Software Update Scheduling Using Stand-by Period Detection
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Solution Overview
Problem
Existing methods for scheduling software updates on receiving devices often disrupt user experience by performing updates during active usage or when the device is in an unsuitable mode, leading to performance issues and inefficiencies.
Innovation Solution
Determining the longest stand-by periods over multiple days and calculating a moving average to identify the optimal time for software updates, ensuring updates occur when the device is least likely to be in use, by monitoring usage patterns and determining the middle of the longest stand-by period as the desired update time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If software updates are performed during active usage periods, then update speed and productivity are improved, but user experience deteriorates and device reliability decreases
Solution Approach 1:
The system dynamically adjusts update scheduling based on real-time monitoring of device usage patterns. By continuously analyzing when the device transitions between active and stand-by modes, the system adapts the update timing to match actual device state, resolving the contradiction between update speed and user experience.
Solution Approach 2:
The system implements feedback loops that monitor device usage patterns and use this information to optimize update scheduling. The monitoring mechanism provides continuous feedback about device state transitions, enabling the system to learn from actual usage behavior and schedule updates accordingly to minimize disruption while maintaining productivity.
2Ease of operation
If software updates are scheduled during stand-by mode, then user disruption is minimized, but update timing precision and reliability decrease
Solution Approach 1:
The system replaces simple time-based scheduling mechanisms with an intelligent monitoring and analysis system. Instead of relying on fixed time slots, the system uses software-based monitoring of device state transitions to determine optimal update timing, achieving both minimal user disruption and high timing reliability.
Solution Approach 2:
The device monitors its own usage patterns and automatically determines optimal update timing without external intervention. The self-service mechanism involves the device tracking its own state transitions and using this information to schedule updates at the most appropriate moments, ensuring both user-friendly timing and operational reliability.
3Loss of time
If update timing is determined based on single-day data, then response time is reduced, but accuracy and precision of timing determination deteriorate
Solution Approach 1:
The system performs preliminary data collection and analysis over multiple days before finalizing update scheduling decisions. By pre-processing usage pattern data from extended periods, the system builds a comprehensive understanding of device behavior, enabling accurate timing determinations while maintaining efficient response through automated analysis.
Solution Approach 2:
The system implements periodic monitoring and analysis of device usage patterns over multiple days. This periodic action allows the system to accumulate sufficient data to determine accurate timing patterns while maintaining a structured, manageable process. The periodic analysis cycles through data collection, pattern recognition, and scheduling optimization in a rhythmic fashion.
Data Source
AI summary
A monitoring system determines when a receiving device is in stand-by mode by monitoring usage of the receiving device on a daily basis. Periodic status checks are performed (e.g., every minute) and based on these status checks, it is determined what mode the receiving device was in for each half hour (or other selected interval) of the day. The middle of the longest period of the day the receiving device was determined to be in stand-by mode is determined as a desired time of day for software updates or other automated actions to occur. This process repeats every day and these determined times of day for software updates are averaged over the number of days the usage data was collected to use as the time of day going forward (based on a moving average) for software updates or other automated actions to be performed on the receiving device.