United Services Automobile Association (usaa)

Southwest Research Institute

Brian Keith Anderson

Maland Keith Mortensen

Alexander B Nagelberg

Joseph Gregory Delong

David Lindley

Shantanu K Tripathy

Kasey T Moore

Zachary T Golden

Kevin D Love

Ryan N Wilson

Wayne D Music

John Whipple

Theodore R Wilmes

Methods and systems described in this disclosure electronically notarize a document. The system can receive biometric information from a user, extract characteristics from the biometric information, and compare the characteristics of the biometric information with previously stored characteristics of the user's biometric information. When the characteristics of the biometric information match the previously stored characteristics to a threshold, the system can create an identity of the user using the characteristics of the biometric information. The system can send a document to the user for cryptographic signature and receive an indication that the document has been signed. The cryptographic signature can be generated with a digest of the document, the identity, and a cryptographic key associated with the user. The system can inspect the digest of the document, the cryptographic key, and the identity associated with the document to verify authenticity of the document and the identity of the user.

Virtual notarization using cryptographic techniques and biometric information

A system, method and computer program product for signal emitter identification using Higher-Order Cumulants (HOC) is presented. At least one transient signal is received from an emitter when the emitter turns on. The at least one transient signal is processed using at least one Higher-Order Cumulant (HOC) to provide a set of entries for a vector table. A feature vector is derived from the vector table, the feature vector identifying the emitter. The feature vector is used to identify an emitter.

System and method for signal emitter identification using higher-order cumulants

An apparatus and method for estimating a location of an object within a mobile reference frame. Sensor data is received from an accelerometer associated with an object followed by determining from the sensor data that the mobile reference frame is executing one of a set of predetermined maneuvers. In response to such determination: (1) the sensor data is applied to a mathematical model associated with the executed maneuver, the model trained with previously obtained data from one or more reference accelerometers positioned at known locations within the mobile reference frame and estimating the location of the object by applying the sensor data to the mathematical model; and/or (2) incorporating reference accelerometers in the mobile reference frame and comparing the sensor data with the reference accelerometers and estimating the location of the object.

Autonomous location of objects in a mobile reference frame

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San Antonio, TX, United States of America

Brian Keith Anderson