Medical Imaging Fault Diagnosis Using Prioritized Log Retrieval
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Solution Overview
Problem
Medical imaging devices often malfunction, leading to significant downtime due to the time-consuming process of retrieving and analyzing large log files, which hinders efficient fault diagnosis.
Innovation Solution
A method utilizing machine learning to predict the relevance and severity of log files, generating a prioritized list for transmission to a remote processing device, thereby reducing unnecessary data retrieval and accelerating fault diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all log files are retrieved for fault diagnosis, then complete diagnostic information is obtained, but data retrieval time and bandwidth consumption increase significantly
Solution Approach 1:
The patent extracts only the relevant log files needed for fault diagnosis by using machine learning to predict which log files are most likely to contain diagnostic information. This selective extraction approach retrieves only necessary data rather than all log files, reducing retrieval time and bandwidth consumption while maintaining diagnostic effectiveness.
Solution Approach 2:
The patent performs preliminary analysis by generating relevance scores for log files before actual retrieval. The machine learning model predicts which log files are relevant to the fault, allowing the system to prioritize and retrieve only those files in advance, thereby reducing overall diagnostic time and information loss.
2Loss of information
If all log files are retrieved for fault diagnosis, then complete diagnostic information is obtained, but bandwidth demand increases significantly
Solution Approach 1:
The patent extracts only the relevant log files needed for fault diagnosis by using machine learning to predict which log files are most likely to contain diagnostic information. This selective extraction approach retrieves only necessary data rather than all log files, reducing retrieval time and bandwidth consumption while maintaining diagnostic effectiveness.
Solution Approach 2:
The patent applies partial action by retrieving only a subset of log files that are predicted to be relevant to the fault, rather than retrieving all available log files. This partial retrieval strategy reduces bandwidth consumption significantly while still obtaining sufficient diagnostic information through the prioritization of relevant files.
3Productivity
If log files are prioritized using machine learning analysis, then data retrieval efficiency improves, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary machine learning model that acts as a mediator between the fault report and log file retrieval process. This intermediary component analyzes the fault report, generates relevance scores for different log files, and guides the retrieval process, thereby improving diagnostic efficiency while managing system complexity through modular architecture.
Solution Approach 2:
The system performs self-service by automatically analyzing fault reports and determining which log files are relevant without requiring manual intervention. The machine learning model autonomously generates relevance scores and prioritizes log files, reducing the need for complex manual configuration and simplifying the overall system operation.
Data Source
AI summary
Proposed are concepts pertaining to aiding fault diagnosis through the consideration of the relevance of different log files to a fault. In particular, embodiments of the invention propose generating predicted relevance scores for different log files, and/or content contained therein. The most relevant log file(s), or parts thereof, may then be prioritized for provision to an engineer, for example.