Amazon Technologies, Inc.

Oracle International Corporation

Kripa Kanchana Sivakumar

Vernon Germano

Xiang Hao

Emily Evon McAninly

Michael Donoser

Loris Bazzani

Bojan Pepik

Matthew Norman

Prateek Ramesh Chandra Shah

Eric Orme

Benjamin Cheung

Benjamin Chung Yen Cheung

Antariksha Bhaduri

A unified schema, such as a common metrics schema, is provided that can universally cater to different kinds of ML metrics generated by different ML pipelines and platforms. In certain implementations, a metrics management system is provided. The metrics management system is based upon the unified schema and provides a repository for storing ML-related metrics in which the metrics may be generated by different disparate pipelines or platforms. The metrics management system may include adapters, converters, layers, libraries, or combinations thereof that can receive metric data and can provide generalized data that can be consumed by various different types of downstream systems. The generalized data may be provided to a downstream system, such as a user interface, an adjustment module, etc.

Metrics management system

Methods and apparatus are relating to the use of machine learning techniques to identify unrated content that will be appealing to particular demographics. Using explicit feedback (e.g., star ratings) and implicit feedback (e.g., viewing behavior) for a given demographic, feature sets are extracted from the rated titles and then used to recognize similar titles in unrated content.

Content appeal prediction using machine learning

Methods and apparatus are described that relate to the use of machine learning techniques to accurately predict ratings for various types of content across multiple mature themes.

Mature themes prediction for online content

Techniques for providing video content are described, where portions of the video content may be determined based an artificial intelligence model. In an example, a computer system may input, to a neural network, a vector representation of a frame. The neural network may be configured based on an error of associating the frame with an incorrect category. The error may be based on timing of the frame and on an error time margin relative to a start time or an end time of the incorrect category. The computer system may receive back an association between the vector representation and a category and may generate, based on the association, an identifier of a portion of the video content. The identifier may associate the portion of the video content with the category and identify a start time or an end time of the portion of the video content.

Determining portions of video content based on artificial intelligence model

Techniques are described for a content rating system that allows for automatic assignment of maturity ratings for media content.

Automated video ratings

Growing community of inventors

Seattle, WA, United States of America

Kripa Kanchana Sivakumar