Video Highlight Generation via User Interaction Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manually identifying highlight moments in videos is time-consuming and may discourage users from doing so, making it inefficient for video applications.
Innovation Solution
A system that generates highlights for videos by accessing user interaction data, comparing it with predefined criteria, and associating moments of interest within the video based on user interactions such as viewing, selecting, sharing, or modifying video content, using machine-readable instructions executed by physical processors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually identify highlight moments in videos, then the accuracy of highlight identification can be ensured, but the time consumption and user effort increase significantly
Solution Approach 1:
The system performs preliminary analysis of user interaction data (views, likes, shares, comments) before the user needs to identify highlights. By pre-processing this data and establishing criteria-based identification, the system prepares highlight candidates in advance, reducing the time users would need to spend on manual identification while maintaining accuracy through multiple interaction metrics.
Solution Approach 2:
The system enables video content to serve itself by automatically identifying highlight moments based on embedded user interaction data. The video content and its associated interaction metrics self-generate highlight information without requiring external manual intervention, thus eliminating time consumption while preserving identification accuracy through objective interaction-based criteria.
2Reliability
If users manually identify highlight moments in videos, then the quality of highlights can be controlled, but user engagement and motivation decrease due to the effort required
Solution Approach 1:
The system allows video content to automatically generate high-quality highlights through self-service mechanisms. User interaction data (views, likes, shares, comments) serves as objective quality indicators, eliminating the need for users to manually evaluate and select highlights. This maintains reliability through multiple interaction metrics while dramatically improving ease of operation by removing manual effort entirely.
Solution Approach 2:
The system uses user interaction data as feedback to automatically identify and quality-assess highlight moments. By incorporating likes, shares, comments, and view duration as feedback signals, the system objectively determines highlight quality without requiring user judgment effort, thus maintaining reliability while improving ease of operation.
3Productivity
If automated methods are used to identify highlight moments, then user effort and time are reduced, but the accuracy and relevance of identified highlights may decrease
Solution Approach 1:
The system introduces user interaction data as an intermediary between automated processing and highlight identification. Rather than directly automating highlight selection without user input, the system uses interaction metrics (views, likes, shares, comments) as intermediate signals that bridge automated analysis and accurate highlight identification, maintaining both productivity and precision.
Solution Approach 2:
The system employs user interaction data as feedback to guide automated highlight identification. By continuously incorporating interaction metrics into the identification process, the automated system maintains high accuracy while preserving productivity, as the feedback loop enables accurate identification without requiring manual user intervention for each video.
4Measurement precision
If comprehensive user interaction data is collected and analyzed, then the accuracy of highlight identification improves, but the system complexity and data processing requirements increase
Solution Approach 1:
The system segments the analysis process by evaluating different interaction metrics (views, likes, shares, comments) separately and then combining their results. This segmentation allows the system to handle comprehensive data through manageable components, reducing processing complexity while maintaining identification accuracy through the aggregated information from multiple segmented metrics.
Solution Approach 2:
The system implements a tiered approach where basic highlight identification can function with minimal interaction data, while more comprehensive analysis optionally incorporates additional metrics. This partial action approach allows the system to achieve acceptable accuracy with lower complexity when needed, while enabling high-precision identification through comprehensive data analysis when resources permit.
Data Source
AI summary
This disclosure relates to systems and methods that generate highlights for a video. A video may be accessed. Criteria for identifying a moment of interest within the video based on a user interaction with a portion of the video may be obtained. Interaction information indicating the user interaction with the portion of the video may be received. The interaction information for the portion of the video may be compared with the criteria. Responsive to the interaction information for the portion of the video indicating the user interaction with the portion of the video satisfying the criteria, a moment in the video corresponding to the portion of the video may be associated with the moment of interest.