Video Recommendation Using Physiological Affect Analysis
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Solution Overview
Problem
Current video recommendation systems rely on imprecise and subjective star ratings, making it difficult to accurately evaluate and recommend videos based on user responses, especially for brief periods of content.
Innovation Solution
A computer-implemented method that captures mental state data, including physiological and facial data, while playing a media presentation, and recommends subsequent media based on this data, correlating individual responses with aggregated data from others who viewed the same content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If star ratings are used for video recommendations, then the system is simple to operate, but the measurement precision of user response is poor
Solution Approach 1:
The patent replaces manual star rating input with automated physiological sensing systems. Sensors detect biological signals (heart rate, skin conductance, facial expressions) to automatically determine user engagement levels, substituting the mechanical rating system with a physiological measurement system that provides more precise objective data without requiring user effort
Solution Approach 2:
The system performs self-evaluation by automatically analyzing physiological data to determine user engagement and generate recommendations without requiring explicit user input. The system serves itself by using sensed data to both evaluate content quality and generate recommendations, eliminating the need for manual rating mechanisms
2Reliability
If star ratings are collected from users, then ease of operation is maintained, but reliability of recommendations deteriorates
Solution Approach 1:
The patent replaces subjective manual rating with objective physiological measurement. Sensors continuously monitor biological signals during video playback, providing reliable objective data about user engagement that cannot be faked or inconsistently applied, thereby improving recommendation reliability without requiring user participation
Solution Approach 2:
The system implements continuous feedback loops where physiological data is constantly monitored and fed back to the recommendation engine. This real-time feedback mechanism allows the system to dynamically adjust recommendations based on actual user response patterns, significantly improving reliability compared to static star ratings
3Measurement precision
If detailed mental state analysis is performed, then measurement precision of user response improves, but loss of time for data processing increases
Solution Approach 1:
The patent performs preliminary classification of physiological signals into discrete engagement states (engaged, neutral, bored) using pre-trained models. This preliminary action reduces complex continuous data into categorized states before detailed analysis, enabling rapid processing while maintaining measurement precision through the use of established physiological markers for each state
Solution Approach 2:
The system segments the analysis into distinct temporal phases: real-time classification during video playback for immediate recommendations, and post-viewing detailed analysis for comprehensive profiling. This segmentation allows the system to provide timely recommendations using simplified real-time data while performing more detailed precision measurements afterward without delaying the recommendation function
Data Source
AI summary
Analysis of mental states is provided to enable data analysis pertaining to video recommendation based on affect. Analysis and recommendation can be for socially shared livestream video. Video response may be evaluated based on viewing and sampling various videos. Data is captured for viewers of a video where the data includes facial information and/or physiological data. Facial and physiological information may be gathered for a group of viewers. In some embodiments, demographics information is collected and used as a criterion for visualization of affect responses to videos. In some embodiments, data captured from an individual viewer or group of viewers is used to rank videos.