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.

Date of Patent:
Oct. 12, 2021

Filed:

Oct. 09, 2018
Applicant:

Sony Corporation, Tokyo, JP;

Inventors:

Ming-Chang Liu, San Jose, CA (US);

Ahmad Khodayari-Rostamabad, San Jose, CA (US);

Assignee:

SONY CORPORATION, Tokyo, JP;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
A61B 5/16 (2006.01); A61B 5/00 (2006.01); G06F 3/01 (2006.01); G06K 9/00 (2006.01); G06K 9/62 (2006.01); G06N 3/04 (2006.01); A61B 5/316 (2021.01); A61B 5/369 (2021.01); A61B 5/0205 (2006.01);
U.S. Cl.
CPC ...
A61B 5/165 (2013.01); A61B 5/316 (2021.01); A61B 5/369 (2021.01); A61B 5/6814 (2013.01); A61B 5/7267 (2013.01); G06F 3/015 (2013.01); G06K 9/00892 (2013.01); G06K 9/6227 (2013.01); G06K 9/6254 (2013.01); G06N 3/0454 (2013.01); A61B 5/0205 (2013.01);
Abstract

An electronic device that handles recognition of mental behavioral, affect, emotional, mental states, mental health, or mood-based attributes based on deep neural networks (DNNs), stores a set of EEG signals and a set of bio-signals associated with a subject. The electronic device trains a plurality of first recognition models on a training set of EEG signals and a training set of bio-signals associated with different training subjects. The electronic device trains a second recognition model on a feature vector from output layers of the plurality of first recognition models. The electronic device estimates a plurality of dependency or relationship data by application of the trained plurality of first recognition models on the set of EEG signals and bio-signals. The electronic device identifies a mental behavioral attribute of the subject by application of the trained second recognition model on the plurality of signals and their relationship data.


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