Cognitive Root Cause Analysis for IT Problem Resolution
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Solution Overview
Problem
Conventional root cause analysis (RCA) technologies in large IT deployments face challenges with subjective and unstructured user input, leading to inaccurate and misclassified root cause identification, resulting in inefficient problem resolution and repeated IT issues, which increases costs and resource wastage.
Innovation Solution
A computer-implemented method using cognitive natural language processing (CNLP) and guided input sequences to identify and categorize root causes, applying scoring algorithms and quality indicators to ensure precision and consistency in user input, generating RCA documents that accurately identify the root cause of IT problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional root cause analysis technologies are used with unstructured user input, then the process is simple and fast, but the accuracy and precision of root cause identification deteriorates
Solution Approach 1:
The patent introduces an intermediary processing layer between unstructured user input and root cause identification. This layer applies structured methodologies (5 Whys, Fishbone, Fault Tree) and cognitive NLP to transform free-text input into organized analysis frameworks, thereby improving accuracy without requiring users to directly create complex structured documents.
Solution Approach 2:
The patent replaces manual, mechanical text analysis with cognitive NLP systems that can automatically interpret unstructured input, identify causal relationships, and apply analytical methodologies. This substitution enables the system to handle complex analysis tasks while maintaining ease of use.
2Reliability
If subjective unstructured user input is accepted, then ease of operation is improved, but reliability of root cause analysis deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the system evaluates the quality and consistency of user input against established RCA methodologies. When input lacks sufficient reliability, the system provides guidance and requests additional information, ensuring that only adequate data proceeds to root cause identification while maintaining user-friendly interaction.
Solution Approach 2:
The patent applies preliminary processing and validation of user input before it is used for root cause analysis. The system pre-processes unstructured text to identify key elements, applies initial filtering based on RCA methodology requirements, and prepares the data in advance, ensuring reliability is established before the main analysis occurs.
3Manufacturing precision
If manual root cause analysis is performed without structured criteria, then flexibility is maintained, but manufacturing precision of RCA documentation deteriorates
Solution Approach 1:
The patent segments the RCA documentation process into distinct components corresponding to established methodologies (5 Whys questions, Fishbone categories, Fault Tree elements). Each segment is processed and validated independently, ensuring precision in each aspect of the analysis while maintaining the overall flexibility of the approach.
Solution Approach 2:
The patent transforms unstructured textual input into structured parameters and categories that align with RCA methodologies. By changing the form of the data from free-text to structured elements (causal relationships, categories, hierarchical structures), the system achieves precise documentation without imposing rigid complexity on the user interface.
4Measurement precision
If unstructured user input is used for RCA, then adaptability to different IT problems is improved, but measurement precision of cause categorization deteriorates
Solution Approach 1:
The patent implements a universal processing framework that handles diverse IT problems through common RCA methodologies. The system can apply the same structured approaches (5 Whys, Fishbone, Fault Tree) to different types of IT issues while adapting to the specific characteristics of each problem through cognitive NLP interpretation of unstructured input.
Data Source
AI summary
An ordered set of root cause analysis (RCA) document entry criteria is identified. RCA input segments are specified using unstructured natural language input, including at least: incident descriptive elements, a single problem statement, a set of why questions and answers, and a single cause categorization. A guided input sequence of the RCA input segments is performed interactively with a user. Quality indicators of content of user input entered during a respective RCA input segment are determined using a scoring algorithm, and the user is assisted with improving precision and consistency of the user input. Responsive to a threshold of consistent user input across the RCA input segments resulting in identification of a single cause categorization of an information technology (IT) problem, an RCA document is generated that identifies the single cause categorization of the new IT problem.