Pointer Activity Aggregation for Video Interestingness Prediction
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Solution Overview
Problem
Existing systems face challenges in automatically processing and predicting user interest in diverse online video content, particularly due to scalability issues and the difficulty in categorizing varied video genres, including user-generated content, and require efficient and non-intrusive methods to collect user feedback.
Innovation Solution
A method that aggregates pointer activity from thousands of viewers to estimate video interestingness by tracking and analyzing pointer movements during video playback, generating an aggregate pointer signal, and extracting features to produce an interestingness score, which can be used to identify regions of interest and generate video previews or advertisements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video content analysis is performed to infer interestingness, then category-specific assumptions can be made about user interest, but the system becomes difficult to scale and cannot handle diverse video genres effectively
Solution Approach 1:
The patent replaces the mechanical system of manual video content analysis with an optical/electromagnetic system by using computer vision algorithms and automated feature extraction. This substitution enables scalable processing of diverse video genres without requiring category-specific assumptions, as the system automatically identifies visual patterns and extracts relevant features across different video types.
Solution Approach 2:
The patent implements a universal video processing framework that can handle multiple video genres and categories through a single system. By using general-purpose feature extraction techniques that work across different video types (sports, news, entertainment, etc.), the system achieves both scalability and adaptability without needing separate analysis pipelines for each category.
2Measurement precision
If user feedback is collected through psychological assessments, then accurate user interest data can be obtained, but the collection process is intrusive and limited to controlled settings with few users
Solution Approach 1:
The patent implements a self-service measurement approach where users passively provide data through their natural interactions with the video player and device. The system automatically collects pointer activity, playback behavior, and device interaction data without requiring users to complete surveys or participate in controlled assessments. This eliminates the intrusiveness of psychological assessments while gathering data from large numbers of users in natural viewing conditions.
Solution Approach 2:
The patent establishes a continuous feedback loop where user interactions with the video player and device are automatically tracked and used to infer interestingness. The system collects real-time data on pointer movements, playback pauses, and other behavioral signals, then uses this feedback to dynamically adjust and improve interestingness predictions without interfering with the user experience.
3Quantity of substance
If video player interactions are used as feedback signals, then data can be collected at scale during online video watching, but the interactions are very sparse and provide limited information
Solution Approach 1:
The patent adds new dimensions to the feedback data by incorporating pointer activity tracking and device interaction metrics beyond simple video player controls. Instead of relying only on sparse playback actions (play, pause, stop), the system captures continuous pointer movement data, touch interactions, and device orientation information, transforming limited interaction data into a rich multi-dimensional signal that provides detailed insights into user attention and interest.
Data Source
AI summary
A method is provided, that initiates with providing a video over a network to a plurality of client devices, wherein each client device is configured to render the video and track movements of a pointer during the rendering of the video. Movement data that is indicative of the tracked movements of the pointer is received over the network from each client device. The movement data from the plurality of client devices is processed to determine aggregate pointer movement versus elapsed time of the video. The aggregate pointer movement is analyzed to identify a region of interest of the video. A preview of the video is generated based on the identified region of interest.