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:
Feb. 21, 2023

Filed:

Mar. 14, 2019
Applicant:

Fujifilm Business Innovation Corp., Tokyo, JP;

Inventors:

Qiong Liu, Cupertino, CA (US);

Ray Yuan, San Ramon, CA (US);

Hao Hu, Orlando, FL (US);

Yanxia Zhang, Cupertino, CA (US);

Yin-Ying Chen, San Jose, CA (US);

Francine Chen, Menlo Park, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/774 (2022.01); G06K 9/62 (2022.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); B25J 9/00 (2006.01);
U.S. Cl.
CPC ...
G06V 10/7747 (2022.01); G06K 9/6257 (2013.01); G06K 9/6259 (2013.01); G06N 3/0454 (2013.01); G06N 3/08 (2013.01); G06V 10/7753 (2022.01); B25J 9/00 (2013.01);
Abstract

A computer-implemented method of learning sensory media association includes receiving a first type of nontext input and a second type of nontext input; encoding and decoding the first type of nontext input using a first autoencoder having a first convolutional neural network, and the second type of nontext input using a second autoencoder having a second convolutional neural network; bridging first autoencoder representations and second autoencoder representations by a deep neural network that learns mappings between the first autoencoder representations associated with a first modality and the second autoencoder representations associated with a second modality; and based on the encoding, decoding, and the bridging, generating a first type of nontext output and a second type of nontext output based on the first type of nontext input or the second type of nontext input in either the first modality or the second modality.


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