Microscopy Log Analysis Using AI Error Pattern Detection
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Solution Overview
Problem
Microscopy systems experience errors that are difficult to identify and resolve due to large log files, leading to time-consuming analysis and potential downtime.
Innovation Solution
A computer-implemented method using an AI-based module to analyze log data for known errors, quickly identifying and providing recommendations to resolve or prevent errors in microscopy systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual log file analysis is used to identify errors, then thorough error detection can be achieved, but time consumption and operational downtime increase significantly
Solution Approach 1:
The patent replaces manual mechanical analysis of log files with an automated AI-based analysis system. The AI module processes log data, identifies error patterns, and generates diagnostic reports automatically, eliminating the need for technicians to manually review thousands of log entries while maintaining or improving detection accuracy.
Solution Approach 2:
The patent introduces an AI-based analysis module as an intermediary between the microscopy system's log generation and the technician's error identification. This intermediary automatically processes raw log data, extracts meaningful error information, and presents structured findings, reducing the time technicians need to spend on analysis.
2Loss of information
If comprehensive log files are generated to capture all system operations, then complete error information is available, but log file size and complexity increase making analysis more difficult
Solution Approach 1:
The AI-based analysis module extracts only the relevant error information from comprehensive log files. It identifies and isolates specific error patterns, problematic parameter combinations, and critical system states, presenting only the essential information needed for diagnosis rather than requiring technicians to navigate entire log files.
Solution Approach 2:
The patent segments the complex log analysis task into manageable components through the AI module, which divides log data into meaningful sections such as error events, warning patterns, system state changes, and component failures. This segmentation makes the information more structured and easier to analyze.
3Measurement precision
If service technicians manually analyze log files to resolve errors, then detailed understanding of system behavior is achieved, but productivity and response time decrease
Solution Approach 1:
The AI-based analysis module performs preliminary error analysis and generates diagnostic reports before technicians begin their work. It pre-identifies potential causes, suggests probable solutions, and prepares structured findings, allowing technicians to start with a head start and resolve errors more quickly while maintaining their expert analysis capabilities.
Data Source
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AI summary
A computer-implemented method of operating a microscopy system (100) is described, the method comprises obtaining log data indicative of at least one error occurring at the microscopy system; evaluating, by means of an Artificial Intelligence-based, Al-based, module the obtained log data with respect to one or more known errors; and determining, based on the evaluation, whether the error indicated by the log data is associated with at least one known error.