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:
Feb. 18, 2025

Filed:

Aug. 17, 2023
Applicant:

Discord Inc., San Francisco, CA (US);

Inventors:

Michele Banko, San Francisco, CA (US);

Alok Puranik, San Francisco, CA (US);

Taylor Rhyne, San Francisco, CA (US);

Assignee:

Discord Inc., San Francisco, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/20 (2020.01); G06F 18/214 (2023.01); G06F 18/2431 (2023.01); G06Q 50/00 (2012.01); G10L 15/08 (2006.01);
U.S. Cl.
CPC ...
G06Q 50/01 (2013.01); G06F 18/214 (2023.01); G06F 18/2431 (2023.01); G10L 15/08 (2013.01);
Abstract

The present technology extends to methods, systems, and computer program products for expanding semantic classes via user feedback. Aspects of the technology learn how a set of labels can be expanded from user-generated tags. Text labels applied by human reviewers to digital content can be inspected and compared to one another. When a threshold of human-generated text tags contain similar terminology, the set of labels can be expanded to define a representation of the similar terminology. Similar terminology can include terms that originate from the same base term, are synonyms, are more specific terms related to a general term category, etc. Similar terminology can be consolidated into a defining term that is used to generate a new (more granular) label or a new top level label. Accordingly, new semantic classes can be discovered from user-generated feedback. New semantic classes can provide a more granular representation of content item classification.


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