Machine Learning Input Device Wake-Up for Zero-Latency Response
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Solution Overview
Problem
Existing corded and cordless input devices, such as mice and keyboards, experience latency when transitioning from a low-power state to an active state, leading to delayed input recognition.
Innovation Solution
The implementation of a power management system that detects the approach of a user's hand and initiates a power-on sequence, allowing the input device to be fully operational by the time the hand touches the device, thereby eliminating latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the input device enters a low-power state to save battery power, then energy consumption is reduced, but response latency increases when the user needs to use the device
Solution Approach 1:
The system performs preliminary action by detecting the user's hand approach and initiating the power-on sequence before the user actually needs to use the device. The machine learning model predicts the user's intent based on hand movement patterns, allowing the device to wake up in advance and eliminate latency while still maintaining low-power state for extended periods.
Solution Approach 2:
The system uses feedback from the machine learning model that analyzes hand approach patterns to dynamically adjust power management decisions. The model continuously learns from user behavior and provides feedback to optimize the balance between power consumption and response time, improving the prediction accuracy over time.
2Speed
If the input device remains in active state to ensure immediate response, then response latency is reduced, but energy consumption increases
Solution Approach 1:
The system activates components in advance based on predicted user intent. The machine learning model detects hand approach patterns and triggers the power-on sequence before the user makes contact, ensuring immediate response when needed while avoiding continuous active state that would drain battery.
Solution Approach 2:
The system dynamically adjusts its power state based on real-time predictions from the machine learning model. Instead of static power management, the device transitions between low-power and active states dynamically, optimizing the balance between response speed and energy consumption based on actual user behavior patterns.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution ensures zero-latency operation of input devices by proactively powering up the device based on user proximity, maintaining performance without compromising battery life.
Implementation Method 1
An integrated capacitive sensor, including a single electrode on a surface of the apparatus, may be configured to detect a presence of an approaching hand at a certain distance
Data Source
Figure 1A~1B
Figure 2A~2B
Figure 3
AI summary
A method, a computer-readable medium, and an apparatus for power management are provided. The apparatus may determine an activation distance based on operator behavior in relation to operating the apparatus. The apparatus may detect the presence of an approaching operator at the activation distance. The apparatus may wake up from a low-power state in response to the detecting of the presence of the approaching operator at the activation distance. The apparatus may determine a deactivation distance based on the operator behavior. The apparatus may detect the presence of a departing operator of the apparatus at the deactivation distance. The apparatus may enter into the low-power state in response to the detecting of the presence of the departing operator at the deactivation distance.