Adaptive Charge Notifications Using User Charging Patterns
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Solution Overview
Problem
Existing battery-powered electronic devices often fail to notify users in time to charge, as fixed threshold-based notifications do not account for individual charging patterns, leading to insufficient charge before the next charging opportunity.
Innovation Solution
A computing system learns user charging patterns by analyzing state of charge data, location, and system activity to predict when the device will run out of energy, sending timely notifications to charge before the next high-probability charging time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed threshold-based notification system is used, then the device structure remains simple, but the notification timing is inaccurate and does not account for individual charging patterns
Solution Approach 1:
The system performs preliminary learning of user charging patterns during an initial period, building a predictive model before actual notification needs arise. This allows the system to anticipate when users will charge their devices and provide timely notifications in advance, rather than waiting for fixed threshold conditions to be met.
Solution Approach 2:
The system continuously monitors actual charging behavior and uses this feedback to refine and update the predictive model over time. By comparing predicted charging times with actual user actions, the system adapts to changing patterns and improves notification accuracy while maintaining a manageable complexity level through iterative optimization.
2Reliability
If a fixed threshold notification is sent, then the notification system remains simple, but the user does not receive timely advice to charge before leaving
Solution Approach 1:
The notification system transitions from a static fixed-threshold approach to a dynamic adaptive system that adjusts notification timing based on learned user patterns. The system dynamically determines optimal notification times by analyzing historical charging data and predicting future charging behavior, ensuring reliable charge assurance while adapting to individual user needs.
Solution Approach 2:
The system performs preliminary analysis of charging patterns and predicts future charging times before notifications are needed. This advance preparation allows the system to provide reliable notifications that ensure users charge their devices before leaving, rather than reacting to fixed threshold conditions at the last moment.
3Reliability
If the notification is sent at a fixed charge level, then the notification logic remains simple, but the probability of running out of charge increases
Solution Approach 1:
The system replaces the simple mechanical threshold-comparison mechanism with an intelligent prediction system that uses machine learning algorithms. Instead of merely comparing current charge levels against fixed thresholds, the system analyzes patterns in user behavior, device usage, and charging history to predict when charge will be depleted and when users will charge, providing more reliable charge sufficiency assurance.
Solution Approach 2:
The system performs preliminary prediction of charge depletion and future charging events before the actual charging decision point. By forecasting when the device will run out of charge and when the user is likely to charge based on learned patterns, the system can provide timely notifications that ensure sufficient charge levels, rather than waiting for fixed threshold conditions to be met.
Data Source
AI summary
Systems and methods are disclosed for advising a user when an energy storage device in a computing system needs charging. State of charge data of the energy storage device can be measured and stored at regular intervals. The historic state of charge data can be queried over a plurality of intervals and a state of charge curve generated that is representative of a user's charging habits over time. The state of charge curve can be used to generate a rate of charge histogram and an acceleration of charge histogram. These can be used to predict when a user will charge next, and whether the energy storage device will have an amount of energy below a predetermined threshold amount before the next predicted charging time. A first device can determine when a second device typically charges and whether the energy storage device in the second device will have an amount of energy below the predetermined threshold amount before the next predicted charge time for the second device. The first device can generate an advice to charge notification to the user on either, or both, devices.