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:
Dec. 02, 2025

Filed:

Jul. 31, 2024
Applicant:

Dell Products L.p., Round Rock, TX (US);

Inventors:

Shweta Goyal, Bangalore, IN;

Kailash Talreja, Mumbai, IN;

Sailendu Kumar Patra, Bangalore, IN;

Saurabh Jha, Austin, TX (US);

Assignee:

Dell Products L.P., Round Rock, TX (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/2452 (2019.01); G06F 16/21 (2019.01); G06F 40/58 (2020.01);
U.S. Cl.
CPC ...
G06F 16/24522 (2019.01); G06F 16/212 (2019.01); G06F 40/58 (2020.01);
Abstract

A method for training a model to generate a structured query language (SQL) output using a natural language input includes receiving a training data set that includes natural language queries, corresponding SQL statements, and a corresponding database. The method also includes extracting query-specific schema from the corresponding database. Further, the method includes embedding the natural language queries and the query-specific schema to generate initialized embeddings, where each initialized embedding corresponds to a natural language query of the natural language queries, and where the corresponding SQL statement is one of the corresponding SQL statements; generating subgraphs based on the initialized embeddings and the natural language queries; training a graph neural network (GNN) using the subgraphs as inputs; generating, using the GNN, refined outputs based on the subgraphs; and training a transformer model using the refined outputs and the natural language queries to obtain a trained model.


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