Tree-Based Log Analysis for Predicting Network Service Outages

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Solution Overview

Problem

Existing IT systems lack predictive analytics to anticipate computing system problems, leading to reactive responses after significant network issues have already occurred, such as bandwidth problems, hardware errors, or bottlenecks.

Innovation Solution

A system utilizing tree-based machine learning classifiers to analyze log data and metrics from networked computing systems, predicting future critical events and outages by training on historical data, and providing proactive mitigation strategies.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional monitoring systems are used to detect system problems, then system health can be gauged, but problems are only detected after they have already occurred

Engineering Contradiction:

Improvesystem health monitoringVSAvoidtime to detect problems

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent applies preliminary action by training machine learning models on historical log data to predict future system failures before they occur. The system analyzes patterns in historical data and generates predictions about upcoming issues, allowing IT professionals to take preventive measures before actual failures happen, thus resolving the contradiction between reliable monitoring and timely detection.

Inventive Principle:

Principle #10Preliminary action

2Ease of operation

If reactive problem response is used, then system alerts can be generated, but mitigation can only occur after critical errors have arisen

Engineering Contradiction:

Improveproblem response capabilityVSAvoidsystem operation continuity

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The patent implements preliminary anti-action by using machine learning predictions to identify potential failures and generate alerts before critical errors occur. The system proactively recommends mitigation actions based on predicted issues, allowing IT professionals to counteract potential problems before they disrupt system operation, thus maintaining reliability while improving ease of operation through automated prediction and recommendation.

Inventive Principle:

Principle #9Preliminary anti-action

3Loss of information

If historical log data is analyzed using traditional methods, then system metrics can be calculated, but predictive analytics cannot be achieved

Engineering Contradiction:

Improvesystem data utilizationVSAvoidprediction accuracy

Core Design Contradiction:

Loss of informationVSMeasurement precision

Solution Approach 1:

The patent replaces traditional mechanical data analysis methods with machine learning-based predictive analytics. Instead of simply calculating metrics from historical logs, the system uses trained machine learning models to automatically identify patterns and predict future failures, substituting conventional analysis with intelligent prediction to achieve both full data utilization and high measurement precision.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12430198B1Tree-based system problem prediction and mitigation

Publication Date: 2025.09.30 FREDDIE MAC

AI summary

In an illustrative embodiment, systems and methods for predicting network service outages gather log data from processes executing on computing device(s), produce training data from the log data for training tree-based machine learning model(s) and metrics calculated therefrom, periodically apply data sets derived from future log data and metrics calculated therefrom to the machine learning model(s) to predict critical error(s)/system outage(s), and periodically update the trained machine learning models using the periodically applied data sets.