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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 11941581 B1

PDFFull Text
Date of Patent:
Mar. 26, 2024

Filed:

Feb. 09, 2022

System and method for classifier training and retrieval from classifier database for large scale product identification

Applicant:

Tracxpoint Llc., Coral Gables, FL (US);

Inventors:

Moshe Meidar, Florida, FL (US);

Gidon Moshkovitz, Haifa, IL;

Edi Bahous, Haifa, IL;

Itai Winkler, Haifa, IL;

Assignee:

TRACXPOINT LLC., Coral Gables, FL (US);

Attorney:

The IP Law Firm of Guy Levi, LLC

Primary Examiner:

David F Dunphy

Int. Cl.
CPC ...
G06K 9/32 (2006.01); B62B 3/14 (2006.01); G06F 18/22 (2023.01); G06F 18/23213 (2023.01); G06F 18/2413 (2023.01); G06K 9/00 (2022.01); G06K 9/62 (2022.01); G06N 3/02 (2006.01); G06N 3/04 (2023.01); G06Q 10/087 (2023.01); G06V 10/70 (2022.01); G06V 20/20 (2022.01); G06V 20/52 (2022.01); G06V 30/194 (2022.01);
U.S. Cl.
CPC ...
G06Q 10/087 (2013.01); B62B 3/14 (2013.01); G06F 18/22 (2023.01); G06F 18/23213 (2023.01); G06F 18/24147 (2023.01); G06N 3/02 (2013.01); G06N 3/04 (2013.01); G06V 10/87 (2022.01); G06V 20/20 (2022.01); G06V 20/52 (2022.01); G06V 30/194 (2022.01); G06V 2201/09 (2022.01);
Abstract

The disclosure relates to systems and methods for real-time detection of a very large number of items in a given constrained volume. Specifically, the disclosure relates to systems and methods for retrieving an optimized set of classifiers from a self-updating classifiers' database, configured to selectively and specifically identify products inserted into a cart in real time, from a database comprising a large number of stock-keeping items, whereby the inserted items' captured images serve simultaneously as training dataset, validation dataset and test dataset for the recognition/identification/re-identification of the product.

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