Abstract:
Some embodiments of the invention provide a novel prediction engine that (1) can formulate predictions about current or future destinations and/or routes to such destinations for a user, and (2) can relay information to the user about these predictions. In some embodiments, this engine includes a machine-learning engine that facilitates the formulation of predicted future destinations and/or future routes to destinations based on stored, user-specific data. The user-specific data is different in different embodiments. In some embodiments, the stored, user-specific data includes data about any combination of the following: (1) previous destinations traveled to by the user, (2) previous routes taken by the user, (3) locations of calendared events in the user's calendar, (4) locations of events for which the user has electronic tickets, and (5) addresses parsed from recent e-mails and/or messages sent to the user. In some embodiments, the prediction engine only relies on user-specific data stored on the device on which this engine executes. Alternatively, in other embodiments, it relies only on user-specific data stored outside of the device by external devices/servers. In still other embodiments, the prediction engine relies on user-specific data stored both by the device and by other devices/servers.
Abstract:
Apps may be tagged with location data when they are used. Mobile device may anonymously submit app usage data. Aggregated app usage data from many mobile devices may be analyzed to determine apps that are particularly relevant to a given location (i.e., exhibiting a high degree of localization). Analysis may include determining the app usage intensity, whether hotspots exist or not at a given location, the spatial entropy of a particular app, the device populations in a particular area, etc. Based on the localized app analysis, apps may be ranked according to local relevance, and, based on this ranking, app recommendations may be provided to a user.