Dynamic Game State Configuration via Biometric Feedback
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Solution Overview
Problem
Video games often fail to engage users effectively due to individual differences in preferences and emotional responses to game challenges, leading to low user engagement and retention.
Innovation Solution
A computer-implemented method that uses sensory data from users playing video games to predict emotional states, dynamically modifying game states to adjust user experience, such as difficulty levels, atmosphere, and features, based on machine learning algorithms and prediction models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a video game is designed with fixed challenge levels and features, then the game development process is simple and straightforward, but different users react differently leading to low user engagement and retention
Solution Approach 1:
The patent implements dynamic game state configuration by continuously monitoring user biometric data (heart rate, skin conductance, temperature) and automatically adjusting game parameters such as difficulty level, challenge type, and feature availability. This transforms the static game into a dynamic system that adapts in real-time to individual user emotional states and physiological responses, thereby improving engagement without requiring complex manual reconfiguration
Solution Approach 2:
The system incorporates continuous feedback loops where user biometric data is collected during gameplay, analyzed to determine emotional state, and used to trigger appropriate game state modifications. This closed-loop feedback mechanism enables the game to respond adaptively to user reactions, maintaining optimal engagement levels while managing complexity through automated decision-making algorithms
2Duration of action of stationary object
If the game state is dynamically modified based on user emotional state, then user engagement and retention improve, but the system complexity and computational requirements increase
Solution Approach 1:
The patent employs pre-trained machine learning prediction models that have been previously trained on extensive biometric data sets to recognize patterns associated with different emotional states. These pre-computed models enable rapid real-time prediction during gameplay without requiring complex calculations during actual use, thus extending user retention while managing system complexity through prior preparation
Solution Approach 2:
The system monitors changes in biometric parameters (heart rate, skin conductance, temperature) and uses these parameter variations to infer emotional state transitions. By focusing on specific key parameters rather than analyzing all possible data points, the system achieves accurate emotional detection with reduced computational complexity, enabling longer user retention through efficient real-time adaptation
3Measurement precision
If multiple types of sensory data are collected and analyzed, then the accuracy of emotional state prediction improves, but the data processing complexity and time requirements increase
Solution Approach 1:
The patent applies different processing weights and analysis methods to different types of sensory data based on their relative importance for emotional state detection. Biometric data such as heart rate and skin conductance are given higher priority and processed with more sophisticated algorithms, while other sensory inputs are processed with simpler methods. This localized quality approach improves overall detection accuracy while minimizing unnecessary processing time on less critical data
Data Source
AI summary
Systems presented herein may automatically and dynamically modify a video game being played by a user based at least in part on a determined or predicted emotional state of a user. Using one or more machine learning algorithms, a parameter function can be generated that uses sensory and/or biometric data obtained by monitoring a user playing a video game. Based on the sensory and/or biometric data, an emotional state of the user can be predicted. For example, it can be determined whether a user is likely to be bored, happy, or frightened while playing the video game. Based at least in part on the determination of the user's emotional state, the video game can be modified to improve positive feelings and reduce negative feelings occurring in response to the video game.