Abstract: A system and method are provided for maintaining a historical record of anonymized user profile data for mobile device users. In one embodiment, a central system, which includes one or more servers, operates to obtain current locations and user profiles for users of mobile devices. The central system processes the current locations and the user profiles of the users over time to maintain a historical record of anonymized user profile data by location. By anonymizing the user data, privacy of the users of the mobile devices is maintained. The central system may then use the historical record of anonymized user profile data to respond to historical requests. The historical requests may be made by users of the mobile devices, subscribers, and/or third party services.

Abstract: Systems and methods are provided for following status updates of users. In one embodiment, a requestor selects a crowd of users. In response, one or more crowds of users that match the selected crowd of users to at least a predefined threshold degree are identified. Status updates sent by users in at least one of the one or more matching crowds of users are then delivered to the requestor. In another embodiment, a requestor selects a Point of Interest (POI). In response, one or more POIs that match the selected POI to at least a predefined threshold degree are identified. Status updates sent by users located at one or more POIs of the one or more matching POIs are delivered to the requestor.

Abstract: A system and method that maintain current locations for users of a number of mobile devices and compute confidence levels for the current locations of the users are provided. In one embodiment, the confidence levels of the users are computed as a function of amounts of time since location updates were received for the users. In addition, detection of location confidence events may be used to modify the confidence levels for the current locations of the users. The location confidence events may include positive location confidence events that result in an increase in the confidence levels of the current locations of corresponding users, negative location confidence events that result in a decrease in the confidence levels of the current locations of corresponding users, or both. Once computed, the confidence levels of the current locations of the users may then be utilized in a desired manner.

Abstract: Systems and methods are disclosed for forming crowds of users based on wireless contexts of corresponding mobile devices of the users. In general, wireless contexts of mobile devices of a number of users are obtained. For each mobile device, the wireless context of the mobile device includes a wireless Personal Area Network (PAN) context of the mobile device, a wireless Local Area Network (LAN) context of the mobile device, or both. The wireless contexts of the mobile devices of the users are then utilized to form crowds of users. More specifically, in one embodiment, users of mobile devices having sufficiently similar wireless contexts are determined to be in the same crowd of users.

Abstract: Systems and methods are disclosed for providing a Graphical User Interface (GUI) for representing a reference item and a number of items of interest. In one embodiment, each item of interest is assigned to one of a number of concentric regions in a two-dimensional space based on one or more attributes of the item of interest. The concentric regions in the two-dimensional space are centered at a location in the two-dimensional space that corresponds to the reference item. A GUI is then generated such that the GUI includes concentric display regions that correspond to the concentric regions in the two-dimensional space, where a select concentric display region provides an expanded view of the items of interest located within the corresponding region in the two-dimensional space and the remaining concentric display region(s) provide collapsed view(s) of the items of interest in the corresponding region(s) of the two-dimensional space.

Abstract: A system and method for predicting a future location of a user are provided. In general, a prediction basis including crowd data describing crowds in which a user was included during a number of reoccurring time windows is generated. Each reoccurring time window may be, for example, a day of the week (i.e., Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday), a portion of a day of the week (e.g., Monday Morning, Monday Afternoon, Monday Evening, Tuesday Evening, etc.), a day of the month (e.g., 1st day of the month), a day of the year (e.g., March 25th), or the like. The prediction basis of the user is then used to predict one or more future locations of the user.

Abstract: Systems and methods are disclosed for forming crowds of users based on wireless contexts of corresponding mobile devices of the users. In general, wireless contexts of mobile devices of a number of users are obtained. For each mobile device, the wireless context of the mobile device includes a wireless Personal Area Network (PAN) context of the mobile device, a wireless Local Area Network (LAN) context of the mobile device, or both. The wireless contexts of the mobile devices of the users are then utilized to form crowds of users. More specifically, in one embodiment, users of mobile devices having sufficiently similar wireless contexts are determined to be in the same crowd of users.

Abstract: Systems and methods are disclosed for creating a user profile for a subject user based on historical aggregate profile data for locations at which the subject user was previously located. In one embodiment, one or more location and time period pairs are determined for the subject user, each defining a previous location of the subject user and a time period during which the subject user was at the location. For a plurality of location and time period pairs, a plurality of historical aggregate profiles for a corresponding plurality of sub-bands within the time period defined by each location and time period pair in the plurality of location and time period pairs is obtained. A user profile for the subject user is created based on the plurality of historical aggregate profiles for the corresponding plurality of sub-bands based on relevancy ratings for the plurality of sub-bands.

Abstract: Systems and methods relating to location-based applications, such as widgets, are disclosed. In general, in one embodiment, location-based application service obtains a current location of a mobile device and determines whether the current location of the mobile device is associated with one of a plurality of non-adjacent geographic locations associated with an establishment. An application identifier associated with the plurality of non-adjacent geographic locations associated with the establishment is provided based on the determination, where the application identifier at least one of identifies an application for the mobile device to activate and references a remote source for the mobile device to obtain the application.

Abstract: Systems and methods are disclosed for monitoring hashtags in micro-blog posts, or status updates, from users to provide one or more crowd-based features. In one embodiment, a micro-blog post of a user in a crowd of users is obtained. The crowd of users is a group of users within a predefined spatial proximity to one another. A hashtag in the micro-blog post is then obtained, and a crowd-based feature is provided based on the hashtag.

Abstract: Systems and methods for providing passive crowd-sourced alternate route recommendations. In one embodiment, locations of users of a number of mobile location-aware devices are tracked over time. Upon receiving a request, users of mobile location-aware devices that have traveled from a desired start location to a desired stop location are identified. Location histories for the identified users are analyzed to determine one or more different routes taken from the desired start location to the desired stop location. The one or more different routes, or a select subset thereof, are then returned to the requestor as recommended alternate routes.

Abstract: A system and method for predicting a future location of a user are provided. In general, a prediction basis including crowd data describing crowds in which a user was included during a number of reoccurring time windows is generated. Each reoccurring time window may be, for example, a day of the week (i.e., Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday), a portion of a day of the week (e.g., Monday Morning, Monday Afternoon, Monday Evening, Tuesday Evening, etc.), a day of the month (e.g., 1st day of the month), a day of the year (e.g., March 25th), or the like. The prediction basis of the user is then used to predict one or more future locations of the user.

Abstract: Systems and methods relating to location-based applications, such as widgets, are disclosed. In general, in one embodiment, a mobile device obtains a list of applications associated with a current or anticipated location of the mobile device from a centralized or distributed application service. One or more desired applications to be activated are either manually or automatically selected from the list of applications associated with the current location or the anticipated location of the mobile device. The mobile device then activates the one or more desired applications. Optionally, for each desired application that has been activated, the desired application may be deactivated once one or more deactivation criteria are satisfied.

Abstract: Systems and methods are disclosed for monitoring hashtags in micro-blog posts, or status updates, from users to provide one or more crowd-based features. In one embodiment, a micro-blog post of a user in a crowd of users is obtained. The crowd of users is a group of users within a predefined spatial proximity to one another. A hashtag in the micro-blog post is then obtained, and a crowd-based feature is provided based on the hashtag.

Abstract: The present disclosure relates to forming crowds of users taking into account known physical boundaries. In general, current locations of a number of users are obtained. A crowd of users is then formed based on the current locations of the users while taking into account one or more known physical boundaries such that the crowd does not include spatially proximate users on opposite sides of the one or more known physical boundaries. By utilizing known physical boundaries in a spatial crowd formation process, users that are spatially proximate to one another but are separated by a physical boundary are not included in the same crowd. In this manner, the spatial crowd formation process provides accurate and meaningful crowd formation in environments such as, but not limited to, buildings with multiple rooms, shopping malls, or the like.

Abstract: Systems and methods are disclosed for creating a user profile for a subject user based on historical aggregate profile data for locations at which the subject user was previously located. In one embodiment, one or more location and time period pairs are determined for the subject user. Each location and time period pair defines a previous location of the subject user and a time period during which the subject user was at the previous location. Historical aggregate profile data is obtained for the location and time period pairs. For each location and time period pair, the historical aggregate profile data is generally an aggregation of user profiles of a number of users relevant to the location and time period pair. A user profile for the subject user is then created based on the historical aggregate profile data for the one or more location and time period pairs.

Abstract: Contiguous location based user networks are disclosed herein. A method includes receiving an identification of an area of interest associated with a user of, and transmitting the identification of the area of interest to a server. The method includes determining a location of the user device and transmitting the location to the server. The method includes receiving information identifying a proximity group comprising the user and a plurality of other users. The proximity group includes least one other user being within a proximate area of the location of the user device and having an area of interest corresponding to the area of interest of the user. The proximate area corresponds to a second geographic region smaller than the area of interest. The proximity group further comprising at least one additional other user identified as a neighbor of each of the user and the at least one other user.

Abstract: A system and method are provided for maintaining a historical record of anonymized user profile data for mobile device users. In one embodiment, a central system, which includes one or more servers, operates to obtain current locations and user profiles for users of mobile devices. The central system processes the current locations and the user profiles of the users over time to maintain a historical record of anonymized user profile data by location. By anonymizing the user data, privacy of the users of the mobile devices is maintained. The central system may then use the historical record of anonymized user profile data to respond to historical requests. The historical requests may be made by users of the mobile devices, subscribers, and/or third-party services.

Abstract: Systems and methods are provided for maintaining a dynamic profile slice of a user profile of a user. In one embodiment, a real-time user-generated context of the user is monitored over time to accumulate keywords in the dynamic profile slice of the user that are representative of dynamic interests of the user. Weights are assigned to the keywords in the dynamic profile slice using a time and/or location weighting function.

Abstract: A system and method are provided for creating location-based user networks. In general, a proximity group including a number of users is identified. Each user in the proximity group is within a proximate area of at least one other user in the proximity group and has an area of interest. The areas of interest of the users in the proximity group are aggregated to provide an aggregate area of interest for the proximity group. Other users within the aggregate area of interest are identified as neighbors of each of the users in the proximity group. Once the neighbors are identified, each of the users in the proximity group may use the neighbors as members, or potential members, for a user network.