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:
Mar. 03, 2026

Filed:

May. 16, 2023
Applicant:

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

Inventors:

Praneet Pabolu, Bangalore, IN;

Karan Dua, Najibabad, 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 (2025.01); 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

A computer-implemented method includes obtaining, from text corpus including article-summary pairs in a plurality of languages, a plurality of article-summary pairs in a target language among the plurality of languages, to form an article-summary pairs dataset in which each article corresponds to a summary; inputting articles from the article-summary pairs to a machine learning model; generating, by the machine learning model, embeddings for sentences of the articles; extracting, by the machine learning model, keywords from the articles with a probability that varies based on lengths of the sentences, respectively; outputting, by the machine learning model, the keywords; applying a maximal marginal relevance algorithm to the extracted keywords, to select relevant keywords; and generating a keyword-text pairs dataset that includes the relevant keywords and text from the articles, the text corresponding to the relevant keywords in each of keyword-text pairs of the keyword-text pairs dataset.


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