System Event Conflict Detection Using Invalidity Code Feedback
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Solution Overview
Problem
Conventional methods fail to reliably identify system event conflicts in a timely manner, leading to negative impacts on downstream actions and increased computational load, as they often require remedial actions after the occurrence of errors rather than preventative analysis.
Innovation Solution
A system that extracts and analyzes data associated with previous system errors, using text-analyzing algorithms to convert narrative text into machine-readable invalidity codes, and updates a system error database with entity-cause pairs to predict and prevent future errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional methods are used to identify system errors, then errors are eventually detected, but detection occurs too late and requires remedial actions after errors have already impacted downstream actions
Solution Approach 1:
The system performs preliminary analysis of query data before approval to identify potential errors. By extracting features and checking against error patterns in advance, the system detects errors before they impact downstream actions, transforming reactive error detection into proactive prevention.
Solution Approach 2:
The system implements feedback mechanisms where detected errors and their characteristics are fed back into the error database. This continuous feedback loop allows the system to learn from past errors and improve future error detection, enhancing both timeliness and reliability of error identification.
2Measurement precision
If comprehensive error analysis is performed on all query data, then error detection accuracy improves, but computational load and processing time increase
Solution Approach 1:
The system extracts only the most relevant features from query data that are indicative of potential errors, rather than analyzing all data comprehensively. This selective extraction maintains high error detection accuracy while significantly reducing computational load and processing time.
Solution Approach 2:
The system applies partial analysis by focusing computational resources on high-risk query patterns identified through feature extraction and error pattern matching. Instead of uniform comprehensive analysis, it performs targeted analysis where most needed, improving both accuracy and efficiency.
Data Source
AI summary
A computer-implemented method includes receiving first query data associated with a first query, identifying a system error that is associated with the first unique entity identifier based on an indication that the first query was improperly approved, extracting a cause of the system error from the first query data, determining a machine-readable invalidity code that corresponds to the cause of the system error, updating a system error database with the first unique entity identifier and the machine-readable invalidity code as a first pair, and outputting, the first pair including the first unique entity identifier and the machine-readable invalidity code from the system error database, the first pair being configured to be processed by a front-end system for identifying a potential system error associated with second query data for a second query.