Advertisement Boundary Detection via Interaction Analysis
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Solution Overview
Problem
Current methods are inefficient in accurately determining advertisement boundaries in media content, leading to challenges in modifying or predicting advertisement placement opportunities, as they rely on incomplete or unreliable user interaction data.
Innovation Solution
A system that analyzes user interactions such as fast-forwarding, tuning out, and volume changes to identify scene changes and determine advertisement boundaries by assigning weights based on content genre, thereby accurately identifying advertisement start and end points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to determine advertisement boundaries, then the process is simpler, but the accuracy of advertisement location determination is poor
Solution Approach 1:
The patent segments the advertisement detection process into multiple independent analysis components: scene change detection, user interaction event classification (fast-forward, tune-out, pause, volume changes), and weighted scoring mechanisms. Each component processes specific aspects of the data independently, then combines results to determine advertisement boundaries, improving accuracy while maintaining manageable system complexity
Solution Approach 2:
The patent introduces an intermediary analysis layer that processes user interaction events and scene change data before determining advertisement boundaries. This intermediary layer applies weighted algorithms to intermediate data points, transforming raw interaction data into meaningful advertisement location indicators without requiring direct complex processing of all raw data
2Measurement precision
If more user interaction data is collected to improve advertisement detection accuracy, then the precision improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary classification and filtering of user interaction events before full analysis. Scene changes are detected and categorized in advance, and user interactions are pre-sorted by type (fast-forward, tune-out, etc.), allowing the main advertisement detection algorithm to work with pre-processed data rather than raw data, reducing processing time while maintaining accuracy
Solution Approach 2:
The patent applies weighted scoring where only certain user interactions and scene changes are given significant weight in determining advertisement boundaries. Not all data points are processed with equal depth - the system focuses computational resources on the most indicative signals, achieving accurate advertisement detection without processing every data point exhaustively
Data Source
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AI summary
A system may determine the locations of advertisements and advertisement placement opportunities based on user interactions. User interactions may correspond to scene changes in the content item. Events such as tune outs and fast forwarding may indicate that a content item has begun or ended, or that an advertisement section has begun or ended. By aggregating and analyzing user interactions and other events, the system may accurately determine which portions of the content item contain advertisements and advertisement placement opportunities.