ML Game Element Evaluation System
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Solution Overview
Problem
The conventional game development process is time-consuming and costly due to the need for extensive playtesting by human testers to evaluate new game elements, which can be particularly burdensome for smaller game development organizations lacking resources.
Innovation Solution
A game evaluation system utilizing machine learning models trained on gameplay data to predict player responses to new game elements, allowing for accurate predictions without the need to fully build the elements, and enabling evaluation of textual descriptions to reduce development time and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human playtesting is used to evaluate game elements, then evaluation accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent creates virtual copies of human players through machine learning models that simulate player behavior and responses. These digital twins replicate human testing capabilities without requiring actual human testers, thereby maintaining evaluation accuracy while eliminating time consumption and cost associated with human playtesting
Solution Approach 2:
The patent replaces the mechanical system of human playtesting with an automated machine learning-based evaluation system. The ML models process game element data and generate predictions about player responses, substituting the manual, time-consuming human testing process with an automated computational approach that delivers results instantly
2Measurement precision
If human playtesting is used to evaluate game elements, then evaluation accuracy is improved, but cost increases significantly
Solution Approach 1:
The patent creates virtual copies of human players through machine learning models that simulate player behavior and responses. These digital twins replicate human testing capabilities without requiring actual human testers, thereby maintaining evaluation accuracy while eliminating cost associated with human playtesting
Solution Approach 2:
The patent uses computationally inexpensive ML models that can be rapidly deployed and executed. These models serve as cheap alternatives to expensive human testers, providing accurate evaluations at a fraction of the cost while requiring minimal computational resources
3Reliability
If game elements are fully built before evaluation, then evaluation realism is improved, but development time increases
Solution Approach 1:
The patent performs evaluation actions before the game elements are fully built. The ML models evaluate game elements based on available data such as design documents, prototypes, or partial implementations, allowing developers to get feedback early in the development process without waiting for complete implementation
Solution Approach 2:
The patent creates virtual representations of game elements that can be evaluated by ML models before the actual game elements are fully built. These digital copies allow for realistic evaluation of design concepts, mechanics, and features without requiring full implementation
Data Source
AI summary
A method for evaluating a game element is disclosed herein. In one example, the method includes receiving a data set representing the game element and generating a predicted response to the game element by inputting the data set into a machine learning model. In some embodiments, the machine learning model is trained on a plurality of training samples derived from previous gameplay data. In some embodiments, each training sample includes a gameplay event and a player response associated with the gameplay event.