Viewer Reaction Detection for Personalized Content Control
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Solution Overview
Problem
Existing content rating systems do not account for individual viewer preferences and may allow unsuitable content to be viewed by unauthorized viewers, such as young children, due to their broad-based approach.
Innovation Solution
A reaction detection system that uses machine learning and sensors to identify primary and secondary viewers, detecting reactions to content and intrusions, and modifies content output based on predefined criteria to prevent unauthorized viewing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a broad based content rating system is used, then content suitability for general audiences is improved, but individual viewer preferences and unauthorized viewing prevention deteriorate
Solution Approach 1:
The system segments the general content rating into individual viewer-specific ratings by detecting each viewer's reactions and preferences through sensors and machine learning, creating personalized content suitability assessments that maintain general appropriateness while accounting for individual differences
Solution Approach 2:
The system implements real-time feedback loops by continuously monitoring viewer reactions through sensors (camera, microphone, motion sensors) and adjusting content playback decisions dynamically based on detected emotional states, surprise reactions, and engagement levels to prevent unauthorized viewing while maintaining personalization
2Adaptability or versatility
If content ratings are applied to the entirety of content, then overall content appropriateness is improved, but identification of specific unsuitable portions deteriorates
Solution Approach 1:
The system divides content into discrete segments or moments by analyzing viewer reactions at specific time points, identifying precisely which portions of content cause adverse reactions rather than treating the entire content as uniformly suitable or unsuitable
Solution Approach 2:
The system applies content control actions (pausing, blocking, warning) only to specific portions of content that trigger adverse reactions, rather than controlling the entire content stream, thereby maintaining precision in identifying unsuitable portions while preserving overall content accessibility
3Adaptability or versatility
If a reaction detection system with machine learning and sensors is implemented, then personalized content control is improved, but device complexity deteriorates
Solution Approach 1:
The system uses multi-functional sensors (camera for facial expressions, microphone for vocal reactions, motion sensors for physical responses) that serve multiple detection purposes simultaneously, reducing the need for separate specialized components while achieving comprehensive viewer reaction monitoring
Solution Approach 2:
The machine learning model automatically trains and adapts to individual viewer preferences through continuous observation of reactions, eliminating the need for manual configuration or complex setup procedures, thereby reducing operational complexity while maintaining high personalization
Data Source
AI summary
Systems, apparatuses, and methods for detecting the reactions of primary and secondary viewers of content are described. Reactions of a primary or secondary viewer of content may be detected through use of a sensor and machine learning model. Based on the reaction of the primary or secondary viewer satisfying some criteria, outputting of the content may be modified and/or alternative content may be provided. Furthermore, metadata may be generated based on the detection of adverse reactions to intrusions by a viewer of content that is associated with an indication that the outputted content is associated with certain predefined types.