User-Selectable Game Function Battery Estimates Before Execution
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Solution Overview
Problem
Existing technologies struggle to accurately estimate battery consumption on a shorter-term basis for specific functions of applications, particularly those with complex functionalities like game applications, before the processing is initiated, making it difficult for users to determine if the processing will deplete the battery before charging.
Innovation Solution
A processing device that accumulates execution-time state information and battery consumption data to generate an estimation model, allowing it to predict battery consumption for specific content within an application before execution, using machine learning techniques to provide accurate estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If battery consumption is estimated based on long-term usage patterns, then prediction accuracy for general trends is improved, but the ability to predict short-term consumption for specific functions before execution is worsened
Solution Approach 1:
The patent segments battery consumption prediction into two distinct models: a long-term prediction model that analyzes usage patterns over days to identify habitual behaviors, and a short-term prediction model that estimates consumption for specific functions before execution. This segmentation allows each model to specialize in its respective time scale, resolving the contradiction between long-term accuracy and short-term timeliness.
Solution Approach 2:
The patent implements preliminary action by performing short-term battery consumption estimation before a specific function is executed. The system calculates predicted consumption for individual functions (e.g., game modes, application features) and presents this information to the user in advance, enabling informed decisions about whether to proceed with the function based on current battery levels.
2Measurement precision
If detailed state information is collected for each function execution, then prediction accuracy for specific content is improved, but device complexity and data processing requirements are worsened
Solution Approach 1:
The patent creates a universal information accumulation mechanism that collects state information (CPU usage, display brightness, network activity, audio output) applicable across all functions and applications. This multi-functional data collection system serves multiple purposes: long-term pattern recognition, short-term prediction, and various optimization functions, thereby managing complexity through reusability rather than creating separate systems for each function.
Solution Approach 2:
The patent introduces an intermediary estimation model that processes raw state information and transforms it into meaningful battery consumption predictions. This intermediary layer aggregates and analyzes multiple state parameters (CPU, display, network, audio) to produce a unified consumption estimate, simplifying the complexity of handling individual parameters while maintaining accurate function-specific predictions.
Data Source
AI summary
A processing device may include: an information accumulating unit that obtains execution-time state information and battery information; an estimation-model generating unit that generates an estimation model for estimating a battery consumption amount when the piece of content is executed, from the information indicating the state of the terminal device on the basis of the execution-time state information and the battery information, which are accumulated in the storage unit; an estimation unit that obtains estimation-time state information indicating the state of the terminal device at a predetermined estimation timing and that estimates a battery consumption amount when the piece of content is executed at the terminal device in the state indicated by the estimation-time state information, on the basis of the estimation-time state information and the estimation model; and an output unit that outputs an estimation result obtained from the estimation unit.