Dynamic Input Classification for I/O Devices
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Solution Overview
Problem
Existing information handling systems face challenges in seamlessly transitioning and configuring user inputs across different environments and devices within a home, leading to potential accidental inputs and decreased user experience, especially with touch-based devices.
Innovation Solution
A dynamic input categorization method using machine learning models that classify user interactions as valid or invalid, adapting to individual users and device operating modes, and updating models based on user behavior to reject unintended inputs and ensure accurate processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system accepts all user inputs from I/O devices across different environments, then the user experience remains seamless and responsive, but accidental or invalid inputs increase leading to errors and decreased reliability
Solution Approach 1:
The system performs preliminary classification of user inputs using machine learning models before executing commands. By analyzing interaction data patterns in advance and comparing them against learned valid input patterns, the system can preemptively identify and reject invalid inputs before they cause errors, ensuring reliable operation while maintaining seamless user experience through automatic validation
Solution Approach 2:
The system continuously updates machine learning models based on observed user behavior and interaction patterns. By incorporating feedback from actual usage data, the system refines its ability to distinguish valid from invalid inputs over time, improving classification accuracy and reliability while adapting to individual user preferences and behaviors across different environments
2Adaptability or versatility
If the system uses fixed input validation rules, then the processing is simple and fast, but the system cannot adapt to different users and environments leading to false-positive rejections
Solution Approach 1:
The system transitions from static validation rules to dynamic machine learning models that continuously adapt to different users and environments. By selecting appropriate models based on device operating modes and updating them with observed user behavior, the system achieves high adaptability to individual users and environmental contexts while managing complexity through automated model selection and update mechanisms
Solution Approach 2:
The system changes the parameters of input validation by using different machine learning models for different operating modes and devices. By adjusting the classification approach based on device type, operating mode, and environmental context, the system achieves versatile adaptation across multiple users and environments while keeping the underlying validation mechanism consistent and manageable
Data Source
AI summary
Systems and methods described herein may provide a system that enables improved input classification and processing for I/O devices. In one aspect, a computing device may receive interaction data from an I/O device. The interaction data may be associated with a first user interaction. The computing device may classify, with a first model and based on the interaction data, the first user interaction as an invalid input and may reject the first user interaction. In certain aspects, the computing device is the I/O device.