NLP Classifier for Automated Error Log Diagnosis

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Solution Overview

Problem

Diagnosing execution problems from large and complex error logs is challenging due to the vast amount of detailed information, making it difficult for investigators to quickly identify relevant log statements, which is time-consuming and prone to human errors.

Innovation Solution

Employing machine learning and natural language processing to classify log statements into categories, enabling the automatic identification and filtering of problem-related statements, reducing the need for manual sifting through extensive log data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual analysis of error logs is performed, then investigators can identify problem-related log statements, but the process is time-consuming and prone to human errors due to the massive amount of detailed information

Engineering Contradiction:

Improveaccuracy of problem identificationVSAvoidtime required for log analysis

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces the manual mechanical process of log analysis with an automated computer-based system using natural language processing and machine learning. The system automatically classifies log statements into categories (environment information, execution results, problems, generation information) and identifies problem-related statements, eliminating human effort and reducing errors while significantly decreasing analysis time

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service by automatically analyzing and classifying error logs without requiring human investigators to manually examine each log statement. The automated classification and problem identification mechanisms allow the system to serve itself in diagnosing execution problems, freeing investigators from time-consuming manual analysis

Inventive Principle:

Principle #25Self-service

2Loss of information

If all log statements are retained and analyzed, then complete information is available for diagnosis, but the complexity and difficulty of finding relevant information increases significantly

Engineering Contradiction:

Improvecompleteness of diagnostic informationVSAvoiddifficulty of finding relevant log statements

Core Design Contradiction:

Loss of informationVSDifficulty of detecting and measuring

Solution Approach 1:

The patent segments the massive error log into categorized groups using automated classification. Log statements are divided into distinct categories (environment information, execution results, problems, generation information), making it easier to locate and analyze specific types of information without being overwhelmed by the complete log content

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system extracts and isolates problem-related log statements from the complete log file using natural language processing and classification algorithms. By automatically identifying and extracting relevant problem information, the system maintains completeness of diagnostic data while reducing the complexity of finding and analyzing relevant statements

Inventive Principle:

Principle #2Taking out (Extraction)

3Productivity

If automated classification systems are implemented, then analysis time is reduced and productivity increases, but the system complexity and computational resources required increase

Engineering Contradiction:

Improvespeed of problem diagnosisVSAvoidcomplexity of analysis system

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent implements automated classification systems that replace manual analysis processes, significantly increasing productivity in problem diagnosis. The system uses natural language processing and machine learning algorithms to automatically classify log statements and identify problems, reducing analysis time from hours or days to minutes while managing computational complexity through efficient algorithm design

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12001788B2Systems and methods for diagnosing problems from error logs using natural language processing

Publication Date: 2024.06.04 OPEN TEXT CORP

AI summary

Disclosed is a solution for diagnosing problems from logs used in an application development environment. A random sample of log statements is collected. The log statements can be completely unstructured and/or do not conform to any natural language. The log statements are tagged with predefined classifications. A natural language processing (NLP) classifier model is trained utilizing the log statements tagged with the predefined classification. New log statements can be classified into the plurality of predefined classifications utilizing the trained NLP classifier model. From the log statements thus classified, statements having a problem classification can be identified and presented through a dashboard running in a browser. Outputs from the trained NLP classifier model can be provided as input to another trained model for automatically and quickly identifying a type of problem associated with the statements, eliminating a need to manually sift through tens or hundreds of thousands of lines of logs.