Senseye, Inc.

Seth Weisberg

Joseph Brown

David Bobbak Zakariaie

Jared B Bowden

Andrew R Sommerlot

Patricia Herrmann

Alexander Rowe

Taumer Anabtawi

Kathryn McNeil

Veronica Choi

Derrik Asher

Jacqueline Parzivand

Steven Thurman

Kyle Grier

A method of discovering relationships between iris physiology and cognitive states and/or emotional states of a subject includes providing a computing device and a video camera to record a close-up view of the subject's eye. A first light is held to the lower eyelid skin and a second light a distance away illuminating the stroma of the eye. The first and second light are electronically synced together and configured to flash alternatively. The user engages in a plurality of tasks while recording ocular information which is processed to identify correlations between the responses in the iris musculature and the distortions in the stroma through the use optimized algorithms. One can then identifying at least one predictive distortion is identified in the stroma capturable solely with a visible-spectrum camera correlating to a predicted responses in the iris musculature.

Transillumination of iris muscles to infer stroma deformation

A method to measure a cognitive load based upon ocular information of a subject includes the steps of: providing a video camera configured to record a close-up view of at least one eye of the subject; providing a computing device electronically connected to the video camera and the electronic display; recording, via the video camera, the ocular information of the at least one eye of the subject; processing, via the computing device, the ocular information to identify changes in ocular signals of the subject through the use of convolutional neural networks; evaluating, via the computing device, the changes in ocular signals from the convolutional neural networks by a machine learning algorithm; determining, via the machine learning algorithm, the cognitive load for the subject; and displaying, to the subject and/or to a supervisor, the cognitive load for the subject.

Ocular system to optimize learning

A method of deception detection based upon ocular information of a subject provides a video camera configured to record a close-up view of a subject's eye. A cognitive state model is configured to determine a high to a low cognitive load experienced by the subject. An emotional state model is configured to determine a high to a low state of arousal experienced by the subject. After asking a question, the ocular information is processed to identify changes in ocular signals of the subject. The cognitive state and emotional state models are evaluated based solely on the changes in ocular signals where a probability of the subject being either truthful or deceptive is estimated for a binary output.

Ocular system for deception detection

A method to optimize learning based upon ocular information of a subject includes providing a video camera for recording a close-up view of a subject's eye. A first electronic display shows a plurality of educational subject matter to the subject. A second electronic display shows an output to an instructor. Changes in ocular signals of the subject are processed through the use optimized algorithms. A cognitive state model determines a low to a high cognitive load experienced by the subject. The cognitive state model is evaluated based on the changes in the ocular signals for determining a probability of the low to the high cognitive load experienced by the subject. The probability of the low to the high cognitive load experienced by the subject is displayed to the instructor.

A method of discovering relationships between eye physiology and cognitive and/or emotional responses of a user starts with engaging the user in a plurality of tasks configured to elicit a predicted specific cognitive and/or emotional response. A first camera films at least one eye of the user recording a time series of events of eye movements of the user, the camera not being in physical contact with the user. The first time series of eye movements are sent to a computing device which compares the eye movements and the plurality of events. The computing device can then identify at least one relationship between eye movements that correlate to an actual specific cognitive and/or emotional response.

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Austin, TX, United States of America

Seth Weisberg