IHS Standby Wake Prediction Using Sensor Likelihood Analysis
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Solution Overview
Problem
Mobile Information Handling Systems (IHSs) face challenges in quickly initializing after being removed from bags, as existing technologies do not effectively anticipate and prepare for user intentions based on sensor data and location information, leading to delayed response times.
Innovation Solution
Implementing a method that uses a plurality of sensors to determine the likelihood of the IHS being in a bag and whether it will be removed, allowing the system to wake up from a standby power state based on sensor readings, network signal information, and user schedules, thereby optimizing power management and initialization for immediate use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the IHS remains in standby power state to conserve energy, then power consumption is reduced, but response time when removed from bag increases
Solution Approach 1:
The system performs preliminary actions by analyzing sensor data (accelerometer, gyroscope, light sensors) and location information while in standby state to predict when the device will be removed from the bag. When removal is anticipated, the system proactively transitions from standby to active state before the user actually removes it, thus reducing response time without requiring continuous operation.
Solution Approach 2:
The system continuously monitors sensor readings and location data to provide feedback about the device's state and context. This feedback loop enables the system to adjust its power state dynamically - remaining in standby when no removal is predicted, and transitioning to active when sensors indicate the device is being taken out of the bag, optimizing both power consumption and response time.
2Loss of time
If the IHS continuously monitors sensor data to detect bag removal, then response time is improved, but power consumption in standby state increases
Solution Approach 1:
Instead of continuous monitoring, the system employs periodic sampling of sensor data while in standby state. The sensors (accelerometer, gyroscope, light sensors) are activated at intervals to collect data about movement patterns and environmental context, then returned to low-power state. This periodic approach maintains detection capability while significantly reducing standby power consumption compared to continuous operation.
3Ease of operation
If the IHS wakes up immediately upon removal from bag, then user experience is improved, but power is wasted if removal is not intended
Solution Approach 1:
The system introduces an intermediary analysis layer between sensor detection and power state transition. Instead of directly waking upon any movement detection, the system analyzes patterns from multiple sensors (accelerometer, gyroscope, light sensors) and location data through machine learning models to determine the likelihood of intended removal. This intermediary intelligence layer filters out false positives (e.g., accidental drops, brief handling) and only triggers wake-up when removal is genuinely intended, improving user experience while preventing wasted power.
Data Source
AI summary
An IHS Handling System (IHS) may be transported within various types of bags. Upon reaching a new location, a user removes the IHS from the bag and prefers that the IHS is ready for use as quickly as possible. Embodiments reduce response times of an IHS that is transported within a bag. While the IHS is configured in a standby power state, sensor readings are collected from sensors of the IHS. Based on the collected sensor readings, a first likelihood is determined of whether the IHS is located in a computer bag. Further based on the collected sensor readings, a second likelihood is determined of whether the IHS will be removed from the computer bag. The IHS is woken from the standby power state and configured for use based on the first likelihood or the second likelihood.