Tubi Limited

Haofei Wang

Chang She

Jaya Kawale

Marios Assiotis

Amir Mazaheri

Joseph Gallagher

Hailong Geng

Yu Tao Zhao

Chiyu Zhong

Yoshihiro Tanaka

Khaldun Matter Ahmad AlDarabsah

Mark Alden Rotblat

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.

Training data generation for advanced frequency management

Systems and methods for frequency management, including: an online media service configured to receive a request for a media item, the request comprising a recipient identifier of a recipient, and identify a set of candidate media items ranked based at least partially on relevance to the recipient; and a frequency management service configured to: (i) identify a quantity of impressions associated with a first candidate media item of the set of candidate media items and the recipient identifier over a preceding duration of time, (ii) identify a maximum frequency threshold, (iii) determine, based on the quantity of impressions, that the maximum frequency threshold is exceeded, (iv) exclude the first candidate media item from a result set based on the frequency threshold being exceeded, and (v) provide the result set comprising an identifier of a second candidate media item in response to the request.

Machine learning techniques for advanced frequency management

Systems and methods for entity detection using artificial intelligence, including: a deep learning model service configured to: select and analyze a set of frames from a media item to determine a set of candidate brand-probability pairs; a voting engine configured to: determining that a first brand-probability pair of a set of candidate brand-probability pairs based on at least one obtained hyperparameter value does not meet a threshold for determining whether candidate brand-probability pairs are to be included in a result set; excluding the first brand-probability pair from the result set based on the determination; sorting the result set; and selecting at least one final brand-probability pair from the result set; and an offline transcoding service configured to: store the final brand-probability pair in a repository with a relation to an identifier of the media item.

Model serving for advanced frequency management

Systems and methods for frequency management, including: an online media service configured to: receive a request for a media item, the request including a recipient identifier; identify a set of candidate media items ranked by a set of matching criteria; a frequency management service configured to: perform a query against a lookup service, where the query includes (i) an entity identifier of at least one candidate media item of the set of candidate media items, and (ii) the recipient identifier; receive a response from the lookup service including a quantity of impressions associated with the entity identifier and the recipient identifier; identify a predefined frequency threshold; determine that the frequency threshold is exceeded and exclude the at least one candidate media item from a result set based on the determination; and provide the result set including an identifier of at least one other candidate media item.

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Redwood City, CA, United States of America

Haofei Wang