Machine Learning Root Cause Analysis for IT Incident Data

The patent introduces machine learning models as intermediary components between raw machine-generated data and human operators. These models process and enrich incident data automatically, serving as a mediator that handles the complexity of data analysis while presenting simplified insights to users. This resolves the contradiction by automating the analytical workload without requiring human operators to directly manage system complexity.

The system enables self-service analysis through automated machine learning pipelines that independently process incidents, generate enrichments, and provide recommendations without human intervention. The machine learning models autonomously learn from historical data and continuously improve their analysis capabilities, allowing the system to serve itself while maintaining high productivity and reducing the need for complex manual analysis procedures.

The system performs preliminary actions by pre-processing and enriching incident data using machine learning models before human operators need to review it. The models generate preliminary diagnoses, identify potential root causes, and prepare enriched incident records in advance, so when operators do review incidents, the work is already partially completed. This reduces response time without requiring operators to perform complex analysis manually.

The patent replaces the mechanical system of manual human analysis with an automated machine learning-based analysis system. Instead of operators manually examining logs and metrics, machine learning algorithms automatically process the data, identify patterns, and generate insights. This substitution dramatically reduces response time while maintaining ease of operation, as the automated system handles the time-consuming analysis tasks.

The machine learning models extract only the most relevant features and insights from comprehensive incident data, separating signal from noise. Instead of requiring operators to analyze all collected data, the models automatically identify and extract key diagnostic information, root cause indicators, and actionable insights. This extraction process maintains measurement precision by focusing on critical data points while reducing the apparent complexity for human operators.

The system transforms raw incident data into enriched parameters and features through machine learning processing. By changing the parameter representation from raw logs and metrics to structured, enriched incident records with pre-computed features and insights, the system improves measurement precision while making the data more manageable and less complex for both automated processing and human review.