Dynamic Power Management for Mobile Platforms
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Solution Overview
Problem
Mobile computing devices face limitations in battery life due to inefficient power management, as existing manual switching mechanisms between power saving states are inefficient and frustrating for users, leading to reduced utility during low-power modes like standby or hibernation.
Innovation Solution
A user-activity-based dynamic power management scheme that monitors sensor values to predict user states and automatically switches between power management policies, adjusting time-out parameters to optimize power consumption based on user activity, using machine learning to update user state models and select appropriate power schemes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If the device enters low power state (Stand-by/Hibernate) to conserve battery power, then battery life is extended, but system utility and CPU execution capability are lost
Solution Approach 1:
The patent implements dynamic power management by continuously monitoring sensor data and automatically adjusting power states based on detected user activities. The system transitions between active and low-power states dynamically rather than statically, allowing the device to adapt its power consumption profile to actual usage patterns. This resolves the contradiction by enabling the system to maintain high utility during active periods while maximizing battery life during inactive periods.
Solution Approach 2:
The system employs machine learning models that automatically analyze sensor data and predict user states without manual intervention. The power management system serves itself by autonomously determining when to transition between power states based on learned patterns of user behavior. This eliminates the need for users to manually switch between power modes while optimizing both battery life and system utility based on actual usage.
2Loss of energy
If manual switching between power saving states is implemented, then power consumption is reduced, but user convenience and operational efficiency deteriorate
Solution Approach 1:
The system automatically monitors sensor data and adjusts power states without requiring user intervention. Machine learning models predict user activities and autonomously optimize power consumption by transitioning between active and low-power states. This self-service approach reduces power loss while maintaining user convenience, as the system handles power management independently based on learned behavioral patterns.
Solution Approach 2:
The system continuously collects sensor data and uses machine learning models to analyze user behavior patterns. This feedback loop enables the system to adapt its power management strategy in real-time, adjusting power states based on detected activities. The feedback mechanism ensures optimal power consumption while maintaining user convenience through automatic adaptation to usage patterns.
3Productivity
If the CPU remains active to maintain system utility, then execution capability is preserved, but battery life is reduced
Solution Approach 1:
The system dynamically adjusts CPU activity based on detected user behaviors through sensor monitoring and machine learning analysis. The CPU transitions between active and low-power states according to predicted user states, maintaining execution capability during active periods while conserving battery life during inactive periods. This dynamic approach optimizes the trade-off between productivity and battery duration.
Solution Approach 2:
The machine learning models predict user states in advance based on sensor data patterns, allowing the system to proactively transition the CPU to appropriate power states. By anticipating user activities, the system can maintain execution capability when needed while avoiding unnecessary CPU activity that would consume battery life, thus optimizing the balance between productivity and battery duration.
Data Source
AI summary
A method and apparatus for user activity-based dynamic power management and policy creation for mobile platforms are described. In one embodiment, the method includes the monitoring of one or more sensor values of a mobile platform device to gather user activity data. Once the user activity data is gathered, the user state may be predicted according to the gathered user activity and an updated user state model. In one embodiment, the user state model is updated according to the user activity data. In one embodiment, a switch occurs from the current power management policy to a new power management policy if the new user state differs from a current user state by a predetermined amount. In one embodiment, at least one time-out parameter of a selected power management policy may be adjusted to comply with a predicted user state. Other embodiments are described and claimed.