Adaptive Power Profile Controller for Dynamic Energy Management
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Solution Overview
Problem
Existing power management tools for computing devices often compromise between battery life and system performance, as they rely on static power policies that are not tailored to specific usage models, leading to suboptimal energy management and user anxiety related to low battery levels.
Innovation Solution
A power profile controller that uses artificial intelligence and machine learning models, such as neural networks, to adapt power policies based on user behavior and device usage patterns, prioritizing performance or battery life depending on user anxiety levels and battery capacity thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If static power policies are used to manage energy expenditure, then battery life is extended, but system performance deteriorates
Solution Approach 1:
The patent applies dynamics by transitioning from static power policies to dynamic power policies that adapt in real-time based on usage patterns. The system continuously monitors telemetry data and adjusts power settings dynamically, allowing the power management approach to evolve from fixed to flexible, resolving the contradiction between extended battery life and maintained system performance.
Solution Approach 2:
The system changes power parameters such as CPU frequency, display brightness, and processor sleep states based on learned usage patterns. By adjusting these parameters dynamically rather than using fixed values, the system optimizes the balance between energy consumption and performance, resolving the contradiction between battery life extension and system performance maintenance.
2Use of energy by stationary object
If power management tools are deployed to extend battery life, then energy expenditure is managed, but system performance is compromised
Solution Approach 1:
The system implements feedback mechanisms by collecting telemetry data about actual usage patterns and using this information to refine power management decisions. The feedback loop allows the system to learn from real-world behavior and adjust power settings accordingly, optimizing the balance between energy expenditure management and system performance maintenance.
Solution Approach 2:
The power management system performs self-service by autonomously learning usage patterns and adjusting policies without requiring manual user configuration. The system self-optimizes based on observed behavior, reducing the need for user intervention while achieving better balance between energy management and performance.
3Device complexity
If static power policies are used, then power management is simplified, but adaptability to specific usage models deteriorates
Solution Approach 1:
The system achieves self-service by automatically learning and adapting to individual usage patterns without requiring complex manual configuration. The autonomous learning mechanism simplifies the user experience while providing high adaptability, resolving the contradiction between management simplicity and usage-specific adaptability.
Solution Approach 2:
The feedback mechanism continuously monitors actual usage and adjusts power policies accordingly, enabling the system to adapt to specific usage models automatically. This feedback loop provides adaptability without increasing user-facing complexity, as the system handles adaptation autonomously based on observed behavior.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed to improve computing device power management. An example apparatus includes a usage classifier to classify usage of a computing system, a low battery probability determiner to determine a probability of the computing system operating with a low battery capacity based on the classification, a policy reward determiner to determine an adjustment of a policy based on at least one of the classification or the probability, and determine a battery capacity of the computing system in response to the adjustment, and a policy adjustor to adjust the policy in response to the battery capacity satisfying a threshold.