AI Hand-Gesture Translation Using Temporal Game Context
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video game systems face challenges in timely and accurate processing of hand-gestures due to rapid changes in game state and context, leading to potential misinterpretation and latency in conveying messages, which can confuse or adversely affect players.
Innovation Solution
Implementing an AI-assisted system with three AI model components for hand-gesture tracking, intent determination, and translation, which processes hand-gestures in real-time, considering a temporal window of game state and context to ensure accurate and timely communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hand-gesture processing is performed in real-time without AI assistance, then system complexity is reduced, but processing accuracy and timeliness deteriorate due to rapid game state changes
Solution Approach 1:
The system divides hand-gesture processing into three distinct AI model components: hand-gesture tracking engine (first AI model), hand-gesture intent engine (second AI model), and hand-gesture translation engine (third AI model). Each component handles a specific aspect of processing, improving overall accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces AI model components as intermediary processing layers between hand-gesture detection and communication generation. These intermediaries analyze temporal context and game state to accurately interpret gestures, resolving the contradiction between processing speed and accuracy.
2Measurement precision
If hand-gesture processing considers temporal context of game state, then communication accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary analysis of temporal context and game state changes before final gesture interpretation. By pre-processing contextual information and maintaining an understanding of recent game events, the system can quickly interpret gestures without significant latency while ensuring accurate contextual relevance.
Solution Approach 2:
The AI models continuously monitor and process temporal context of game state changes, maintaining an ongoing understanding of the gaming environment. This continuous processing enables rapid gesture interpretation by leveraging already-analyzed contextual information rather than analyzing everything from scratch.
3Reliability
If hand-gesture tracking uses traditional methods, then device complexity is lower, but communication reliability deteriorates due to misinterpretation
Solution Approach 1:
The hand-gesture intent engine analyzes the output of the tracking engine and provides feedback to adjust interpretations based on game state context. This feedback mechanism ensures reliable communication by continuously validating gesture meanings against current gaming conditions, preventing misinterpretation.
Solution Approach 2:
The system dynamically adjusts interpretation parameters based on changing game state and temporal context. The AI models adapt gesture meanings according to current gaming conditions, ensuring reliable communication while managing complexity through parameter adaptation rather than complex fixed rules.
Data Source
AI summary
A hand-gesture tracking engine includes a first artificial intelligence (AI) model component configured and trained to automatically identify a hand-gesture made by a source player within a video game. A hand-gesture intent engine includes a second AI model component configured and trained to automatically determine a message for communication to a target player within the video game as conveyed by the hand-gesture identified by the hand-gesture tracking engine. A hand-gesture translation engine having a third AI model component configured and trained to automatically generate a communication to the target player that conveys the message as determined by the hand-gesture intent engine. The third AI model component is configured and trained to automatically control sending of the communication to the target player.