Automated Operations Manager for Imminent Device Failure Prediction
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Solution Overview
Problem
Existing techniques fail to effectively manage and mitigate computing device failures and performance issues in smartphones and other mobile devices due to complex hardware components and varying configuration settings, leading to reduced reliability and battery life.
Innovation Solution
An Automated Operations Manager (AOM) system that automatically identifies imminent device failures by generating state-space outcome models based on observed hardware attribute states, allowing for dynamic modification of configuration settings and operations to prevent or mitigate failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If device operations are managed with complex hardware components and configuration settings, then device functionality and performance are improved, but device reliability and battery life are reduced
Solution Approach 1:
The system performs preliminary actions by continuously monitoring hardware attribute states and predicting potential failures before they occur. The AOM system analyzes sequences of hardware states (CPU temperature, battery voltage, memory usage, etc.) to identify patterns indicating imminent failure, and proactively modifies device operations or notifies users to prevent actual failures from occurring.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring hardware attribute states and using this information to dynamically adjust device operations. The AOM system creates a closed-loop control system where hardware state monitoring feeds into failure prediction models, which then trigger operational modifications that feed back to affect hardware states, creating a self-regulating system that improves reliability while maintaining functionality.
2Productivity
If device operations are managed with complex hardware components and configuration settings, then device functionality is improved, but battery life is reduced
Solution Approach 1:
The system applies dynamics by making device operations adaptable and adjustable based on real-time hardware states. The AOM system dynamically modifies configuration settings such as CPU frequency scaling, display refresh rates, and network transmission powers according to predicted failure risks and current battery levels, allowing the device to optimize between performance and power consumption based on changing conditions.
Solution Approach 2:
The system changes operational parameters to extend battery life while maintaining functionality. The AOM system modifies parameters such as processor clock speeds, memory allocation, network data rates, and peripheral power states based on failure predictions and battery status, thereby adjusting device operations to conserve energy when failure risks are high or battery levels are low.
3Reliability
If automated corrective actions are taken to prevent device failure, then device reliability is improved, but device complexity increases
Solution Approach 1:
The system implements self-service by enabling the device to automatically monitor its own hardware states, predict potential failures, and execute corrective actions without external intervention. The AOM system incorporates failure prediction models and operational modification capabilities directly into the device firmware or operating system, allowing the device to self-diagnose and self-correct issues, thereby improving reliability without requiring additional external management systems.
Data Source
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AI summary
Techniques are described for automatically and dynamically modifying ongoing operations of computing devices in device-specific manners, such as based on an automated identification of a computing device's status (e.g., identifying a likely ongoing or imminent failure of a smart phone or other computing device based on a series of observed hardware states of the computing device, and taking automated corrective actions to prevent or otherwise mitigate such device failure, such as by modifying configuration settings on the computing device or on associated systems). The techniques may include, for each of multiple device status outcomes of interest (e.g., device failure versus device non-failure), generating a state-space outcome model representing devices that reach that status outcome within a time period of interest, and using such outcome models to identify a likely ongoing or imminent outcome of a current device, with corresponding automated corrective actions then taken.