Automated Defect Identification Using Machine Learning

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Identifying and resolving system defects and optimizations in complex computer systems is tedious, time-consuming, and costly due to the vast amount of data in log files, requiring manual review of gigabytes of information to find hidden debug information.

Innovation Solution

A system where multiple computer systems communicate data to a central repository with a machine-learning algorithm that processes system performance and configuration data to identify defects and optimizations, allowing for automated identification and distribution of solutions, and proactive prevention of defects.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual review of log files is used to identify defects, then detailed analysis can be performed, but the process becomes time-consuming and labor-intensive

Engineering Contradiction:

Improvedefect detection accuracyVSAvoidtime to identify and resolve defects

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical review of log files with an automated computer-based system that uses machine learning algorithms and natural language processing to analyze log data, extract defect information, and generate resolutions automatically

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service defect resolution by automatically analyzing log files, identifying defects, generating resolutions, and distributing fixes without requiring manual intervention from system administrators or developers

Inventive Principle:

Principle #25Self-service

2Productivity

If automated systems are deployed to resolve defects across multiple systems, then productivity increases, but system complexity increases

Engineering Contradiction:

Improvedefect resolution speedVSAvoidsystem architecture complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent creates a universal defect resolution platform that can analyze log files from multiple different computer systems, applications, and defect types using a single automated system, making the complex system applicable across diverse scenarios

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system introduces a centralized defect management platform as an intermediary between log file generation and defect resolution, which standardizes the defect identification and resolution distribution process across multiple systems

Inventive Principle:

Principle #24Intermediary (Mediator)

3Reliability

If comprehensive log file analysis is performed to ensure system health, then defect detection improves, but data processing requirements increase

Engineering Contradiction:

Improvesystem health monitoringVSAvoidcomputational resources for log analysis

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The patent extracts only the relevant defect information from vast amounts of log file data using natural language processing and machine learning algorithms, rather than processing and analyzing every single log entry in detail

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system performs preliminary processing and filtering of log files to identify potential defect patterns before conducting detailed analysis, reducing the overall computational burden by pre-sorting and prioritizing log entries

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS9202167B1Automated defect identification and resolution

Publication Date: 2015.12.01 EMC IP HLDG CO LLC

AI summary

A method, article of manufacture, and apparatus for identifying and resolving defects in multiple clusters based on a reported defect instance is discussed. Computer systems may be grouped into clusters. A report of a defective system may be received, and the defective system's cluster may be identified. A defect solution may then be distributed to all the systems in that cluster.