Adaptive Idle Time Adjustment Based on User Activity Patterns
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Solution Overview
Problem
Current computing devices often inaccurately determine user inactivity, leading to unnecessary power shutdowns or reduced brightness, which can annoy users or waste power, as they do not differentiate between active engagement and mere proximity.
Innovation Solution
A system that uses sensors to detect user presence and activity patterns to adjust the power consumption of computing devices, extending or shortening the idle time based on the user's typical absence or presence patterns, thereby optimizing power management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the computing device turns off or reduces brightness after a predetermined idle time, then power consumption is reduced, but user convenience deteriorates when the user is merely reading content
Solution Approach 1:
The system performs preliminary sensing of user presence and determines activity patterns before making power management decisions. By analyzing sensor data in advance to establish whether the user is truly absent or merely inactive, the system可以避免 unnecessary power reduction that would annoy users who are still engaged with the device
Solution Approach 2:
The system continuously monitors sensor signals indicating user presence and activity patterns, using this feedback to dynamically adjust the idle time threshold. When the system detects patterns suggesting the user is still engaged (even if not actively interacting), it extends the idle time to prevent premature power reduction, thereby maintaining user convenience while still managing power consumption
2Ease of operation
If the computing device extends idle time to prevent unnecessary shutdowns, then user convenience is improved, but power consumption increases
Solution Approach 1:
The system dynamically adjusts the idle time threshold based on real-time sensor data and learned activity patterns. Rather than using a fixed idle time, the system adapts the threshold to match the user's actual behavior patterns, extending idle time only when patterns suggest the user is still engaged while maintaining shorter thresholds when true absence is detected
Solution Approach 2:
The system changes the idle time parameter based on detected activity patterns. When sensor data indicates the user is merely inactive rather than absent, the system increases the idle time parameter to prevent unnecessary shutdowns. When patterns indicate true absence, the system reduces the parameter to enable power savings
3Loss of energy
If the device shuts down during extended user absence, then power resources are conserved, but productivity decreases due to restart time
Solution Approach 1:
The system performs preliminary determination of activity patterns and predicts when the user will return based on historical sensor data. By making advance predictions about user absence duration, the system can confidently shut down during confirmed extended absences without risking unnecessary restarts, thereby conserving power resources while maintaining productivity during actual user needs
Data Source
AI summary
A system and method is disclosed for adjusting power consumption of a computing device. The computing device is configured with one or more sensors to sense when a user moves away and returns to the computing device. Over a period of time, the computing device determines a pattern of activity related to how long the user is normally away from the computing device during particular times of the day. The computing device may then adjust power consumption of the device or associated components during times of the day for which a pattern of activity has been determined. For example, the computing device may adjust a duration that the computing device will remain idle before power to the computing device is limited.