Arris Enterprises LLC

Google Inc.

Arris Technology, Inc.

Motorola Corporation

Motorola Mobility LLC

General Instrument Corporation

Andrew Wireless Systems Gmbh

Arris International IP Ltd

Commscope UK Limited

Magdi A. Mohamed

Carl M. Danielsen

Mark A. Tarlton

Renxiang Li

Eric R. Buhrke

General Instrument Holdings, Inc.

Faisal Ishtiaq

Stephen P Emeott

Kevin Lynn Baum

Michael L Needham

Anthony J Braskich

Benedito J Fonseca, Jr

Alfonso Martinez Smith

Benedito J Fonseca

Isselmou Ould Dellahy

Carl M Danielsen

Venugopal Vasudevan

Jehan Wickramasuriya

Bhavan Gandhi

Magdi A Mohamed

Eric Rolse Buhrke

Mark A Tarlton

Shivajit Mohapatra

Cuneyt M Taskiran

Eric R Buhrke

Kevin L Baum

Jr Fonseca Benedito J

Disclosed is a method of associating, at a secondary device, secondary media content with primary media content being output at a primary device. The method includes receiving, at the secondary device, first information based upon the primary content being output at the primary device, wherein the first information includes at least one of an audio and a visual signal, determining at the secondary device second information corresponding to the first information, receiving at the secondary device one or more portions of secondary media content that have been made available by a third device, determining at the secondary device whether one or more of the portions of the secondary media content match one or more portions of the second information, and taking at least one further action upon determining that there is a match.

Identifying media on a mobile device

A method of detecting frames in a video that demarcate a pre-determined type of video segment within the video is provided. The method includes identifying visually distinctive candidate marker frames within the video, grouping the candidate marker frames into a plurality of groups based on visual similarity, computing a collective score for each of the groups based on temporal proximity of each of the candidate marker frames within the group to related events occurring within the video, and selecting at least one of the groups based on the collective proximity scores as marker frames that demarcate the pre-determined type of video segment. A video processing electronic device and at least one non-transitory computer readable storage medium having computer program instructions stored thereon for performing the method are also provided.

Detection of demarcating segments in video

Particular embodiments extract features for frames from a video. Then, a sequence of frames is identified based on a pattern analysis of frames based on the features. This pattern may be used to select sentinel features from the sequence of frames. For example, a pattern may include a first sentinel sequence followed by a transitional frame and then a second sentinel sequence. The transitional frame may be a black frame that is used to identify back-to-back sentinel sequences. Then, a sentinel frame that demarks a transition from a first content type to a second content type is identified. For example, the frames on either side of the black frame may be very similar and be identified as sentinel frames that mark a transition from program content to advertisement content. The above process may allow automated detection of sentinel frames that can run without user supervision.

Detecting sentinel frames in video delivery using a pattern analysis

A method for creating derivative advertisements is provided. The method includes identifying a visual element based on an analysis of a primary advertisement asset such that the visual element is representative of a branding message provided within the primary advertisement. The method also includes identifying a call-to-action (CTA) data element based on the analysis and creating a derivative advertisement asset having the visual element and CTA data element. A system and a non-transitory computer-readable storage medium containing software for executing the method are also provided.

Creating derivative advertisements

Particular embodiments analyze logos found in a video program to determine video demarcations in the video program. For example, a video demarcation may be content that marks ('marker content') a transition from a first video content type to a second video content type. Marker content may be used so the user knows that a transition is occurring. Particular embodiments analyze the logos found in a video program to determine the video demarcations in the video. The video is first analyzed to determine logos in the video program. Once these logos are determined, particular embodiments may re-analyze the video program to identify marker frames that include the marker content that signal the transitions to a different video content types. The marker frames may be determined without any prior knowledge of the marker content. Then, particular embodiments may use the marker frames to determine video segments.

Detecting of graphical objects to identify video demarcations

A method is provided for detecting static graphical elements in a sequence of video frames that compares a selected frame in the sequence to each of a plurality of previous frames in the sequence to identify a graphical element that can be a logo. For each pair of frames compared, an absolute difference frame is determined by acquiring an absolute difference value between pixel values for corresponding pixels over at least a portion of the frames in the frame pair. A metric associated with each absolute difference frame is generated, which reflects a degree of dissimilarity. At least some of the absolute difference frames weighted in accordance with the metric associated therewith are summed to generate an accumulation difference frame such that pairs of frames that are more dissimilar have a greater weight. A static graphical element is then identified over a region of the accumulation difference frame in which pixel values satisfy specified criteria.

Detection of logos in a sequence of video frames

Particular embodiments can refine a seed sentinel frame signature for a seed sentinel frame. The seed sentinel frame may be predictable or partially predictable content that demarks a beginning and/or end of certain content in a video program. The seed sentinel frame may be first used to detect other sentinel frames in the video program. However, other sentinel frames throughout the video program, or in other video programs, may be slightly different from the given sentinel frame due to different reasons. The seed sentinel frame signature may not detect the sentinel frames of a video program with a desired accuracy. Accordingly, particular embodiments may refine the sentinel frame signature to a synthetic sentinel frame signature. The synthetic sentinel frame signature may then be used to analyze the current video program or other video programs. The synthetic sentinel frame signature may more accurately detect the sentinel frames within the video program.

Generating synthetic frame features for sentinel frame matching

Particular embodiments detect a solid color frame, such as a black frame, that may include visible content other than the solid color in a portion of the frame. These frames may conventionally not be detected as a solid color frame because of the visible content in the portion of the frame. However, these solid color frames may be 'functional' black or white frames, in that the solid color frames are performing the function of the solid color frame even though the frames include the visible content. The visible content may be content that may always be displayed on the screen even if the video content is transitioning to an advertisement. Particular embodiments use techniques to detect the functional solid color frames even when visible content appears in the solid color frames. Particular embodiments use color layout information and edge distribution information to detect solid color frames.

Detection of solid color frames for determining transitions in video content

A method receives video content and metadata associated with video content. The method then extracts features of the video content based on the metadata. Portions of the visual, audio, and textual features are fused into composite features that include multiple features from the visual, audio, and textual features. A set of video segments of the video content is identified based on the composite features of the video content. Also, the segments may be identified based on a user query.

Content based video content segmentation

A method implemented in a computer system for controlling the delivery of data and audio/video content. The method delivers primary content to the subscriber device for viewing by a subscriber. The method also delivers secondary content to the companion device for viewing by the subscriber in parallel with the subscriber viewing the primary content, where the secondary content relates to the primary content. The method extracts attention estimation features from the primary content, and monitors the companion device to determine an interaction measurement for the subscriber viewing the secondary content on the companion device. The method calculates an attention measurement for the subscriber viewing the primary content based on the attention estimation features, and the interaction measurement, and controls the delivery of the secondary content to the companion device based on the attention measurement.

Attention estimation to control the delivery of data and audio/video content

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Lake Zurich, IL, United States of America

Renxiang Li