Causal Change Analysis for Faster Incident Root Cause Ranking

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Solution Overview

Problem

Identifying the root cause of major technology incidents caused by changes is challenging due to the large number of recent changes, prolonging outages and consuming significant time and resources.

Innovation Solution

A method and system for causal change analysis using Structured Query Language (SQL) queries and machine learning algorithms to rank potential root cause change requests based on similarity and relevance scores, supported by a chat-based interface and AI tools for efficient root cause identification.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If support teams manually review all recent changes to identify root cause, then they can find the correct root cause, but the process becomes time-consuming and prolongs outages

Engineering Contradiction:

Improveroot cause identification accuracyVSAvoidincident resolution time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent introduces an intermediary system that automatically analyzes change records and incidents using machine learning models. This intermediary processes the relationship between changes and incidents, generating relevance scores and ranked lists of potential root causes, thereby eliminating the need for manual review while maintaining high identification accuracy.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces the mechanical manual review process with an automated computer-based system that uses machine learning algorithms and natural language processing. The system automatically queries databases, compares change records with incident data, and generates analytical results, substituting human effort with computational processes that are both faster and equally accurate.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If the number of recent changes is large, then more potential root causes can be identified, but the complexity of analysis increases significantly

Engineering Contradiction:

Improveroot cause identification completenessVSAvoidanalysis process complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent extracts only the most relevant change records by comparing them against incident descriptions using machine learning similarity measures. Instead of analyzing all changes equally, the system identifies and extracts the subset of changes that have the highest relevance to the current incident, reducing analysis complexity while maintaining completeness.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent transforms the analysis by introducing relevance scores as a new parameter that ranks changes based on their likelihood of being the root cause. By changing the parameter from simple change enumeration to scored relevance ranking, the system manages large numbers of changes efficiently while maintaining identification completeness.

Inventive Principle:

Principle #35Parameter changes

3Loss of information

If manual analysis of change records is performed, then detailed examination is possible, but significant time and resources are consumed

Engineering Contradiction:

Improvechange record analysis thoroughnessVSAvoidtime and resource consumption

Core Design Contradiction:

Loss of informationVSLoss of energy

Solution Approach 1:

The patent implements a self-service system where the computer automatically performs the complete analysis of change records without human intervention. The system queries databases, retrieves relevant data, applies machine learning models, and generates results autonomously, eliminating the need for human analysts to invest time and resources in manual examination while maintaining thorough analysis quality.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20260003869A1Systems and methods for causal change analysis of incidents

Publication Date: 2026.01.01 JPMORGAN CHASE BANK NA

AI summary

A method may include: receiving an identification of a current incident involving an affected element; generating a first Structured Query Language (SQL) query for recently-implemented change records for the affected element; executing the first SQL query on a database; receiving the recently-implemented change records for the affected element; generating a second SQL query for past incidents that are similar to the current incident; executing the second SQL query on the database; receiving the past incidents that are similar to the current incident, together with associated change records that were identified to be a cause of the past incidents; calculating a relevance score of each recently-implemented change record to the current incident, based on a similarity of the past change records that caused similar incidents; and returning the past change record for the affected element that is similar to the past change record for any element with a highest score.