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The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Patent No.:

US 11640572 B1

PDFFull Text
Date of Patent:
May. 02, 2023

Filed:

Dec. 18, 2020

Ocular system to optimize learning

Applicant:

Senseye, Inc., Austin, TX (US);

Inventors:

David Zakariaie, Austin, TX (US);

Kathryn McNeil, Austin, TX (US);

Alexander Rowe, Austin, TX (US);

Joseph Brown, Austin, TX (US);

Patricia Herrmann, Austin, TX (US);

Jared Bowden, Austin, TX (US);

Taumer Anabtawi, Austin, TX (US);

Andrew R. Sommerlot, Austin, TX (US);

Seth Weisberg, Austin, TX (US);

Veronica Choi, Austin, TX (US);

Assignee:

Senseye, Inc., Austin, TX (US);

Attorney:

Orbit IP

Primary Examiner:

Xin Jia

Int. Cl.
CPC ...
G06K 9/62 (2022.01); G06N 3/08 (2023.01); G06T 7/73 (2017.01); A61B 5/16 (2006.01); G16H 50/50 (2018.01); G16H 30/20 (2018.01); G16H 50/70 (2018.01); G16H 50/20 (2018.01); A61B 3/00 (2006.01); A61B 3/14 (2006.01); A61B 5/00 (2006.01); G06N 3/04 (2023.01); A61B 3/11 (2006.01); G16H 15/00 (2018.01); A61B 3/113 (2006.01); A61B 5/11 (2006.01); G06Q 10/0635 (2023.01); G06Q 10/0639 (2023.01); G06Q 10/10 (2023.01); G06V 20/40 (2022.01); G06V 40/18 (2022.01); G06V 40/19 (2022.01); G16H 50/30 (2018.01); G06N 3/088 (2023.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); A61B 3/0025 (2013.01); A61B 3/0041 (2013.01); A61B 3/0091 (2013.01); A61B 3/112 (2013.01); A61B 3/113 (2013.01); A61B 3/145 (2013.01); A61B 5/1103 (2013.01); A61B 5/161 (2013.01); A61B 5/163 (2017.08); A61B 5/165 (2013.01); A61B 5/4845 (2013.01); A61B 5/4863 (2013.01); A61B 5/6898 (2013.01); A61B 5/7246 (2013.01); G06N 3/0454 (2013.01); G06Q 10/0635 (2013.01); G06Q 10/06398 (2013.01); G06Q 10/10 (2013.01); G06T 7/73 (2017.01); G06V 20/46 (2022.01); G06V 40/18 (2022.01); G06V 40/19 (2022.01); G16H 15/00 (2018.01); G16H 30/20 (2018.01); G16H 50/20 (2018.01); G16H 50/50 (2018.01); G16H 50/70 (2018.01); A61B 5/7267 (2013.01); A61B 2503/20 (2013.01); G06N 3/088 (2013.01); G06T 2207/10016 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30041 (2013.01); G16H 50/30 (2018.01);
Abstract

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.

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