Comcast Cable Communications, LLC

Comcast Interactive Media, LLC

Agency for Science, Technology and Research

Nbcuniversal Media, LLC

Oliver Jojic

David F Houghton

Bageshree Shevade

Evelyne Tzoukermann

Amit Bagga

Jan Neumann

Hongzhong Zhou

Ryan M Farrell

Ehsan Younessian

Hans Sayyadi

Geetu Ambwani

Anthony R Davis

Leslie Eugene Chipman

Brian Curtis

Jeanine Heck

Muthu Manickam

Vladimir Kronrod

Mevan Samarasinghe

Larry Cynkin

Corey Farrell

Hassan Sayyadi-Harikandehei

Sheau Bao Ng

Wei Yun Yau

Manu Shukla

Sujoy Roy

Sridhar Alla

Kong Wah Wan

Shekhar Agrawal

Hongzhon Zhou

Ryan Matthew Farrell

Content streams may be segmented to provide automatic extraction and storage of content items without intervening commercials or other unrelated content. These content items may then be stored in a database and made accessible to subscribers through, for example, an on-demand service. Automatic segmentation may include the identification of program boundaries, segmentation of a content stream based on the boundaries and the subsequent classification of the segments into content types. For example, audio and video duplication detection may be used to identify commercials since commercials tend to repeat frequently over a relatively short amount of time. A system may further identify an end of program indicator in a video stream to determine when a program ends. Accordingly, if a program ends after a scheduled end time, a recording device (e.g., the program is being recorded) may automatically extend the recording time to capture the entire program.

Program segmentation of linear transmission

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

Aspects described herein relate to identifying media items relevant to a selected subject matter including, for example, determining the subject matter of a first media item, which may comprise at least one of audio content or video content; determining the classification within an ontology of the subject matter of the first media item; analyzing the ontology to identify other subject matter related to the subject matter of the first media item; and performing a search for other media items relevant to the subject matter of the first media item as a function of at least the other related subject matter according to the ontology.

Searching for segments based on an ontology

Systems and methods are described for improving content classifications and metadata for a plurality of content items. The content items may comprise various forms of media content. A content recommendation processing system may automatically annotate and/or classify content items with a plurality of metadata tag information describing content items. The disclosed system and methods may also determine and/or assign applicability scores indicating a level or degree of suitability for annotating a content item with particular metadata tag information, and may utilize applicability scores to provide content to calibrate metadata information for a plurality of content items, and further to provide content recommendations to users.

Content recommendation system with weighted metadata annotations

A text detection process may involve comparing high-contrast pixel densities of areas of images of a video to determine quantities of text-containing areas in the images. Based on a difference between quantities of text-containing areas of subsets of the images, an image of the video may be selected for modification.

Image modification based on text recognition

According to some aspects described herein, content and service providers in a media delivery network may provide improved recommendations and/or personalize a user's experience based on the real-time activity of that user as well as other users. In this way, ever increasing amounts of content may be optimally managed in a way that provides users with the improved and/or personalized experience.

Real-time recommendations for altering content output

Detecting the start of a credit roll within video program may allow for the automatic extension of video recordings among other functions. The start of the credit roll may be detected by determining the number of text blocks within a sequence of frames and identifying a point in the sequence of frames where a difference between the number of text blocks in frames occurring before the point and the number of text blocks in frames occurring after the point is greatest and exceeds a specified threshold. Text blocks may be identified within each frame by partitioning the frame into one or more segments and recording the segments having a pixel of a sufficiently high contrast. Contiguous segments may be merged or combined into single blocks, which may then be filtered to remove noise and false positives. Additional content may be inserted into the credit roll frames.

Detection of transitions between text and non-text frames in a video stream

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Annandale, VA, United States of America

Oliver Jojic