AI Gaming Ecosystem Calibration via Player Sensor Data
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Solution Overview
Problem
Video games often fail to provide a balanced and enjoyable experience for players, leading to frustration and stress, which can negatively impact mental and physical health due to unadjustable difficulty levels and unpleasant audio-visual aspects.
Innovation Solution
A system that uses AI models to analyze player data from various sensors, adjusting game difficulty, visual, and audio elements in real-time to enhance gameplay experience and reduce stress, by modifying game rules, attributes, or providing hints and recommendations based on player satisfaction indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual difficulty adjustment options are provided, then player control over game challenge is improved, but player satisfaction and playability are not guaranteed to improve
Solution Approach 1:
The system continuously monitors player performance metrics, stress indicators, and engagement levels to dynamically adjust difficulty parameters. This closed-loop feedback mechanism ensures difficulty adaptation is responsive to actual player state rather than relying on static manual adjustments, thereby reliably improving player satisfaction and playability.
Solution Approach 2:
The patent implements dynamic difficulty adjustment where game parameters such as enemy health, player resources, and mission objectives are continuously modified based on real-time analysis of player capability and stress levels. This transforms the static difficulty setting into a dynamic system that adapts to player needs, ensuring optimal playability without requiring manual intervention.
2Productivity
If difficulty level is increased to provide challenge, then game engagement is improved, but player stress and dissatisfaction increase
Solution Approach 1:
The system analyzes multiple player parameters including performance metrics, physiological stress indicators, and behavioral patterns to dynamically adjust difficulty parameters. By monitoring stress levels through sensors and game behavior analysis, the system modifies challenge parameters to maintain optimal engagement without exceeding stress thresholds, thereby preventing dissatisfaction while preserving engagement.
Solution Approach 2:
The system uses continuous feedback loops where player stress indicators and performance data are monitored in real-time, and difficulty parameters are adjusted accordingly. This ensures challenge levels remain engaging while preventing stress from becoming excessive, balancing engagement and well-being through responsive adaptation.
3Stability of the object's composition
If static game difficulty is maintained, then game design consistency is preserved, but player satisfaction varies widely across different skill levels
Solution Approach 1:
The patent transforms static difficulty settings into dynamic systems that adapt to individual player capabilities while preserving core game design integrity. The system adjusts parameters such as enemy behavior, resource availability, and mission parameters based on real-time player analysis, allowing the same game design to provide optimal experiences across diverse skill levels without compromising design consistency.
Solution Approach 2:
The system applies localized adjustments to specific game parameters rather than overhauling the entire game design. By modifying individual elements such as enemy health, player resources, or mission objectives based on player skill level, the system maintains overall game design consistency while adapting local parameters to suit different player capabilities, thereby improving playability across skill levels.
Data Source
AI summary
An artificial intelligence (AI) based method for calibration of a gaming ecosystem and to maximize satisfaction of a player involved in a gaming session such as single-player gaming session, multi-player online gaming session, and a videogame recommendation system is provided. The system detects the gaming session that includes an execution of a video game for a first gameplay and acquires sensor data associated with a player involved in the first gameplay. The sensor data corresponds to a duration of the detected gaming session. The system determines one or more indicators of a dissatisfaction of the player with the first gameplay based on application of one or more Artificial Intelligence (AI) models on the sensor data. The system controls the execution of the video game to modify one or more aspects associated with the first gameplay or a second gameplay of the video game that is different from the first gameplay.