Adaptive Media Skip and Delay Based on User Interest Signals
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Solution Overview
Problem
Existing content presentation systems fail to dynamically tailor the presentation of auxiliary content based on user interest, often leading to user frustration due to forced interruptions or the inability to skip uninteresting content at inappropriate times.
Innovation Solution
A content presentation application that determines user interest through monitoring activity and user profiles to selectively offer options to skip or delay content, such as advertisements, based on user engagement and viewing history, allowing users to avoid uninteresting segments and continue watching without interruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If auxiliary content is presented to users prior to or during desired media content, then content providers can generate revenue through advertisements, but user satisfaction deteriorates due to forced interruptions and inability to skip uninteresting content
Solution Approach 1:
The system dynamically adjusts the presentation of auxiliary content based on real-time user engagement metrics. When user engagement drops below a threshold, the system automatically modifies content delivery (e.g., skipping ads, reducing interruptions), transforming a static ad-delivery system into an adaptive one that responds to user state changes.
Solution Approach 2:
The system implements feedback loops by monitoring user engagement metrics (viewing duration, interaction patterns, pause/rewind behavior) and using this feedback to adjust auxiliary content presentation. This closed-loop control allows the system to optimize between revenue generation and user satisfaction based on actual user response data.
2Reliability
If users are forced to consume auxiliary content during desired content, then content providers ensure ad delivery, but user frustration increases due to interruptions at critical moments
Solution Approach 1:
The system performs preliminary analysis of the desired content to identify critical moments (climaxes, key plot points) before auxiliary content is scheduled. By pre-processing the content structure and user engagement patterns, the system can proactively avoid scheduling interruptions during these critical segments, preventing frustration before it occurs.
Solution Approach 2:
The system changes the timing parameter of auxiliary content delivery based on content analysis and user engagement data. Instead of fixed scheduling, the system dynamically adjusts when ads are presented, modifying the time parameter to align with low-engagement periods in the desired content, thereby maintaining ad delivery while reducing interruptions at critical moments.
3Device complexity
If up-next functionality is triggered at the same point for all users, then system complexity is reduced, but adaptability to individual user preferences deteriorates
Solution Approach 1:
The system segments the user base into different groups based on viewing behavior patterns, content preferences, and engagement metrics. Instead of treating all users uniformly, it creates distinct segments (e.g., binge-watchers, casual viewers, early leavers) and applies different up-next triggering strategies to each segment, enabling personalized experiences without requiring complex individualized processing for every user.
Data Source
AI summary
Systems and methods are described for managing presentation of content. A determination may be made, based on user information related to the current media asset, whether the presentation of the current media asset is not likely to be of interest to the user. In response to determining that the presentation of the current media asset is not likely to be of interest to the user, a selectable option may be presented to skip current content (e.g., advertisement content, or another currently presented media asset) and start presentation of a next media asset (e.g., a selected media asset, or a media asset similar to the current media asset).


