Battery Usage History Modeling for Future Charge Prediction
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Solution Overview
Problem
Existing technologies do not provide a user with a reliable method to predict future remaining battery amount, making it difficult to manage battery usage effectively.
Innovation Solution
An information processing apparatus that calculates a future prediction value of remaining battery amount based on user usage history and presents this information to the user, utilizing a control unit to aggregate and analyze usage data to provide accurate predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the remaining battery amount is simply monitored without prediction, then the device structure remains simple, but the user cannot predict future battery amount
Solution Approach 1:
The system performs preliminary analysis of usage history data to predict future battery amounts before the user needs to know them. The prediction function analyzes past usage patterns in advance and prepares forecast information, allowing users to see future battery status without adding complex real-time computation requirements.
Solution Approach 2:
The system creates a simplified model or copy of usage patterns from historical data to predict future battery consumption. Instead of complex real-time analysis, it replicates past usage behavior patterns to generate accurate predictions with minimal computational overhead.
2Measurement precision
If detailed usage history is collected for accurate prediction, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The system extracts only the essential and relevant features from detailed usage history data that are necessary for accurate prediction. It identifies and isolates key patterns such as typical usage durations, high-consumption applications, and temporal patterns, discarding redundant information to maintain accuracy while reducing processing complexity.
Solution Approach 2:
The system collects more usage data than strictly necessary (excessive action) to ensure sufficient statistical basis for prediction, but then applies selective processing to use only the most relevant portions. This approach ensures prediction accuracy by having abundant data while avoiding the complexity of processing every single data point in detail.
Data Source
AI summary
The present disclosure provides novel and improved information processing apparatus, information processing method, and program with which it is easy for a user to predict a future remaining battery amount. According to the present disclosure, there is provided an information processing apparatus including a control unit that performs control to calculate a future prediction value of remaining battery amount on the basis of a use history of an information processing apparatus by a user and to present prediction value related information related to the prediction value to the user. According to the present disclosure, the user can easily predict the future remaining battery amount. Note that the effects described above are not necessarily limitative. With or in the place of the above effects, there may be achieved any one of the effects described in this specification or other effects that may be grasped from this specification.