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:
Jul. 27, 2021

Filed:

Mar. 13, 2019
Applicant:

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

Inventors:

Miquel Angel Farre Guiu, Bern, CH;

Matthew Petrillo, Sandy Hook, CT (US);

Monica Alfaro Vendrell, Barcelona, ES;

Marc Junyent Martin, Barcelona, ES;

Daniel Fojo, Barcelona, ES;

Anthony M. Accardo, Los Angeles, CA (US);

Avner Swerdlow, Los Angeles, CA (US);

Katharine Navarre, Santa Monica, CA (US);

Assignee:

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

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06K 9/62 (2006.01); G06T 7/70 (2017.01); G06T 7/60 (2017.01); G06T 7/73 (2017.01); G06T 7/174 (2017.01); G06T 7/11 (2017.01);
U.S. Cl.
CPC ...
G06K 9/00751 (2013.01); G06K 9/62 (2013.01); G06K 9/6254 (2013.01); G06K 9/6255 (2013.01); G06K 9/6256 (2013.01); G06T 7/60 (2013.01); G06T 7/70 (2017.01); G06T 7/11 (2017.01); G06T 7/174 (2017.01); G06T 7/73 (2017.01); G06T 7/74 (2017.01);
Abstract

According to one implementation, a system for automating content annotation includes a computing platform having a hardware processor and a system memory storing an automation training software code. The hardware processor executes the automation training software code to initially train a content annotation engine using labeled content, test the content annotation engine using a first test set of content obtained from a training database, and receive corrections to a first automatically annotated content set resulting from the test. The hardware processor further executes the automation training software code to further train the content annotation engine based on the corrections, determine one or more prioritization criteria for selecting a second test set of content for testing the content annotation engine based on the statistics relating to the first automatically annotated content, and select the second test set of content from the training database based on the prioritization criteria.


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