Game-State Gesture Mapping for Adaptive Input Macros
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Solution Overview
Problem
Existing game controllers often lack intuitive and efficient means for executing complex in-game actions, particularly for novice and young players, and static gesture controls do not account for varying game contexts or player preferences.
Innovation Solution
A system that dynamically maps player gestures to input sequences based on game state using machine learning models, incorporating factors like current menu state, character state, and game events, allowing for adaptive and context-aware control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional game controllers with multiple buttons and joysticks are used, then complex in-game actions can be executed, but the controller becomes overwhelming and difficult to operate for novice and young players
Solution Approach 1:
The patent replaces traditional mechanical controller inputs (buttons, joysticks) with gesture-based control mechanisms. Players perform gestures in the air or with the device itself, which are captured by sensors and translated into game commands, eliminating the need to physically manipulate complex controller components.
Solution Approach 2:
The patent introduces gesture recognition software and processing algorithms as an intermediary layer between the player's physical movements and the game's control system. This intermediary translates natural gestures into meaningful game commands, bridging the gap between simple player actions and complex game responses.
2Adaptability or versatility
If static gesture controls are implemented, then the control method is simple to implement, but the controls do not account for varying game contexts or player preferences
Solution Approach 1:
The patent implements dynamic gesture mapping where the meaning and effect of gestures change based on the current game state, context, and player preferences. The system continuously adapts gesture interpretations to match the ongoing game situation, making the same gesture produce context-appropriate actions.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system monitors player responses and game outcomes to refine and adapt gesture mappings over time. The control system learns from player behavior patterns and adjusts gesture interpretations to better match player intentions and preferences.
Data Source
AI summary
A machine learning-based model is configured to make inferences about computer game actions to execute based on dynamic, varying player gestures and to translate those game actions into input sequence macros. In some instances, the button sequence mapping for the macros can even dynamically change based on game state so that different macros for the same computer game action might be inferred by the model depending on game state.