Personalized Game Guidance Using Cluster Maps and Reinforcement Learning
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Solution Overview
Problem
Existing video game tutorials are often not personalized, failing to address the specific difficulties users face, and users may not be aware of content that aligns with their interests, leading to reduced engagement and retention.
Innovation Solution
A computer-implemented method using machine learning techniques to generate user-specific guidance by clustering user interaction data onto a map, applying reinforcement learning to determine personalized tutorials and content recommendations based on user skill and interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional non-personalized tutorials are used, then device complexity is reduced, but user engagement and retention deteriorate
Solution Approach 1:
The system changes parameters by extracting feature vectors from user interaction data and using machine learning models to dynamically adjust tutorial content based on user skill level, interests, and behavior patterns. This enables personalized guidance without requiring complete system redesign
Solution Approach 2:
The patent introduces machine learning models and feature vector representations as intermediaries between raw user data and tutorial content delivery. These intermediaries process and interpret user behavior to generate personalized guidance, bridging the gap between simple data collection and complex personalization
2Measurement precision
If comprehensive user interaction data is collected, then guidance personalization is improved, but loss of time for data processing increases
Solution Approach 1:
The system performs preliminary actions by continuously collecting and preprocessing user interaction data in the background during normal gameplay. Feature vectors are extracted and stored for future use, so when personalization is needed, the data is already prepared and ready for quick retrieval
Solution Approach 2:
The patent segments user interaction data into distinct feature vectors representing different aspects of user behavior (skill level, interests, preferences). This segmentation allows the system to process and retrieve only relevant features for specific personalization needs rather than processing entire datasets
3Adaptability or versatility
If generic content recommendations are provided, then device complexity is minimized, but user engagement deteriorates
Solution Approach 1:
The system applies local quality by providing different content recommendations to different users based on their specific feature vectors. Each user receives tailored recommendations that match their individual interests and skill level, rather than uniform generic content. This is achieved through machine learning models that analyze user data and generate personalized recommendations
Data Source
AI summary
A device may access a feature vector generated based on interactions by a user with a video game. The device may access a cluster map comprising a mapping of user clusters, wherein each location within the cluster map is associated with a set of users whose feature vectors are within a threshold degree of similarity of each other. The cluster map may be generated using a plurality of extracted feature vectors obtained from interaction information. A device may determine a map location within the cluster map associated with the user based at least in part on the feature vector. A device may determine a target map location within the cluster map. A device may determine a guidance action based at least in part on the target map location and the map location associated with the user. A device may execute the guidance action.