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:
Jun. 27, 2023

Filed:

May. 20, 2019
Applicants:

Synaptics Incorporated, San Jose, CA (US);

The Trustees of Indiana University, Bloomington, IN (US);

Inventors:

Francesco Nesta, Aliso Viejo, CA (US);

Lijiang Guo, Bloomington, IN (US);

Minje Kim, Bloomington, IN (US);

Assignees:

SYNAPTICS INCORPORATED, San Jose, CA (US);

THE TRUSTEES OF INDIANA UNIVERSITY, Bloomington, IN (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2023.01); G06V 10/40 (2022.01); G06V 10/764 (2022.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01); G06F 18/2113 (2023.01); G06F 18/25 (2023.01); G06N 3/045 (2023.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06F 18/2113 (2023.01); G06F 18/254 (2023.01); G06N 3/045 (2023.01); G06V 10/40 (2022.01); G06V 10/764 (2022.01); G06V 10/811 (2022.01); G06V 10/82 (2022.01);
Abstract

Systems and methods for multimodal classification include a plurality of expert modules, each expert module configured to receive data corresponding to one of a plurality of input modalities and extract associated features, a plurality of class prediction modules, each class prediction module configured to receive extracted features from a corresponding one of the expert modules and predict an associated class, a gate expert configured to receive the extracted features from the plurality of expert modules and output a set of weights for the input modalities, and a fusion module configured to generate a weighted prediction based on the class predictions and the set of weights. Various embodiments include one or more of an image expert, a video expert, an audio expert, class prediction modules, a gate expert, and a co-learning framework.


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