Media Breakpoint Generation Using ML Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing media playback systems face challenges in minimizing disruption to users when inserting breakpoints for presenting digital components, as current methods often pause media at inappropriate times, leading to a higher likelihood of user abandonment and wastage of computing resources.
Innovation Solution
A machine learning model, incorporating bi-directional gradient recurring units and fully connected neural networks, evaluates media items to identify optimal breakpoint locations based on presentation features and relative locations, reducing disruptiveness and optimizing breakpoint placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If breakpoints are inserted during media playback to present digital components, then user engagement and additional content delivery are improved, but user disruption and media playback interruption increase
Solution Approach 1:
The system performs preliminary analysis of the media item before playback to identify optimal breakpoint locations. By analyzing audio tracks, video content, and media metadata in advance, the system determines when to insert digital components to minimize user disruption while maintaining engagement.
Solution Approach 2:
The system applies different breakpoint insertion strategies to different segments of the media item based on their local characteristics. High-energy segments like action scenes or dialogue-heavy portions are identified as optimal locations for breakpoints, while low-energy segments are avoided, ensuring minimal disruption to user experience.
2Productivity
If breakpoints are placed at frequent intervals to maximize digital component delivery, then content engagement is improved, but computing resource wastage increases
Solution Approach 1:
The system dynamically adjusts breakpoint insertion parameters based on media characteristics and user behavior patterns. By analyzing features like audio energy levels, video motion intensity, and temporal patterns, the system optimizes the timing and frequency of breakpoints to deliver maximum content engagement while minimizing unnecessary computational resources.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor user interaction with digital components and adjust breakpoint placement accordingly. By analyzing user engagement data and playback patterns, the system refines future breakpoint selections to improve delivery efficiency and reduce resource wastage.
3Device complexity
If breakpoints are inserted without analyzing media content features, then system complexity is reduced, but breakpoint effectiveness and user experience deteriorate
Solution Approach 1:
The system divides the media item into temporal segments and analyzes each segment's characteristics independently. By segmenting the media into audio tracks, video clips, and metadata components, the system can apply targeted analysis to identify optimal breakpoint locations without overwhelming computational complexity.
Solution Approach 2:
The system introduces an intermediary analysis layer that processes media features and translates them into breakpoint placement decisions. This intermediary layer, which analyzes audio energy, video motion, and temporal patterns, acts as a mediator between raw media data and breakpoint selection, improving effectiveness while managing system complexity.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining breakpoints in a media item. Methods can include determining a candidate set of breakpoints within a media item. A machine learning model is used to generate a score for each particular candidate breakpoint in the set of candidate breakpoints based on presentation features of the media item. A subset of candidate breakpoints is selected from the set of candidate breakpoints based on the score. A final set of breakpoints is selected from the subset of candidate breakpoints based on a combination of the score for each particular candidate breakpoint and a location of the particular candidate breakpoint relative to a different candidate breakpoint. The final set of breakpoints is stored in a database and during playback of the media item, a digital component is presented when the media item reaches a stored breakpoint.