Intertrust Technologies Corporation

Citrix Systems, Inc.

Jiawen Su

Yiming Ma

Janardhanan Jawahar

Shaunak Mistry

Prashant Batra

Abbas Mahyari

Systems and methods for recognizing and/or predicting activities of a user of a mobile device are disclosed. In certain embodiments, the systems and methods may predict a future activity and/or location of a mobile device user based on current and/or historical device data and/or other personal information relating to the user. In some embodiments, probabilistic determinations and/or other statistical models may be used to predict future activities and locations of a mobile device user. The disclosed systems and methods may further utilize location and/or activity recognition and/or prediction methods to deliver personalized services to a user of a mobile device at a particular time and/or location.

Activity recognition systems and methods

A user device obtains a set of one or more public key certificates for a server received from a directory service, and a current public key certificate of the server received from the server. The user device compares the current public key certificate received from the server with the set of public key certificates received from the directory service. If the current public key certificate of the server matches one of the public key certificates in the set of public key certificates for the server, the authenticity of the server is confirmed, and communications are permitted between the user device and the server. Communications between the user device and the server may be prevented unless the current public key certificate from the server matches a public key certificate in the set of public key certificates received from the directory service.

Certificate pinning using a directory service

Methods, systems, computer-readable media, and apparatuses may provide a mobile device with the ability to locally detect fraudulent activity by an unauthorized user. A mobile device may include a fraud detection module that may build a historical usage pattern of a user of the mobile device. The fraud detection module may monitor usage of the mobile device for multiple parameters and record events pertaining to the parameters. Periodically or in response to each event, the fraud detection module may compute a current usage pattern using each of the current parameter values. The fraud detection module may compare the current usage pattern with the historical usage pattern and may use the comparison result to compute a confidence score. The fraud detection module may then compare the confidence score with a preset confidence score to determine whether fraudulent activity is occurring at the mobile device.

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Sunnyvale, CA, United States of America

Jiawen Su