Self-Evolving AI Playstyle Models from Gameplay and Emotion Data
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Solution Overview
Problem
Existing gaming systems lack intelligent, self-evolving playstyle models that can adapt to player behavioral shifts and emotional states, leading to suboptimal gaming experiences and reduced player engagement.
Innovation Solution
A gaming system that utilizes a machine learning network to analyze gameplay decisions and emotional states, modifying a playstyle model in real-time to enhance player experience through intelligent recommendations and adaptations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional static playstyle models are used in gaming systems, then device complexity is reduced and ease of manufacture is improved, but adaptability to player behavioral shifts and emotional states deteriorates
Solution Approach 1:
The patent transforms static playstyle models into dynamic, self-evolving models that continuously adapt to player behavior. The machine learning network automatically updates playstyle models in real-time based on player gameplay data and emotional states, enabling the system to evolve without manual intervention while maintaining manageable complexity through automated processes.
Solution Approach 2:
The playstyle model system performs self-updates and self-evolution through machine learning algorithms that automatically process gameplay data and adjust models without external intervention. This self-service capability allows the system to improve adaptability while reducing the operational burden of model maintenance and updates.
2Productivity
If manual intervention is required for playstyle model updates, then manufacturing precision and control are improved, but productivity and response time to player behavior changes deteriorate
Solution Approach 1:
The system implements self-updating playstyle models through machine learning networks that automatically process gameplay data and evolve models without manual intervention. This eliminates the need for operators to manually update models, dramatically increasing productivity and response time while maintaining operational simplicity through automated processes.
Solution Approach 2:
The system continuously collects feedback from player gameplay data and emotional states, processes this information through machine learning networks, and automatically adjusts playstyle models in real-time. This closed-loop feedback mechanism enables rapid adaptation to player behavior changes while simplifying operations through automated decision-making.
3Measurement precision
If generic playstyle models are used, then ease of operation and implementation are improved, but measurement precision of player-specific behaviors deteriorates
Solution Approach 1:
The patent transitions from generic playstyle models to player-specific customized models that precisely capture individual behavior patterns. The machine learning network processes player-specific gameplay data to generate tailored playstyle models, achieving high measurement precision while managing complexity through automated data processing and individualized model generation.
Data Source
AI summary
The present disclosure relates generally to a gaming system, device, and method supportive of a self-evolving, AI-based playstyle models. A gaming system, device, and method are provided that identify data associated with a gameplay session at a gaming device, provide the data to a machine learning network; receive an output from the machine learning network in response to the machine learning network processing the data using a playstyle model, the output including an indication associated with modifying the playstyle model; and modify the playstyle model based on the indication. The data includes at least one of: a gameplay decision received during the gameplay session; and an emotional state of a user in association with the gameplay decision.