Causality Network for Automated Root Cause Identification

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Solution Overview

Problem

Existing methods for detecting errors in computer networks are time-consuming, inefficient, and often inaccurate, relying on manual analysis that is slow and prone to subjective judgment, leading to prolonged downtime and increased costs.

Innovation Solution

The deployment of a causality network, such as a Bayesian belief network, to automate the identification of root causes of events by analyzing data points and determining causal relations, allowing for efficient and accurate root cause analysis.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual analysis methods are used to identify root causes of events, then personnel can review and analyze data, but the process becomes time-consuming and inefficient

Engineering Contradiction:

Improveaccuracy of root cause identificationVSAvoidtime to identify root cause

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical analysis by personnel with an automated computer-based analysis system. The system automatically receives event data, identifies relevant data points, determines causal relations, and identifies root causes without human intervention, thereby eliminating the time loss associated with manual processes while maintaining or improving identification accuracy through systematic automated analysis.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service by automatically performing the entire root cause identification process without requiring personnel intervention. The computing device autonomously receives data, analyzes causal relations, and generates root cause identifications, making the system serve itself rather than relying on external human analysts.

Inventive Principle:

Principle #25Self-service

2Measurement precision

If multiple personnel review large amounts of information to determine root cause, then comprehensive analysis may be achieved, but operational costs increase

Engineering Contradiction:

Improveaccuracy of root cause identificationVSAvoidoperational cost

Core Design Contradiction:

Measurement precisionVSLoss of energy

Solution Approach 1:

The patent replaces multiple human personnel with a single automated computing device that performs comprehensive data analysis. The system receives event data, automatically identifies relevant data points, determines causal relations through systematic processing, and identifies root causes, thereby eliminating the operational costs associated with multiple personnel while maintaining comprehensive analysis capabilities through automated algorithms.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system uses data representations and models to copy and analyze information structures. By creating data point objects that represent events and their relationships, the system can analyze comprehensive information sets without requiring multiple human reviewers, reducing operational costs while maintaining analytical thoroughness.

Inventive Principle:

Principle #26Copying

3Adaptability or versatility

If manual analysis is used, then personnel can apply judgment and experience, but the process relies on subjective judgement and may misdiagnose problems

Engineering Contradiction:

Improveability to apply judgment and experienceVSAvoidaccuracy of root cause identification

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The patent replaces subjective human judgment with objective automated analysis algorithms. The computing device systematically processes event data, identifies causal relations based on defined criteria, and determines root causes without subjective bias. This substitution eliminates misdiagnosis risks while maintaining adaptability through programmable analysis rules that can be configured for different event types and scenarios.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system incorporates feedback mechanisms where the automated analysis process continuously refines its root cause identification based on analyzed data patterns and causal relations. The system learns from analyzed events and improves its accuracy over time, providing objective adaptability that replaces subjective human judgment while enhancing measurement precision through systematic feedback-driven refinement.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11531581B2Event root cause identification for computing environments

Publication Date: 2022.12.20 CITRIX SYSTEMS INC

AI summary

Systems and apparatuses for identifying root causes of events within an computing environment described herein. A causality network may be generated based on detected events in the computing environment. The causality network may be nodes for the events and directed edges showing the casual relations between the nodes. Conditional probability tables (CPTs) for the nodes may show the strength of the causal relations. When an event occurs, computing device may identify the node for the event in the causal network and traverse the causal network until a root cause node is identified. The computing device may output the root cause node and the path of traversal.