Customized Video Recap Using Previously Viewed Scenes
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Solution Overview
Problem
Viewers face challenges in re-engaging with interrupted streaming content due to decreased recollection of previous events, requiring effective and computationally efficient methods to generate customized summaries.
Innovation Solution
Utilizing existing summaries and machine learning to identify relevant scenes for customized content summaries, reducing computational burden by focusing on existing data rather than complete content analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complete content analysis is performed to generate customized summaries, then summary accuracy and relevance are improved, but computational cost and processing time increase
Solution Approach 1:
The patent segments the content analysis process into two distinct stages: (1) pre-computation stage where comprehensive scene analysis and summary generation is performed and stored, and (2) runtime stage where pre-generated summaries are retrieved and presented to users. This segmentation allows complete content analysis to be performed once offline, improving summary accuracy without imposing high computational costs during streaming operations.
Solution Approach 2:
The patent applies preliminary action by pre-computing and storing scene summaries, key events, and metadata before the actual streaming content is consumed. This preliminary analysis includes identifying important scenes, generating descriptive summaries, and organizing content structure in advance, so that when users need customized summaries, the computationally intensive work has already been completed, significantly reducing real-time processing requirements.
2Loss of information
If detailed scene analysis is performed for every content item, then recollection accuracy is improved, but processing time and system complexity increase
Solution Approach 1:
The patent extracts only the most essential and informative elements from complete content analysis, such as key scenes, pivotal moments, and critical plot points, rather than analyzing and presenting all content details. This extraction approach maintains high recollection accuracy by focusing on what matters most to viewer understanding, while significantly reducing the complexity of the analysis system compared to processing every scene in detail.
Solution Approach 2:
The patent applies local quality by providing different levels of analysis depth for different types of scenes and content segments. High-detail analysis is applied to critical plot points and key moments that require accurate recollection, while less critical segments receive simplified or aggregated treatment. This differentiated approach maintains recollection accuracy for important information while reducing overall system complexity.
3Adaptability or versatility
If customized summaries are generated for each user based on viewing history, then viewer engagement is improved, but computational resources required increase
Solution Approach 1:
The patent merges the customization logic with pre-computed content metadata by integrating user viewing history analysis with pre-generated scene summaries and key event data. Instead of performing separate complete content analysis for each user, the system combines user-specific viewing patterns with pre-prepared content information, achieving high adaptability and personalized summaries while maintaining processing efficiency through the reuse of pre-computed data structures.
Data Source
AI summary
Techniques for identifying a plurality of scenes relating to a first content item and selecting, using a computing system, two or more scenes, of the plurality of scenes, for a customized content summary for a user are disclosed. This includes identifying a point in time when the user stopped consuming the first content item and selecting the two or more scenes based on the identified point in time. The selected two or more scenes were previously presented to the user, prior to the point in time when the user stopped consuming the first content item. The techniques further include generating the customized content summary, using the computing system, by combining the selected two or more scenes.