Xad, Inc.

Guoxin Li

Can Liang

Pravesh Katyal

Yilin Chen

A system coupled to a packet-based network is configured to predict the locations of one or more mobile devices communicating with the packet-based network. The system comprises a request processor configured to detect location events associated with mobile devices communicating with the packet-based network, each location event corresponding to a time stamp and identifying a geo-place in a geo database. The system further comprises an off-line prediction subsystem configured to train a plurality of off-line prediction models and an on-line prediction model using various historical location events. The off-line prediction subsystem is further configured to generate off-line prediction results corresponding to the off-line prediction models. The system further comprises an on-line prediction subsystem configured to construct a feature vector using the off-line prediction results and recently detected location events in response to a request received in real-time, and to generate on-line prediction results by applying the on-line prediction model to the feature vector.

System and method for real-time prediction of mobile device locations

Systems and methods for real-time prediction of mobile device locations

A system coupled to a packet-based network is configured to predict the locations of mobile devices that have communicated with the packet-based network. The system includes a request processor configured to detect location events associated with mobile devices communicating with the packet-based network, each location event corresponding to a time stamp and identifying a geo-place in a geo database. The geo-places include geo-blocks and geo-fences. The system further comprises a location prediction subsystem configured to construct first feature space using first location events and second feature space using second location events, and to extract a set of labels from third location events. The location prediction subsystem is further configured to train a prediction model using the first feature space and the set of labels, and to apply the prediction model to the second feature space to obtain prediction results.

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Mountain View, CA, United States of America

Guoxin Li