Dynamic Input Modifier for Cross-Platform Gaming Fairness
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Solution Overview
Problem
Existing input devices for computer systems, such as controllers and mice, have inherent differences in accuracy, precision, and speed, leading to imbalances in online multiplayer games, where aim assist features can favor certain players, causing frustration.
Innovation Solution
A system that determines an input modifier based on data sets from multiple users, correlating the distance between current and target locations with the time taken to move between them, allowing for dynamic adjustments to input signals to balance play across different input devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If aim assist features are implemented to compensate for input device differences, then fairness in cross-platform gaming is improved, but player frustration increases due to perceived cheating
Solution Approach 1:
The system dynamically adjusts input parameters (speed, accuracy, precision) based on measured user performance metrics. By changing these parameters in real-time according to actual gameplay data, the system provides fair compensation for input device differences without creating unfair advantages, thus resolving the contradiction between fairness and player frustration.
Solution Approach 2:
The system continuously monitors user input performance and uses this feedback to dynamically adjust aim assist parameters. This closed-loop approach ensures that aim assist is calibrated based on actual user skill and input device characteristics, preventing both under-compensation (frustration) and over-compensation (perceived cheating).
2Reliability
If input modifiers are dynamically adjusted based on user performance data, then cross-platform fairness is improved, but system complexity increases
Solution Approach 1:
The system segments the adjustment process into distinct modules: data collection, performance analysis, modifier calculation, and input application. This segmentation allows each component to be optimized independently and simplifies the overall system architecture while achieving complex fairness goals through coordinated operation of simpler subsystems.
Solution Approach 2:
The system automatically collects performance data, analyzes it to determine appropriate modifiers, and applies adjustments without manual configuration. This self-service approach reduces the operational complexity burden on administrators while maintaining high fairness standards through automated, data-driven decision-making.
3Measurement precision
If input data is collected and analyzed from multiple users to determine modifiers, then input accuracy is improved, but data processing requirements increase
Solution Approach 1:
The system processes and analyzes only the necessary subset of user data required to determine accurate modifiers, rather than processing all available data. This selective approach maintains high input accuracy for the specific metrics needed (speed, accuracy, precision) while reducing overall data processing volume and computational requirements.
Data Source
AI summary
A system for determining an input modifier for an input to a computer based on data sets from multiple users, including a correlation unit configured to: receive data sets from multiple users, each data set relating to display output data including a current location and a target location for a user, and corresponding user input data responsive to the display output data; and determine an input modifier based on the data sets, relating a distance between the current location and the target location for a user, to a time taken for the user to move from the current location to the target location.