Battery Charging Intervals Using Synchronized User Context
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Solution Overview
Problem
Existing synchronization techniques among multiple electronic devices suffer from inefficiencies related to query frequency and network bandwidth as the number of items to be synchronized increases, leading to potential power consumption issues.
Innovation Solution
Implementing a method for synchronizing context data among devices by analyzing device usage data to predict user inactivity and entering an enhanced reduced power state, utilizing power saving optimizations such as turning off radios, postponing tasks, and altering notifications, and synchronizing context information to manage power consumption and network usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If existing synchronization techniques are used among multiple electronic devices, then data synchronization is achieved, but power consumption increases and network bandwidth is excessive
Solution Approach 1:
The system performs preliminary actions by predicting future user inactivity periods using historical usage data and present usage signals. This prediction enables the device to proactively enter enhanced reduced power states before actual inactivity occurs, and to pre-schedule battery charging during predicted disconnection periods, thereby reducing power consumption while maintaining synchronization reliability.
Solution Approach 2:
The system dynamically adjusts the device's operational state based on predicted user behavior. It transitions between normal operation and enhanced reduced power states depending on predicted inactivity periods, and dynamically schedules charging operations based on predicted disconnection times. This dynamic adaptation resolves the contradiction by optimizing power consumption without compromising synchronization reliability.
2Loss of information
If query frequency is increased for synchronization, then data freshness is improved, but network bandwidth consumption increases
Solution Approach 1:
Instead of continuous or high-frequency querying, the system implements periodic synchronization actions scheduled during predicted user disconnection periods. This periodic approach maintains data freshness by performing synchronization at appropriate intervals while significantly reducing network bandwidth consumption compared to continuous querying.
Solution Approach 2:
The system performs synchronization as a preliminary action during predicted disconnection periods before the user returns. This timing strategy ensures data is fresh and synchronized when needed, while avoiding unnecessary network traffic during active usage periods, thus resolving the contradiction between data freshness and bandwidth consumption.
3Loss of energy
If the device remains in normal operational state, then user activity response is immediate, but power consumption is excessive during inactivity
Solution Approach 1:
The system performs preliminary prediction of user inactivity using historical data and present signals. Based on this prediction, it proactively enters enhanced reduced power states before actual inactivity begins, ensuring that when the user does return, the system can quickly exit the low-power state and resume normal operation, thus maintaining responsiveness while reducing power consumption during predicted inactivity periods.
Solution Approach 2:
The system dynamically transitions between normal operational state and enhanced reduced power state based on predicted user behavior. This dynamic state adjustment reduces power consumption during predicted inactivity while maintaining the ability to quickly respond when user activity resumes, resolving the contradiction between power savings and operational responsiveness.
4Quantity of substance
If battery charging is performed continuously when connected to power source, then battery capacity is maximized, but battery lifespan is reduced due to prolonged full charge state
Solution Approach 1:
The system performs preliminary prediction of user disconnection times using historical charging patterns and present context. Based on this prediction, it proactively schedules battery charging to complete before the predicted disconnection time, rather than charging continuously. This preliminary scheduling ensures the battery is fully charged when needed while avoiding prolonged full charge states that would reduce battery lifespan, thus resolving the contradiction between capacity and lifespan.
Solution Approach 2:
The system dynamically adjusts charging operations based on predicted disconnection times. Instead of continuous charging, it modulates charging activity to achieve full charge optimally before predicted disconnection, then reduces or pauses charging to extend battery lifespan. This dynamic charging management resolves the contradiction between maximizing capacity and extending lifespan.
Data Source
AI summary
An electronic device can include a power system including a battery and a processor programmed to: receive synchronized context data from one or more other devices associated with a user of the device, determine, at least in part based on the synchronized context data, one or more battery charging intervals, and operate the power system to charge the battery from the external power source during the identified one or more battery charging intervals. The processor can be programmed to determine the one or more battery charging intervals using a machine learning model. The synchronized context data can provide indication of the user's location. If the synchronized context data indicates that the user is at a different location than the device, the one or more battery charging intervals determined based at least in part on an expected time for the user to return to the location of the device.