Intertrust Technologies Corporation

Google Inc.

Nokia Corporation

Nokia Technologies Oy

Yiming Ma

Ramakrishnan Srikant

Roberto Javier Bayardo

Jiawen Su

Dennis Su

David P Maher

Rudi Cilibrasi

David L Racz

Irfan Mohammed

Rich Hankins

Julien Bellanger

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

The present disclosure relates generally to systems and methods for the secure management of electronic information relating to a user. In certain embodiments, systems and methods disclosed herein may allow for personal information related to a user to be used in connection with information targeting systems and methods configured to match and/or target information for delivery to a user based on the user's interests. In some embodiments, personal information relating to a user may be stored and/or managed in a personal ontology graph or other data structure including, among other things, various inferred interests of the user derived from available personal information.

Information targeting systems and methods

This disclosure relates to systems and methods for managing resources of a mobile device based on a state of the device. In certain embodiments, systems and methods disclosed herein may adjust utilization of one more device systems and/or sensors based on a state of a device to better utilize device power or other resources. In further embodiments, systems and methods disclosed herein may utilize one or more power-efficient systems to determine when a device is in a transitory state and to schedule precise location determination events at times when the device is more likely to be located at a point of interest.

Portable resource management systems and methods

Techniques to derive or predict location context for a user of a mobile device include receiving signal data that indicates a set of one or more distinct signal sources from which signals are received at the mobile device for each of multiple different times. The method further comprises determining whether the mobile device is moving outside a specified area at a current time based on the signal data. The method further comprises incrementing a count for a stationary state associated with the set of one or more distinct signal sources at the current time, if the mobile device is determined to be not moving outside the specified area. The method also comprises delivering a service to the mobile device based on the stationary state.

Method and apparatus for incrementally determining location context

Pairs of similar objects in a population of objects can be found using a process that includes identifying a comparison vector x in a set of vectors having non-zero features, determining an estimated similarity contribution of a subset of features of the comparison vector x to a similarity between the comparison vector x and each vector in the set of vectors, generating an index that includes features based on a comparison of the similarity contribution with a similarity threshold, and identifying another vector in the set that is similar to the vector x using the index.

Similarity-based searching

Pairs of similar vectors in a set of vectors are identified. A comparison vector x is identified, and a set of candidate vectors corresponding to the vector x is identified. For each candidate vector y in the set, a similarity estimate between the comparison vector x and the candidate vector y is determined; if the similarity estimate meets a similarity threshold, a similarity score of the comparison vector x and the candidate vector y is determined; and if the similarity score meets the similarity threshold, the pair of vectors (x, y) is included in a list of similar pairs of vectors.

Pairs of similar vectors (or objects) in a set of vectors (or objects) are identified. A comparison vector x in a set of vectors is identified; a size threshold is determined such that if a similarity between the vector x and a vector y in the set of vectors is equal to or greater than a similarity threshold, then the vector y has a size at least equal to the size threshold, the size of the candidate vector y being determined based on a number of non-zero features in the vector y. A vector having a size less than the size threshold is removed from the set of candidate vectors.

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Fremont, CA, United States of America

Yiming Ma