Sensor-Learned Wake Scheduling for Responsive, Low-Power Computing Systems
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Solution Overview
Problem
Existing computing systems lack an efficient dynamic wake period schedule that balances battery life and responsiveness, leading to unnecessary power consumption and reduced user experience.
Innovation Solution
Implementing an Integrated Sensor Hub (ISH) that analyzes user day-to-day routines through sensor measurements, correlates these routines with operating system states, and uses machine learning to generate a dynamic wake period schedule.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system uses a fixed wake period schedule to periodically wake from standby mode, then the system maintains responsiveness and readiness, but unnecessary power consumption increases and battery life decreases
Solution Approach 1:
The patent implements a dynamic wake period schedule that adapts to user behavior patterns rather than using a fixed schedule. The system learns user routines and adjusts wake periods accordingly, making the wake-up frequency variable rather than constant. This resolves the contradiction by allowing the system to be responsive when needed (maintaining reliability) while reducing wake-ups during periods when users are unlikely to use the device (reducing energy loss).
Solution Approach 2:
The system changes the parameter of wake period duration based on learned user behavior patterns. Instead of using a static wake period, the system dynamically adjusts the wake period parameter according to contextual factors such as time of day, user activity patterns, and device usage history. This allows optimization of both responsiveness and power consumption by adapting the wake period parameter to current conditions.
2Ease of operation
If the system frequently wakes from standby mode to maintain readiness, then user experience and responsiveness improve, but battery life is reduced
Solution Approach 1:
The system performs preliminary learning of user behavior patterns during an initial period, building a model of when the user is likely to use the device. This preliminary action allows the system to predict optimal wake periods in advance, ensuring good user experience when the device is actually needed while avoiding unnecessary wake-ups that would consume battery. The preliminary learning phase enables subsequent energy-efficient operation without sacrificing user experience.
3Loss of energy
If the system uses sensor measurements and machine learning to dynamically adjust wake periods, then power consumption is optimized and battery life extends, but device complexity increases
Solution Approach 1:
The system implements self-service through automated machine learning models that learn user behavior patterns without requiring manual configuration or intervention. The system autonomously analyzes sensor data, identifies patterns, and adjusts wake periods based on learned insights. This self-service capability reduces the need for complex user interfaces or manual tuning, offsetting the added complexity with automation that ultimately simplifies the user's interaction while achieving energy optimization.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed to dynamically schedule a wake pattern in a computing system. An example apparatus includes a device state controller to determine contexts of a device based on sensor measurements collected at a first time, an associator to associate a state of an operating system with the contexts of the device, the state obtained at a second time after the first time, a training controller to generate a prediction model based on the association, the prediction model to predict a third time when the state of the operating system will be active based on the contexts, and a schedule controller to reduce power consumption of the device by triggering a wake event before the third time, the wake event to prepare the device for exiting an inactive state.