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:
Oct. 10, 2023

Filed:

Nov. 06, 2020
Applicant:

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

Inventors:

Rajiv Jain, Vienna, VA (US);

Varun Manjunatha, Potomac, MD (US);

Joseph Barrow, College Park, MD (US);

Vlad Ion Morariu, Potomac, MD (US);

Franck Dernoncourt, Sunnyvale, CA (US);

Sasha Spala, Newton, MA (US);

Nicholas Miller, Newton, MA (US);

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 18/214 (2023.01); G06F 40/30 (2020.01); G06F 40/117 (2020.01); G06V 30/413 (2022.01); G06F 18/21 (2023.01); G06F 18/2415 (2023.01); G06F 16/33 (2019.01);
U.S. Cl.
CPC ...
G06F 18/2148 (2023.01); G06F 18/217 (2023.01); G06F 18/2415 (2023.01); G06F 40/117 (2020.01); G06F 40/30 (2020.01); G06V 30/413 (2022.01); G06F 16/33 (2019.01); G06V 2201/10 (2022.01);
Abstract

Certain embodiments involve using a machine-learning tool to generate metadata identifying segments and topics for text within a document. For instance, in some embodiments, a text processing system obtains input text and applies a segmentation-and-labeling model to the input text. The segmentation-and-labeling model is trained to generate a predicted segment for the input text using a segmentation network. The segmentation-and-labeling model is also trained to generate a topic for the predicted segment using a pooling network of the model to the predicted segment. The output of the model is usable for generating metadata identifying the predicted segment and the associated topic.


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