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:
Jan. 27, 2026

Filed:

Aug. 11, 2020
Applicant:

Adobe Inc., San Jose, CA (US);

Inventors:

Amir Pouran Ben Veyseh, Eugene, OR (US);

Franck Dernoncourt, Sunnyvale, CA (US);

Quan Tran, San Jose, CA (US);

Yiming Yang, Santa Clara, CA (US);

Lidan Wang, San Jose, CA (US);

Rajiv Jain, Vienna, VA (US);

Vlad Morariu, Potomac, MD (US);

Walter Chang, San Jose, CA (US);

Assignee:

Adobe Inc., San Jose, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 40/205 (2020.01); G06F 40/216 (2020.01); G06F 40/242 (2020.01); G06F 40/253 (2020.01); G06F 40/268 (2020.01); G06F 40/279 (2020.01); G06F 40/284 (2020.01); G06F 40/289 (2020.01); G06N 3/044 (2023.01); G06N 3/0442 (2023.01); G06N 3/045 (2023.01); G06N 3/0464 (2023.01); G06N 3/08 (2023.01); G06N 3/09 (2023.01); G06N 7/01 (2023.01);
U.S. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 40/205 (2020.01); G06F 40/216 (2020.01); G06F 40/242 (2020.01); G06F 40/253 (2020.01); G06F 40/268 (2020.01); G06F 40/279 (2020.01); G06F 40/284 (2020.01); G06F 40/289 (2020.01); G06N 3/044 (2023.01); G06N 3/0442 (2023.01); G06N 3/045 (2023.01); G06N 3/0464 (2023.01); G06N 3/09 (2023.01); G06N 3/08 (2013.01); G06N 7/01 (2023.01);
Abstract

This disclosure describes methods, non-transitory computer readable storage media, and systems that extract a definition for a term from a source document by utilizing a single machine-learning framework to classify a word sequence from the source document as including a term definition and to label words from the word sequence. To illustrate, the disclosed system can receive a source document including a word sequence arranged in one or more sentences. The disclosed systems can utilize a machine-learning model to classify the word sequence as comprising a definition for a term and generate labels for the words from the word sequence corresponding to the term and the definition. Based on classifying the word sequence and the generated labels, the disclosed system can extract the definition for the term from the source document.


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