Patent Publication Number: US-2013238727-A1

Title: System and method for context enhanced messaging

Description:
This application includes material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent disclosure, as it appears in the Patent and Trademark Office files or records, but otherwise reserves all copyright rights whatsoever. 
     FIELD OF THE INVENTION 
     The present invention relates to systems and methods for delivering media enhanced messages on a network and, more particularly, to systems and methods for messages that include media which relates to a user defined context using, in part, data collected and stored by multiple devices on a network. 
     BACKGROUND OF THE INVENTION 
     A great deal of information is generated when people use electronic devices, such as when people use mobile phones and cable set-top boxes. Such information, such as location, applications used, social network, physical and online locations visited, to name a few, could be used to deliver useful services and information to end users, and provide commercial opportunities to advertisers and retailers. However, most of this information is effectively abandoned due to deficiencies in the way such information can be captured. For example, and with respect to a mobile phone, information is generally not gathered while the mobile phone is idle (i.e., not being used by a user). Other information, such as presence of others in the immediate vicinity, time and frequency of messages to other users, and activities of a user&#39;s social network are also not captured effectively. 
     SUMMARY OF THE INVENTION 
     In one embodiment, the invention is a method. A message from a message sender to be delivered to at least one message recipient is received over a network. The message comprises at least one content criteria. A query is formulated based on the content criteria so as to search, via the network, for user profile data, social network data, spatial data, temporal data and topical data that is available via the network and relates to the content criteria and to media files so as to identify at least one media file that is relevant to the content criteria. The media files are inserted into the message, and the message is transmitted over the network to the recipient. 
     In another embodiment, the invention is a method. A message from a message sender to be delivered to at least one message recipient is received over a network. The message comprises at least one delivery criteria and at least one content criteria. When it is determined, via the network that the delivery criteria of the message has been satisfied, a query is formulated based on the content criteria so as to search, via the network, for user profile data, social network data, spatial data, temporal data and topical data that is available via the network and relates to the content criteria and to media files so as to identify at least one media file that is relevant to the content criteria. The media files are inserted into the message and the message is transmitted over the network to the recipient. 
     In another embodiment, the invention is a system comprising: a context enhanced message receiving module that receives messages from senders, wherein the requests each contains at least one recipient, at least one delivery criteria and at least one content criteria; a delivery criteria evaluation and tracking module that determines for each message received by the context enhanced message receiving module if the delivery criteria of the message is satisfied; a media retrieval module that uses content criteria on each message received by the context enhanced message receiving module to formulate a query based on the content criteria so as to search, via the network, for user profile data, social network data, spatial data, temporal data and topical data that is available via the network and relates to the content criteria and to media files so as to identify at least one media file that is relevant to the content criteria; a context enhanced message update module that, for each message, inserts the media files identified by the media retrieval module into the message; and context enhanced message transmission module that transmits each the message updated by the context enhanced message update module to the at least one recipient of the message. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The foregoing and other objects, features, and advantages of the invention will be apparent from the following more particular description of preferred embodiments as illustrated in the accompanying drawings, in which reference characters refer to the same parts throughout the various views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating principles of the invention. 
         FIG. 1  illustrates relationships between real-world entities (RWE) and information objects (IO) on one embodiment of a W4 Communications Network (W4 COMN.) 
         FIG. 2  illustrates metadata defining the relationships between RWEs and IOs on one embodiment of a W4 COMN. 
         FIG. 3  illustrates a conceptual model of one embodiment of a W4 COMN. 
         FIG. 4  illustrates the functional layers of one embodiment of the W4 COMN architecture. 
         FIG. 5  illustrates the analysis components of one embodiment of a W4 engine as shown in  FIG. 2 . 
         FIG. 6  illustrates one embodiment of a W4 engine showing different components within the sub-engines shown in  FIG. 5 . 
         FIG. 7  illustrates one embodiment of a data model showing how a W4 COMN can store media files and relate such files to RWEs, such as persons and places, and IOs, such as topics and other types of metadata. 
         FIG. 8  illustrates one embodiment of a system capable of supporting context-enhanced messaging between users known to a network. 
         FIG. 9  illustrates one embodiment of a process of how a network containing temporal, spatial, and social network and topical data for a plurality of users, devices, and media, such as a W4 COMN, can be used to enable media enhanced messages having complex user contexts having delivery and content criteria. 
         FIG. 10  illustrates one embodiment of a context enhanced message engine capable of supporting the process illustrated in  FIG. 9 . 
     
    
    
     DETAILED DESCRIPTION 
     The present invention is described below with reference to block diagrams and operational illustrations of methods and devices to select and present media related to a specific topic. It is understood that each block of the block diagrams or operational illustrations, and combinations of blocks in the block diagrams or operational illustrations, can be implemented by means of analog or digital hardware and computer program instructions. 
     These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, ASIC, or other programmable data processing apparatus, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, implements the functions/acts specified in the block diagrams or operational block or blocks. 
     In some alternate implementations, the functions/acts noted in the blocks can occur out of the order noted in the operational illustrations. For example, two blocks shown in succession can in fact be executed substantially concurrently or the blocks can sometimes be executed in the reverse order, depending upon the functionality/acts involved. 
     For the purposes of this disclosure the term “server” should be understood to refer to a service point which provides processing, database, and communication facilities. By way of example, and not limitation, the term “server” can refer to a single, physical processor with associated communications and data storage and database facilities, or it can refer to a networked or clustered complex of processors and associated network and storage devices, as well as operating software and one or more database systems and applications software which support the services provided by the server. 
     For the purposes of this disclosure the term “end user” or “user” should be understood to refer to a consumer of data supplied by a data provider. By way of example, and not limitation, the term “end user” can refer to a person who receives data provided by the data provider over the Internet in a browser session, or can refer to an automated software application which receives the data and stores or processes the data. 
     For the purposes of this disclosure the term “media” and “media content” should be understood to refer to binary data which contains content which can be of interest to an end user. By way of example, and not limitation, the term “media” and “media content” can refer to multimedia data, such as video data or audio data, or any other form of data capable of being transformed into a form perceivable by an end user. Such data can, furthermore, be encoded in any manner currently known, or which can be developed in the future, for specific purposes. By way of example, and not limitation, the data can be encrypted, compressed, and/or can contained embedded metadata. 
     For the purposes of this disclosure, a computer readable medium stores computer data in machine readable form. By way of example, and not limitation, a computer readable medium can comprise computer storage media and communication media. Computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid-state memory technology, CD-ROM, DVD, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other mass storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computer. 
     For the purposes of this disclosure a module is a software, hardware, or firmware (or combinations thereof) system, process or functionality, or component thereof, that performs or facilitates the processes, features, and/or functions described herein (with or without human interaction or augmentation). A module can include sub-modules. Software components of a module may be stored on a computer readable medium. Modules may be integral to one or more servers, or be loaded and executed by one or more servers. One or more modules may grouped into an engine or an application. 
     For the purposes of this disclosure an engine is a software, hardware, or firmware (or combinations thereof) system, process or functionality that performs or facilitates the processes, features, and/or functions described herein (with or without human interaction or augmentation). 
     Embodiments of the present invention utilize information provided by a network which is capable of providing data collected and stored by multiple devices on a network. Such information may include, without limitation, temporal information, spatial information, and user information relating to a specific user or hardware device. User information may include, without limitation, user demographics, user preferences, user social networks, and user behavior. One embodiment of such a network is a W4 Communications Network. 
     A “W4 Communications Network” or W4 COMN, provides information related to the “Who, What, When and Where” of interactions within the network. In one embodiment, the W4 COMN is a collection of users, devices and processes that foster both synchronous and asynchronous communications between users and their proxies providing an instrumented network of sensors providing data recognition and collection in real-world environments about any subject, location, user or combination thereof. 
     In one embodiment, the W4 COMN can handle the routing/addressing, scheduling, filtering, prioritization, replying, forwarding, storing, deleting, privacy, transacting, triggering of a new message, propagating changes, transcoding and linking Furthermore, these actions can be performed on any communication channel accessible by the W4 COMN. 
     In one embodiment, the W4 COMN uses a data modeling strategy for creating profiles for not only users and locations, but also any device on the network and any kind of user-defined data with user-specified conditions. Using Social, Spatial, Temporal and Logical data available about a specific user, topic or logical data object, every entity known to the W4 COMN can be mapped and represented against all other known entities and data objects in order to create both a micro graph for every entity as well as a global graph that relates all known entities with one another. In one embodiment, such relationships between entities and data objects are stored in a global index within the W4 COMN. 
     In one embodiment, a W4 COMN network relates to what may be termed “real-world entities”, hereinafter referred to as RWEs. A RWE refers to, without limitation, a person, device, location, or other physical thing known to a W4 COMN. In one embodiment, each RWE known to a W4 COMN is assigned a unique W4 identification number that identifies the RWE within the W4 COMN. 
     RWEs can interact with the network directly or through proxies, which can themselves be RWEs. Examples of RWEs that interact directly with the W4 COMN include any device such as a sensor, motor, or other piece of hardware connected to the W4 COMN in order to receive or transmit data or control signals. RWE may include all devices that can serve as network nodes or generate, request and/or consume data in a networked environment or that can be controlled through a network. Such devices include any kind of “dumb” device purpose-designed to interact with a network (e.g., cell phones, cable television set top boxes, fax machines, telephones, and radio frequency identification (RFID) tags, sensors, etc.). 
     Examples of RWEs that may use proxies to interact with W4 COMN network include non-electronic entities including physical entities, such as people, locations (e.g., states, cities, houses, buildings, airports, roads, etc.) and things (e.g., animals, pets, livestock, gardens, physical objects, cars, airplanes, works of art, etc.), and intangible entities such as business entities, legal entities, groups of people or sports teams. In addition, “smart” devices (e.g., computing devices such as smart phones, smart set top boxes, smart cars that support communication with other devices or networks, laptop computers, personal computers, server computers, satellites, etc.) may be considered RWE that use proxies to interact with the network, where software applications executing on the device that serve as the devices&#39; proxies. 
     In one embodiment, a W4 COMN may allow associations between RWEs to be determined and tracked. For example, a given user (an RWE) can be associated with any number and type of other RWEs including other people, cell phones, smart credit cards, personal data assistants, email and other communication service accounts, networked computers, smart appliances, set top boxes and receivers for cable television and other media services, and any other networked device. This association can be made explicitly by the user, such as when the RWE is installed into the W4 COMN. 
     An example of this is the set up of a new cell phone, cable television service or email account in which a user explicitly identifies an RWE (e.g., the user&#39;s phone for the cell phone service, the user&#39;s set top box and/or a location for cable service, or a username and password for the online service) as being directly associated with the user. This explicit association can include the user identifying a specific relationship between the user and the RWE (e.g., this is my device, this is my home appliance, this person is my friend/father/son/etc., this device is shared between me and other users, etc.). RWEs can also be implicitly associated with a user based on a current situation. For example, a weather sensor on the W4 COMN can be implicitly associated with a user based on information indicating that the user lives or is passing near the sensor&#39;s location. 
     In one embodiment, a W4 COMN network may additionally include what may be termed “information-objects”, hereinafter referred to as IOs. An information object (IO) is a logical object that may store, maintain, generate or otherwise provides data for use by RWEs and/or the W4 COMN. In one embodiment, data within in an IO can be revised by the act of an RWE An IO within in a W4 COMN can be provided a unique W4 identification number that identifies the IO within the W4 COMN. 
     In one embodiment, IOs include passive objects such as communication signals (e.g., digital and analog telephone signals, streaming media and interprocess communications), email messages, transaction records, virtual cards, event records (e.g., a data file identifying a time, possibly in combination with one or more RWEs such as users and locations, that can further be associated with a known topic/activity/significance such as a concert, rally, meeting, sporting event, etc.), recordings of phone calls, calendar entries, web pages, database entries, electronic media objects (e.g., media files containing songs, videos, pictures, images, audio messages, phone calls, etc.), electronic files and associated metadata. 
     In one embodiment, IOs include any executing process or application that consumes or generates data such as an email communication application (such as OUTLOOK by MICROSOFT, or YAHOO! MAIL by YAHOO!), a calendaring application, a word processing application, an image editing application, a media player application, a weather monitoring application, a browser application and a web page server application. Such active IOs can or can not serve as a proxy for one or more RWEs. For example, voice communication software on a smart phone can serve as the proxy for both the smart phone and for the owner of the smart phone. 
     In one embodiment, for every IO there are at least three classes of associated RWEs. The first is the RWE that owns or controls the IO, whether as the creator or a rights holder (e.g., an RWE with editing rights or use rights to the IO). The second is the RWE(s) that the IO relates to, for example by containing information about the RWE or that identifies the RWE. The third are any RWEs that access the IO in order to obtain data from the IO for some purpose. 
     Within the context of a W4 COMN, “available data” and “W4 data” means data that exists in an IO or data that can be collected from a known IO or RWE such as a deployed sensor. Within the context of a W4 COMN, “sensor” means any source of W4 data including PCs, phones, portable PCs or other wireless devices, household devices, cars, appliances, security scanners, video surveillance, RFID tags in clothes, products and locations, online data or any other source of information about a real-world user/topic/thing (RWE) or logic-based agent/process/topic/thing (IO). 
       FIG. 1  illustrates one embodiment of relationships between RWEs and IOs on a W4 COMN. A user  102  is a RWE provided with a unique network ID. The user  102  may be a human that communicates with the network using proxy devices  104 ,  106 ,  108 ,  110  associated with the user  102 , all of which are RWEs having a unique network ID. These proxies can communicate directly with the W4 COMN or can communicate with the W4 COMN using IOs such as applications executed on or by a proxy device. 
     In one embodiment, the proxy devices  104 ,  106 ,  108 ,  110  can be explicitly associated with the user  102 . For example, one device  104  can be a smart phone connected by a cellular service provider to the network and another device  106  can be a smart vehicle that is connected to the network. Other devices can be implicitly associated with the user  102 . 
     For example, one device  108  can be a “dumb” weather sensor at a location matching the current location of the user&#39;s cell phone  104 , and thus implicitly associated with the user  102  while the two RWEs  104 ,  108  are co-located. Another implicitly associated device  110  can be a sensor  110  for physical location  112  known to the W4 COMN. The location  112  is known, either explicitly (through a user-designated relationship, e.g., this is my home, place of employment, parent, etc.) or implicitly (the user  102  is often co-located with the RWE  112  as evidenced by data from the sensor  110  at that location  112 ), to be associated with the first user  102 . 
     The user  102  can be directly associated with one or more persons  140 , and indirectly associated with still more persons  142 ,  144  through a chain of direct associations. Such associations can be explicit (e.g., the user  102  can have identified the associated person  140  as his/her father, or can have identified the person  140  as a member of the user&#39;s social network) or implicit (e.g., they share the same address). Tracking the associations between people (and other RWEs as well) allows the creation of the concept of “intimacy”, where intimacy may be defined as a measure of the degree of association between two people or RWEs. For example, each degree of removal between RWEs can be considered a lower level of intimacy, and assigned lower intimacy score. Intimacy can be based solely on explicit social data or can be expanded to include all W4 data including spatial data and temporal data. 
     In one embodiment, each RWE  102 ,  104 ,  106 ,  108 ,  110 ,  112 ,  140 ,  142 ,  144  of a W4 COMN can be associated with one or more IOs as shown.  FIG. 1  illustrates two IOs  122 ,  124  as associated with the cell phone device  104 . One IO  122  can be a passive data object such as an event record that is used by scheduling/calendaring software on the cell phone, a contact IO used by an address book application, a historical record of a transaction made using the device  104  or a copy of a message sent from the device  104 . The other IO  124  can be an active software process or application that serves as the device&#39;s proxy to the W4 COMN by transmitting or receiving data via the W4 COMN. Voice communication software, scheduling/calendaring software, an address book application or a text messaging application are all examples of IOs that can communicate with other IOs and RWEs on the network. IOs may additionally relate to topics of interest to one or more RWEs, such topics including, without limitation, musical artists, genre of music, a location and so forth. 
     The IOs  122 ,  124  can be locally stored on the device  104  or stored remotely on some node or datastore accessible to the W4 COMN, such as a message server or cell phone service datacenter. The IO  126  associated with the vehicle  108  can be an electronic file containing the specifications and/or current status of the vehicle  108 , such as make, model, identification number, current location, current speed, current condition, current owner, etc. The IO  128  associated with sensor  108  can identify the current state of the subject(s) monitored by the sensor  108 , such as current weather or current traffic. The IO  130  associated with the cell phone  110  can be information in a database identifying recent calls or the amount of charges on the current bill. 
     RWEs which can only interact with the W4 COMN through proxies, such as people  102 ,  140 ,  142 ,  144 , computing devices  104 ,  106  and locations  112 , can have one or more IOs  132 ,  134 ,  146 ,  148 ,  150  directly associated with them which contain RWE-specific information for the associated RWE. For example, IOs associated with a person  132 ,  146 ,  148 ,  150  can include a user profile containing email addresses, telephone numbers, physical addresses, user preferences, identification of devices and other RWEs associated with the user. The IOs may additionally include records of the user&#39;s past interactions with other RWE&#39;s on the W4 COMN (e.g., transaction records, copies of messages, listings of time and location combinations recording the user&#39;s whereabouts in the past), the unique W4 COMN identifier for the location and/or any relationship information (e.g., explicit user-designations of the user&#39;s relationships with relatives, employers, co-workers, neighbors, service providers, etc.). 
     Another example of IOs associated with a person  132 ,  146 ,  148 ,  150  includes remote applications through which a person can communicate with the W4 COMN such as an account with a web-based email service such as Yahoo! Mail. A location&#39;s IO  134  can contain information such as the exact coordinates of the location, driving directions to the location, a classification of the location (residence, place of business, public, non-public, etc.), information about the services or products that can be obtained at the location, the unique W4 COMN identifier for the location, businesses located at the location, photographs of the location, etc. 
     In one embodiment, RWEs and IOs are correlated to identify relationships between them. RWEs and IOs may be correlated using metadata. For example, if an IO is a music file, metadata for the file can include data identifying the artist, song, etc., album art, and the format of the music data. This metadata can be stored as part of the music file or in one or more different IOs that are associated with the music file or both. W4 metadata can additionally include the owner of the music file and the rights the owner has in the music file. As another example, if the IO is a picture taken by an electronic camera, the picture can include in addition to the primary image data from which an image can be created on a display, metadata identifying when the picture was taken, where the camera was when the picture was taken, what camera took the picture, who, if anyone, is associated (e.g., designated as the camera&#39;s owner) with the camera, and who and what are the subjects of/in the picture. The W4 COMN uses all the available metadata in order to identify implicit and explicit associations between entities and data objects. 
       FIG. 2  illustrates one embodiment of metadata defining the relationships between RWEs and IOs on the W4 COMN. In the embodiment shown, an IO  202  includes object data  204  and five discrete items of metadata  206 ,  208 ,  210 ,  212 ,  214 . Some items of metadata  208 ,  210 ,  212  can contain information related only to the object data  204  and unrelated to any other IO or RWE. For example, a creation date, text or an image that is to be associated with the object data  204  of the IO  202 . 
     Some of items of metadata  206 ,  214 , on the other hand, can identify relationships between the IO  202  and other RWEs and IOs. As illustrated, the IO  202  is associated by one item of metadata  206  with an RWE  220  that RWE  220  is further associated with two IOs  224 ,  226  and a second RWE  222  based on some information known to the W4 COMN. For example, could describe the relations between an image (IO  202 ) containing metadata  206  that identifies the electronic camera (the first RWE  220 ) and the user (the second RWE  224 ) that is known by the system to be the owner of the camera  220 . Such ownership information can be determined, for example, from one or another of the IOs  224 ,  226  associated with the camera  220 . 
       FIG. 2  also illustrates metadata  214  that associates the IO  202  with another IO  230 . This IO  230  is itself associated with three other IOs  232 ,  234 ,  236  that are further associated with different RWEs  242 ,  244 ,  246 . This part of  FIG. 2 , for example, could describe the relations between a music file (IO  202 ) containing metadata  206  that identifies the digital rights file (the first IO  230 ) that defines the scope of the rights of use associated with this music file  202 . The other IOs  232 ,  234 ,  236  are other music files that are associated with the rights of use and which are currently associated with specific owners (RWEs  242 ,  244 ,  246 ). 
       FIG. 3  illustrates one embodiment of a conceptual model of a W4 COMN. The W4 COMN  300  creates an instrumented messaging infrastructure in the form of a global logical network cloud conceptually sub-divided into networked-clouds for each of the 4Ws: Who, Where, What and When. In the Who cloud  302  are all users whether acting as senders, receivers, data points or confirmation/certification sources as well as user proxies in the forms of user-program processes, devices, agents, calendars, etc. 
     In the Where cloud  304  are all physical locations, events, sensors or other RWEs associated with a spatial reference point or location. The When cloud  306  is composed of natural temporal events (that is events that are not associated with particular location or person such as days, times, seasons) as well as collective user temporal events (holidays, anniversaries, elections, etc.) and user-defined temporal events (birthdays, smart-timing programs). 
     The What cloud  308  is comprised of all known data—web or private, commercial or user—accessible to the W4 COMN, including for example environmental data like weather and news, RWE-generated data, IOs and IO data, user data, models, processes and applications. Thus, conceptually, most data is contained in the What cloud  308 . 
     Some entities, sensors or data may potentially exist in multiple clouds either disparate in time or simultaneously. Additionally, some IOs and RWEs can be composites in that they combine elements from one or more clouds. Such composites can be classified as appropriate to facilitate the determination of associations between RWEs and IOs. For example, an event consisting of a location and time could be equally classified within the When cloud  306 , the What cloud  308  and/or the Where cloud  304 . 
     In one embodiment, a W4 engine  310  is center of the W4 COMN&#39;s intelligence for making all decisions in the W4 COMN. The W4 engine  310  controls all interactions between each layer of the W4 COMN and is responsible for executing any approved user or application objective enabled by W4 COMN operations or interoperating applications. In an embodiment, the W4 COMN is an open platform with standardized, published APIs for requesting (among other things) synchronization, disambiguation, user or topic addressing, access rights, prioritization or other value-based ranking, smart scheduling, automation and topical, social, spatial or temporal alerts. 
     One function of the W4 COMN is to collect data concerning all communications and interactions conducted via the W4 COMN, which can include storing copies of IOs and information identifying all RWEs and other information related to the IOs (e.g., who, what, when, where information). Other data collected by the W4 COMN can include information about the status of any given RWE and IO at any given time, such as the location, operational state, monitored conditions (e.g., for an RWE that is a weather sensor, the current weather conditions being monitored or for an RWE that is a cell phone, its current location based on the cellular towers it is in contact with) and current status. 
     The W4 engine  310  is also responsible for identifying RWEs and relationships between RWEs and IOs from the data and communication streams passing through the W4 COMN. The function of identifying RWEs associated with or implicated by IOs and actions performed by other RWEs may be referred to as entity extraction. Entity extraction can include both simple actions, such as identifying the sender and receivers of a particular IO, and more complicated analyses of the data collected by and/or available to the W4 COMN, for example determining that a message listed the time and location of an upcoming event and associating that event with the sender and receiver(s) of the message based on the context of the message or determining that an RWE is stuck in a traffic jam based on a correlation of the RWE&#39;s location with the status of a co-located traffic monitor. 
     It should be noted that when performing entity extraction from an IO, the IO can be an opaque object with only where only W4 metadata related to the object is visible, but internal data of the IO (i.e., the actual primary or object data contained within the object) are not, and thus metadata extraction is limited to the metadata. Alternatively, if internal data of the IO is visible, it can also be used in entity extraction, e.g. strings within an email are extracted and associated as RWEs to for use in determining the relationships between the sender, user, topic or other RWE or IO impacted by the object or process. 
     In the embodiment shown, the W4 engine  310  can be one or a group of distributed computing devices, such as a general-purpose personal computers (PCs) or purpose built server computers, connected to the W4 COMN by communication hardware and/or software. Such computing devices can be a single device or a group of devices acting together. Computing devices can be provided with any number of program modules and data files stored in a local or remote mass storage device and local memory (e.g., RAM) of the computing device. For example, as mentioned above, a computing device can include an operating system suitable for controlling the operation of a networked computer, such as the WINDOWS XP or WINDOWS SERVER operating systems from MICROSOFT CORPORATION. 
     Some RWEs can also be computing devices such as, without limitation, smart phones, web-enabled appliances, PCs, laptop computers, and personal data assistants (PDAs). Computing devices can be connected to one or more communications networks such as the Internet, a publicly switched telephone network, a cellular telephone network, a satellite communication network, a wired communication network such as a cable television or private area network. Computing devices can be connected any such network via a wired data connection or wireless connection such as a wi-fi, a WiMAX (802.36), a Bluetooth or a cellular telephone connection. 
     Local data structures, including discrete IOs, can be stored on a computer-readable medium (not shown) that is connected to, or part of, any of the computing devices described herein including the W4 engine  310 . For example, in one embodiment, the data backbone of the W4 COMN, discussed below, includes multiple mass storage devices that maintain the IOs, metadata and data necessary to determine relationships between RWEs and IOs as described herein. 
       FIG. 4  illustrates one embodiment of the functional layers of a W4 COMN architecture. At the lowest layer, referred to as the sensor layer  402 , is the network  404  of the actual devices, users, nodes and other RWEs. Sensors include known technologies like web analytics, GPS, cell-tower pings, use logs, credit card transactions, online purchases, explicit user profiles and implicit user profiling achieved through behavioral targeting, search analysis and other analytics models used to optimize specific network applications or functions. 
     The data layer  406  stores and catalogs the data produced by the sensor layer  402 . The data can be managed by either the network  404  of sensors or the network infrastructure  406  that is built on top of the instrumented network of users, devices, agents, locations, processes and sensors. The network infrastructure  408  is the core under-the-covers network infrastructure that includes the hardware and software necessary to receive that transmit data from the sensors, devices, etc. of the network  404 . It further includes the processing and storage capability necessary to meaningfully categorize and track the data created by the network  404 . 
     The user profiling layer  410  performs the W4 COMN&#39;s user profiling functions. This layer  410  can further be distributed between the network infrastructure  408  and user applications/processes  412  executing on the W4 engine or disparate user computing devices. Personalization is enabled across any single or combination of communication channels and modes including email, IM, texting (SMS, etc.), photobloging, audio (e.g. telephone call), video (teleconferencing, live broadcast), games, data confidence processes, security, certification or any other W4 COMM process call for available data. 
     In one embodiment, the user profiling layer  410  is a logic-based layer above all sensors to which sensor data are sent in the rawest form to be mapped and placed into the W4 COMN data backbone  420 . The data (collected and refined, related and deduplicated, synchronized and disambiguated) are then stored in one or a collection of related databases available applications approved on the W4 COMN. Network-originating actions and communications are based upon the fields of the data backbone, and some of these actions are such that they themselves become records somewhere in the backbone, e.g. invoicing, while others, e.g. fraud detection, synchronization, disambiguation, can be done without an impact to profiles and models within the backbone. 
     Actions originating from outside the network, e.g., RWEs such as users, locations, proxies and processes, come from the applications layer  414  of the W4 COMN. Some applications can be developed by the W4 COMN operator and appear to be implemented as part of the communications infrastructure  408 , e.g. email or calendar programs because of how closely they operate with the sensor processing and user profiling layer  410 . The applications  412  also serve as a sensor in that they, through their actions, generate data back to the data layer  406  via the data backbone concerning any data created or available due to the applications execution. 
     In one embodiment, the applications layer  414  can also provide a user interface (UI) based on device, network, carrier as well as user-selected or security-based customizations. Any UI can operate within the W4 COMN if it is instrumented to provide data on user interactions or actions back to the network. In the case of W4 COMN enabled mobile devices, the UI can also be used to confirm or disambiguate incomplete W4 data in real-time, as well as correlation, triangulation and synchronization sensors for other nearby enabled or non-enabled devices. 
     At some point, the network effects enough enabled devices allow the network to gather complete or nearly complete data (sufficient for profiling and tracking) of a non-enabled device because of its regular intersection and sensing by enabled devices in its real-world location. 
     Above the applications layer  414 , or hosted within it, is the communications delivery network  416 . The communications delivery network can be operated by the W4 COMN operator or be independent third-party carrier service. Data may be delivered via synchronous or asynchronous communication. In every case, the communication delivery network  414  will be sending or receiving data on behalf of a specific application or network infrastructure  408  request. 
     The communication delivery layer  418  also has elements that act as sensors including W4 entity extraction from phone calls, emails, blogs, etc. as well as specific user commands within the delivery network context. For example, “save and prioritize this call” said before end of call can trigger a recording of the previous conversation to be saved and for the W4 entities within the conversation to analyzed and increased in weighting prioritization decisions in the personalization/user profiling layer  410 . 
       FIG. 5  illustrates one embodiment of the analysis components of a W4 engine as shown in  FIG. 3 . As discussed above, the W4 Engine is responsible for identifying RWEs and relationships between RWEs and IOs from the data and communication streams passing through the W4 COMN. 
     In one embodiment the W4 engine connects, interoperates and instruments all network participants through a series of sub-engines that perform different operations in the entity extraction process. The attribution engine  504  tracks the real-world ownership, control, publishing or other conditional rights of any RWE in any IO. Whenever a new IO is detected by the W4 engine  502 , e.g., through creation or transmission of a new message, a new transaction record, a new image file, etc., ownership is assigned to the IO. The attribution engine  504  creates this ownership information and further allows this information to be determined for each IO known to the W4 COMN. 
     The correlation engine  506  can operates two capacities: first, to identify associated RWEs and IOs and their relationships (such as by creating a combined graph of any combination of RWEs and IOs and their attributes, relationships and reputations within contexts or situations) and second, as a sensor analytics pre-processor for attention events from any internal or external source. 
     In one embodiment, the identification of associated RWEs and IOs function of the correlation engine  506  is done by graphing the available data, using, for example, one or more histograms A histogram is a mapping technique that counts the number of observations that fall into various disjoint categories (i.e. bins.). By selecting each IO, RWE, and other known parameters (e.g., times, dates, locations, etc.) as different bins and mapping the available data, relationships between RWEs, IOs and the other parameters can be identified. A histogram of all RWEs and IOs is created, from which correlations based on the graph can be made. 
     As a pre-processor, the correlation engine  506  monitors the information provided by RWEs in order to determine if any conditions are identified that can trigger an action on the part of the W4 engine  502 . For example, if a delivery condition has been associated with a message, when the correlation engine  506  determines that the condition is met, it can transmit the appropriate trigger information to the W4 engine  502  that triggers delivery of the message. 
     The attention engine  508  instruments all appropriate network nodes, clouds, users, applications or any combination thereof and includes close interaction with both the correlation engine  506  and the attribution engine  504 . 
       FIG. 6  illustrates one embodiment of a W4 engine showing different components within the sub-engines described above with reference to  FIG. 4 . In one embodiment the W4 engine  602  includes an attention engine  608 , attribution engine  604  and correlation engine  606  with several sub-managers based upon basic function. 
     The attention engine  608  includes a message intake and generation manager  610  as well as a message delivery manager  612  that work closely with both a message matching manager  614  and a real-time communications manager  616  to deliver and instrument all communications across the W4 COMN. 
     The attribution engine  604  works within the user profile manager  618  and in conjunction with all other modules to identify, process/verify and represent ownership and rights information related to RWEs, IOs and combinations thereof. 
     The correlation engine  606  dumps data from both of its channels (sensors and processes) into the same data backbone  620  which is organized and controlled by the W4 analytics manager  622 . The data backbone  620  includes both aggregated and individualized archived versions of data from all network operations including user logs  624 , attention rank place logs  626 , web indices and environmental logs  618 , e-commerce and financial transaction information  630 , search indexes and logs  632 , sponsor content or conditionals, ad copy and any and all other data used in any W4COMN process, IO or event. Because of the amount of data that the W4 COMN will potentially store, the data backbone  620  includes numerous database servers and datastores in communication with the W4 COMN to provide sufficient storage capacity. 
     The data collected by the W4 COMN includes spatial data, temporal data, RWE interaction data, IO content data (e.g., media data), and user data including explicitly-provided and deduced social and relationship data. Spatial data can be any data identifying a location associated with an RWE. For example, the spatial data can include any passively collected location data, such as cell tower data, global packet radio service (GPRS) data, global positioning service (GPS) data, WI-FI data, personal area network data, IP address data and data from other network access points, or actively collected location data, such as location data entered by the user. 
     Temporal data is time based data (e.g., time stamps) that relate to specific times and/or events associated with a user and/or the electronic device. For example, the temporal data can be passively collected time data (e.g., time data from a clock resident on the electronic device, or time data from a network clock), or the temporal data can be actively collected time data, such as time data entered by the user of the electronic device (e.g., a user maintained calendar). 
     Logical and IO data refers to the data contained by an IO as well as data associated with the IO such as creation time, owner, associated RWEs, when the IO was last accessed, the topic or subject of the IO (from message content or “re” or subject line, as some examples) etc. For example, an IO may relate to media data. Media data can include any data relating to presentable media, such as audio data, visual data, and audiovisual data. Audio data can be data relating to downloaded music, such as genre, artist, album and the like, and includes data regarding ringtones, ringbacks, media purchased, playlists, and media shared, to name a few. The visual data can be data relating to images and/or text received by the electronic device (e.g., via the Internet or other network). The visual data can be data relating to images and/or text sent from and/or captured at the electronic device. 
     Audiovisual data can be data associated with any videos captured at, downloaded to, or otherwise associated with the electronic device. The media data includes media presented to the user via a network, such as use of the Internet, and includes data relating to text entered and/or received by the user using the network (e.g., search terms), and interaction with the network media, such as click data (e.g., advertisement banner clicks, bookmarks, click patterns and the like). Thus, the media data can include data relating to the user&#39;s RSS feeds, subscriptions, group memberships, game services, alerts, and the like. 
     The media data can include non-network activity, such as image capture and/or video capture using an electronic device, such as a mobile phone. The image data can include metadata added by the user, or other data associated with the image, such as, with respect to photos, location when the photos were taken, direction of the shot, content of the shot, and time of day, to name a few. Media data can be used, for example, to deduce activities information or preferences information, such as cultural and/or buying preferences information. 
     Relationship data can include data relating to the relationships of an RWE or IO to another RWE or IO. For example, the relationship data can include user identity data, such as gender, age, race, name, social security number, photographs and other information associated with the user&#39;s identity. User identity information can also include e-mail addresses, login names and passwords. Relationship data can further include data identifying explicitly associated RWEs. For example, relationship data for a cell phone can indicate the user that owns the cell phone and the company that provides the service to the phone. As another example, relationship data for a smart car can identify the owner, a credit card associated with the owner for payment of electronic tolls, those users permitted to drive the car and the service station for the car. 
     Relationship data can also include social network data. Social network data includes data relating to any relationship that is explicitly defined by a user or other RWE, such as data relating to a user&#39;s friends, family, co-workers, business relations, and the like. Social network data can include, for example, data corresponding with a user-maintained electronic address book. Relationship data can be correlated with, for example, location data to deduce social network information, such as primary relationships (e.g., user-spouse, user-children and user-parent relationships) or other relationships (e.g., user-friends, user-co-worker, user-business associate relationships). Relationship data also can be utilized to deduce, for example, activities information. 
     Interaction data can be any data associated with user interaction of the electronic device, whether active or passive. Examples of interaction data include interpersonal communication data, media data, relationship data, transactional data and device interaction data, all of which are described in further detail below. Table 1, below, is a non-exhaustive list including examples of electronic data. 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Examples of Electronic Data 
               
            
           
           
               
               
               
            
               
                 Spatial Data 
                 Temporal Data 
                 Interaction Data 
               
               
                   
               
               
                 Cell tower 
                 Time stamps 
                 Interpersonal 
               
               
                 GPRS 
                 Local clock 
                 communications 
               
               
                 GPS 
                 Network clock 
                 Media 
               
               
                 WiFi 
                 User input of time 
                 Relationships 
               
               
                 Personal area network 
                   
                 Transactions 
               
               
                 Network access points 
                   
                 Device interactions 
               
               
                 User input of location 
               
               
                 Geo-coordinates 
               
               
                   
               
            
           
         
       
     
     Interaction data includes communication data between any RWEs that is transferred via the W4 COMN. For example, the communication data can be data associated with an incoming or outgoing short message service (SMS) message, email message, voice call (e.g., a cell phone call, a voice over IP call), or other type of interpersonal communication related to an RWE. Communication data can be correlated with, for example, temporal data to deduce information regarding frequency of communications, including concentrated communication patterns, which can indicate user activity information. 
     The interaction data can also include transactional data. The transactional data can be any data associated with commercial transactions undertaken by or at the mobile electronic device, such as vendor information, financial institution information (e.g., bank information), financial account information (e.g., credit card information), merchandise information and costs/prices information, and purchase frequency information, to name a few. The transactional data can be utilized, for example, to deduce activities and preferences information. The transactional information can also be used to deduce types of devices and/or services the user owns and/or in which the user can have an interest. 
     The interaction data can also include device or other RWE interaction data. Such data includes both data generated by interactions between a user and a RWE on the W4 COMN and interactions between the RWE and the W4 COMN. RWE interaction data can be any data relating to an RWE&#39;s interaction with the electronic device not included in any of the above categories, such as habitual patterns associated with use of an electronic device data of other modules/applications, such as data regarding which applications are used on an electronic device and how often and when those applications are used. As described in further detail below, device interaction data can be correlated with other data to deduce information regarding user activities and patterns associated therewith. Table 2, below, is a non-exhaustive list including examples of interaction data. 
     
       
         
           
               
             
               
                 TABLE 2 
               
             
            
               
                   
               
               
                 Examples of Interaction Data 
               
            
           
           
               
               
            
               
                 Type of Data 
                 Example(s) 
               
               
                   
               
               
                 Interpersonal 
                 Text-based communications, such as SMS and e- 
               
               
                 communication 
                 mail 
               
               
                 data 
                 Audio-based communications, such as voice 
               
               
                   
                 calls, voice notes, voice mail 
               
               
                   
                 Media-based communications, such as 
               
               
                   
                 multimedia messaging service (MMS) 
               
               
                   
                 communications 
               
               
                   
                 Unique identifiers associated with a 
               
               
                   
                 communication, such as phone numbers, e-mail 
               
               
                   
                 addresses, and network addresses 
               
               
                 Media data 
                 Audio data, such as music data (artist, genre, 
               
               
                   
                 track, album, etc.) 
               
               
                   
                 Visual data, such as any text, images and video 
               
               
                   
                 data, including Internet data, picture data, 
               
               
                   
                 podcast data and playlist data 
               
               
                   
                 Network interaction data, such as click patterns 
               
               
                   
                 and channel viewing patterns 
               
               
                 Relationship 
                 User identifying information, such as name, age, 
               
               
                 data 
                 gender, race, and social security number 
               
               
                   
                 Social network data 
               
               
                 Transactional 
                 Vendors 
               
               
                 data 
                 Financial accounts, such as credit cards and banks 
               
               
                   
                 data 
               
               
                   
                 Type of merchandise/services purchased 
               
               
                   
                 Cost of purchases 
               
               
                   
                 Inventory of purchases 
               
               
                 Device interaction 
                 Any data not captured above dealing with user 
               
               
                 data 
                 interaction of the device, such as patterns of use 
               
               
                   
                 of the device, applications utilized, and so forth 
               
               
                   
               
            
           
         
       
     
     Context Enhanced Messaging 
     One of the most important functions of many communications and data networks is the ability for users to send messages to one another. Methods of communication include, among others, email, instant messaging, photo messaging, video conferencing and telephonic or cellular voice communications. Such methods of communication are, however, inherently limited. There is often more that a network user wishes to communicate than can be readily expressed in words. Messages can be significantly enhanced by including highly personalized media content such as music and images that are fine tuned to both the sender&#39;s message and the recipient&#39;s personal preferences. 
     The right media can evoke deep seated memories in users and create a picture, an impression, a feeling, of a time or place, a person or a group of persons, or even an abstract idea to users that evokes a call to action of some kind, commercial and/or personal. It is simple enough to retrieve a playlist or list of videos for a single musical artist. But a person may wish to capture a more complex concept, for example, a person may wish to create a playlist of songs representing the favorite music of the recipient that relates to a subject, time, place or a mood. Furthermore, messaging can be further enhanced by fine-tuning the delivery of the message to correspond to a specific time or time and date. 
     When a user creates an enhanced message, the user may be said to have a specific context in mind for the content or delivery of the message. In one embodiment, the message context can be defined as a set of criteria that describe or circumscribe one or more related ideas central to the message, the sender and the recipient in that context, and which can thus be used to create a model of for message content and delivery options for that instance. The criteria can be conceptually divided into four categories: Who, What, When and Where. 
     “Who” criteria are persons, devices, or proxies who are related to the ideas embodied in the context. “Who” may be a known person, such as the message sender, the message recipients, or a specific person known by the user. “Who” may also be a list of specific persons, such as the contact list stored on the PDA of a user, the guest list of a party, or persons listed on a user&#39;s social network profile as friends. Alternatively, “Who” can be a general description of persons of interest, such as persons who are interested in surfing, single women in their 40&#39;s who drive motorcycles and like yoga, men who like football and commute by bus, persons who pass by a billboard more than three times a week and/or customers of a specific restaurant who also drive BMWs. 
     “What” criteria are objects or topics, concrete or abstract that relate to the ideas embodied in the context. “What” may be the form of media the message sender or the message recipients are interested in, such as photos, music or videos. “What” may be an object such as a car, a piece of jewelry or other object of shared interest. “What” may be a genre of music or video, such as country or rock. “What” may be subject matter addressed in media, such as love songs or even specific lyrical phrases. Alternatively, “What” may be a mood or atmosphere, such as happy, sad, energetic, or relaxed. As an indicator of topical relevance, “What” criteria are an unbounded set of things determined by human creation, attention and association or tagging. 
     “When” criteria are temporal constructs such as dates and times which are related to the ideas embodied in the context. “When” may be the current date and time. “When” may also be a specific date and time in the past or the future, or a range of dates and times in the past or the future, such as a duration, e.g. two hours, four weeks, one year. “When” may be a conditional occurrence if specified conditions or criteria are met. “When” may be an offset from a specific date, for example, ten days in the past, or an offset from a conditional occurrence, ten days after a mortgage payment is late. Alternatively, “When” can be an event on a calendar, such as a birthday, a season or a holiday, or an event of personal or societal/social importance, such as the last time a favorite sports team won a championship. 
     “Where” criteria are physical locations which are related to the ideas embodied in the context. “Where” may be a user&#39;s current location. “Where” may be specific places, such as a country, a state, a city, a neighborhood. “Where” may be defined as the location of an event, such as a concert or some other newsworthy occurrence, or alternatively the personal location of a user when they learned of an event, e.g. where were you when you heard about 9/11. Alternatively, “Where” can be a general description of places of interest, such as blues or jazz clubs, or a conditional location depending on the satisfaction or resolution of specified criteria. For example, “where” can be the real-time most popular club for 24-35 year olds, or “where” can be the research lab where breast cancer is finally cured. 
     In one embodiment, a context-enhanced message comprises four elements: a recipient, a message body, delivery criteria, and content criteria. The recipient is one or more real world entities that are to receive the message. The recipient may be, without limitation, one or more specific persons, may be a group email address, or may be a general description of a type of recipient, such as parents of children on my child&#39;s soccer team, or everyone in a person&#39;s social network. The recipient may be, or may include, the message sender, such as, for example, a message reminder to one&#39;s self The message body is a text or media object that expresses a specific message. For example, if a context-enhanced message is an email, the message body will typically contain some kind of text message of arbitrary length such as “Meet me @ 7:00 PM” or “Happy Birthday.” The message body may include an audio file containing, for example, a voice message. The message body may include an image file containing, for example, a picture of the sender, or a video message from the user. 
     Delivery criteria are the conditions under which the message is to be delivered to the recipients. Such conditions may include “Where” or spatial conditions such as, for example, when a recipient is at a specific location, within a certain proximity of a location, person or object. Such conditions may include “When” or temporal conditions such as a specific time or date or when a specific event occurs. Such criteria may also include “Who” or social criteria, such as, for example, music preferred by one or more of the sender&#39;s social network. Such criteria may also utilize “What” or topical criteria, such as, for example, when the recipient&#39;s mood as judged, for example, by the content of recent messages sent by the recipient, appears to be sad. 
     Content criteria describe the media files that are to be included with the message. Such messages may contain criteria keyed to the recipient&#39;s or sender&#39;s context at the time the message is sent, the context of the subject of the message or the context when the message is to be delivered. Such criteria may include spatial criteria, for example, different media files are included in the message depending on the sender&#39;s or recipient&#39;s physical location at the time the message is sent or received. Such criteria may include temporal criteria, for example, different media files are included in the message depending on the time of day, the day of the week, or if it is the recipient&#39;s birthday. Such criteria may include social criteria, for example, different media files are included in the message depending on the recipient&#39;s favorite music. Such criteria may include topical criteria, for example, different media files are included in the message depending on the recipient&#39;s mood. 
     Content criteria may also contain any combination of criteria spatial, temporal, social or topical criteria that are unrelated to the recipient&#39;s or sender&#39;s context at the time the message is sent or delivered. For example, the message may include a criteria describing the type of media files to be delivered. For example, the criteria may define a playlist for songs in the top 10 in the 1970&#39;s whose lyrics reference New York City. 
     The embodiments of the present invention discussed below illustrate application of the present invention within a W4 COMN. Nevertheless, it is understood that the invention can be implemented using any networked system, virtual or real, integrated or distributed through multiple parties, that is capable of collecting, storing accessing and/or processing user profile data, as well as temporal, spatial, topical and social data relating to users and their devices. Thus, the term W4 COMN is used herein for convenience to describe a system and/or network having the features, functions and/or components described herein throughout. 
       FIG. 7  illustrates one embodiment of a data model showing how a W4 COMN can store media files and relate such files to RWEs, such as persons and places, and IOs, such as topics and other types of metadata. 
     In the illustrated embodiment, media is stored as media objects  710 . Media objects are passive IOs relating to media files containing audio content, visual content, or both. Such media files can contain content such as songs, videos, pictures, images, audio messages, phone calls, and so forth. The media objects themselves contain metadata  712 . Such data may be specific to the to the object data  710  and unrelated to any other IO or RWE. At the simplest level, such metadata may relate to basic file properties such as creation date, text or an image that is associated with a media file to which an IO relates. 
     Additionally, there are existing databases  720  which can reside within or outside of the network that can provide an extensive set of descriptive metadata relating to specific songs, videos and other types of media. For example, the Allmusic database (formerly the All Music Guide, owned by All Media Guide) provides metadata which includes:
         Basic metadata such as names, genres, credits, copyright information, product numbers.   Descriptive content such as styles, tones, moods, themes, nationalities, etc.   Relational content such as similar artists and albums, influences, etc.   Editorial content such as biographies, reviews, rankings, etc.       

     Other types of databases that can be used as sources for metadata relating to songs and video include:
         Historical billboard rankings at a local, regional, or national level, or on foreign billboards.   Music and video industry news.   Music lyrics.   Music blogs or industry data aggregation services   Music consumption transactions and trends data from both online and offline sources       

     In one embodiment, metadata originating from such databases can be extracted from source databases and embedded  712  in the media objects  710  themselves. Alternatively or additionally, the media objects may be related to IOs that contain or relate to metadata  740 . Metadata can include one or more keywords or topics that describe or classify data including rating or ranking information for one or more users. For example, an IO relating to metadata can be topics that relate to all songs within a genre, such as rock, or all songs performed at a specific festival, such as Woodstock. Topic IOs relating to metadata can be associated with IOs relating to higher level topics  742 . For example, a composer may be associated with a topic such as baroque music, which is itself associated with a higher-level IO for classical music. 
     Alternatively or additionally, a metadata server with its associated databases can be defined as an RWE  722  within the W4 COMN, and media objects and other IOs can be associated with the RWE  722 . In one embodiment, metadata relating to a media object can be retrieved on demand, rather than being stored in static metadata or in a persistent IO. Metadata retrieved on demand can be chosen based on needs of users who have a potential interest in the media object. For example, a user who initially selects a media object based on a topic can then retrieve metadata on demand relating to tone or mood of the music associated with the media object. 
     If a user wishes to select media objects using a topic for which no topics exists, for example, the top 10 hits in the U.K. in 1975, a metadata server which is capable of providing such information can be queried to retrieve a list of such songs. In one embodiment, the list of songs can be used to create an IO relating to a topic, such as IO  740 , by associating media objects relating to the list of songs with a newly created IO. In one embodiment, such an IO is created by a correlation engine within a W4 engine (see above and  FIGS. 4 through 6 , for example). The IO can then be used in subsequent queries. 
     In one embodiment, media objects are associated with other RWEs, such as musical rights holders  730  (i.e. owners and licensees), and interested listeners  750 . In one embodiment, where an owner  730  of a media object can be identified, an attribution engine within a W4 engine tracks the real-world ownership, control, publishing or other conditional rights of any RWE in any media IO whenever a new object is detected. 
     In one embodiment, users  750 ,  752 , and  754  can be identified as having an interest in a specific song  710  or a topic IO  740  or  742  by a correlation engine within a W4 engine. In one embodiment, the correlation engine identifies relationships between user RWEs and media or IOs relating to metadata by creating a combined graph of the RWEs and IOs and their attributes, relationships and reputations. For example, a user can explicitly state in a user profile that they have an interest in a specific musical artist. Alternatively, the correlation engine can determine a user&#39;s interest in a topic or a song or view based on the content of the user&#39;s interaction data, sensing attention events from any internal or external source including transaction history, online path and browsing history as well as physical real-world path and attention data. 
     In one embodiment, the W4 COMN builds a profile of a user over time by collecting data from the user or from information sources available to the network so as to gain an understanding of where they were born, where they have lived, and where they live today. Using social data, the W4 COMN can also create an overlapping social network profile which places the user in a temporal, geographic and social graph, thus determining where a user lived when and with whom. User RWEs can be also be associated with other RWEs through interaction data, co-location data or co-presence data. Users who are interested in the same time/place can declare their interests and be connected to a topic-based social network through, for example, an IO relating to that topic. In the illustrated embodiment in  FIG. 7 , users  750  and  752  are identified as being within a social network,  760 . 
     Thus, media objects can be stored and associated with temporal, spatial, social network and topical data derived from, without limitation, traditional metadata sources, user profile data, social networks, and interaction data, building a network of relationships across the universe of media and users. Such relationships may be built on demand, if necessary, or alternatively constantly updated based upon real-time receipt of a continuous stream of data related to the user, their proxies, declared and implied interests and the rest of the real and online worlds. Such relationships can then enable queries for media that satisfy the criteria of simple or complex contexts. 
       FIG. 8  illustrates one embodiment of a system  800  capable of supporting context-enhanced messaging between users known to a network. 
     The hub of the system is a W4 COMN  850  or similar network that provides data storage, processing, and real-time tracking capabilities. Within the W4 COMN are servers that provide context-based messaging facilities as will be described in greater detail below. The data relationships described in  FIG. 7  above are stored within the W4 COMN. In one embodiment, data relationships between all real world entities and logical data are stored in a global index within the W4 COMN  850  which is maintained by processes within the W4 COMN. 
     Media objects may be stored by servers within the W4 COMN  850 , may be stored in a distributed manner on end user devices, or may be stored by third party data providers  840 , or all of the above. Third party data providers  840  may provide additional data to the network  850 , such as metadata providers or social networking sites known to the network. 
     A message sender  802  who wishes to send a context enhanced message to a recipient enters a message with context criteria including delivery criteria and content criteria into a user proxy device  804  which transmits the message to the network  850 . The message is processed by servers within the network to add context specified content to the message and the enhanced message is delivered to the message recipient&#39;s  810  proxy device  812  under conditions satisfying the delivery criteria. 
     Real world entities which include the message sender  802 , the message recipient  810 , the message sender&#39;s and message recipient&#39;s proxy devices  804  and  812  respectively, the message sender&#39;s friends  826  and  830 , a retail location  820 , a restaurant  824  and a friend&#39;s home  828  are known to the network. For each of the entities, the network, without limitation, tracks the physical location of the entity, builds and stores profile data and stores and analyzes interaction data. The network also receives data from remote sensors  832 , which can include traffic sensors, GPS devices, weather sensors, video surveillance, cell towers, Bluetooth, Wi-Fi and so forth. 
       FIG. 9  illustrates one embodiment of a process of how a network containing temporal, spatial, and social network and topical data for a plurality of users, devices, and media, such as a W4 COMN, can be used to enable media enhanced messages having complex user contexts having delivery and content criteria. 
     The process begins when a message is received  910  from a message sender containing at least one recipient, and delivery criteria and content criteria. The message sender may enter the message, delivery and content criteria using any type of proxy device such as, for example, a portable media player, PDA, computer, or cell phone. The delivery criteria and the content criteria can be any combination of spatial, temporal, social or topical criteria. 
     In one embodiment, the criteria can be related to one another using standard relational or set operators. In one embodiment, the criteria can be stated as a natural language query. In one embodiment, criteria can be ranked in relative importance for each request and prioritized appropriately in the resulting population of enhanced content. The request can be regarded as containing, by default, criteria which specifies the requesting user (i.e. the request is taken from the point of view of the requesting user.) The message need not contain both delivery criteria and content criteria, but may contain only content criteria or only delivery criteria. Multiple recipients may also have multiple divergent or overlapping sets of content and delivery criteria. For example, a requesting user may send a message for a party with instructions for the system to enhance the content of the invitation with each recipients current favorite “party” mood music. Thus, each recipient may receive a different song added to their invitation, and yet each is created by the singular criteria request. 
     The process then determines if delivery criteria have been satisfied  920  using data available to the network, which includes network databases  922  and sensors  924 . Where delivery criteria are not initially met  930 , the process retains the message for a fixed length of time and periodically, or continuously reevaluates delivery criteria until delivery conditions are satisfied. The process can monitor any spatial, temporal, social or topical data known to the network using databases  920  and sensors  924  available to the network. 
     When delivery conditions are satisfied  930 , the process retrieves media related to content criteria  940 . The criteria are used to search, via the network, for user profile data, social network data, spatial data, temporal data and topical data that is available via the network  922  and  924  and relates to the content criteria and to media files so as to identify at least one media file that is relevant to the content criteria. The media files are then inserted into the message  950  and the message is then transmitted to the message recipient  960 . In alternative embodiments, media files related to the content criteria can be retrieved before delivery conditions are evaluated, and the message can be updated and transmitted when delivery conditions are satisfied. 
       FIG. 10  illustrates one embodiment of a context enhanced message engine capable of supporting the process illustrated in  FIG. 9 . 
     A context enhanced message engine  1000  resides on a server within the W4 COMN. The context query engine  1000  can be defined to the W4 COMN as an RWE, or alternatively, an active IO. The context query engine can be a component of a W4 engine, or, alternatively, may use services provided by components of a W4 engine or any of its constituent engines. 
     The context enhanced message engine  1000  includes: a content enhanced message receiving module  1100  that receives messages from message senders containing delivery and content criteria; a delivery criteria evaluation and tracking module  1200  that that determines if delivery criteria are satisfied and tracks data related to delivery criteria; a media retrieval module  1400  that retrieves media related to content criteria; a context enhanced message update module  1500  that inserts media files into messages; and a context enhanced message transmission module  1600  that transmits the updated messages to the intended recipient. Any of the aforementioned modules or the communications between modules (e.g. the delivery or context criteria) can be stored on computer readable media, for transient, temporary or permanent storage. 
     The coxtent enhanced message receiving module  1100  can provide a user interface for message senders to enter context enhanced message requests. The interface provided may be a graphical user interface displayable on mobile phones, gaming devices, computers or PDAs, including HTTP documents accessible over the Internet. Such interfaces may also take other forms, including text files, such as SMS, emails, and APIs usable by software applications located on computing devices. The interface provides for entry of delivery or content criteria that include spatial, temporal, social, or topical criteria. 
     In one embodiment, delivery and content criteria can be related to one another using standard relational or set operators. In one embodiment, temporal and spatial data obtained from sensors within user devices can be included in the delivery or content criteria. For example, the current location of a device associated with a user can be automatically identified and included in the criteria, the current time and date, etc. The message sender creating the context can be automatically identified through the association of the proxy device with a user within the network and automatically included in the context. 
     The delivery criteria evaluation and tracking module  1200  uses all data known to the network to evaluate delivery conditions. Such data may include network databases  1220  and real-time sensors  1240 . Sensor data can include data relating to the physical position of any real-world entity and can include the message sender and the message recipient as well as any other known RWEs who may be specified in the delivery conditions. The end user devices may contain positioning or other sensors that detect various aspects of the physical environment surrounding the user, such as, for example, the user&#39;s geographical location, altitude and directional vector. Sensors can also include other environmental sensors such as temperature and lighting sensors, or can also include biometric sensors such as heart-rate, brain waves, etc. 
     The delivery criteria may relate to any combination of spatial, temporal, social or topical data available to the network. In one embodiment, where delivery criteria are not immediately satisfied, the delivery criteria evaluation and tracking module  1200  tracks data related to the delivery criteria in the message. In one embodiment, the delivery criteria are periodically reevaluated. In another embodiment, data relating to delivery conditions are tracked in real-time, and a change in value triggers reevaluation of the delivery conditions. 
     For example, delivery criteria can specify that the message be processed at a future point in time, periodically, or on the occurrence of a specific event. For example, a delivery may specify that the message be reprocessed on the occurrence of a trigger condition, such as hourly, when the physical location of the entity associated with the delivery condition changes, when a calendared event occurs (e.g. an anniversary), when a news event occurs (e.g. a favorite sports team wins a game), where a spatial, social, temporal or topical intersection occurs (e.g. when two or more friends arrive at favorite bar to watch football) or when the sender&#39;s or recipient&#39;s mood changes. 
     The media retrieval module  1400  searches one or more network databases  1220  and sensors  1240 , for user profile data, social network data, spatial data, temporal data and topical data that is available via the network and relates to the context and to media files so as to identify at least one media file that is relevant to the content criteria. Such searches are performed using the capabilities of the network databases  1220  and their supporting infrastructure. 
     In one embodiment, the criteria are interpreted to take advantage of the best available data within the network. For example, if data relevant to the context resides on a relational database, the query module can execute a series of SQL statements for retrieving data from a relational database or a procedural language containing embedded SQL. Queries may be nested or otherwise constructed to retrieve data from one set of entities, and to use the result set to drive additional queries against other entities, or to use recursive data retrieval. 
     In the case of a W4 COMN, the content criteria can be mapped and represented against all other known entities and data objects in order to create both a micro graph for every entity as well as a global graph that relates all known entities with one another, and media objects relevant to the context are thereby identified. In one embodiment, such relationships between entities and data objects are stored in a global index within the W4 COMN. 
     Where query criteria relate to simple descriptive matter, such as date and time of creation, relationships can be identified using metadata embedded in media objects. Where criteria relate to a topic, such as a genre of music, relationships can be identified through IOs (whether currently existing or dynamically generated) relating to the topic which may then be used to identify media objects associated with the topic. 
     Where criteria relate to relationships between two or more IOs or RWEs, such as all friends of a particular user, related IOs and RWEs can be identified using social network relationships supported by the W4 COMN. When a specific media object is selected, the media search module can further determine if the message recipient or the message recipient&#39;s proxy receiving the context is permitted to access the content of the media file using ownership data in or associated with the media object. 
     The context enhanced message update module  1500  can update the context enhanced message in any manner that allows the message recipient to access the selected media files. In one embodiment, the actual media files are inserted into the message and open or begin playing upon opening of the enhanced message by the recipient. In one embodiment, the inserted files comprise links to the media files. In one embodiment, the media files comprise one or more playlists of multiple objects or files. In an alternative implementation, the content criteria are inserted into the message and are not evaluated until the message recipient opens the message. In one such embodiment, media retrieval module  1400  does not process the content criteria until the message recipient opens the message. 
     The context enhanced message transmission module  1600  can transmit message to a single recipient or a group of recipients having a set of characteristics that define a finite set users known to the network. For example, a message may be sent to users in the sender&#39;s social network that are single and like rock music, or to fans of last night&#39;s band, who were at the show and also have their own blog. 
     In one embodiment, an enhanced message can contain an advertisement with enhanced content criteria relating to the advertisement. Thus, an advertisement may supplement basic ad content with media tailored for a specific user. For example, an advertisement for a sports car may be associated with a context specifying the users favorite musical artist and songs with a fast tempo or explicit references to speed, or the year 1975 when the user last owned a sports car. 
     Context enhanced messages can provide additional data on message senders and recipients to the network. Gathering interaction data contained in context enhanced messages on the preferences and communications patterns between individuals and groups can provide data that can inform advertiser communications to the pair or group or the individuals within that pair or group. For example, a context enhanced message may reveal a user loves butterflies, and an advertiser with butterfly themed merchandise may wish to contact the user. 
     Specific Examples of Contexts 
     The disclosure will now discuss specific examples of the above principles. The examples given below are intended to be illustrative, and not limiting. 
     In one example, if a user wished to send a message containing a song which automatically plays for a recipient at a specified time (e.g. a musical wakeup call.), the message sender could create a message having a delivery criteria of a specific time and a content criteria specifying a specific song. The delivery criteria evaluation and tracking module would track the current time and pass the message on to the media retrieval module for processing when the specified time arrives. The media retrieval module would retrieve the specific song for insertion into the message. 
     In another example, if a message sender wished to send a message that plays a media object when the recipient reaches a specific location, (e.g. a message that plays a shopping list audio file when the recipient reaches the grocery store), the message sender could create a message having a delivery criteria that specifies delivery when the recipient is in a specific location and a content criteria specifying a specific media object. The delivery criteria evaluation and tracking module would track the recipient&#39;s current location and pass the message on to the media retrieval module for processing when the user arrives at the location. The media retrieval module would retrieve the specific media object for insertion into the message. 
     Alternatively, a message sender may wish to send a message containing a romantic song that plays when the recipient drives past a place that has a romantic meaning to the sender and recipient, such as where they were engaged. The message sender could create a message having delivery criteria that specify delivery when the recipient is in a specific location and a content criteria specifying a romantic song. The delivery criteria evaluation and tracking module would track the recipient&#39;s current location and pass the message on to the media retrieval module for processing when the user arrives at the location. The media retrieval module could then search, for example, the recipient&#39;s profile data and recent playlists for songs having metadata indicating a romantic or sentimental song. 
     In another example, a message sender wishes to send a message including an event-based set of tracks to play for the recipient which varies based on actions or locations of the recipient at various times of the day. A message sender could create a message wherein the delivery criteria specify delivery on certain days, such as the recipient&#39;s birthday, and content criteria that specify different sets of tracks for breakfast, lunch, and dinner. Content criteria could further specify that the tracks that are played vary with the recipient&#39;s location. For example, content criteria could specify one track if the recipient goes to a That restaurant for lunch, and another track if the recipient eats at a French restaurant. 
     In each case, the delivery criteria evaluation and tracking module would track the current date and time, as well as the recipient&#39;s current location and pass the message on to the media retrieval module for processing when the delivery criteria are satisfied. The media retrieval module could then search, for example, for media satisfying content criteria, such as, for example, music that relates to food and Thailand or France. 
     A sender may wish to emote using context enhanced messaging. The message sender could create a message for immediate delivery that specifies content criteria that selects a song reflecting the sender&#39;s current mood. The media retrieval module could attempt to determine the sender&#39;s current mood by scanning text in their recent emails and text messages. The media retrieval module could then select a song from the sender&#39;s favorite songs (e.g. in the user&#39;s profile, or most frequent historical play&#39;s) that has associations suggesting it is responsive to that mood. 
     Such a context could define a push or a pull operation. The sender may wish to express his mood to his fiancé with a media enhanced message the system sends on his behalf as described above, or, alternatively, the sender&#39;s fiancé may wish to poll his mood. For example, the sender&#39;s fiancé could send herself a message that contains content criteria that specifies her fiancé&#39;s mood. For example, if he misses her, the system could respond with a song that expresses that emotion in both of them. 
     In another example, suppose a message sender wishes to send a message incorporating text matching of lyrics of songs in order to express an emotional connection between the sender and the recipient. For example, a sender knows that the recipient loves butterflies. The message sender could create a message for immediate delivery with content criteria specifying a romantic song with lyrics containing the word “butterfly.” The media retrieval module could then search, for example, songs whose lyrics includes “butterfly” or “butterflies” and having metadata indicating a romantic or sentimental song and then rank them personally for this specific user based upon their user profile and past consumption data. 
     Those skilled in the art will recognize that the methods and systems of the present disclosure may be implemented in many manners and as such are not to be limited by the foregoing exemplary embodiments and examples. In other words, functional elements being performed by single or multiple components, in various combinations of hardware and software or firmware, and individual functions, may be distributed among software applications at either the client level or server level or both. In this regard, any number of the features of the different embodiments described herein may be combined into single or multiple embodiments, and alternate embodiments having fewer than, or more than, all of the features described herein are possible. Functionality may also be, in whole or in part, distributed among multiple components, in manners now known or to become known. Thus, myriad software/hardware/firmware combinations are possible in achieving the functions, features, interfaces and preferences described herein. Moreover, the scope of the present disclosure covers conventionally known manners for carrying out the described features and functions and interfaces, as well as those variations and modifications that may be made to the hardware or software or firmware components described herein as would be understood by those skilled in the art now and hereafter. 
     Furthermore, the embodiments of methods presented and described as flowcharts in this disclosure are provided by way of example in order to provide a more complete understanding of the technology. The disclosed methods are not limited to the operations and logical flow presented herein. Alternative embodiments are contemplated in which the order of the various operations is altered and in which sub-operations described as being part of a larger operation are performed independently. 
     While various embodiments have been described for purposes of this disclosure, such embodiments should not be deemed to limit the teaching of this disclosure to those embodiments. Various changes and modifications may be made to the elements and operations described above to obtain a result that remains within the scope of the systems and processes described in this disclosure.