Personalized Game Content Filtering With Biometric Feedback
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Solution Overview
Problem
Viewers and gamers, particularly those who should not be exposed to mature content, often encounter inappropriate content due to inadequate age rating systems.
Innovation Solution
An intelligent dynamic personalized system that utilizes artificial intelligence and machine learning to analyze user personality traits, gaming behavior, and stress levels to edit media content in real-time, modify video games, and provide personalized content based on user preferences and emotional states, while also initiating interventions when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional age rating systems are used to control content access, then content classification is simplified, but users can still access inappropriate content due to system inadequacy
Solution Approach 1:
The patent implements dynamic content filtering that adapts in real-time to user emotional states and personality traits. The system continuously monitors user biometric data (heart rate, galvanic skin response, facial expressions) and adjusts content delivery dynamically, transitioning from static age ratings to adaptive content control based on actual user state
Solution Approach 2:
The system incorporates continuous feedback loops where user biometric responses to content are measured and fed back into the filtering algorithm. This creates a closed-loop system that learns from user reactions and refines content recommendations, improving reliability through iterative adaptation rather than one-time age verification
2Adaptability or versatility
If AI and machine learning are used to analyze user personality traits and behavior, then content personalization is improved, but system complexity increases
Solution Approach 1:
The patent segments the content filtering system into distinct functional modules: biometric data acquisition, personality trait analysis, emotional state detection, and content recommendation engines. Each module operates semi-independently, allowing the system to achieve high adaptability while managing complexity through modular architecture
Solution Approach 2:
The system employs universal AI/ML models that can analyze multiple types of biometric data (facial expressions, heart rate, GSR) and apply them across different content types (movies, games, TV shows). This multi-functional approach achieves versatile personalization without requiring separate specialized systems for each content category
3Object-affected harmful factors
If real-time content editing is performed based on user emotional state, then user stress is minimized, but processing time and system resources increase
Solution Approach 1:
The system performs preliminary analysis of user personality traits and preferences before content delivery, establishing baseline profiles that predict stress responses. This pre-processing allows the real-time system to make faster decisions by comparing actual biometric data against pre-established patterns rather than analyzing everything from scratch
Solution Approach 2:
The patent applies real-time editing selectively to specific content segments rather than entire content streams. When stress indicators are detected, the system locally modifies only the problematic portions (e.g., muting loud sounds, blurring intense visuals) while leaving other segments unchanged, reducing overall processing requirements
4Reliability
If comprehensive biometric monitoring is implemented to detect stress levels, then content appropriateness is improved, but user privacy concerns increase
Solution Approach 1:
The system extracts only the minimum necessary biometric data required for content filtering (basic emotional state indicators) while excluding sensitive personal information. It separates essential monitoring data from optional detailed biometric profiles, allowing users to grant selective permission for content-appropriate data collection
Data Source
AI summary
Aspects of the subject disclosure may include, for example: obtaining training data associated with a video game player, where the training data is indicative of control inputs provided by the video game player during a course of playing a plurality of video games; receiving, via a communications network from a user device of the video game player, current data indicative of current control inputs being provided by the video game player during a course of playing a current video game; determining, based at least in part upon the training data and the current data, that the video game player is currently behaving irregularly as compared to behavior indicated by the training data; and responsive to the determination being that the video game player is currently behaving irregularly as compared to the behavior indicated by the training data, initiating a communication to a third-party requesting intervention. Other embodiments are disclosed.