Personalized Game Challenge Generation via Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual curation of personalized challenges in computer games is laborious and unscalable, failing to effectively cater to individual player preferences due to the lack of efficient challenge generation methods.
Innovation Solution
A computer-implemented method using machine-learned models to generate personalized challenges by processing input data, determining candidate challenges' difficulty scores, and iteratively updating search constraints to meet a target difficulty threshold, incorporating game-specific logic and real-world data through natural language processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual curation of challenges is used, then challenge quality and personalization can be achieved, but the process becomes laborious and unscalable
Solution Approach 1:
The patent replaces the manual mechanical process of challenge curation with an automated machine learning system. The ML model automatically generates and evaluates challenge parameters based on player data, substituting human manual work with computational processes that can scale indefinitely without proportional increases in labor.
Solution Approach 2:
The system enables challenges to generate themselves automatically through the machine learning model. The model takes player data as input and autonomously produces personalized challenge parameters without requiring manual intervention, making the challenge generation process self-service and highly scalable.
2Adaptability or versatility
If manual curation of challenges is used, then individual player preferences can be considered, but the task becomes very laborious
Solution Approach 1:
The patent replaces the complex manual process of analyzing player preferences and creating personalized challenges with a machine learning system. The ML model automatically processes player data and generates appropriate challenges, eliminating the labor-intensive manual analysis while maintaining personalization quality.
3Productivity
If automated challenge generation is implemented, then scalability is improved, but context-awareness and personalization may be reduced
Solution Approach 1:
The patent uses machine learning to substitute manual context analysis with automated pattern recognition. The ML model learns from player data to understand context and preferences, enabling scalable automated generation that maintains or even improves context-awareness compared to manual methods.
Solution Approach 2:
The system incorporates feedback loops where the ML model continuously learns from player responses and performance data. This feedback mechanism enables the automated system to improve its context-awareness over time, generating increasingly personalized challenges while maintaining scalability.
Data Source
AI summary
According to an aspect of this specification, there is described a computer implemented method comprising: receiving input data, the input data comprising data relating to a user of a computer game; generating, based on the input data, one or more candidate challenges for the computer game; determining, using a machine-learned model, whether each of the one or more of the candidate challenges satisfies a threshold condition, wherein the threshold condition is based on a target challenge difficultly; in response to a positive determination, outputting the one or more candidate challenges that satisfy the threshold condition for use in the computer game by the user.