Architecture Diagram Analysis Tool for Software Systems
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Solution Overview
Problem
Existing computer systems face challenges in identifying and resolving issues quickly, leading to significant downtime and reduced throughput due to the complexity of troubleshooting unique configuration-related problems, which can arise from non-compliant or non-scalable components.
Innovation Solution
An architecture diagram analysis tool that uses a combination of unsupervised and supervised learning techniques to detect issues and autonomously implement solutions by converting features into vector points, classifying them, and updating the architecture diagram with compliant and scalable alternatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual troubleshooting is used to identify and resolve issues in computer systems, then accuracy of issue detection can be maintained, but the time required to detect and resolve issues increases significantly
Solution Approach 1:
The system performs self-diagnosis by automatically analyzing architecture diagrams and detecting issues without human intervention. The computer system identifies its own configuration problems and resolves them autonomously, eliminating the need for manual troubleshooting while maintaining detection accuracy and significantly reducing downtime.
Solution Approach 2:
The patent replaces manual mechanical troubleshooting processes with automated machine learning-based analysis. The system uses unsupervised and supervised learning algorithms to automatically detect and resolve configuration issues, substituting human operators with an automated intelligent system that operates continuously without interruption.
2Ease of repair
If the computer system is shut down to allow repairs, then issues can be resolved, but throughput and performance decrease during the shutdown period
Solution Approach 1:
The system performs preliminary analysis of architecture diagrams to detect configuration issues before they cause system failures. By identifying and resolving potential problems in advance, the system prevents the need for shutdowns and maintains continuous operation at full throughput.
Solution Approach 2:
The system autonomously identifies and resolves configuration issues without requiring shutdowns or human intervention. The automated detection and resolution process allows the system to maintain full operational capacity while fixing problems, eliminating the productivity loss associated with traditional repair shutdowns.
3Reliability
If complex troubleshooting processes are used to handle unique configuration issues, then comprehensive issue detection is achieved, but the complexity of the repair process increases
Solution Approach 1:
The patent replaces complex manual troubleshooting processes with automated machine learning algorithms. The system uses unsupervised learning to identify unusual patterns and supervised learning to classify configuration issues, automatically handling the complexity of unique configuration problems without requiring complex human troubleshooting procedures.
Solution Approach 2:
The system transforms the troubleshooting approach by changing from manual analysis parameters to automated learning parameters. By using machine learning models that can process and analyze architecture diagram data automatically, the system maintains comprehensive issue detection while eliminating the complexity of manual troubleshooting processes.
Data Source
AI summary
A device configured to obtain an architecture diagram that includes features that are configured to form a workflow for a computer system. The device is further configured to identify the features within the architecture diagram and their metadata. The device is further configured to convert the features into vector points based on the metadata and to generate a vector map that associates vector points with their metadata. The device is further configured to input the vector points into a machine learning model and to obtain classification results for the vector points. The device is further configured to identify non-compliant features that correspond with vector points that are associated with a non-compliant classification. The device is further configured to identify alternative features for the non-compliant features, to update the vector map with the alternative features, and to update the architecture diagram based on the updated vector map.