Hardware Life Extension Through AI Configuration Recommendations
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Solution Overview
Problem
The increasing demand for minimizing greenhouse gas emissions during the manufacture and usage of information handling systems is challenged by the frequent replacement of underperforming hardware components, leading to unnecessary GHG emissions from manufacturing and disposal of these parts.
Innovation Solution
A machine learning system that recommends software configuration adjustments based on user-approved recommendations from similar systems to extend the life of hardware components, while rewarding users for implementing these adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hardware components are frequently replaced to maintain system performance, then system reliability is improved, but greenhouse gas emissions from manufacturing and disposal increase
Solution Approach 1:
The patent changes the operational parameters of hardware components by adjusting software configurations, power settings, and usage patterns to extend hardware lifespan. This allows the system to maintain reliability through parameter optimization rather than physical replacement, thereby reducing manufacturing emissions.
Solution Approach 2:
The system implements self-service mechanisms where AI models automatically analyze hardware performance data and generate optimization recommendations. This enables the system to self-diagnose and self-optimize hardware usage patterns, extending component life without manual intervention or physical replacement.
2Duration of action of stationary object
If software configuration adjustments are made to extend hardware life, then hardware replacement frequency is reduced, but system performance may deteriorate
Solution Approach 1:
The patent applies partial optimization by selectively adjusting only those software parameters and configurations that directly impact hardware stress and lifespan. Not all performance parameters are reduced - only those that can be optimized without significant productivity loss, while maintaining acceptable system performance levels.
Solution Approach 2:
The system dynamically adjusts configuration parameters based on real-time hardware performance data and usage patterns. Rather than static optimizations, the AI models continuously adapt settings to balance hardware extension goals with current performance requirements, allowing the system to respond to changing conditions.
3Measurement precision
If AI models are trained on user-approved recommendations from multiple systems, then recommendation accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the training data into distinct categories from multiple information handling systems, organizing recommendations by hardware type, failure mode, and success criteria. This structured segmentation enables the AI models to process complex multi-source data systematically, improving accuracy while managing complexity through organized data architecture.
Data Source
AI summary
A hardware life extension learning recommendation and reward system of an information handling system may comprise a network interface device to receive operational telemetries from a first and a second computing device that each include a high temperature warning, an error for a labeled hardware type, and execution of a specific application, and a processor to determine, based on these telemetries, that failure of a first component of the labeled hardware type previously occurred at the first device, failure of a second component of the labeled hardware type has occurred at the second device, and execution at the first device of a previous recommendation to limit access to resources of the first component made available to the specific application extended the first component lifetime. A new recommendation may be transmitted to the second device to limit access to resources of the second component to the specific application.