Amazon Technologies, Inc.

Ryan Barlow Dall

Christian Garcia Siagian

Hooman Mahyar

Ramakanth Mudumba

Charles Effinger

Christian Ciabattoni

Ritesh Pase

Lawrence Kyuil Chang

Yang Yu

Andrew Jeremy Crews

Stefan Joseph Wojciechowski

Keith Gargano

Yik Pui Suen

Jonathan Y Ito

Joshua Anthony Marsh

Brady Court Tsurutani

Vadim Volovik

Moussa El Chater

Shubham Kansal

Described are systems and processes providing meaningful responses to natural language (NL) prompts issued by a user and providing the responses to the user with configurable interactive components selected based in part on a role of the user that enables the user to engage with the responses in meaningful ways. The configurable interactive components may enable a user to perform certain tasks with output data in a response, such as play content, edit content, and/or perform actions by implementation of software code added to the configurable interactive components. The actions may include implementing approvals, sending electronic messages, and/or scheduling meetings, among many other actions.

Configurable interactive components for structured query results

Techniques for identifying synchronization errors between audio and video are described herein. Audio portions in audio for media content may be identified based at least in part on a sound level associated with first respective segments of the audio portions. A subset of the audio portions may be selected based at least in part on a duration associated with the audio portions. For a segment of the subset a first number of frames in the audio and a second number of frames in the video for the segment may be determined. A determination may be made that the segment includes a conversation segment based at least in part on the first number of frames, the second number of frames, and a first threshold. A synchronization error may be identified in the conversation segment based on a difference between the audio and the video of the conversation segment.

Techniques for detecting non-synchronization between audio and video

This disclosure is directed to a system and method that detects duplicated content and/or media items. A media item can be split into media item portions. Based on the media item portions, features can be determined. Using the features, media item portion signatures can be determined to generate a media item signature. The media item signature can be compared with a different media item signature to determine duplicated content within the media items.

Automated detection of duplicate content in media items

This disclosure is directed to a system and method that automatically detects repeated content within multiple media items. Content providers often include content, such as an introduction, near the beginning of a media item. In some circumstances, such as in the case of a series of television episodes, the content providers use the same content in each episode of the series. By dividing the media items into portions and analyzing the portions, the systems and methods described can automatically detect the repeated content. Using the detection of the repeated content, a user interface can then allow a user to bypass the repeated content during playback.

Automated detection of repeated content within a media series

A subtitle management system is provided that analyzes and adjusts subtitles for video content to improve the experience of viewers. Subtitles may be optimized or otherwise adjusted to display in particular regions of the video content, to display in synchronization with audio presentation of the spoken dialogue represented by the subtitles, to display in particular colors, and the like. Subtitles that are permanently integrated into the video content may be identified and addressed. These and other adjustments may be applied to address any of a variety of subtitle issues and shortcomings with conventional methods of generating subtitles.

Optimization of subtitles for video content

This disclosure is directed to systems and methods that automatically detects static content within a media item. While consuming a media item, such as a movie, a user might notice, unexpectedly, that a portion of the movie does not change resulting in a poor user experience. By dividing the media item into portions and analyzing the portions, the systems and methods described can automatically detect the static content and, in some instances, correct the static content.

Automated detection of static content within portions of media content

Techniques for automated content validation are provided. In some examples, a media file and a metadata file associated with a title of the media file may be received. One or more scene paragraphs may be identified based at least in part on information in the metadata file. Scenes may be identified at least in part by using a transformer model implemented in a neural network. One or more scene files may be generated from the media file. One or more characters in a scene file of the one or more scene files may be identified. A match score may be determined, based at least in part on an association of the scene file to a scene paragraph of the plurality of scene paragraphs. A validity criterion may be determined for the title associated with the media file based at least in part on the match score.

Techniques for validating digital media content

Technologies are provided for generation of points of insertion of directed content into a video asset. In some embodiments, multiple time offsets within an interval spanned by the video asset can be determined using audio data corresponding to the video asset. A time offset defines a boundary between first and second segments of the video asset. Using image data corresponding to the video asset, respective pairs of video clips for the multiple time offsets can be generated. Visual features, aural features, and language features pertaining to the respective pairs of video clips can then be generated. Scores for the multiple time offsets can be generated using the visual features, the aural features, and the language features. A score represents an assessment of suitability to insert directed content into the video asset at a time offset. A file that contains specific time offsets can be generated.

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Los Angeles, CA, United States of America

Ryan Barlow Dall