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:
Nov. 05, 2024

Filed:

Aug. 12, 2021
Applicant:

Mastercard International Incorporated, Purchase, NY (US);

Inventors:

Shreyansh Singh, Noida, IN;

Gaurav Dhama, Gurgaon, IN;

Ankur Arora, New Delhi, IN;

Kanishka Kayathwal, Kota, IN;

Jessica Carta, Stamford, CT (US);

Ganesh Nagendra Prasad, West New York, NJ (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 30/02 (2023.01); G06F 16/24 (2019.01); G06F 16/2457 (2019.01); G06F 18/21 (2023.01); G06F 18/22 (2023.01); G06F 40/20 (2020.01); G06F 40/284 (2020.01); G06F 40/30 (2020.01); G06Q 30/0279 (2023.01);
U.S. Cl.
CPC ...
G06Q 30/0279 (2013.01); G06F 18/2178 (2023.01); G06F 18/22 (2023.01); G06F 40/284 (2020.01); G06F 40/30 (2020.01);
Abstract

The disclosure relates to AI-based machine-learning and natural language modeling to identify semantic similarities between sets of content having natural language text. For example, a system may generate a relevance classification that indicates whether content such as articles are non-specifically relevant to charities without identifying a particular charity. If the content is non-specifically relevant to charities, the system may apply a natural language model to generate sentence embeddings based on the content and determine a level similarity between the sentence embeddings and a query embedding generated from a charity query. The charity query may itself be generated from a full description of the charity through an encoder-decoder architecture with reinforcement learning.


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