Linguistic Semantic Alert Correlation Engine

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Solution Overview

Problem

The integration of applications with computing devices is time-intensive due to the need for human intervention in understanding and analyzing system management artifacts, leading to inefficient alert correlation in monitoring software, which often results in failed operation with all available firmware functionality.

Innovation Solution

An Information Handling System (IHS) equipped with a linguistic semantic analysis alert correlation engine that classifies and correlates alerts using domain-specific language terms from alert dictionary databases, eliminating the need for human intervention and 'hand-coding' by associating alerts with common subsystems.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual analysis and hand-coding of system management artifacts is used for alert correlation, then accuracy of correlation can be maintained, but integration time and effort increase significantly

Engineering Contradiction:

Improvecorrelation accuracyVSAvoidintegration time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical analysis and hand-coding processes with automated natural language processing and semantic analysis systems. The system automatically parses system management artifacts, extracts meanings, and generates correlation rules without human intervention, thereby reducing integration time while maintaining correlation accuracy through sophisticated linguistic analysis algorithms.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service automation where the alert correlation engine independently analyzes system management artifacts, understands their meanings, and generates correlation rules autonomously. This eliminates the need for manual coding while maintaining high accuracy through advanced semantic analysis and machine learning techniques embedded in the engine.

Inventive Principle:

Principle #25Self-service

2Adaptability or versatility

If comprehensive alert correlation rules are manually created for all monitoring software, then complete coverage is achieved, but the complexity and effort of maintenance increase

Engineering Contradiction:

Improvecoverage completenessVSAvoidrule complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements a universal alert correlation engine that can automatically adapt to multiple monitoring software platforms and system management artifacts. The system uses general-purpose natural language processing and semantic analysis capabilities to handle diverse alert types and correlation scenarios across different domains, eliminating the need for platform-specific hand-coding while maintaining comprehensive coverage.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system dynamically adjusts its analysis parameters and semantic models based on the specific monitoring software and alert catalogs being processed. By changing linguistic parameters, domain-specific vocabularies, and correlation thresholds adaptively, the system achieves comprehensive coverage across different platforms without requiring complex manual rule configurations for each scenario.

Inventive Principle:

Principle #35Parameter changes

3Reliability

If frequent integration requests are handled through traditional manual processes, then integration quality can be maintained, but productivity decreases

Engineering Contradiction:

Improveintegration qualityVSAvoidintegration throughput

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent replaces manual integration processes with automated semantic analysis and rule generation systems that can rapidly process multiple integration requests simultaneously. The system maintains integration quality through sophisticated linguistic analysis and validation mechanisms while dramatically increasing throughput by eliminating sequential manual analysis and coding steps.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs preliminary automated analysis of system management artifacts and pre-generates correlation rules and semantic models in advance. This preliminary action creates reusable templates and knowledge bases that can be quickly applied to subsequent integration requests, maintaining high quality standards while significantly improving productivity through batch processing and reuse of analyzed patterns.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS10936822B2Linguistic semantic analysis alert correlation system

Publication Date: 2021.03.02 DELL PROD LP

AI summary

A linguistic semantic alert correlation analysis system includes a storage system storing alert dictionary databases that include domain-specific language information that identifies domain-specific language terms utilized in providing alerts within different domains. A linguistic semantic alert correlation analysis engine is coupled to the at least one storage device, and receives alert catalogs that are each utilized one of the different domains. The domain specific language terms are used to classify alerts in each of the alert catalogs and, based on the classification, determine that a first alert in a first alert catalog and a second alert in a second alert catalog are each associated with a common subsystem. Based on the first alert and the second alert being associated with the common subsystem, the first alert and the second alert are correlated such that each is associated with the common subsystem.