Automated Controller Configuration via Machine Learning
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Solution Overview
Problem
Users face limitations in interacting with software due to default controller configurations that are not optimized for individual user tendencies, leading to cumbersome customization and reduced user experience, especially in competitive games where default controls are not suited for skilled performance.
Innovation Solution
A machine learning system provides automated controller configuration recommendations based on user profiles, including skill level and input tendencies, which can be shared and updated, allowing for dynamic adjustments to improve user performance and accessibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If controller settings are customized for each user, then user performance is improved, but device complexity increases
Solution Approach 1:
The system automatically analyzes user input patterns and generates optimized controller configurations without requiring manual user setup. The controller settings adjust themselves based on observed usage patterns, eliminating the need for users to navigate complex customization options while still achieving personalized optimization.
Solution Approach 2:
The system dynamically modifies controller parameters such as sensitivity levels, dead zones, and button assignments based on analyzed user tendencies. By automatically adjusting these parameters according to observed input patterns, the system delivers personalized performance optimization without exposing users to the underlying complexity of multiple configurable parameters.
2Adaptability or versatility
If controller settings are made customizable, then adaptability is improved, but ease of operation worsens
Solution Approach 1:
The system performs automatic configuration analysis and generation, eliminating the need for users to manually adjust settings. The controller settings service itself analyzes usage data and self-adjusts configurations, providing high adaptability without requiring user interaction with complex setup procedures.
Solution Approach 2:
The system pre-analyzes user input patterns and prepares optimized controller configurations in advance, before the user needs them. By performing the configuration work beforehand based on observed usage, the system makes adaptability available automatically without requiring users to spend time on setup or customization at the point of need.
3Ease of operation
If automated configuration recommendations are provided, then ease of operation is improved, but loss of information increases
Solution Approach 1:
The system presents generated controller configuration recommendations to users for review and approval before implementation. This feedback loop allows users to see what changes are being made, understand the rationale based on their usage patterns, and maintain control by accepting or rejecting recommendations, thus preventing information loss while preserving ease of operation.
Data Source
AI summary
Various aspects of the subject technology relate to systems, methods, and machine- readable media for adjusting controller settings. The method includes receiving, through a controller associated with a user, controller input for software. The method also includes determining, based on the controller input, a user profile for the user comprising at least a skill level and an input tendency of the user. The method also includes providing suggested adjustments to the controller settings intended to improve performance of the user in relation to the software, the controller settings comprising at least one of controller sensitivity or controller assignments. The method also includes receiving approval of the user to implement the suggested adjustments to the controller settings. The method also includes adjusting the controller settings based on the approval of the user.