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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 12406521 B1

PDFFull Text
Date of Patent:
Sep. 02, 2025

Filed:

Apr. 11, 2024

Entity identification using machine learning

Applicant:

Tidalx AL Inc., San Ramon, CA (US);

Inventors:

Barnaby John James, Campbell, CA (US);

Grace Taixi Brentano, Redwood City, CA (US);

Christopher Thornton, Vancouver, CA;

Assignee:

TidalX AI Inc., Mountain View, CA (US);

Attorney:

Fish & Richardson P.C.

Primary Examiner:

Michael R Neff

Int. Cl.
CPC ...
G06V 40/10 (2022.01); A01K 61/95 (2017.01); A01K 63/02 (2006.01); F24F 11/30 (2018.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06F 18/2413 (2023.01); G06N 20/00 (2019.01); G06T 3/40 (2006.01); G06V 10/24 (2022.01); G06V 10/25 (2022.01); G06V 10/40 (2022.01); G06V 10/44 (2022.01); G06V 10/762 (2022.01); G06V 10/764 (2022.01); G06V 10/77 (2022.01); G06V 20/52 (2022.01); G06V 20/80 (2022.01);
U.S. Cl.
CPC ...
G06V 40/10 (2022.01); A01K 61/95 (2017.01); A01K 63/02 (2013.01); F24F 11/30 (2018.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06F 18/2413 (2023.01); G06N 20/00 (2019.01); G06T 3/40 (2013.01); G06V 10/245 (2022.01); G06V 10/25 (2022.01); G06V 10/40 (2022.01); G06V 10/44 (2022.01); G06V 10/762 (2022.01); G06V 10/764 (2022.01); G06V 10/7715 (2022.01); G06V 20/52 (2022.01); G06V 20/80 (2022.01); F24F 2221/225 (2013.01);
Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media for identification and re-identification of fish. In some implementations, first media representative of aquatic cargo is received. Second media based on the first media is generated, wherein a resolution of the second media is higher than a resolution of the first media. A cropped representation of the second media is generated. The cropped representation is provided to the machine learning model. In response to providing the cropped representation to the machine learning model, an embedding representing the cropped representation is generated using the machine learning model. The embedding is mapped to a high dimensional space. Data identifying the aquatic cargo is provided to a database, wherein the data identifying the aquatic cargo comprises an identifier of the aquatic cargo, the embedding, and a mapped region of the high dimensional space.

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