Transformative Power Management Inference
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Solution Overview
Problem
Current power management systems for handheld devices rely on user proactive configuration and memory, which is inefficient due to increasing app power demands, multiple app usage, individual user differences, and mission-specific requirements, leading to suboptimal battery usage.
Innovation Solution
The Transformative Power Management (TPM) system applies pattern recognition and learning algorithms to infer user behavior and mission states, dynamically prioritize power resources, and suggest optimal power modes for apps, enabling autonomous or interactive energy management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional power management systems rely on user proactive configuration and memory, then system simplicity is maintained, but power management efficiency deteriorates due to increasing app power demands and multiple app usage
Solution Approach 1:
The power management system automatically infers user behavior patterns and mission states without requiring explicit user configuration. The system self-adjusts power allocation by monitoring app usage patterns, determining user behaviors, and dynamically modifying power settings based on inferred mission contexts, enabling autonomous power optimization
Solution Approach 2:
The patent replaces manual user configuration mechanisms with automated pattern recognition and machine learning algorithms. Instead of relying on user-provided power management settings, the system uses computational models to infer user intentions and automatically optimize power distribution across applications
2Measurement precision
If power management systems account for individual user differences and mission-specific requirements, then power allocation accuracy is improved, but system complexity increases
Solution Approach 1:
The system pre-establishes a comprehensive user behavior pattern library and mission state database before actual power management operations. By预先 collecting and organizing user behavior patterns across different missions and applications, the system prepares inference models in advance, enabling accurate real-time power allocation without complex on-the-fly calculations
Solution Approach 2:
The patent introduces an inference service as an intermediary layer between raw app usage data and power management decisions. This intermediary component processes app usage information, determines user behaviors, infers mission states, and translates these inferences into power allocation instructions, simplifying the overall system architecture while improving accuracy
3Use of energy by moving object
If the system dynamically prioritizes power resources based on inferred user behavior, then energy efficiency is improved, but computational overhead increases
Solution Approach 1:
The system applies partial inference and power adjustment strategies by focusing computational resources on the most power-intensive applications and critical mission states. Instead of continuously analyzing all app usage data, the system selectively infers user behaviors for high-impact applications and adjusts power settings primarily for resources consuming significant energy, reducing overall computational overhead while maintaining energy efficiency
Solution Approach 2:
The power management system implements continuous feedback loops where power allocation decisions are monitored and adjusted based on actual user behavior patterns and mission state changes. The system learns from observed user responses to power adjustments and refines its inference models over time, improving energy efficiency while reducing the computational power required for inference through iterative optimization
Data Source
AI summary
Methods to infer user behavior are disclosed comprising a process of predefining one or more activities for a user application, providing a processor based device and user interface configured to operate with the device to support the user with tasks using the user application, the user application communicating an intent message to a transformative power management (TPM) application and the TPM configured to define and output an instruction for the application given the intention and the predefined activities. In some embodiments, the methods are implemented on a processor based device. In some embodiments, the systems and methods apply pattern recognition algorithms and pattern learning algorithms to manage the power allocation to power consuming devices.