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:
May. 12, 2026

Filed:

Jul. 30, 2025
Applicant:

Intuit Inc., Mountain View, CA (US);

Inventors:

Kaiwen Dong, Granger, IN (US);

Xiang Gao, Clyde Hill, WA (US);

Maria Kissa, Mountain View, CA (US);

Ayan Acharya, Hopkinton, MA (US);

Hilaf Hasson, Mountain View, CA (US);

Mauricio Flores, San Jose, CA (US);

Heather Simpson, San Jose, CA (US);

Byron Tang, Mountain View, CA (US);

Kamalika Das, Saratoga, CA (US);

Assignee:

Intuit Inc., Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/901 (2019.01); G06F 16/28 (2019.01); G06N 3/045 (2023.01);
U.S. Cl.
CPC ...
G06F 16/9024 (2019.01); G06F 16/284 (2019.01); G06N 3/045 (2023.01);
Abstract

Certain aspects of the disclosure provide techniques for categorization by a device. An example method includes receiving input information regarding a plurality of classification targets and a plurality of categories for classification of the plurality of classification targets; generating a plurality of embeddings for the input information using a first model, the plurality of embeddings including: a set of first embeddings associated with the plurality of classification targets, and a set of second embeddings associated with the plurality of categories; determining that a similarity score for the set of first embeddings and the set of second embeddings fails to satisfy a threshold; generating, based on the similarity score failing to satisfy the threshold and using a graph neural network (GNN), a classification of the plurality of classification targets in accordance with the plurality of categories; and outputting information regarding the classification.


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