US 12,413,796 B2
Training data generation for advanced frequency management
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); and Amir Mazaheri, Mountain View, CA (US)
Assigned to Tubi, Inc., San Francisco, CA (US)
Filed by Tubi, Inc., San Francisco, CA (US)
Filed on Feb. 21, 2022, as Appl. No. 17/676,759.
Claims priority of provisional application 63/213,177, filed on Jun. 21, 2021.
Prior Publication US 2022/0406038 A1, Dec. 22, 2022
Int. Cl. 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)
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)] 18 Claims
OG exemplary drawing
 
1. A system for programmatic generation of training data, comprising:
a computer processor; and
a training data generation engine executing on the computer processor and configured to enable the computer processor 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, wherein the procedure evaluates presence of surfaces suitable for content overlay;
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 comprising 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; and
a deep learning model service configured to execute the trained artificial intelligence model on a set of video advertisements to identify brand identifiers associated with a set of entities; and
an offline transcoding service configured to store the brand identifiers associated with the set of entities in a repository; and
an online media service configured to:
identify a set of frequency thresholds associated with the brand identifiers;
calculate frequency metrics of the brand identifiers based on frequency of serving media content associated with the brand identifiers to user clients; and
regulate serving of media content to user clients to avoid exceeding the frequency thresholds based on the calculated frequency metrics.