Video Highlight Prediction via Crowdsourced Engagement Data
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Solution Overview
Problem
Existing systems struggle to predict noteworthy portions of video content across diverse contexts, relying solely on video analysis, which is insufficient for determining engaging moments in various types of media content.
Innovation Solution
A computing system predicts noteworthy portions by crowdsourcing user engagement information, such as reactions, comments, and views, to identify and extract highlights that can be shared on social networks, encouraging user interaction and discovery of related videos.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a computing system relies solely on video analysis to determine noteworthy portions, then the system complexity remains low, but the precision of identifying engaging moments deteriorates across diverse contexts
Solution Approach 1:
The patent introduces user engagement data as an intermediary element that bridges the gap between simple video analysis and accurate identification of noteworthy portions. By incorporating reactions, comments, and views from users as mediating information, the system achieves higher precision in identifying engaging moments without directly increasing the complexity of video analysis algorithms themselves.
Solution Approach 2:
The system implements feedback loops by collecting user engagement data (reactions, comments, views) and using this feedback to refine and improve the identification of noteworthy portions. This feedback mechanism allows the system to continuously learn and adapt, improving measurement precision over time while maintaining relatively simple video analysis components.
2Adaptability or versatility
If the system uses diverse user engagement information to predict noteworthy portions, then the adaptability to different contexts improves, but the difficulty of detecting and measuring noteworthy portions increases
Solution Approach 1:
The patent applies universality by using a unified approach of collecting and analyzing user engagement data across multiple different video contexts and types. The same data collection and analysis framework works universally for sports videos, entertainment videos, news videos, and other types, enabling the system to adapt to diverse contexts without creating context-specific detection mechanisms.
3Productivity
If the system extracts and shares highlights on social networks, then user engagement increases, but the loss of time for processing and managing video content worsens
Solution Approach 1:
The system performs preliminary actions by automatically extracting and preparing highlight portions before they are needed for sharing. By proactively identifying and segmenting noteworthy portions using user engagement data, the system reduces the time required for on-demand processing and enables faster sharing on social networks, thereby increasing productivity without proportionally increasing processing time investment.
Data Source
AI summary
In one embodiment, a method includes accessing a first video; predicting a first noteworthy portion for the first video, wherein the first noteworthy portion is a portion of the first video that is predicted based on user-engagement information associated with the portion of the first video; extracting a first highlight from the first video, wherein the first highlight corresponds to the first noteworthy portion; and sending, to a client system of a user, information configured to render the first highlight and a first interactive element that is configured to launch the first video on the client system.