Endpoint-Specific Diagnostic Plans for Information Handling Systems
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Solution Overview
Problem
Conventional diagnostic strategies for information handling systems are inefficient and ineffective due to reliance on passive test models and multiple static test algorithms, leading to slow, inaccurate, and costly diagnostics, especially with the increasing complexity and variability of computer systems.
Innovation Solution
A cloud-based diagnostics optimization method and platform that employs machine learning resources to generate endpoint-specific diagnostic plans, prioritizing execution time and frequency, and differentiating between hardware and software issues by utilizing health data from endpoints and operational contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If passive test models with multiple static test algorithms are used, then comprehensive device coverage is achieved, but diagnostic execution time increases and efficiency decreases
Solution Approach 1:
The patent transitions from static test algorithms to dynamic, adaptive diagnostic plans that are generated in real-time based on device health data, operational context, and machine learning predictions. The diagnostic plan changes dynamically according to the specific needs of each device and endpoint, optimizing execution time while maintaining comprehensive coverage.
Solution Approach 2:
The system changes multiple parameters simultaneously including test selection, test sequence, execution timing, and resource allocation based on analyzed health data and predictive models. This allows the diagnostic process to adapt its parameters to minimize execution time while maintaining accuracy.
2Measurement precision
If comprehensive health data collection from all endpoints is performed, then diagnostic accuracy improves, but data processing complexity and resource requirements increase
Solution Approach 1:
The patent extracts only the most relevant health data and features needed for specific diagnostic purposes using machine learning models. Rather than processing all collected data uniformly, the system identifies and extracts critical parameters that contribute most to diagnostic accuracy, reducing processing complexity.
Solution Approach 2:
The centralized platform performs multiple functions including data collection, analysis, model training, diagnostic plan generation, and execution coordination. This multi-functional approach consolidates complexity into a single system rather than distributing it across multiple components.
3Productivity
If endpoint-specific diagnostic plans are generated using machine learning, then diagnostic efficiency and accuracy improve, but computational resource requirements and system complexity increase
Solution Approach 1:
The system performs preliminary machine learning model training and health data analysis in advance to generate predictive insights and pre-configured diagnostic plans. This preliminary computation reduces the computational burden during actual diagnostic execution, as the heavy lifting is done beforehand.
Solution Approach 2:
The centralized diagnostic platform acts as an intermediary between endpoint devices and diagnostic execution. It handles the computationally intensive machine learning operations centrally, allowing endpoints to benefit from advanced analytics without bearing the full computational burden themselves.
4Ease of manufacture
If traditional static diagnostic algorithms are used across all devices, then implementation simplicity is maintained, but adaptability to different device configurations and issues decreases
Solution Approach 1:
The system implements dynamic diagnostic plans that automatically adapt to different device configurations, operational contexts, and detected issues. The machine learning models continuously learn from new data, enabling the system to adapt its diagnostic approach to each specific situation while maintaining a unified implementation framework.
Data Source
AI summary
A diagnostics optimization platform employs cloud-based resources, including a diagnostics repository that accumulates health data from managed endpoints, and machine learning (ML) resources that generate endpoint-specific diagnostic plans based on the accumulated health data. The ML resources may be configured to generate diagnostic plans that prioritize any appropriate diagnostic testing parameter or objective including, as a non-limiting example, a reduction in diagnostic testing execution time and/or diagnostic testing frequency. The ML resources may maintain a continually updated training database derived from the collected health data to develop endpoint-specific data collection and diagnostic testing models. The ML resources may include a diagnostics optimization module to develop diagnostic testing models and provide corresponding endpoint-specific diagnostic plans to each endpoint. The ML resources may further include a data collection module to develop data collection models and generate endpoint-specific data collection plans for each of the managed endpoints.