Context-induced wireless network transport for content and profile information

#The present disclosure relates generally to wireless networks and more particularly to context-induced information and content transport in wireless networks.

#Referring now to FIG. 1, a functional block diagram of exemplary Internet connectivity is shown. A distributed communications system, such as the Internet 102, is connected to by various mobile phone provider networks, such as those shown at 104-1 and 104-2. In addition, Internet service providers, such as Internet service provider 106 connect to the Internet 102.

#Mobile devices 108 connect to the mobile phone provider networks 104. Mobile devices 108-1 and 108-2 communicate with the mobile phone provider network 104-1. The mobile devices 108-1 and 108-2 communicate with each other and access the Internet 102 via the mobile phone provider network 104-1. Similarly, mobile devices 108-3, 108-4, and 108-5 communicate with the mobile phone provider network 104-2. The Internet service provider 106 provides connectivity to the Internet 102 for a desktop computer 110 and a laptop computer 112. A wireless access point (not shown) schematically located between the Internet service provider 106 and the laptop 112 provide wireless connectivity to the laptop computer 112.

#The present disclosure will become more fully understood from the detailed description and the accompanying drawings, wherein:

#FIG. 1 is a functional block diagram of exemplary Internet connectivity according to the prior art;

#FIG. 2A is an information flow diagram presents a traditional mode of advertising content delivery according to the prior art;

#Context-aware content delivery is being actively investigated and implemented by a number of electronic and online advertising delivery companies, such as Google Inc. and Yahoo! Inc. Their Internet-based approaches rely either on voluntary input from the consumer or on tracking web browsing habits to profile consumers' interests for targeted content delivery. These approaches are incomplete in that they are not able to infer a consumer's interest level by observing behavioral context from the consumer's day-to-day activities and inter-personal interaction. Internet-based approaches are not aware of a consumer's community interactions, such as at home, work, gym, malls, restaurants, etc.

#When consumers carry mobile devices, such as mobile phones, their location is tracked. The mobile phones determine position using global positioning system (GPS), Wi-Fi hot spot detection, and mobile communications tower triangulation. A database could then be created that maps position to buildings, such as homes, stores, and restaurants. In addition, if date and time information is incorporated into the database, individual events are identified. For example, when a mobile device is located in a stadium at one time, the consumer may be participating in a cheerleading competition. However, at a different time, the consumer may be watching a football game in the same location.

#This approach, however, is still limited by the client-server paradigm. Although the locations and attendance records of individual consumers can be analyzed by a server, the social interaction of consumers in close proximity to each other is not being leveraged. Further, any commonality of interests between interacting consumers is difficult to analyze in a client-server model.