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:
Jun. 24, 2025

Filed:

May. 16, 2023
Applicant:

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

Inventors:

Praneet Pabolu, Bangalore, IN;

Sriram Chaudhury, Bangalore, IN;

Assignee:

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

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 21/62 (2013.01); G06F 16/34 (2019.01); G06F 40/166 (2020.01); G06F 40/216 (2020.01); G06F 40/284 (2020.01); G06F 40/40 (2020.01); G06F 40/47 (2020.01); G06F 40/56 (2020.01); G06F 40/58 (2020.01); G06N 3/045 (2023.01); G06N 3/09 (2023.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06F 21/6254 (2013.01); G06F 16/345 (2019.01); G06F 40/166 (2020.01); G06F 40/216 (2020.01); G06F 40/284 (2020.01); G06F 40/40 (2020.01); G06F 40/47 (2020.01); G06F 40/56 (2020.01); G06F 40/58 (2020.01); G06N 3/045 (2023.01); G06N 3/09 (2023.01); G06N 20/00 (2019.01);
Abstract

Method includes populating fake value for each of entities, to generate string of fake entity values that correspond to entities; inserting sentinel token between adjacent fake values included in the string to generate first input data; generating, by natural language generation model, natural language sentences based on first input data, natural language sentences including one or more fake values from the string; analyzing natural language sentences to determine whether any fake value from the string is missing; based on the fake value missing, summarizing, using text summarization model, natural language sentences to generate text summary; concatenating the text summary with the fake value, to generate second input data; and generating, by a next sentence generation model, additional natural language sentence using the second input data, the additional natural language sentence including the fake value. Additional natural language sentence is combined with natural language sentences to generate final natural language sentences.


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