System Tuner for Personalized Game Variants via Player Segmentation
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Solution Overview
Problem
Existing gaming systems fail to provide personalized user experiences by optimizing game settings and content based on individual player segments, leading to suboptimal engagement and satisfaction.
Innovation Solution
The System Tuner employs machine learning algorithms to analyze player data and generate game variants by modifying elements such as bonus amounts, difficulty levels, and in-game music, creating tailored experiences for specific player segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If game settings and content are customized for individual player segments using machine learning, then user engagement and satisfaction are enhanced, but system complexity and computational resources increase
Solution Approach 1:
The patent segments players into distinct player segments based on their behavior patterns, preferences, and gameplay characteristics. By dividing the player base into segments, the system can apply different game settings and content optimizations to each segment, achieving personalized experiences without needing to completely customize everything for every individual player, thus managing system complexity while improving adaptability.
Solution Approach 2:
The system dynamically adjusts game parameters such as bonus amounts, difficulty levels, and in-game music based on machine learning analysis of player data. By changing these parameters according to player segments, the system achieves adaptability and personalization while working within existing game architecture, avoiding the need to rebuild the entire game system.
2Reliability
If machine learning algorithms analyze player data to generate optimized game variants, then player satisfaction increases, but processing time and computational energy consumption increase
Solution Approach 1:
The system performs machine learning analysis and generates optimized game variants in advance, before players actually play. By pre-processing player data and creating personalized game configurations ahead of time, the system reduces the computational burden during actual gameplay, lowering energy consumption while still delivering personalized experiences that increase player satisfaction.
Solution Approach 2:
The machine learning system automatically analyzes player data and generates optimized game variants without requiring manual intervention. This self-service approach allows the system to efficiently process player data and create personalized experiences while minimizing the need for additional computational resources during peak gameplay periods.
3Productivity
If game variants are created by modifying bonus amounts, difficulty levels, and in-game music, then user engagement improves, but development and maintenance complexity increases
Solution Approach 1:
The patent applies different game settings and modifications to specific player segments rather than creating entirely separate game versions. By making localized changes to bonus amounts, difficulty levels, and in-game music for specific segments, the system improves user engagement while reusing the core game codebase, thereby reducing development and maintenance complexity compared to creating fully separate game variants.
Solution Approach 2:
The system dynamically adjusts game parameters such as bonus amounts and difficulty levels based on real-time player behavior and segment classification. This dynamic approach allows the game to adapt to different player needs without requiring static pre-programmed variants for every possible scenario, reducing development complexity while maintaining high user engagement through personalized experiences.
Data Source
AI summary
A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein for a System Tuner for customizing a player's experience. The System Tuner creates an optimal game model based on game-related data of a plurality of players. The optimal game model corresponds to a player segment in the plurality of players. The System Tuner generates one or more rules for building a game variant based on the optimal game model. The System Tuner detects a first player accessing the game. The System Tuner determines a particular player segment to which the player belongs. The System Tuner generates a game variant based on an optimal game model for the particular player segment and sends the game variant to a client device.