Media Playback Skipping Based on Rewind and Watch Patterns
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Solution Overview
Problem
Current methods for fast forwarding and rewinding media assets require manual operations and lack intelligence to determine the optimal length of fast forwarding or rewinding, often leading to inaccurate positioning and missed or skipped content of interest.
Innovation Solution
A system that analyzes user rewind and fast forward patterns to identify characteristics of interest, generating a machine learning model to automatically fast forward or recommend against skipping segments containing those characteristics, and adapt to user overrides.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual fast forward or rewind operations are used, then the viewer can navigate through media content, but the viewer must perform manual operations and is unaware of the optimal length to fast forward or rewind
Solution Approach 1:
The system performs fast forwarding and rewinding automatically without requiring manual user input. The media player analyzes consumption patterns and autonomously determines when and how long to fast forward or rewind, making the system self-serve the user's navigation needs.
Solution Approach 2:
The system uses consumption history and pattern analysis as feedback to continuously improve fast forwarding and rewinding decisions. By monitoring what content the viewer actually watches and skips, the system learns optimal navigation strategies and adjusts its behavior accordingly.
2Loss of time
If the viewer fast forwards a segment perceived as uninteresting, then time is saved, but the viewer may miss essential plot portions or content of interest
Solution Approach 1:
The system performs preliminary analysis of media content by examining consumption patterns across multiple viewings before making fast forwarding decisions. By pre-identifying which segments are consistently skipped and which are always watched, the system reliably determines safe fast forward targets before the viewer actually fast forwards.
Solution Approach 2:
The system replaces manual viewer judgment about content interest with automated pattern recognition algorithms. Instead of relying on the viewer's perception of what is uninteresting, the system uses consumption history data to objectively identify segments that are reliably uninteresting based on actual viewing behavior.
3Ease of operation
If the viewer manually fast forwards to skip uninteresting portions, then navigation is possible, but the viewer must determine the exact length to fast forward which requires trial and error
Solution Approach 1:
The system automatically determines the precise fast forward length without user input. By analyzing consumption patterns to identify exact boundaries of interesting and uninteresting content, the system self-calculates the optimal fast forward distance to skip precisely the right amount of content.
Solution Approach 2:
The system pre-determines fast forward lengths by analyzing consumption patterns before the viewer needs to navigate. By examining what portions are consistently skipped versus watched across multiple viewings, the system prepares accurate fast forward instructions in advance.
4Extent of automation
If the system automatically fast forwards based on consumption patterns, then manual intervention is reduced, but the system must accurately identify characteristics of interest which requires complex analysis
Solution Approach 1:
The system replaces complex manual analysis with automated pattern recognition using consumption history data. Instead of requiring sophisticated real-time content analysis, the system uses simpler pattern matching based on what the viewer has actually watched and skipped in previous viewings.
Solution Approach 2:
The system uses consumption patterns from previous viewings as templates to guide fast forwarding in current viewings. By copying successful navigation strategies from past behavior, the system avoids the need for complex real-time decision-making algorithms.
Data Source
AI summary
Systems and methods for providing fast forwarding recommendations based on the user's consumption history are disclosed. The consumption history includes data relating to attributes that were previously rewinded and watched and those that were skipped and forwarded. It also includes scores for attributes that were present and absent in a portion that was previously rewinded or forwarded. A score is assigned to the attributes and used for determining a consumption pattern. If the consumption pattern indicates that the user previously rewinded and watched the attribute, then a recommendation not to skip an upcoming portion that includes the attribute is provided. A graphical timeline that depicts the amount of time saved by skipping the portion of the media asset with the attribute is also provided.