Mobile Battery Consumption Analysis via Delta Charge Sampling
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Solution Overview
Problem
Conventional methods fail to accurately identify which applications or device components are responsible for battery drain in mobile devices, making it difficult to determine if battery consumption is within reasonable usage patterns or if specific activities or conditions, such as applications, voice calls, or poor coverage, are causing excessive drain.
Innovation Solution
A method is developed to process disparate data streams from mobile devices to generate battery use timelines, allowing for the identification of applications or components that correlate with high battery drain by sampling battery discharge at equal increments rather than elapsed time, and subtracting voice call and other wireless function battery use from total battery use to isolate application-specific consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional equal time increments are used for sampling battery discharge, then data collection is simple, but measurement precision of battery consumption patterns is insufficient
Solution Approach 1:
The patent changes the sampling parameter from equal time increments to equal battery discharge increments. This transforms the independent variable in the sampling process, allowing for more accurate measurement of battery consumption patterns by aligning sample points with actual discharge events rather than arbitrary time intervals. The system now samples at consistent energy consumption thresholds, improving measurement precision.
Solution Approach 2:
The patent introduces a new dimension for data analysis by creating separate timelines for different battery discharge scenarios (e.g., charging vs. discharging, different usage conditions). This multi-dimensional approach allows for more nuanced analysis of battery consumption patterns under various conditions, resolving the contradiction by adding analytical depth without fundamentally complicating the core sampling mechanism.
2Loss of information
If total battery use is analyzed without separation, then analysis is straightforward, but identification of specific application consumption is lost
Solution Approach 1:
The patent segments the total battery consumption data into distinct components attributable to different applications and system functions. By dividing the aggregate battery use timeline into application-specific segments, the system recovers lost information about individual application consumption patterns while maintaining a structured approach that manages complexity through systematic organization of data segments.
Solution Approach 2:
The patent extracts specific application consumption data from the total battery use timeline by identifying and isolating segments corresponding to individual applications. This extraction process retrieves the previously lost application-specific information without requiring complete redesign of the analysis system,而是 by selectively separating relevant data portions from the aggregate timeline.
3Measurement precision
If voice call and wireless function battery use is included in total battery use, then total consumption is accurate, but application-specific consumption cannot be isolated
Solution Approach 1:
The patent extracts and separates the battery consumption attributable to voice calls and wireless functions from the total battery use timeline. By removing these specific components, the system isolates the remaining consumption to application-specific activities, enabling precise measurement of application battery usage while accounting for the energy consumed by wireless functions through separate tracking.
Solution Approach 2:
The patent segments the total battery consumption into distinct categories: wireless function consumption (voice calls, data transmission), application consumption, and baseline system consumption. This segmentation allows for accurate measurement of application-specific consumption by clearly separating it from wireless function energy usage, resolving the contradiction through systematic categorization.
Data Source
AI summary
A method for operation of a battery consumption analysis server communicatively coupled to a plurality of mobile devices over radio channels to receive packages of data recorded at each mobile device containing application use, and battery charging and discharging events. By receiving independent sets of information from the mobile device wherein independent sets comprise at least one of wireless phone activities and battery use, combining this information in such a way that the battery consumption or over consumption of specific applications are identified, the apparatus and method configures a display to identify the correspondingly bad applications to a user.