Personalized Gaming Experience System for Dynamic Difficulty Adjustment
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Solution Overview
Problem
Traditional video games have fixed difficulty levels and monetization settings, leading to inconsistent player experiences as different players may find the same game too easy or too challenging, resulting in poor engagement and retention.
Innovation Solution
A personalized gaming experience system that uses machine learning to dynamically adjust game settings based on user data and game state, optimizing metrics such as engagement, satisfaction, and revenue by adapting game elements like difficulty and asset display in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If fixed difficulty levels are used in games, then game development is simplified and consistent, but player engagement and satisfaction deteriorate because different players find the same game too easy or too challenging
Solution Approach 1:
The patent implements dynamic difficulty adjustment where game parameters automatically change based on real-time player performance data. The system monitors player actions, success rates, and engagement metrics to continuously adapt difficulty levels, transforming static game settings into dynamic ones that respond to individual player capabilities without requiring manual intervention or complex development processes.
Solution Approach 2:
The system changes multiple game parameters simultaneously based on player performance, including difficulty level, resource availability, enemy behavior patterns, and task complexity. By adjusting these parameters dynamically according to measured player metrics, the game adapts to individual players while maintaining consistent development workflows through automated parameter management.
2Ease of operation
If manual difficulty setting changes are required, then players have control over their experience, but engagement deteriorates because players must interrupt gameplay to adjust settings
Solution Approach 1:
The game system automatically monitors player performance and adjusts difficulty settings without requiring player intervention. The system serves itself by collecting gameplay data, analyzing player capability trends, and autonomously modifying game parameters, thereby eliminating the need for players to pause and manually adjust settings while maintaining full player control over the adaptive experience.
Solution Approach 2:
The system implements continuous feedback loops where player performance data is collected, analyzed, and used to automatically adjust difficulty in real-time. This closed-loop feedback mechanism ensures the game responds to player needs dynamically without interrupting gameplay, as adjustments are made seamlessly based on ongoing performance monitoring rather than requiring manual player input.
3Device complexity
If fixed monetization targets are set, then revenue prediction is simplified, but player satisfaction deteriorates because the same monetization approach may be too aggressive or too lenient for different players
Solution Approach 1:
The patent applies different monetization strategies to different player segments based on their individual characteristics, spending patterns, and engagement levels. Rather than using a uniform monetization approach, the system tailors monetization parameters locally to each player's profile, offering personalized deals, discounts, and content recommendations that match their specific preferences and spending capacity.
Solution Approach 2:
The system dynamically adjusts monetization parameters including pricing levels, discount offerings, content availability, and promotional timing based on real-time analysis of player behavior and spending patterns. This allows the monetization system to adapt to individual players while maintaining manageable complexity through automated parameter adjustment based on measured player metrics.
Data Source
AI summary
A method for optimizing a computer-implemented game for a target metric is disclosed. Based on a detecting that an optimization point has been reached during a runtime of the computer game, user data, game state data, and personalized gaming experience (PGE) question data is communicated to a PGE server. The PGE question data is linked to the optimization point. An answer corresponding to the PGE question data is received from the PGE server. The answer is selected from a plurality of answers linked to the optimization point based on an application of a machine-learned model to the user data, game state data, and PGE question data. The received answer is implemented within the computer-implemented game.