Automated Video Timeline Curation via User Interaction Telemetry
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Solution Overview
Problem
Manual video curation is time-consuming and expensive, making it impractical to create highlight videos that cater to individual user preferences for large amounts of video content.
Innovation Solution
A telemetry server collects statistical data on user interactions with video segments, processing this information to determine interest levels, which are then used by a playlist generator to automatically curate videos by selecting the most interesting segments and assembling them into curated videos.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual video curation is performed by human curators, then the quality and accuracy of selected video segments are high, but the time consumption and cost increase significantly
Solution Approach 1:
The patent replaces the manual mechanical process of human curators watching and selecting video segments with an automated electronic system. The system uses computer processors to analyze video data, apply selection criteria, and generate curated playlists automatically, eliminating the need for human time investment while maintaining consistent selection quality through algorithmic precision.
Solution Approach 2:
The system enables video curation to serve itself through automated feedback loops. User interactions with video content (playback, pause, seek behavior) are automatically collected and used to refine selection algorithms, allowing the system to continuously improve its curation accuracy without additional human intervention beyond initial parameter setup.
2Reliability
If manual video curation is performed in professional studios, then the production quality of highlight videos is high, but the cost becomes prohibitively expensive for large volumes of content
Solution Approach 1:
The patent creates virtual copies of the professional curation process through software algorithms that replicate human curator decision-making. Instead of paying multiple human curators to manually select segments, the system uses computational models that copy and generalize expert selection patterns across vast quantities of video content at minimal marginal cost.
Solution Approach 2:
The system maintains production quality by allowing dynamic adjustment of selection parameters and criteria. Users can modify weights for different video attributes, time thresholds, and engagement metrics without requiring additional human resources, enabling consistent high-quality output across different content volumes and types through parameter optimization rather than increased manual labor.
3Productivity
If automated systems are used for video curation, then the speed and scalability of production increase, but the ability to capture nuanced user preferences and interests may be reduced
Solution Approach 1:
The system implements continuous feedback loops where user interactions with curated videos (completion rates, re-watches, pauses, seeks) are automatically captured and fed back into the selection algorithm. This enables the automated system to learn and adapt to nuanced user preferences over time, refining its understanding of individual taste patterns while maintaining high-speed automated operation without losing contextual information about user interests.
Solution Approach 2:
The patent divides user preferences into segmentable attributes and behaviors that can be independently analyzed and weighted. By segmenting user interaction data into discrete measurable elements (time spent on segments, replay frequency, skip patterns), the system can process nuanced preferences through computational algorithms while maintaining the detail and specificity of individual user tastes at scale.
Data Source
AI summary
The subject disclosure is directed towards a technology in which highlight (curated) videos are automatically generated for users to watch based upon estimated interest levels with respect to segments of a longer video or set of videos. In one aspect, statistics are collected with respect to actions of viewers of the video or set of videos. These may include playback-related actions such as the number of times a particular segment is played, and/or behavioral actions of viewers during the playback. Based upon which segments have the highest estimated interest levels, a subset of the segments are selected for a playback list, which when played provides the highlight/curated video.