Excalibur IP, LLC

Oath Inc.

Accumente, LLC

Senaka Buthpitiya

Christian Holz

Haojian Jin

Rajiv Ayyangar

Marius Knaust

Miteshkumar Patel

Ian R Lane

Jike Chong

A user is authenticated based on feature data of a target such as a body-part or other object obtained by a touchscreen of a computing device. When the user positions the target to interact with the touchscreen, interaction data is gathered. Feature data of the target is determined from the gathered interaction data. The feature data is used to identify one or more of the target and the user. Various actions are executed based on the identification and authentication of the user.

User authentication and data encryption

Disclosed are systems and methods for improving interactions with and between computers in an authentication system supported by or configured with authentication servers or platforms. The systems interact to identify access and retrieve data across platforms, which data can be used to improve the quality of results data used in processing interactions between or among processors in such systems. The disclosed anti-spoofing systems and methods provide improved functionality to facial recognition systems by enabling enhanced 'spoof' (or attempts to impersonate a user) detection while authenticating a user. The disclosed systems and method provide additional functionality to existing facial recognition systems that enables such systems to actually determine whether the image being captured and/or recorded is that of an actual person, as opposed to a non-human representation.

Computerized system and method for determining authenticity of users via facial recognition

A method of optimizing the calculation of matching scores between phone states and acoustic frames across a matrix of an expected progression of phone states aligned with an observed progression of acoustic frames within an utterance is provided. The matrix has a plurality of cells associated with a characteristic acoustic frame and a characteristic phone state. A first set and second set of cells that meet a threshold probability of matching a first phone state or a second phone state, respectively, are determined. The phone states are stored on a local cache of a first core and a second core, respectively. The first and second sets of cells are also provided to the first core and second core, respectively. Further, matching scores of each characteristic state and characteristic observation of each cell of the first set of cells and of the second set of cells are calculated.

Utilizing multiple processing units for rapid training of hidden markov models

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Mountain View, CA, United States of America

Senaka Buthpitiya