Comcast Cable Communications, LLC

Ehsan Younessian

Jan Neumann

Oliver Jojic

Amit Bagga

Brian Curtis

Jeanine Heck

Larry Cynkin

Mevan Samarasinghe

Muthu Manickam

Faisal Ishtiaq

Hans Sayyadi

Anthony J Braskich

Benjamin Spier

David S Wahl

Hassan Sayyadi-Harikandehei

Sridhar Alla

Nina Mortazavi

Shekhar Agrawal

Systems, apparatuses, and methods are described for contextually augmenting content, which may be offered based on segment types associated with various sentiments, emotions, and/or excitement levels which may be detected and/or aligned in the content. The contextual content augmentation may comprise in-video contextual advertising, which may be based on advertisement strategies such as segment-based, alignment/detection-based, market-based, and/or user-based strategies.

Augmenting content using contextual features

Systems, methods, and devices relating to determining an introduction portion in a video program are described herein. A method may determine first and second hard-matching pairs of video segments in first and second video content such that video fingerprints of the first hard-matching pair match and video fingerprints of the second hard-matching pair also match. The method may classify a third pair of video segments in the first and second video content, sequentially between the first and second hard-matching pairs, as a soft-matching pair of video segments of an introduction portion. The method may use the classification of the third pair of video segments as a soft-matching pair to determine a model configured to determine that a pair of video segments in two video content items are a soft-matching pair of video segments of an introduction portion.

Systems, methods, and devices for determining an introduction portion in a video program

Content items may be represented in a listing by one or more thumbnails containing images from the content items. The images of a content item may be processed to recognize persons and/or objects, and frames of the content item may be selected based on interest values of the recognized persons and/or objects. For example, frames may be selected based on having uncommon (rare) objects and/or common (popular) persons.

Systems, methods, and apparatuses for selecting/generating visual representations of media content

Systems, apparatuses, and methods are described for intelligent control of video display backlighting that provides environmental lighting illumination of an area behind a video display device, such as on a wall behind a video display. The environmental lighting illumination may be based on recognizing images (e.g., objects, patterns, faces, colors, etc.) in video content being displayed via the video display device, recognizing audio (e.g., words, sounds, voices, etc.) in audio content being output in association with video content being displayed via the video display device, and/or recognizing other triggers such as commands associated with video content.

Video display environmental lighting

Systems, apparatuses, and methods are described for determining boundaries within a content asset. A server may collect user interaction data for one or more content assets. Using the user interaction data, the server may determine commercial breaks or scene changes for the content asset. The server may apply best fit curves to the user interaction data, and determine commercial breaks or scene changes based on the best fit curves.

Crowd-sourced program boundaries

Methods and apparatus for generating personalized menus and a media content interface are provided. In one example, different device specific media asset lists are generated at different user devices for the same user based on the media consumption history at each device. In another example, media asset listings are displayed for multicast and on-demand media assets determined to be of interest to the user. An on-demand media asset is listed at a time when no other multicast media assets are listed. In another example, media asset listings for successive episodes of a media series are displayed in sequential order in response to an input to view successive episodes of a media series. In another example, a text search for media content is performed in which the search priority of media provider results is decreased relative to the search priority of media asset results as additional characters are added to a search string.

Personalized menus and media content interface

Event detection may be carried out to determine one or more content items associated with an event type. A server may determine one or more visually relevant labels, of a plurality of labels, associated with the event type. The server may determine one or more textually relevant labels, of the plurality of labels, associated with the event type. The server may determine an event label distribution based on a correlation of the visually relevant event labels and the textually relevant labels, wherein the event label distribution comprises a plurality of labels. The server may determine a content item label distribution associated with a content item, wherein the content item label distribution comprises a plurality of second labels. The server may send, based on comparing the event label distribution with the content item label distribution, the content item to a computing device.

Systems and methods for ad-hoc event detection in content

The audio content (e.g., an audio track, an audio file, an audio signal, etc.) of a content item (e.g., multimedia content, a movie, streaming content, etc.) may be modified to augment and/or include one or more auditory events, such as a sound, a plurality of sounds, a sound effect(s), a voice(s), and/or music.

Methods and systems for augmenting audio content

Provided herein are methods and systems for improved accessibility scoring for content items. A predicted accessibility score may be based on a plurality of multimodal features present within a content item. The plurality of multimodal features may include video features (e.g., based on video/image analysis), audio features (e.g., based on audio analysis), text-based features (e.g., based on closed-captioning analysis), features indicated by metadata (e.g., duration, genre, etc.), a combination thereof, and/or the like. A predicted accessibility score for a content item may indicate how accessible the content item may be for persons who are visually impaired, hearing impaired, cognitively impaired, etc., as well as for persons who desire to view content that requires less visual attention and/or audio attention as the case may be.

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Washington, DC, United States of America

Ehsan Younessian