Adaptive Sleep State Control via Proximity and Activity Prediction
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Solution Overview
Problem
Conventional computing devices experience lengthy delays when re-entering an awake state due to the time required to power on various components from sleep states, leading to user inconvenience and inefficiency in energy usage.
Innovation Solution
The techniques involve adjusting sleep states of a computing device based on proximity detection and historical user behavior, using a proximity detector and power controller to switch between high and low power states, and scheduling deep and light sleep signals based on user activity patterns to optimize power usage and reduce wake-up times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the computing device increasingly lowers or switches off power to internal components during extended sleep periods, then energy efficiency is improved, but the time required to re-enter an awake state increases
Solution Approach 1:
The patent applies dynamics by making the sleep state adjustable and adaptive rather than static. The system dynamically transitions between light sleep and deep sleep states based on detected user presence and historical activity patterns, allowing the power consumption and wake-up time characteristics to change according to current conditions
Solution Approach 2:
The system performs preliminary action by maintaining a light sleep state when user presence is detected or when recent activity suggests imminent use. This prepares the system in advance by keeping components partially powered, so when the user actually interacts with the device, the wake-up time is minimized
Solution Approach 3:
The patent implements feedback through proximity sensors that detect user presence and through analysis of historical activity data. This feedback loop allows the system to continuously adjust its sleep state based on real-time and historical information about user behavior patterns
2Loss of energy
If the computing device enters a deep sleep state to maximize power savings, then energy efficiency is improved, but user experience deteriorates due to lengthy delays when resuming activity
Solution Approach 1:
The system dynamically adjusts between deep and light sleep states based on detected user presence. When a user is detected nearby, the system transitions to or maintains a light sleep state, ensuring quick responsiveness and maintaining good user experience while still providing power savings during extended idle periods
Solution Approach 2:
Proximity sensors provide feedback about user presence that triggers appropriate sleep state selection. This feedback mechanism ensures the system adapts its power consumption characteristics to match actual usage patterns, preventing unnecessarily long wake-up delays
Data Source
AI summary
This application relates to techniques that adjust the sleep states of a computing device based on proximity detection and predicted user activity. Proximity detection procedures can be used to determine a proximity between the computing device and a remote computing device coupled to the user. Based on these proximity detection procedures, the computing device can either correspondingly increase or decrease the amount power supplied to the various components during either a low-power sleep state or a high-power sleep state. Additionally, historical user activity data gathered on the computing device can be used to predict when the user will likely use the computing device. Based on the gathered historical user activity, deep sleep signals and light sleep signals can be issued at a time when the computing device is placed within a sleep state which can cause it to immediately enter either a low-power sleep state or a high-power sleep state.