule contextual data  266  to sync with a calendar that is managed by the API  260 . Schedule updates, alerts and reminders can be utilized and shared between users and devices including remote and local servers  250 , networked computers  252 , and mobile communication devices  254  in the system  240 . 
     The API  260  may be assigned an electronic marker  282  to identify contextual data  266 , behavioral data  264 , experiential data  262 , media content  242 , collective media experience data  259 , ranked contextual data  268 , and rendered data  278 . A marker  282  may be used to identify data, groups of data, an index, or indices. A marker  282  may be used to identify a user profile  280  and associated data. A marker may be used by the data analysis, in aggregation, indexing, assigning and storing functions of the system  240 . A marker  282  may be assigned to the location of a media content source  243 . A marker may be used to identify various devices, networks, or storage mediums on the system  240 . A marker  282  may be used to establish filters for search queries, sorting data, and identifying specific data from media content sources. A marker  282  may be used to assign media content, media contextual data, ranked contextual data, and other information rendered  278  based in an electronic queue of for presentation from various media sources  243 . 
     The API  260  (which may be the same as or similar to the API  210 ) may be used to initiate a web chat, video conference, or video phone application using the presentation device  209  and camera  114  with applicable programming. The API  260  may be used to initiate a login sequence on a web service  229 , media aggregator  230 , or EPG  225  that connects and synchronizes the presentation device  209  to the media selection  202  and activities of other users of those systems. For example, the API  260  may be used to manage a login sequence to a social network that enables media content and information to be sent automatically to the presentation device. The API  260  (and API  210 ) may be used to manage downloaded program applications that remotely operate devices on the system  240 . The API  260  (and API  210 ) may be used in conjunction with the data manager  240  to establish and manage an electronic queue, content filters, and presentation schedule for media content presentations in accordance with user preference settings. In one embodiment, the API  260  (and API  210 ) may be downloaded by a computer  252 , members of a social network  253 , or a mobile device  254  to identify and share media content  242  using media experience data  259 . In another embodiment, media experience data  259  and ranked contextual data  268  derived from a social network  253  may be compared and shared based on the sender&#39;s choices of media content  242  to be presented. For example, if three members of a social network send similar media content on the system, each may receive a ranking of their selection compared to the others based on the connectedness data values rendered by media experience data analysis and ranked contextual data analysis, and data rendering results. In another embodiment, a program may automatically analyze media that is stored, viewed, downloaded, shared, or created on a device and compare the media contextual data to media connectedness values associated with a user profile. If the media connectedness values meet a threshold or benchmark, an audio visual or vibrating alert may be sent to a single users and/or the social network. 
     The system  240  may enable comparative analysis of media  242  from various media content sources  243  to establish a rating or ranking based on connectedness data values rendered by media experience data analysis and ranked contextual data analysis, and data rendering results. In one embodiment, users of these various media sources  243  may participate in a reward-based virtual game for sharing media ranked and rated using connectedness data values, by volume, highest value measurements, time based measurements, number of participants, most presented, and any combination of the like. For example, a single or group of remote users  253  of the system  240  may submit multimedia content  243  such as video clips or images to be presented to a subject whom, based on the analysis and presentation of ranked and rated connectedness data, will reveal to the remote group which of the content submissions was more or less favorable, desirable, studied, analyzed, and the like. In another example, multimedia content  243  may be presented to a subject wherein the subject&#39;s behavioral data is measured along with spontaneous comments and speech about the content that is simultaneously time stamped, recorded, transcribed, logged, and ultimately distributed to members of a social network  253 . 
     another delivered interactions. 
       FIG. 3 . is a graphical depiction of a user profile manager  310  that is used for managing the media content and device activities associated with the subject. The user profile manager  310  can be part of a telemetry system or similar system functioning on a network  110  or a communication device  130 . The user profile manager  310  may identify, assign, analyze and associate data or data sets from various components and programming in the system  110 . Data may include preference data  312 , behavioral data  314 , contextual data  316 , experiential data  318 , and media event data  320 . 
     The user profile manager  310  may be used to manage content, content filters, preference data, and analyzed data with various components of the system including a wearable device  322 , a presentation device  324 , and a communication device  326 ; the devices may comprise a network  328  associated with the subject. The user profile manager  310  may be used to assign a unique identity, network administrator, and preferences associated with the subject by maintaining a user profile  330 . The user profile manager  310  may manage preferences for search queries or presented media with a content manager  332 . The content manager  332  may utilize the data aggregator  260  and data analysis block  226  to identify, sort, and direct media from web services  229  or  244 , or media aggregator  230 . The user profile manager  310  may manage access to and content flow with a social network manager  334 . Content may be shared, transferred, or presented on an automated or request basis with devices and users of the system. The user profile manager  310  may create settings and schedules for information exchanges between devices on the system for new user activity, new content availability, search results, updates, countdowns, media event results, activity thresholds and benchmarks with a message/alert manager  336 . In one embodiment, preference data  312  may be used to create parameters for presenting media including but not limited to device type, favorite content, favorite television program, favorite artist/celebrity, time of day, type of device, location, length of program, and/or sleep periods (of inactivity). 
       FIG. 4 -A is a graphical depiction of a system for capturing and analysis of facial expressions, physical movement, and speech audio. A system  400  is shown in which a camera  402  observes a subject  404  and analyzes data that indicates media connectedness. The subject  404  may be human or non-human such as a pet animal kept in a home. Facial expressions  406  may be represented by the upper body, the head, the face or a combination therein that may be observed in real time. Speech audio  407  may be recorded during a media presentation. Physical movement  408  may include a hand gesture, standing, sitting, and the like. The camera  402  may attached to or embedded in a presentation device  410  equipped with instructional programming for recording facial expressions  406  and physical movements  408 . 
       FIG. 4 -B is a block diagram that schematically shows the system  420  for capturing and processing facial expressions, hand and body movements that indicate media connectedness. The system  420  may be attached to or embedded in a device managed by a communication interface  422  and operated in accordance with programmed or downloaded instructions. The system may include a lens  424 , an infrared (IR) illuminator  425 , one or more video sensors  426 , an ambient light sensor  427 , and a motion detection module  428  to detect and measure a change in orientation or movement within a visible field. The IR illuminator  425  may enable video capture in low light or darkness. The ambient light sensor  427  may allow the video sensors  426  to adjust to low light. The motion detection module  428  may process data from a sensor  426  that interprets depth, range, and physical activity including facial expressions, hand and body movements. A facial expression may be a smile, a frown, a laugh, and the like. Hand and body movements may include a wave, hand clap, pointing, laughing, standing, sitting, and the like. In one embodiment, the system  420  may initiate a command based on a change in lighting detected by the ambient light sensor  427  such as sending a message alert to a device on the system or a social network group, video or audio program playback, video recording, presentation of media content stored in a queue, and the like. 
     The system  420  includes a processing unit (central processing unit, CPU or processor)  430 , a graphics processing unit (GPU)  431  and a system bus  432  that couples various system components including the system memory  434 , such as read only memory (ROM)  436  and random access memory (RAM)  437 , to the processor  430 . The processor  430  may utilize a non-volatile or volatile flash memory  434  for temporary storage. The system  420  can include a cache  438  of high-speed memory connected directly with, in close proximity to, or integrated as part of the processor  430 . The system  420  can copy data from the memory  434  and/or the storage device  440  to the cache  438  for quick access by the processor  430 . In this way, the cache can provide a performance boost that avoids processor  430  delays while waiting for data. These and other modules can control or be configured to control the processor  430  and GPU  431  to perform various actions such as capturing video, analyze video and picture images, facial detection programming, collecting sensor data, operating television infrared remote control signals, playing a video file, web browsing, music and audio playback, image and picture presentation, reading an audio book, executing an automated media content search on a database, managing social media access, and the like. The processor  430  and GPU  431  can include any general purpose processor or a special-purpose processor with instructions that are incorporated into the actual processor design such as a hardware module ( 1 )  442  and a software module ( 2 )  444  stored in storage device  440 , configured to control the processor  430 . The processor  430  and GPU  431  may operate according to instructions derived from an activity and expression detection program  448  for identifying gestures and facial expressions, an media data program  449  that analyzes media and media contextual data, or biometric program  450  that interprets biometric sensor activity. The processor  430  may process data using a USB FIFO unit  452  and USB Controller  454 . The USB FIFO unit  452  acts as a buffer between various components that supply data to the USB Controller  454  that manages data flow. An advanced high performance bus module  432  may also be used to carry data from the system  420  to other communication devices using a communication module  456 . The communication module  456  may be configured for wired or wireless connections including USB, Wi-Fi, Bluetooth, HDMI, cellular data network and the like. 
     The system  420  may have an LED light  460  that emits multicolor signals. The system  420  may include a clock  461  that is used to determine the schedule for automated functions and communications between devices on the system  420 . The system  420  may include a microphone  462 . Audio signals captured by the microphone  462  are digitized by an analog to digital converter  463 . The audio signals may be processed in accordance with program instructions provided by an audio detection module  464 . The system  420  may include a fan  465  for reducing heat inside the device. The system  420  may have a proximity sensor  466  to detect other devices within detectable range. The system may have a data port  467  for external memory input. The system  420  may have an infra-red communication module  469  for remote operation of devices controlled with infra-red controlled functions. The infrared (IR) module  469  is comprised of a digital/IR signal converter  470 , a decoder  472 , a microcontroller  474 , an IR transmitter and receiver  476 , port for external IR input/output sensor  478 , IR emitter sensor  480 , program instructions, and program code for learning IR remote commands. In one embodiment the IR module  468  transmits and receives data over a network to communication devices included program instructions, and remote control commands including input source change, channel change, volume change, mute on/off, channel list, closed captioning functions, viewing aspect ratio, system modes/settings menu, and activity status of the television including power on/off and display of program information. The processor  430  may essentially be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric. 
     In one embodiment, the system  420  downloads and stores programming instructions in available memory that enable remote operation of a communication device connected to the system  420  using the communication module  456 . In another embodiment, coupled with the audio output of a connected device, the system  420  may be configured with program instructions that enable a web chat, web conference, web phone, and the like. In yet another embodiment, the expression detection program  448  and activity detection program  450  may establish data benchmarks with captured data that are stored (e.g., in the storage  440 ) and used for referencing previous and future data benchmarks that indicate a connectedness values. In yet another embodiment, the communication module  456  may be configured for operation on a cellular data network using a data plan associated with a user payment account, enabling the system  420  to function where wireless or wired LAN connectivity is not available.  63  interface  6   s (and sizes), colors for backgrounds, borders, and fonts  489  and videos  490  settings  12  settings including levels, measurements, earphone and hearing-aid compatibility  3494  eBook  495  activity The menu  485  may also use the machine learning and artificial intelligence in conjunction with electronic references  497  such as online dictionaries, encyclopedia, news publications, and the like in response to a command to interpret a word or phrase heard during an e-book presentation.  58   
       FIG. 5 -A Is a graphical depiction of a system  500  for capturing physical and physiological data. A system  500  is shown which identifies, records, and measures a subject&#39;s physical movements and biometric responses  501  that indicate media connectedness. A subject  502  may be a person or an animal that is evaluated. The system may include a presentation device  503  and a wearable device  504 . 
       FIG. 5 -B is a diagram of the generalized embodiment of a presentation  505  device that may be used to implement a system  500  for collecting, analyzing and sharing media connectedness data. The presentation device  505  may have a central processing unit  506 , a Read Only Memory (ROM)  507 , Random Access Memory (RAM)  508 , and at least one cache  509  to temporarily store data and improve processing efficiency. The presentation device  505  may have a user interface  536  to manually control device functions. The presentation device  505  may have a graphics processing unit (GPU)  510  and a video encoder/video codec  511  (coder/decoder) to process high resolution graphic data and present on a display  512 . The presentation device  505  may have an audio processing unit  513  and an audio codec  514  for processing and broadcasting high fidelity stereophonic audio to an audio port or external audio speakers  515 . The presentation device  505  may include an embedded video camera  516  and microphone  517  for capturing audio visual content from the subject or surrounding environment. The presentation device  505  may include an I/O controller  518 , network interface controller  519 , memory controller  520 , and system memory  521 , logic module  522 , network interface  523 , analog to digital module  524 , and wireless communications adapter  525 . The I/O controller  518  may manage data input and output to and from the presentation device  505 . The logic module  522  may manage automated functions of the device. The network interface  523  may manage connections between the presentation device  505  and a network. The memory controller  520  manages data to and from the presentation device  505  memory  521 . The system memory  521 , ROM  507 , RAM  508 , and cache  509  may store application program data and operation commands. The analog to digital module  524  may convert analog signals into digital data. The wireless communications adapter  525  may operate with the network interface  523  to enable wireless access to a network (e.g., private network, local network, or internet) and may include any of a variety of various wired or wireless components including Bluetooth, BLE, WiMax, Wi-Fi, ZigBee and the like. 
     The presentation device  505  may include a clock  526  that is used to determine the schedule for automated functions and system  500  communications between devices and presentation device  505  functions. The GPU  510 , central processing unit  506 , network interface controller  519  and various other components of the presentation device  505  are interconnected via one or more buses  527 , including serial and parallel buses, a memory bus, a peripheral bus, and a processor or local bus using a variety of bus architectures. In one or more embodiments, the presentation device  505  may be a smart phone, smart television, cell phone, computer, computer tablet, laptop computer, or video monitor. In one embodiment, the presentation device  505  may include a computer program application embodied in a non-transitory computer readable medium for converting text to speech in an audio broadcast. The computer program application may include code for reading alphanumeric character text and information, code for converting text to speech, and code for rending an audible broadcast of the converted text. For example, if a news article from a web site is sent to a presentation device  505 , the information may be read to a viewer with a wearable device in accordance with user profile preference settings. In another embodiment, an image and accompanying text message describing the image may be sent to a presentation device  505 , and the system  500  will present the audio and visual information simultaneously in accordance with user profile preference settings. In a further embodiment, the presentation device  505 , upon receipt of information or media content data delivered by the system  500 , may initiate an audio visual alert to devices on the system  500  confirming receipt of the data. In yet a further embodiment, the presentation device  505  may use a clock  526  to synchronize with an electronic calendar that is managed by the system  500 . 
       FIG. 5 -C shows a wearable system  550  for collecting physical and physiological behavioral data that relates to media connectedness values. The system  550  may have a central processing unit (CPU or processor)  551 , a Read Only Memory (ROM)  552 , a Random Access Memory (RAM)  553 , and at least one cache  554  to temporarily store data and improve processing efficiency. The processor  551  may utilize a non-volatile or volatile flash memory  555  for temporary storage. The system  550  may include an I/O controller  556 , logic module  558 , analog to digital module  559 , USB FIFO unit  560 , USB controller  561 , clock  562 , graphic processing unit  564 , video codec  565 , wireless communications module  566 , and network interface  567 . The CPU  551  and various other components of the wearable system  550  are interconnected via one or more buses  578 , including serial and parallel buses, a memory bus, a peripheral bus, and a processor or local bus using a variety of bus architectures. The I/O controller  556  may manage data input and output to and from the system  550 . The logic module  558  may manage automated functions of the system  550 . The analog to digital module  559  may convert analog signals into digital data. The USB FIFO unit  560  acts as a buffer between various components that supply data to the USB controller  561  that manages data flow. The clock  562  may be used to determine the schedule for automated functions on the device and system  550  communications between devices. The network interface  567  may manage connections between the system  550  and a network. The wireless communications module  566  may operate to enable wireless access to other devices and/or a network (e.g. private network, wide area network, ISP, local network, internet) and may be any of a variety of various wired or wireless components including Bluetooth, BLE, IR, optical, WiMax, RFID, Wi-Fi and the like. 
     The wearable system  550  may include a user interface  568 , display  570 , ambient light sensor  572 , vibration motor  573 , microphone  574 , and speakers  576 . The user interface  568  may be used to manually control device functions. The display  570  may display graphics, images, pictures, alphanumeric characters, and the like. The microphone  574  may be used to capture audio including audible speech, voice activated speech, voice commands, and ambient sounds. The speakers  576  may be used to broadcast audio sent to the system  550 . The ambient light sensor  572  may be used detect changes in light intensity. The vibration motor  573  may be may be used in conjunction with message and alert functions of the system  550 . 
     The wearable system  550  may include behavioral sensors  575  that detect physical and physiological data. Behavioral sensors  575  that measure physical and physiological information may be worn about the body of the subject including but not limited to a wrist, hand, waist, neck, chest, leg or head. The behavioral sensors  575  may include sensors for collecting physical data indicating horizontal and vertical movement, angular movement such as a multi-axis gyroscope  581 . An accelerometer  583  sensor may be used to record the rate of movement activity and specific movement patterns. A proximity sensor  580  may be used to detect other devices within a specific range. In one embodiment, the gyroscope and accelerometer data may be analyzed to detect when the subject is asleep, awake, active, clapping, waving, or pointing. The behavior sensors  575  may include physiological sensors for collecting data indicating skin temperature, blood pressure, heart rate, galvanic, EEG, and other physiological responses. A photoplethysmographic sensor  582  may be used to monitor heart rate, blood pressure and oxygen levels. An electrochemical sensor  584  may be used to measure body fluids such as sweat, tears, and pH levels. A magnetometer (digital compass)  585  may define a geographical location and coordinate frame of reference oriented from the Earth&#39;s magnetic North pole. A digital temperature thermostat sensor  586  may be used to detect skin temperatures. A Global Positioning System (GPS) receiver  587  can provide the location of the system  550  and define waypoint coordinates. A pressure sensor  588  may be used to detect torsion, bending, or vibrations. An electroencephalogram (EEG) sensor  589  may detect electrical activity in the brain via electrical impulses. An audio recorder  590  may be used to record audio from the subject wearing the system  550 . In one embodiment, an automated program function may sample readings from various sensors in to properly calibrate and determine measure accuracy. 
     The system  550  may use a microphone  574  in conjunction with an audio recorder  590  to enable a program that transcribes voice to text, a program that enables voice activated recording during media content presentations, voice based text messaging, and/or voice activated commands that control functions on the system  550 . In another embodiment, the microphone  574  and speaker  576  may also be used in connection with applications for video chat and video conferencing. In yet another embodiment, the proximity sensor  580  may initiate an audio visual alert through the display  570  and/or speaker  576  indicating the system  550  is in or out of range of another device. In yet a further embodiment, the system  550  with a display  570  may confirm receipt of a message, request or alert signal with activation of the vibration motor  573  and/or signal from the speakers  576 . Similarly, the system  550  may receive an audio, vibrating, or visual alert confirming (search application) discovery, delivery and/or presentation of media content, text information, or media content data that has been sent from other devices or user accounts with access to the system  550 . The vibrating, audio, or visual alert may vary in degree of intensity based upon the degree of media connectedness of the purposed media selection. In still yet a further embodiment, the system  550  may receive time sensitive data, alerts, or messages from devices synchronized with the clock  562  and an electronic calendar managed on a network. For example, the wearable device may receive a countdown timer oriented message indicating the schedule or time of a media presentation, web chat, or other information on the system  550 . 
     The system  550  may have a wireless charging receiver  592  compatible with a rechargeable battery. The wireless charging receiver  592  may use resonant circuits for inductive power transmission. The wireless charging receiver  592  may include communications and control unit  593 , converter  594 , rectifier  595 , and modulator  596 . The communications and control unit  593  regulates the transferred power to the level that is appropriate for the components of the system  550 . The converter  594  converts transferred power into the required DC voltage. In one embodiment, the wireless charging receiver  592  may deliver functional data to the I/O controller  556  and display  570  including power levels, charging status, low power indication, and recharge time. In another embodiment, the system  550  may have a data/power port  598  used for hardwire recharging and transferring data to an external device including but not limited to biometric data, system data, and device function related data. In a further embodiment, the wireless charging receiver activity and functions may be triggered by a specific biometric data profile comprised of a single or combination of behavioral sensor  575  data measurements, e.g.; the subject is asleep or in a resting status. 
       FIG. 6 -A. is a graphical depiction of a system  600  for capturing experiential data  602 . The system  600  may include a presentation device  604 , a camera  606 , and a wearable device  608 . Experiential data  602  may include measurable data that enhances understanding, definition, or clarity of collected behavioral data  610  including but not limited to time of day, device types, media event locations, duration of media events, frequency of media events, device interactivity, media content source, media delivery channel or network, user interactivity and the like. Behavioral data  610  may include physical and physiological data captured by sensors that are worn about the body of a subject including but not limited to a wrist, hand, waist, neck, chest, leg or head. Behavioral data  610  sensors may collect physical data indicating horizontal and vertical movement, angular movement with a multi-axis gyroscope and/or an accelerometer. Behavioral data  610  sensors may collect physiological data indicating skin temperature, blood pressure, heart rate, galvanic, and other physiological responses. 
       FIG. 6 -B. illustrates conditions, elements, attributes and circumstances that may represent experiential data  622  and impact connectedness data values between a subject and presented media before, during, and after a media presentation  620 . Data measurements and analysis  628  may be conducted to determine the influence of experiential data  622  on media connectedness data values derived from a media presentation  620 ; these values are rendered as media experience data or media event data  634 . Media event data  634  may include individual data, indices and/or a collective data set including media contextual data  624 , behavioral data  626  and experiential data  622 . Experiential data  622  may provide clarity, depth, contexts, and refinement to data analysis  628  that evaluates and renders media event data  634 . Surrounding the experiential data  622  in  FIG. 6 -B is a non-exhaustive list of different types of measurable and quantifiable data that may indicate a range of preference values and elements that may impact the media presentation  620  outcome on connectedness data values and interpretations, attributes, inferences that may be applied to media contextual data  624  and behavioral data  626  respectively. Other sources of reference and historical information, such as a user profile  630 , web service or electronic program guide  632  may be analyzed  628  to determine the accuracy and consistency of experiential data  622  values. 
       FIG. 7 -A is a flowchart of a process  700  for processing and analyzing media event data that may be used to evaluate and measure media connectedness. The flow may begin with the process  700  using a user profile account data  702  to create an electronic identifier  704 . The electronic identifier  704  may be used to define individual data, an index, a data set, or indices. The electronic identifier  704  may be associated by the user profile  702  with media contextual data  706 , behavioral data (camera and wearable data)  708  and experiential data  710  to generate a collective media experience data  714 . The media experience data  714  may include data, a data point, an index, a data set, groups of data sets, or group of indices. The processing of data  716  may occur in real time utilizing streaming data or take place once the media experience  714  collection concludes. The data processing  716  may aggregate, index, label, assign, synchronize, correlate, associate, compare, count, measure, or calculate the collective data to determine which portion therein will be presented as media event data  717 . 
     The process  700  may use available analytical instructional data  718  stored in the user profile account to define, refine, add context to, and guide quantitative and qualitative evaluations, inferences, and interpretations of media event data as they relate to connectedness with the subject associated with the user profile. Analytical instructional data  718  may include a combination of preferences  720 , content filters  722  or evaluative parameters  724 . Preferences  720  may determine the priority, hierarchy, or qualifying standard for comparing and associating any or all indices identified in contextual data  706 , behavioral data  708 , or experiential data  710 . Content filters  722  may be used to determine the priority, hierarchy, or qualifying standard for screening or limiting any or all indices associated with media contextual data  706 . Evaluative parameters  724  may be used to guide or customize the process  700  regarding the method of analyzing information to affect a particular result. The process  700  may use a media connectedness analyzer  726  to further process and evaluate media event data  717  and media instructional data  718 . The process may present the analysis results in a data rendering  728 . A data rendering may be presented in a variety of depictions including numerical value, chart, graph, percentage, ratio and the like. Rendered data  728  may also be identified as threshold or benchmark data  730  stored in the user profile  702  for reference, comparison, and evaluation of historical and potential connectedness values. In one embodiment, the data captured and analyzed by the system can be recorded into a standard relational database (e.g., SQL server or the like). 
       FIG. 7 -B is a method  740  for assigning media connectedness data to a user profile. At  742  the user is presented with an option to review user profile data  744  or search for media content  746 . If the user elects to search for profile data  744 , once found, they may be presented with several categories of data related to media connectedness data values. User profile data  744  can be used to set parameters for the search function  746 . For example, if user profile information indicates that a specific media variety is preferred at certain times of the day, then the search function  746  may incorporate those parameters while surveying media content sources. Once a media selection is found, then at  748  the user is presented with an option to evaluate the media selection with a connectedness analysis module  750  and store the media contextual data  752  or present the media  754 . Once the media is presented, behavioral response data is captured  756 , synchronized with contextual data and experiential data  758 , analyzed and evaluated  760 . At  762 , the user is then presented with the option to add the media experience data to the user profile or return to the initial search mode. 
       FIG. 8 -A depicts a model  800  of dependencies which may be used to determine, infer, and/or interpret connectedness values between a subject and presented media using collected media experience data. In the model of  FIG. 8 -A, connectedness values may be generally characterized in a correlation between data plots on axis ranges based on like/dislike and preferred/not preferred. The model  800  may include collecting media experience data before, during, and after a media selection presentation to represent media event data  802 . A media event data set  802  may include physical and physiological data captured from a wearable device and camera, media contextual data, and experiential data. The wearable device may capture physiological information which may include one or more data measurements of heart rate, blood pressure, skin temperature, and perspiration. The wearable device may capture physical information which may include one or more data measurements of body movement, hand movement, audible sounds, and haptic gestures. The camera may capture physical information which may include one or more data measurements of head movement, body movement, hand movement, facial expressions, eye movement, mouth movement, and audible sounds. For example, each media experience may create a unique media event data  802  plot which represents a connectedness value including collected data (wearable device data  810 , camera data  812 , media contextual data  814 , and experiential data  816 ). 
     In one embodiment, baseline data measurements are determined using an algorithmic computerized learning program. For example, a media event plotted at X-2 has the highest evaluation and the media event plotted at X-3 may have the lowest evaluation into a known baseline or norm. Baselines and norms may change over time as more and more data is acquired that refines the correlation of connectedness values to a particular subject and specific media experience. A range of values measured on a continuum between “Like” or “dislike” and “preferred” or “not preferred” may be distinguished based upon one or more measurements of intensity, degree, variance, and frequency of the captured physiological and physical and this data correlation to experiential and media contextual data. 
       FIG. 8 -B depicts a flow diagram of the media connectedness value analysis  820 . Analysis of media connectedness data may include any type of analysis including computation of means, standard deviations, correlations, comparisons, modes, data plots, statistical values, proportions, ratios, or percentages. The parameters that determine computational analysis methods may be standardized or vary depending on sufficient availability of data and the desired analysis outcomes. Methods for parameter input may be by human means or established by computerized learning program applications. The flow may begin with collecting media experience data associated with an electronic user profile  822 . Analyzing media experience user profile data  822  may include measuring  824 , interpreting  826 , and inferring  828  connectedness values that reflect variations of a subject&#39;s preference for or against a presented media selection, and reflect variations of a subject&#39;s like or dislike of a presented media selection. 
     The flow  820  may include developing data baseline  830  and norms  832  using collected media experience data including physical and physiological data captured from a wearable device and camera, media contextual data, and experiential data. Data baselines  830  and norms  832  may be established to optimize one or more methods that include the media connectedness value analysis  836  process. Data baselines  830  and norms  832  may be developed for media connectedness values based on calculations or may be based on historical connectedness values associated with a particular media selection or subject viewing the presented media selection. Data baselines  830  and norms  832  may be developed with human input based on familiarity with the subject&#39;s media tastes, preferences, and lifestyle. 
     The flow  820  may include determining the probability analysis  840  of connectedness between a subject and media they have already experienced or have never experienced. Using a proposed media module  844 , the contextual data of a proposed media selection is processed in conjunction with a probability analysis  840  of one or more of the available media experience data categories to attribute predictions and/or forecasts of connectedness values of a subject to the proposed media selection. The proposed media module  844  and probability analysis  840  may compare and measure historical media experience user profile data  822  with the proposed media selection data using a combination of machine learning, artificial intelligence and/or algorithmic calculation programs. The flow  820  may generate an analysis rendering  846  in various depictions of connectedness values. 
     Connectedness analysis  836  and analysis rendering  846  may be used by computerized search programs  850  to locate media content  852  stored on local or remote servers, web services, media content aggregators, and the like. Once identified, the proposed media selection contextual data may be evaluated, rated and ranked  854  with a combination of machine learning, artificial intelligence and/or algorithmic calculation programs that compare and measure data to determine comparative order and position based on specific attributes and or parameters related to media connectedness values. Based on the search parameter inputs and one or more of connectedness analysis  836 , and probability analysis  840 , rating and rankings analysis  854 , recommendation rendering  856  may be provided for specific media selections in relation to connectedness data values. These steps may also contribute to establishing data benchmarks, filters, qualifiers, and thresholds using a computerized learning program or developed with human input, based on familiarity with the subject&#39;s media tastes, preferences, and lifestyle. Recommendation renderings  856  may be provided to an individual subject, a group of users on a social network, a web service, media aggregator, or a computerized search program in a variety of depictions including numerical value, chart, graph, percentage, ratio and the like. 
     To help clarify the best circumstances for a presentation to a particular subject, the probability analysis  840  may use an optimal conditions module  860  to establish a baseline and thresholds for ideal circumstances for presenting media to a subject. The optimal conditions module  858  may analyze wearable, camera, and experiential data that is available when the proposed media selection data is evaluated. Based on probability analysis  840  results and a combination of machine learning, artificial intelligence and/or algorithmic calculation programs, the optimal conditions module  860  may recommend the best conditions or parameters for presenting the proposed media based on such factors as the type of media, time of day, device type, subject matter, and the like. Methods for establishing probability analysis  840  parameters and thresholds may be input by human means or established by computerized learning program applications. For example, if the proposed media selection is a news program presented in the morning hours and the subject&#39;s media experience profile indicates a preference to show news programming in the evening hours, the proposed media selection will be delivered to a queue for presentation during the evening hours. 
       FIG. 9 -A Illustrates an example implementation of a system  900  for remote access management of media experience data over a communications channel. In the example shown, a communication device  902  may use an application program interface (API)  904  to access a communications channel  906  and manage communications sessions  908  between a server network  910 , a presentation device  912 , and other devices with network connectivity. A communication device  902  may be a computer, cell phone, smart phone, tablet, laptop and the like. The server network  910  may be a server farm, cloud-based network, or the like. The presentation device  912  may have similar functions as a communications device  902  and may include the technical means that enables the capture of media experience data that indicates media connectedness, such as a camera for capturing facial expressions and means for wireless communications with a wearable device that captures physical and physiological behavioral data. The communications channel  906  can be a Universal Serial Bus (USB), Ethernet, a wireless link (e.g., Wi-Fi, WiMax, 4G), an optical link, infrared link, FireWire, or any other known communications channel or media. 
     In one embodiment, a security process  914  may be used to secure communications sessions  908 . A security process  914  may use a cryptographic protocol, such as Secure Sockets Layer (SSL) or Transport Layer Security (TSL) to provide a secure connection between a communications device  902  and a server network  910 , and a presentation device  912 . The system  900  may include a daemon program  916  that works with the API  904  to manage the communication sessions  908 , including the transmission of commands and data, over the communications channel  906  and server network  910 . The API  904  may support a client program  918  that operates on communication devices  902  and presentation devices  912  and provides a set of functions, procedures, commands, and data structures for supporting communication sessions  908  between devices operating on the communications channel  906 . The client program  918  may operate using the user interface of devices on the system  900 . The client program  918  may allow a user to download or update files, software, search databases for media, store user data, select services, browse web services, locate media content, manage device settings, initiate a web chat, set up preference parameters, set up data thresholds and benchmarks, set up user profiles, remotely operate a device on the network, conduct a data survey, perform financial transactions, and engage an online service or function. 
       FIG. 9 -B Illustrates an example process  930  for managing and presenting media connectedness data on a computing device. The process  930  begins with presenting a first page  934  of user interface elements  936  on the display of a computing device  938 . The computing device  938  may be a mobile phone, smart phone, tablet, laptop computer, or desktop computer. The user interface elements  936  may include display objects  940  and/or an application menu  942 . In one embodiment, the user interface may be controlled using touch-sensitive controls. In another embodiment, the user interface may be controlled using computer peripheral hardware, such as a mouse and alphanumeric keyboard. Objects  940  displayed may be graphics, pictures, photos, text, icons, symbols or some type of image. Menu  942  displays may include navigation guides that direct the user to different user interface elements  936  and additional pages  934 . The process  930  may have a menu format of individual pages  934  designated for but not limited to browsing media, sharing media, analyzing media connectedness values, managing devices, setting up media content filters, creating thresholds and benchmarks for media connectedness values, managing network access, assigning administrative rights to users and devices, assigning access rights to users and devices, managing social network communication access rights and parameters, interfacing with an electronic program guide, managing third-party information, sending text and voice messages, purchasing goods and services, accessing a social network, and managing subscription based media services. 
       FIG. 10  illustrates an example implementation of a system  1000  for capturing and analysis of media experience data  1001  in a group or audience setting. The system  1000  may analyze the collected media experience data  1001  and render analyzed data results that indicate connectedness values  1002  for an audience or group of subject&#39;s  1018 . In the example shown, the system  1000  may be comprised of one or more of the following: a network  1009 , a client program  1012 , an application program interface (API)  1016 , a person or subject  1018 , a communications module  1024 , presentation device  1040 , camera  1013 , communications device  1024  and wearable device  1021 . The system  1000  may operate in presentation environments  1002 , including those designed for audio visual presentations  1004  and live activity  1006 , that can accommodate a small group or large audience including but not limited to, for example, a movie theater, a cruise ship, a bus, an airplane, a playhouse, a sports stadium or arena, a concert hall for music, a comedy club, a church, a sports bar and the like. 
     The media experience data  1001 , connectedness values  1002 , network  1008 , API  1016 , communications device  1024  and wearable device  1021  may operate in accordance with the purpose, functions and features depicted in  FIGS. 1-9  and the respective descriptions therein. Similarly to the systems described previously, in the present system  1000  media experience data  1001  may be comprised of behavioral data  1005  that is captured, measured, and collected from a camera  1013  and wearable device  1021 ; experiential data  1008  from the presentation environment  1002  including live venue activity  1006  and presentation device  1040 ; and contextual data  1007  derived from the media selection  1004 . Live venue activity  1006  examples may include but are not limited to an athletic competition, an amusement park, a music concert, an art gallery, a play, a speech or oral presentation, a retail store or shopping center, and the like. 
     The communications module  1024  may enable a wireless ad-hoc network to connect system  1000  devices with the client program  1012 , API  1016 , and network  1009 . Communications module components may include but not be limited to a signal parser; a node core; node table identifier, range finder, and connection storage; peer management code; database adapter; peer to peer hardware adapter; outbox thread; daemon service component for message management, and a broadcast receiver. 
     The camera  1013 , client program  1012 , and network  1009  may individually or collectively be operated or controlled by a multiple facial detection and recognition program in real time to identify, monitor, measure, and record behavioral data  1005 . The camera  1013  may be equipped with a microphone. The client program  1012  may be comprised of computer application algorithms that use mathematical and matricial techniques to convert images into digital format for submission to processing and comparison routines. In one embodiment, the facial recognition components may use popular facial recognition techniques such as geometric, three-dimensional face recognition, photometric, Facial Action Coding System, or Principal Component Analysis (PCA) with Eigen faces derived from the covariance matrix of the probability distribution over the high-dimensional vector space of face images, Linear Discriminate Analysis, Elastic Bunch Graph Matching fisher face, the Hidden Markov model, and the neuronal motivated dynamic link matching, and the like. The client program  1012  may incorporate one or a combination of the aforementioned techniques to identify behavioral data  1005  including facial expressions, vocal expressions and bodily posture. This information can be organized, processed, collated, compared, and analyzed by the client program  1012  or a remote program connected to the network  1009 . The behavioral data  1005  from the camera  1013  can be managed by the client program  1012  or network  1009  program independently or it can be synchronized with behavioral data  1005  from the wearable  1021 . Behavioral data  1005  collected by the system  1000  devices can be analyzed, compared, calculated, measured, rendered and presented as media experience data  1001  and/or connectedness values by the client program  1012 , API  1016  and/or network  1009  program and displayed on system devices  1000  with display capabilities including the communication device  1024 , wearable  1021 , and presentation device  1040 . 
     In one embodiment, the presentation environment  1002  may be enable several hardwired connections between the system  1000  devices using a Universal Serial Bus (USB), Ethernet, an optical link, FireWire, Lightning or any other known power and/or data connector. For remote data access via a network  1009  to the client program  1012 , communications module  1024 , API  1016 , and presentation device  1040  and other system  1000  devices operating within the presentation environment  1002 , the communications module  1024 , presentation devices  1040 , cameras  1013 , and wearable devices  1021  may include any of a variety of various wired or wireless components including Bluetooth, BLE, WiMax, Wi-Fi, ZigBee and the like. The communication module  1024  may operate based on commands from the client program  1012  to interact with, store subject  1018  and system  1000  data, manage information and data transfers between the network  1009 , API  1016 , and various components of the system  1000 . 
     Media  1004  content may be delivered remotely via a network  1009  and/or locally by the presentation devices  1040 . The presentation devices  1040  may be comprised of a variety of components operating to deliver media  1004  to a presentation environment  1002 . Presentation devices  1040  may include but not be limited to a cable or satellite television system, a television/monitor connected to the internet, a video projector and widescreen formatted for display in a theater or large room, and the like. In one embodiment, the system  1000  may enable multiple subjects  1018  to subscribe, login, opt-in, or join a networked connection  1009  using independently or a combination of an API  1016 , a communication device  1024 , a wearable device  1021 . The system  1000  may download or transfer commands, data, control inputs, software updates via a network  1009 . The network  1009  connection to a client program  1012  allows for remote management of the system  1000  components including the wireless module  1024 , camera  1013 , presentation system  1040 , and API  1016 . The camera  1013  may be enabled with motion detection, facial recognition, infra-red and/or night vision technologies. The client program  1012  may enable the camera to capture random subjects  1018  in the presentation environment  1002  or synchronize wirelessly with wearable devices  1021  to identify specific subjects  1018 . Wearable devices  1021  identified by the system  1000  may be periodically synchronized by the client program  1012  and API  1016  with the audio visual program  1004  or live activity  1006  to establish base line data readings, calibrate hardware, improve data measurement and the like to enable more efficient and accurate system  1000  operation, collection of behavioral data  1005 , rendering of media experience data  1001  and connectedness values  1002 . 
     The system  1000  may identify, monitor, measure, record, collect, analyze and store experiential data  1008  before, during and/or after an audio visual  1004  presentation or live activity  1006 . Experiential data  1008  may include but not be limited to the number of subjects  1018  logged in to the system  1000  via communication device  1024 , via wearable device  1021  and/or measured, counted, or estimated by the client program  1012  and/or the camera(s)  1013 . In the present example, experiential data  1008  may include demographic data associated with a subject&#39;s  1018  use of user profile, a communication device  1024  and/or a wearable device  1021  that interacts with the system including GPS location, IP address, images, videos, social media connections, and the like. Experiential data  1008  may also include crowdsourced data  1026  that is actively solicited and/or passively solicited electronically from subjects  1018  and system  1000  devices. For example, at a random or specific point in time before, during and/or after a media  1004  presentation or live activity  1006 , the system  1000  may read, capture, measure and analyze the behavioral data  1005  of the subjects  1018 , communication device  1024  and wearable device  1021 . Crowdsourced data  1026  include user profiles, user information, GPS location data, venue information, opinion surveys, advertisements, promotions, service or product offerings, rank or rating surveys, and the like. The system  1000  may utilize machine learning or artificial intelligence software in the client program  1012  to customize and refine crowdsourced data  1026  interaction and functions with specific subjects  1018  and or devices connected to the system  1000 . For example, if an audience survey response reveals a demographic within the group from a specific geographic area, or users of a specific device type/platform, or preference for a particular type of food, the client program  1012  may refine or customize the ongoing and future interaction with that sub-group based on their previous response. This process may repeat in order to refine crowdsourced data  1026 . 
     In the foregoing specification, embodiments of this disclosure have been described as applicable to an implementation anticipating the passive capture and analysis of media experience data, where the special advantages of the system and method are very attractive to consumers of media and members of their social network. However these embodiments may be applied to existing content aggregators and web services that enable various methods for identifying content through search queries or other means. 
     In the foregoing specification, the invention has been described with reference to specific embodiments thereof. However, various modifications and changes may be made thereto without departing from the broader spirit and scope of the invention. The specification and drawings are, accordingly, to be regarded in an illustrative sense rather than a restrictive sense.