Personalized Haptic Feedback for Guiding In-Game Interactive Tasks
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Solution Overview
Problem
Existing video games fail to provide personalized haptic feedback to users, leading to varied user immersion and missed interactive tasks due to differing play styles, distractions, or slow reaction times.
Innovation Solution
A system that analyzes user play style and game scenarios to provide customized haptic responses via controllers, guiding users to interactive tasks through vibrational cues, spatial cues, and other feedback mechanisms, adjusting based on machine learning and user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If standardized haptic feedback is provided to all users, then device complexity is reduced, but user immersion and engagement deteriorate due to lack of personalization
Solution Approach 1:
The haptic feedback system dynamically adjusts vibration characteristics based on real-time analysis of user play style and game context. The system transitions from static standardized feedback to dynamic personalized feedback by continuously monitoring user interactions and adapting haptic responses accordingly, resolving the contradiction between adaptability and complexity through intelligent automation.
Solution Approach 2:
The system performs self-adjustment by automatically analyzing user play style and game scenarios to determine appropriate haptic feedback without requiring manual configuration. The machine learning algorithms enable the system to serve itself by autonomously optimizing haptic responses based on observed user behavior patterns, reducing the need for complex user input while maintaining high adaptability.
2Adaptability or versatility
If haptic feedback is customized for each user, then user immersion improves, but device complexity increases due to personalized configuration requirements
Solution Approach 1:
The system automatically analyzes user play style and game scenarios to determine appropriate haptic feedback without requiring manual configuration. The machine learning algorithms enable the system to serve itself by autonomously optimizing haptic responses based on observed user behavior patterns, reducing the need for complex user input while maintaining high adaptability.
Solution Approach 2:
The system implements a feedback loop where user interactions with the game are continuously monitored and analyzed to refine haptic feedback settings. This feedback mechanism allows the system to learn from user behavior and automatically adjust haptic responses, eliminating the need for users to manually configure preferences while maintaining personalized immersion.
3Adaptability or versatility
If real-time haptic adjustment is provided, then user engagement improves, but processing time and computational resources increase
Solution Approach 1:
The system pre-analyzes user play style patterns and game scenarios to predict appropriate haptic feedback before critical moments occur. By performing analysis in advance and caching results, the system reduces real-time processing requirements while maintaining responsive and engaging haptic feedback, resolving the contradiction between adaptability and processing time.
Data Source
AI summary
Methods and systems for providing response to a player during game play of a video game includes detecting an interactive task within a game scenario of the video game that requires an action from the player. In response to detecting the interactive task, a profile of the player playing the video game, is identified. A haptic response is provided to the player in accordance to haptic setting defined for the player profile of the player. The haptic response is provided to the player via an input device used by the player for providing game input to the video game. The haptic response that is provided is specific for the player and is provided to guide the player toward the interactive task within the game scenario of the video game.