Amazon Technologies, Inc.

Genband US LLC

Arjun Cholkar

Hooman Mahyar

Ibrahim Dogru

Don Gilchrist

Anthony Robert Jones

Dany Sylvain

Vimal Bhat

Abhinav Jain

Avijit Vajpayee

Rafael Soltanovich

Harshal Dilip Wanjari

Christian Garcia Siagian

Ramakanth Mudumba

Charles Effinger

Shiva Krishnamurthy

David Niu

Nikhil Kajale

Abhishek Patidar

Andrew James McVeigh

Yang Yu

Hani Suleiman

Jayashree Rajagopalan

Shivakumar Krishnamurthy

James C Willeford

Zhikang Zhang

Kumar Sohony

Pemra Narli

Ankit Prem Manocha

Louis Kirk Barker

Craig Critchley

Steven David Prinz

Narayan Sundaram

Anthony Jones

Systems, devices, and methods are provided for context aware graphical subtitles. Techniques described herein may involve determining a representation for a graphical subtitle. The graphical subtitles may have various customizable properties that allow for creative expression. Representations of graphical subtitles may be determined using lower-level information extracted from detectors, such as audio detectors and/or visual detectors, as well as higher-level information determined by encoder-decoders.

Content aware graphical subtitles

Techniques for playing a first audio track for a video on a first audio device of a first audience member and simultaneously playing a second audio track that includes different content for the video than the first audio track on a second audio device of a second audience member by a single media player are described. According to some examples, a computer-implemented method includes receiving, by a media player, a manifest indicating a video, a first audio track for the video, and a second audio track comprising an audio-narrated description of the video; receiving an indication from a user of the media player that indicates a first audio device of a first audience member is to output the first audio track, and that indicates a second audio device of a second audience member is to output the second audio track; sending the video to a display coupled to the media player for displaying of the video to the first audience member and the second audience member; sending the first audio track, concurrently with the sending of the video to the display, to the first audio device of the first audience member by a first audio player of the media player; and sending the second audio track, concurrently with the sending of the video to the display and the sending of the first audio track to the first audio device, to the second audio device of the second audience member by a second audio player of the media player.

Computer-implemented media player to play multiple audio tracks at the same time to enable accessibility experiences

Described are example systems and methods generally directed to systems and methods for a real-time content recommendation service employing active guidance to facilitate efficient and effective recommendations of content to a user. A plurality of characteristic information associated with content items from a corpus of content items can be obtained and used to generate a content graph and a discovery map/decision tree. The discovery maps/decision trees may include content items and/or segments of content items that are arranged and connected in the discovery maps/decision trees based on their associated characteristic information to facilitate guided recommendations of content based on user interactions. The discovery map/decision tree may then be accessed to provide guided recommendations of content.

Actively guided content recommendation

Techniques for using a machine learning model to determine a proper subset of a multimedia file for a viewer based on their interest without the need to actively control a media player timeline are described. As one example, a computer-implemented method includes receiving a request at a content delivery service from a media player of a viewer to play a proper subset of a live multimedia file for a category of content of the live multimedia file without the viewer actively controlling a timeline of the media player of the viewer, determining an indication of a prior multimedia playing interaction of the viewer with the content delivery service, partitioning, by the content delivery service, the live multimedia file into a video portion, an audio portion, and a text portion, determining, by the content delivery service, one or more labels for the video portion, the audio portion, and the text portion, determining, by a machine learning model of the content delivery service, a proper subset of segments of the live multimedia file to send to the viewer based at least in part on the indication and the one or more labels, and live streaming the proper subset of segments of the live multimedia file to the media player of the viewer.

Machine learning model to determine a proper subset of a multimedia file for a viewer based on their interest

Systems, methods, and computer-readable media are disclosed for systems and methods for automated generation and presentation of sign language avatars for video content. Example methods may include determining, by one or more computer processors coupled to memory, a first segment of video content, the first segment including a first set of frames, first audio content, and first subtitle data, where the first subtitle data comprises a first word and a second word. Methods may include determining, using a first machine learning model, a first sign gesture associated with the first word, determining first motion data associated with the first sign gesture, and determining first facial expression data. Methods may include generating an avatar configured to perform the first sign gesture using the first motion data, where a facial expression of the avatar while performing the first sign gesture is based on the first facial expression data.

Automated generation and presentation of sign language avatars for video content

Techniques are described for detecting desynchronization between an audio component and a video component of a media presentation. Feature sets may be determined for portions of the audio component and portions of the video component, which may then be used to generate correlations between portions of the audio component and portions of the video component. Synchronization may then be assessed based on the correlations.

Event based audio-video sync detection

Intelligent reframing techniques are described in which content (e.g., a movie) can be generated in a different aspect ratio than previously provided. These techniques include obtaining various video frames having a first aspect ratio. Various objects can be identified within the frames. An object having the highest degree of importance in a frame can be selected and a focal point can be calculated based at least in part on that object. A modified version of the content can be generated in a second aspect ratio that is different from the first aspect ratio. The modified version can be generated using the focal point calculated based on the object having the greatest degree of importance. Using these techniques, the content can be provided in a different aspect ratio while ensuring that the most important features of the frame still appear in the new version of the content.

Intelligent reframing

A system can be configured to receive entertainment content requested by a user and identify content segments and content features from the entertainment content. The content segments can be utilized to identify portions of the entertainment content for enrichment and/or enhancement by the system. The content features can be utilized to associate the entertainment content and the content segments with supplemental content that includes or is associated with the content features. The content features can indicate genres, scene classifications, significant figures credited with creating the entertainment content, and other points of interests for users interested in the entertainment content. The associations between the entertainment content and the supplemental content can enable the system to engage the users by presenting the supplemental content determined to match interests of the users.

Interactive entertainment content

Systems, methods, and computer-readable media are disclosed for systems and methods for automated selection of color palettes for video content. Example methods may include determining, by one or more computer processors coupled to memory, a first segment of video content, the first segment comprising a first set of frames, determining, using a first video processing algorithm, a first object that is present in the first set of frames, and determining, using a second video processing algorithm, a first semantic characteristic of the first segment. Some example methods may include generating a first vector representing the first object and the first semantic characteristic, and generating, using a first neural network and the first vector, a first color palette recommendation for the first segment. Selection of the first color palette recommendation may cause a color filter to be applied to the first set of frames.

Automated selection of color palettes for video content using artificial intelligence

Techniques for automated up-sampling of media files are provided. In some examples, a title associated with a media file, a metadata file associated with the title, and the media file may be received. The media file may be partitioned into one or more scene files, each scene file including a plurality of frame images in a sequence. One or more up-sampled scene files may be generated, each corresponding to a scene file of the one or more scene files. An up-sampled media file may be generated by combining at least a subset of the one or more up-sampled scene files. Generating one or more up-sampled scene files may include identifying one or more characters in a frame image of the plurality of frame images, based at least in part on implementation of a facial recognition algorithm including deep learning features in a neural network.

Techniques for up-sampling digital media content

A method includes, within a Graphical User Interface (GUI), displaying a contact list and a conversation container to a user, the contact list including a plurality of contact objects, the conversation container being an active object used to represent an ongoing conversation, receiving input from a user through the GUI, the input instructing the client computing system to move a first contact object from the plurality of contact objects over the conversation container and drop the first contact object into the conversation container, in response to dropping the first contact object into the conversation container, instructing a server to open a first communication session between the client system and a first device associated with the one of the contact objects, the first communication session being of a first media type, and engaging in communication through the first communication session with the first device.

Collaborative multimedia communication

A method performed by a first computing device to transfer a multimedia conversation to the first computing device includes sending, to a first service node, a request to transfer a first communication session to the first computing device, the first communication session using a first type of protocol, the first communication session being between a second computing device and a first endpoint, and sending, to a second service node, a request to transfer a second communication session to the first computing device, the second communication session using a second type of protocol that is different from the first type of protocol, the second communication session being between the second computing device and a second endpoint. The multimedia conversation comprises the first communication session and the second communication session.

Multimedia conversation transfer

A method performed by a computing system includes maintaining a record of a terminated conversation, the conversation comprising a plurality of participants and a plurality of media types, wherein the media types used during the conversation changed during the conversation. The method further includes receiving a request from a client system associated with a participant of the terminated conversation, the request being to reestablish the terminated conversation. The method further includes, in response to the request, initiating reestablishment of the terminated conversation by attempting to reestablish the terminated conversation, wherein attempting to reestablish the terminated conversation includes a default setting of initiating communication sessions between a cumulative total of each of the participants using a cumulative total of each media type associated with the terminated conversation.

Multimedia conversation history

A method performed by a conversation manager in a communications network includes establishing a multimedia conversation between a first device associated with a first user and at least one endpoint associated with at least one other user, the multimedia conversation comprising at least two different media types according to respective and different protocol types. The method further includes receiving an instruction to transfer the conversation from the first device of the first user to a second device, adding the second device to the multimedia conversation by establishing connections for the at least two different media types to the second device, and removing the first device from the multimedia conversation by removing connections for the at least two media types to the first device.

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Bothell, WA, United States of America

Arjun Cholkar