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. 20, 2020

Filed:

Nov. 13, 2018
Applicant:

Disney Enterprises, Inc., Burbank, CA (US);

Inventors:

Concetta Maratta, Los Angeles, CA (US);

Brian Kennedy, New York, NY (US);

Zachary Toback, Montclair, NJ (US);

Jared L. Wiener, New York, NY (US);

Fabian Westerwelle, New York, NY (US);

Assignee:

Disney Enterprises, Inc., Burbank, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/205 (2020.01); G06F 16/908 (2019.01); G06N 3/02 (2006.01); G06N 20/00 (2019.01); G06F 40/169 (2020.01); G06F 40/263 (2020.01); G06F 16/45 (2019.01); G06F 16/783 (2019.01); G06F 16/483 (2019.01); G06F 16/65 (2019.01); G06F 16/683 (2019.01); G06F 16/75 (2019.01); G06F 40/30 (2020.01);
U.S. Cl.
CPC ...
G06F 40/205 (2020.01); G06F 16/45 (2019.01); G06F 16/483 (2019.01); G06F 16/65 (2019.01); G06F 16/683 (2019.01); G06F 16/75 (2019.01); G06F 16/783 (2019.01); G06F 16/908 (2019.01); G06F 40/169 (2020.01); G06F 40/263 (2020.01); G06F 40/30 (2020.01); G06N 3/02 (2013.01); G06N 20/00 (2019.01);
Abstract

In one implementation, a content processing system includes a computing platform having a hardware processor and a system memory storing a content classification software code, a natural language processor, and a computer vision analyzer. The hardware processor executes the content classification software code to receive content inputs from multiple content sources, and, for each content input, to parse the content input for metadata describing the content input, obtain a description of language-based content included in the content input from the natural language processor, and obtain a description of visual content included in the content input from the computer vision analyzer. The content classification software code further associates predetermined annotation tags with the content input based on the metadata, the description of the language-based content, and the description of the visual content, and assigns the content input to a predetermined subject matter classification based on the associated annotation tags.


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