Tactile Interaction Personalization via AI Feature Extraction
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Solution Overview
Problem
Current technologies fail to effectively personalize user interactions based on tactile data from interfaces, leading to inconsistent and inefficient communication with users of varying experience levels and contexts.
Innovation Solution
A processor identifies user interactions through tactile sensors, extracts features using an AI model, and classifies the data to output interaction parameters for customized communication, adapting language, tone, and guidance based on user classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If tactile sensors are used to detect user interactions, then user interaction data can be collected, but the system cannot effectively personalize interactions based on this data
Solution Approach 1:
The patent introduces an AI model as an intermediary component that processes raw tactile sensor data and extracts meaningful user characteristics. This mediator translates complex sensor signals into actionable insights about user experience levels and preferences, enabling personalization without requiring the entire system to directly handle the complexity of raw tactile data analysis
Solution Approach 2:
The system extracts specific features from tactile sensor data using the AI model, separating the essential user characteristics (such as experience level indicators) from the raw sensor signals. This extraction process isolates the key information needed for personalization, allowing the system to focus on applying these extracted features rather than processing all raw data
2Loss of information
If the system provides detailed communication for all users, then information completeness is improved, but user engagement decreases due to information overload for experienced users
Solution Approach 1:
The patent applies local quality by tailoring the communication style and information density to match the specific characteristics of each user. Experienced users receive concise, direct information while less experienced users receive more detailed explanations. This localized adaptation of information quality ensures each user receives appropriate detail without overload, maintaining both information completeness and engagement
Solution Approach 2:
The system dynamically adjusts communication parameters based on real-time classification of user experience levels. The interaction management module modifies communication style, detail level, and guidance amount on-the-fly according to the user's classified characteristics, enabling flexible adaptation that maintains optimal engagement across different user types
3Productivity
If the system uses simplified communication for all users, then user engagement is improved, but information completeness decreases for less experienced users
Solution Approach 1:
The system applies local quality by tailoring the communication style and information density to match the specific characteristics of each user. Experienced users receive concise, direct information while less experienced users receive more detailed explanations. This localized adaptation of information quality ensures each user receives appropriate detail without overload, maintaining both information completeness and engagement
Solution Approach 2:
The system uses feedback from tactile sensor data to continuously monitor and adjust communication appropriateness. By analyzing user interaction patterns through the AI model, the system receives feedback about user understanding and experience level, then adjusts information density and communication style accordingly to maintain optimal engagement while ensuring information completeness
Data Source
AI summary
A processor may identify that a user is interacting with a device, where the interacting is identified from the user touching the device. The processor may receive tactile data associated with the user from one or more tactile sensors. The processor may extract, utilizing an AI model, one or more features of the tactile data. The processor may classify, utilizing the AI model, the tactile data as having a tactile data characteristic. The processor may output the classification to an interaction management module.