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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.

Patent No.:

US 12413796 B1

PDFFull Text
Date of Patent:
Sep. 09, 2025

Filed:

Feb. 21, 2022

Training data generation for advanced frequency management

Applicant:

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

Inventors:

Khaldun Matter Ahmad Aldarabsah, Santa Clara, CA (US);

Hailong Geng, Beijing, CN;

Yu Tao Zhao, Olympia, WA (US);

Yoshihiro Tanaka, Redmond, WA (US);

Haofei Wang, Redwood City, CA (US);

Mark Alden Rotblat, Lafayette, CA (US);

Jaya Kawale, San Jose, CA (US);

Chang She, San Francisco, CA (US);

Marios Assiotis, Park City, UT (US);

Joseph Gallagher, San Francisco, CA (US);

Chiyu Zhong, Bloomington, IN (US);

Amir Mazaheri, Mountain View, CA (US);

Assignee:

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

Attorney:

Redbrick IP, P.C.

Primary Examiner:

Nay A Maung

Assistant Examiner:

Heath E Wells

Int. Cl.
CPC ...
H04N 21/234 (2011.01); G06Q 30/0241 (2023.01); G06Q 30/0242 (2023.01); G06Q 30/0251 (2023.01); G06V 10/70 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 20/40 (2022.01); H04N 21/25 (2011.01); H04N 21/262 (2011.01);
U.S. Cl.
CPC ...
H04N 21/23424 (2013.01); G06Q 30/0245 (2013.01); G06Q 30/0251 (2013.01); G06Q 30/0277 (2013.01); G06V 10/70 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 20/41 (2022.01); G06V 20/46 (2022.01); H04N 21/251 (2013.01); H04N 21/26208 (2013.01);
Abstract

Systems and methods for programmatic generation of training data, including: a training data generation engine configured to: identify an image asset corresponding to an entity; identify a training video; select a consecutive subset of frames of the training video based on a procedure for ranking frames on their candidacy for overlaying content; for at least one frame of the subset of frames: perform an augmentation technique on the identified logo image to generate an augmented image asset; overlay at least one variation of the image asset, including the augmented image asset, onto each of the subset of frames to generate a set of overlayed frames; and generate an augmented version of the training video including the overlayed frames; and a model training engine configured to: train an artificial intelligence model for entity detection using the augmented version of the training video.

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