Amazon Technologies, Inc.

Vernon Germano

Xiang Hao

Kripa Kanchana Sivakumar

Jingxiang Chen

Emily Evon McAninly

Muhammad Raffay Hamid

Michael Donoser

Loris Bazzani

Matthew Norman

Bojan Pepik

Eric Orme

Prateek Ramesh Chandra Shah

Benjamin Cheung

Benjamin Chung Yen Cheung

Lakshay Sharma

Devices, systems, and methods are provided for context-based abusive language detection and responses. A method may include identifying text associated with first video content, and determining that a first word in the text matches a first keyword indicative of abusive language. The method may include determining a first label associated with the first word, the first label indicating that the first word is ambiguous. The method may include identifying a first sentence of the text, the first sentence including the first word. The method may include determining first and second context of the first word and the first sentence. The method may include determining, based on the first and second context, using a machine learning model, a second label associated with the first sentence, the second label indicating a probability that the first sentence includes abusive language. The method may include generating second video content for presentation.

Context-based abusive language detection and response for media

Techniques for temporal localization of mature content in long-form videos using only video-level labels are described. According to some embodiments, computer-implemented method includes receiving a request to train a machine learning model on a training video file comprising at least one mature content label, training the machine learning model to generate a feature vector for each of a plurality of video frames of the training video file, generate a plurality of frame-level mature content classification scores of the training video file from the feature vectors of the training video file, and generate a video-level mature content classification score of the training video file from the plurality of frame-level mature content classification scores for the training video file based at least in part on the at least one mature content label of the training video file, receiving a request for an input video file, generating, by the machine learning model in response to the request, a feature vector for each of a plurality of video frames of the input video file, a plurality of frame-level mature content classification scores of the input video file from the feature vectors of the input video file, and a video-level mature content classification score of the input video file from the plurality of frame-level mature content classification scores for the input video file, and transmitting the plurality of frame-level mature content classification scores of the input video file or the video-level mature content classification score of the input video file to a client application or to a storage location.

Temporal localization of mature content in long-form videos using only video-level labels

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.

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Bainbridge Island, WA, United States of America

Vernon Germano