Affectiva, Inc.

Youssef Kashef

Rana El Kaliouby

Niels Haering

Ahmed Adel Osman

Viprali Bhatkar

Boisy G Pitre

File system manipulation using machine learning is described. Access to a machine learning system is obtained. A connection between a file system and an application is structured. The connection is managed through an application programming interface (API). The connection provides two-way data transfer through the API between the application and the file system. The connection provides distribution of one or more data files through the API. The connection provides enablement of processing of the one or more data files. The processing uses classifiers running on the machine learning system. Data files are retrieved from the file system connected through the interface. The file system is network-connected to the application through the interface. The data files comprise image data of one or more people. Cognitive state analysis is performed by the machine learning system. The application programming interface is generated by a software development kit (SDK).

File system manipulation using machine learning

Video of one or more vehicle occupants is obtained and analyzed. Heart rate information is determined from the video. The heart rate information is used in cognitive state analysis. The heart rate information and resulting cognitive state analysis are correlated to stimuli, such as digital media, which is consumed or with which a vehicle occupant interacts. The heart rate information is used to infer cognitive states. The inferred cognitive states are used to output a mood measurement. The cognitive states are used to modify the behavior of a vehicle. The vehicle is an autonomous or semi-autonomous vehicle. Training is employed in the analysis. Machine learning is engaged to facilitate the training. Near-infrared image processing is used to obtain the video. The analysis is augmented by audio information obtained from the vehicle occupant.

Autonomous vehicle control using heart rate collection based on video imagery

Video of one or more people is obtained and analyzed. Heart rate information is determined from the video. The heart rate information is used in mental state analysis. The heart rate information and resulting mental state analysis are correlated to stimuli, such as digital media, which is consumed or with which a person interacts. The heart rate information is used to infer mental states. The inferred mental states are used to output a mood measurement. The mental state analysis, based on the heart rate information, is used to optimize digital media or modify a digital game. Training is employed in the analysis. Machine learning is engaged to facilitate the training.

Mental state mood analysis using heart rate collection based on video imagery

Video of one or more people is obtained and analyzed. Heart rate information is determined from the video and the heart rate information is used in mental state analysis. The heart rate information and resulting mental state analysis are correlated to stimuli, such as digital media which is consumed or with which a person interacts. The heart rate information is used to infer mental states. The mental state analysis, based on the heart rate information, can be used to optimize digital media or modify a digital game.

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Obour, Egypt

Youssef Kashef