Video Segment Popularity Detection via User Operation Feedback
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Solution Overview
Problem
Users face challenges in accessing the most interesting parts of network videos due to time constraints, as existing automated editing technologies rely on subjective video analysis and require significant manual effort, failing to accurately reflect audience interests.
Innovation Solution
A method and apparatus that utilize video player operation information from multiple users to evaluate a popularity measure for video segments, identifying and displaying popular segments, and creating a condensed version of the video containing the most popular segments, ensuring increased accessibility and accuracy in content selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If automated editing technology using video analysis methods is used to reduce video quantity, then users can consume video content in reduced time, but the computing resources required are large and the results may not reflect audience interests
Solution Approach 1:
The system collects video player operation information from multiple users and uses this feedback to evaluate popularity measures for different video segments. This feedback mechanism allows the system to identify interesting portions based on actual user behavior rather than subjective analysis, resolving the contradiction by making the editing process both efficient and audience-relevant
Solution Approach 2:
The system enables users to indirectly select interesting video segments through their natural viewing behaviors (play, pause, seek operations). The aggregation of these self-service actions across multiple users automatically identifies popular segments without requiring external analysis, reducing computing resources while maintaining relevance to audience interests
2Measurement precision
If manual editing and arranging of video is performed to identify interesting portions, then video can be presented in advance, but significant manual effort is required and the determination is subjective
Solution Approach 1:
Users effectively perform the selection task for themselves and other users through their natural video viewing behaviors. The system aggregates these self-service actions to automatically identify interesting portions, eliminating manual editing effort while achieving objective accuracy based on actual user preferences rather than subjective editor judgment
Solution Approach 2:
The patent replaces the mechanical process of manual video editing with an automated system that processes video player operation information. This substitution eliminates the need for human editors to manually analyze and select segments, reducing manual effort to zero while maintaining or improving accuracy through objective user behavior data
3Measurement precision
If video is manually edited and arranged in advance, then interesting portions can be identified, but the process suffers from subjectivity based on editor preferences
Solution Approach 1:
The system continuously collects feedback from multiple users through their video player operations and uses this feedback to evaluate popularity measures. This feedback loop ensures that the identified interesting portions objectively reflect actual audience interests and preferences, resolving the subjectivity problem while maintaining high accuracy in identifying segments that resonate with viewers
Solution Approach 2:
The system dynamically adapts to changing audience preferences by continuously processing new video player operation information. Unlike static manual editing, the system can update popularity measures and reidentify interesting portions as user preferences evolve, ensuring ongoing alignment with audience interests across different time periods and user groups
Data Source
AI summary
A method for identifying popular segments of a network video comprising: receiving video player operation information for a plurality of video players operated by users accessing the network video; evaluating a popularity measure for one or more segments of the network-video using the received operation information so as to identify popular segments of the network video.