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:
Jul. 14, 2026

Filed:

Mar. 27, 2025
Applicant:

Informatica Llc, Redwood City, CA (US);

Inventors:

Sarath S, Ernakulam, IN;

Ankan Dutta, Asansol, IN;

Natesan Karthikeyan, Bangalore, IN;

Vishal Balagangadhar, Bangalore, IN;

Assignee:

INFORMATICA LLC, Redwood City, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/00 (2019.01); G06F 16/242 (2019.01); G06F 16/2457 (2019.01); G06F 16/248 (2019.01);
U.S. Cl.
CPC ...
G06F 16/243 (2019.01); G06F 16/24575 (2019.01); G06F 16/248 (2019.01);
Abstract

A method, apparatus, and computer-readable medium for intent classification of natural language queries in a generative artificial intelligence platform, the method including receiving a natural language query from a user in a user interface of a generative artificial intelligence platform, querying an entity index based on query terms to identify entities corresponding to the natural language query, identifying assets in the enterprise data catalog corresponding to the entities, identifying a metadata context corresponding to the assets, retrieving previous conversations of the user, generating a structured prompt based on the natural language query, the assets, the metadata context corresponding to the assets, and the previous conversations, and determining an intent associated with the natural language query by inputting the structured prompt into a fine-tuned large language model, the fine-tuned large language model being trained based on training queries, training conversations, and training metadata contexts.


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