Smart Video Skipping System Using Content Transition Analysis
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Solution Overview
Problem
Current video playback methods, such as manual fast-forwarding and rewinding, are inefficient and inaccurate for skipping unwanted sections in media content, making it difficult for viewers to quickly access desired scenes or frames.
Innovation Solution
A method and apparatus for smart video skipping, which analyzes content segments and transitions to identify markers and determine viewing characteristics, updating skipping instructions to automatically skip unnecessary sections, using machine learning techniques and historic skipping data to predict user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual fast-forwarding and rewinding are used to skip unwanted sections, then viewers can access different parts of the content, but the process is inefficient and inaccurate
Solution Approach 1:
The system automatically analyzes content segments and generates skipping instructions without user intervention. The processing system identifies content markers, determines viewing characteristics, and creates skipping instructions that enable automatic navigation through unwanted sections, making the system self-sufficient in improving playback efficiency
Solution Approach 2:
The system performs content analysis and generates skipping instructions before the actual playback or viewing session. By pre-processing the media content item to identify content segments and determine which sections should be skipped, the system prepares navigation instructions in advance, enabling efficient and accurate skipping during playback
2Loss of time
If automatic skipping is implemented without analysis, then playback time is reduced, but user preferences and viewing characteristics are not considered
Solution Approach 1:
The system uses historic skipping data as feedback to improve future skipping decisions. By analyzing past user skipping behavior and preferences, the system refines its content analysis algorithms to better predict which sections users want to skip, creating a continuous improvement loop that adapts to individual viewing habits
Solution Approach 2:
The system changes the parameters of content analysis by identifying specific content markers and determining viewing characteristics for different segments. By varying the analysis depth and markers identified based on content type and user history, the system adapts its skipping strategy to maintain both time efficiency and user preference alignment
Data Source
AI summary
Aspects of the subject disclosure may include obtaining a first media content item comprising a plurality of content segments. For each content segment of the plurality of content segments of the first media content item, comparing the content segment and a prior content segment to identify a content transition, analyzing the content segment to identify a content marker in the content segment, determining a viewing characteristic of the content segment according to the content transition and the content marker, determining if the content segment is unnecessary according to the viewing characteristic, and updating a set of skipping instructions associated with the first media content item responsive to the determining the content segment is unnecessary, and presenting the first media content item at a first device according to the set of skipping instructions associated with the first media content item. Other embodiments are disclosed.