In-Game User Input Calibration Using Contextual Feedback
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Solution Overview
Problem
Existing video game calibration methods for user inputs, such as gaze tracking and gesture recognition, often result in poor user experiences when initial calibration is low quality, forcing players to redo the process or endure suboptimal gameplay due to inaccuracies.
Innovation Solution
Implementing a system that allows for dynamic in-game refinement of user input calibration by leveraging game context and user feedback to adjust calibration settings in real-time, using dynamic user interfaces and machine learning models to improve recognition accuracy without disrupting gameplay.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If initial calibration process is performed for user input, then calibration settings are determined, but low quality calibration results force players to redo the process or endure suboptimal gameplay
Solution Approach 1:
The calibration system transitions from a static pre-game calibration to a dynamic in-game calibration that continuously adapts. The system performs initial calibration before gameplay, then dynamically refines calibration settings during gameplay based on contextual information and user interactions, allowing calibration to evolve rather than remaining fixed.
Solution Approach 2:
The system implements feedback mechanisms by analyzing game context, user interactions, and input accuracy during gameplay to continuously refine calibration settings. The calibration system receives feedback about input quality and contextual information, then adjusts calibration parameters accordingly, creating a closed-loop improvement process that eliminates the need to redo calibration.
2Productivity
If calibration settings are applied during gameplay, then input recognition occurs, but inaccuracies disrupt gameplay experience
Solution Approach 1:
The calibration system operates dynamically during gameplay rather than statically before gameplay. It continuously monitors input accuracy and adjusts calibration settings in real-time, ensuring that calibration remains accurate without disrupting gameplay flow. The system adapts calibration on-the-fly based on observed input patterns and game context.
Solution Approach 2:
The calibration system performs self-correction during gameplay by automatically analyzing input accuracy and adjusting its own calibration settings without requiring user intervention. The system monitors its own performance and autonomously refines calibration, eliminating the need for users to manually correct inaccuracies and maintaining seamless gameplay.
3Measurement precision
If dynamic calibration adjustment is performed during gameplay, then input recognition accuracy improves, but system complexity increases
Solution Approach 1:
The calibration system leverages existing game context data and user interaction patterns for multiple purposes: it uses the same information that tracks gameplay progress and user actions to simultaneously refine calibration settings. This multi-functional approach allows the system to improve measurement precision without adding separate complex calibration mechanisms, as the calibration refinement utilizes existing data streams already present in the game system.
Data Source
AI summary
A method is provided for refining calibration of a user input for a video game, including: performing an initial calibration process for a user input for the video game, wherein the initial calibration process determines calibration settings for the user input; initiating gameplay of the video game using the calibration settings determined from the initial calibration, such that the calibration settings are applied to interpret instances of the user input occurring during the gameplay; determining a game context in which the instances of the user input occur; analyzing the determined game context, and adjusting the calibration settings based on the analysis of the determined game context.