Media Content Identification During Unscheduled Segments
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Solution Overview
Problem
Automatic Content Recognition (ACR) systems face difficulties in identifying media content when the same content is displayed on multiple channels simultaneously, leading to ambiguity in determining the specific channel being watched and providing contextually-related information.
Innovation Solution
The implementation of a video matching system that uses identification information from media content displayed before and after the common video segment, along with timeline events like 'talking heads' segments, to accurately identify and provide contextually-related content, even during unscheduled media segments like breaking news.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the ACR system uses identification information from the current media content segment for matching, then the matching speed is fast, but the identification accuracy deteriorates when multiple channels display the same content simultaneously
Solution Approach 1:
The system performs preliminary actions by collecting identification information from media content segments before and after the common segment, and prepares multiple candidate identification results in advance. When a common segment is detected, the system already has pre-collected information from adjacent unique segments ready for comparison and disambiguation, avoiding the need to wait for post-processing.
Solution Approach 2:
The system introduces intermediary elements (timeline events such as 'talking heads' segments, pre-segment and post-segment content) that act as mediators between the ambiguous common segment and the identification process. These intermediaries contain unique channel-specific information that helps distinguish which channel is being viewed, resolving the ambiguity without sacrificing matching speed.
2Measurement precision
If the system disables contextually-related content during common video segments to avoid errors, then the identification accuracy is improved, but the viewer experience and advertising revenue deteriorate
Solution Approach 1:
The system implements feedback by continuously monitoring the disambiguation result in real-time. When the system determines with high confidence that a specific channel is being viewed (through matching identification information from adjacent segments), it immediately enables contextually-related content. This feedback loop allows the system to dynamically adjust content delivery based on identification confidence, maintaining both accuracy and viewer experience.
Solution Approach 2:
The system applies dynamics by making the contextually-related content delivery flexible and adaptive rather than static. Instead of permanently disabling content during common segments, the system dynamically enables or disables content based on real-time disambiguation results. This allows the system to maintain high identification accuracy while minimizing disruption to viewer experience and advertising revenue.
3Device complexity
If the system uses only the current segment for identification, then the device complexity is low, but the ability to handle unscheduled segments deteriorates
Solution Approach 1:
The system applies segmentation by dividing the media content into distinct segments (pre-segment, common segment, post-segment) and processing each segment separately. This allows the system to handle unscheduled segments like breaking news by identifying them as common segments and using the segmented structure to disambiguate channel identity from adjacent unique segments, without requiring complex overall system redesign.
Solution Approach 2:
The system achieves universality by creating a multi-functional identification framework that handles both scheduled and unscheduled segments using the same basic mechanism. The timeline event structure and adjacent-segment comparison approach work universally across different segment types, allowing the system to maintain low complexity while gaining high adaptability to various content scenarios.
Data Source
AI summary
Provided are systems, methods, and computer-program products for identifying a media content stream when the media content stream is playing an unscheduled media segment. A computing device may receive a plurality of media content streams, where at least two of the plurality of media content streams concurrently includes a same unscheduled media segment. The computing device may determine that the media display device is playing the unscheduled media segment by examining the media content available at the current time in each of the plurality of media content streams. The computing device may determine identification information from the media content included in the media content stream. The computing device may determine contextually-related content, which may be disabled while the unscheduled media segment is being played by the media display device. The computing device may display the media content stream and the contextually-related content after the unscheduled media segment has been played.