Dynamic Hovering Keypress Detection via Adaptive Travel Distance
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Solution Overview
Problem
Conventional keyboards with proximity sensors face challenges in detecting hovering keystrokes with low latency, which hinders efficient user interaction, particularly in applications requiring quick responses.
Innovation Solution
The system employs a processor and memory in an Information Handling System to configure and modify the travel distance for hovering events based on user behavior, using machine learning algorithms to classify users and adjust the detection criteria, thereby improving latency and accuracy in detecting hovering keypresses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a fixed travel distance is used for detecting hovering events, then the detection criteria are simple and reliable, but the latency cannot be optimized for different user behaviors
Solution Approach 1:
The patent applies dynamics by making the travel distance configurable and modifiable based on user behavior. The system transitions from a static fixed travel distance to a dynamic adjustable parameter that can be modified in response to detected user patterns, allowing optimization of detection latency without permanently increasing system complexity
Solution Approach 2:
The system implements feedback by detecting user behavior patterns and using this information to modify the travel distance parameter. The machine learning algorithm continuously monitors hovering events and adjusts the travel distance based on learned user behaviors, creating a closed-loop system that reduces latency through adaptive feedback
2Loss of time
If the travel distance is reduced to improve detection speed, then latency decreases, but false detections increase
Solution Approach 1:
The system applies preliminary action by using machine learning algorithms to analyze user behavior patterns before making detection decisions. The system learns from historical hovering events and predicts genuine keypress intentions, allowing it to use smaller travel distances without increasing false detections because the preliminary behavioral analysis filters out spurious signals
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting the travel distance parameter based on detected user behaviors. The system modifies this critical parameter in response to behavioral patterns, allowing optimization of detection sensitivity and specificity by changing the threshold that distinguishes genuine keypresses from false signals
3Reliability
If the travel distance is increased to reduce false detections, then reliability improves, but detection latency increases
Solution Approach 1:
The system performs preliminary behavioral analysis using machine learning to pre-validate hovering events before they trigger keypress detection. This preliminary action allows the system to confidently use smaller travel distances without sacrificing reliability, as the behavioral context has already been verified
Solution Approach 2:
The system uses feedback from machine learning models that continuously learn from user behavior patterns. This feedback mechanism allows the system to adapt the travel distance parameter dynamically, maintaining high reliability even when using smaller distances by leveraging learned patterns that distinguish genuine from spurious inputs
4Measurement precision
If machine learning algorithms are used to classify users and modify travel distance, then detection accuracy improves, but computational requirements increase
Solution Approach 1:
The system applies partial action by implementing machine learning classification only for hovering events that meet certain criteria or show ambiguous characteristics. Routine clear-cut events use standard detection, while only problematic cases undergo complex behavioral analysis, reducing overall computational energy consumption while maintaining high accuracy for critical detections
Data Source
AI summary
Systems and methods for dynamically predicting keypresses on a hovering keyboard based on user behavior are described. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution by the processor, cause the IHS to: configure a travel distance for hovering events detectable by a keyboard coupled to the IHS, and modify the travel distance in response to a user's behavior.