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:
Aug. 25, 2026

Filed:

Nov. 22, 2021
Applicant:

Mercari, Inc., Palo Alto, CA (US);

Inventors:

Sho Arora, Palo Alto, CA (US);

Jeffrey Kenichiro Hara, Santa Clara, CA (US);

Sahil Rishi, San Jose, CA (US);

Yu Ishikawa, San Francisco, CA (US);

Shotaro Kohama, Palo Alto, CA (US);

Lu Sun, Palo Alto, CA (US);

Vishal Kashyap, Tokyo, JP;

Mohammad-Mahdi Moazzami, San Jose, CA (US);

Assignee:

MERCARI, INC., Palo Alto, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/084 (2023.01); G06Q 10/087 (2023.01);
U.S. Cl.
CPC ...
G06N 3/084 (2013.01); G06Q 10/087 (2013.01);
Abstract

Disclosed herein are system, computer-readable storage medium, and method embodiments of automatic ontology generation by embedding representations. A system including at least one processor may be configured to receive a vectorized feature set derived from an embedding and including first and second features, and provide the vectorized feature set to a fuser set including first and second fusers. The system may be configured to generate a representation from the fuser set based on the first and second features, and derive tasks based on the representation, assigning to the tasks respective qualifier sets including a weight value, a loss function, and a feedforward function. The system may be configured to compute respective weighted losses for the tasks, based on the respective qualifier sets, and output a data model based on backpropagating the respective weighted losses through the fuser set, the vectorized feature set, the embedding, or a combination thereof.


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