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. 20, 2025

Filed:

Dec. 13, 2022
Applicant:

Oracle International Corporation, Redwood Shores, CA (US);

Inventors:

Philip Arthur, Sydney, AU;

Vishal Vishnoi, Redwood City, CA (US);

Mark Edward Johnson, Castle Cove, AU;

Thanh Long Duong, Seabrook, AU;

Srinivasa Phani Kumar Gadde, Fremont, CA (US);

Balakota Srinivas Vinnakota, Sunnyvale, CA (US);

Cong Duy Vu Hoang, Melbourne, AU;

Steve Wai-Chun Siu, Melbourne, AU;

Nitika Mathur, Melbourne, AU;

Gioacchino Tangari, Sydney, AU;

Aashna Devang Kanuga, Foster City, CA (US);

Assignee:

Oracle International Corporation, Redwood Shores, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 16/2452 (2019.01); G06F 16/332 (2019.01); G06F 16/3329 (2025.01); G06F 40/205 (2020.01); G06F 40/211 (2020.01); G06F 40/237 (2020.01); G06F 40/284 (2020.01); G06F 40/35 (2020.01); G06F 40/40 (2020.01); G06F 40/47 (2020.01); G06F 40/58 (2020.01);
U.S. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 16/24522 (2019.01); G06F 16/3329 (2019.01); G06F 40/211 (2020.01); G06F 40/237 (2020.01); G06F 40/284 (2020.01); G06F 40/40 (2020.01); G06F 40/47 (2020.01); G06F 40/58 (2020.01); G06F 40/205 (2020.01); G06F 40/35 (2020.01);
Abstract

Techniques are disclosed herein for synthesizing synthetic training data to facilitate training a natural language to logical form model. In one aspect, training data can be synthesized from original under a framework based on templates and a synchronous context-free grammar. In one aspect, training data can be synthesized under a framework based on a probabilistic context-free grammar and a translator. In one aspect, training data can be synthesized under a framework based on tree-to-string translation. In one aspect, the synthetic training data can be combined with original training data in order to train a machine learning model to translate an utterance to a logical form.


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