ORAN Network Backbone Fault Management for Real-Time Degradation Detection

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Solution Overview

Problem

Existing systems lack real-time monitoring and efficient identification of network component status and degradations in Open Radio Access Networks (ORAN), leading to time-consuming data searches and delayed resolution of outages and degradations.

Innovation Solution

A network backbone fault management system utilizing AI/ML and real-time data processing to monitor ORAN components, detect degradations, and facilitate rapid resolution through AI/ML-driven analysis and automated data processing.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual data searching and analysis is used to identify network component status and degradations, then operators can monitor the ORAN, but the process becomes time-consuming and labor-intensive, delaying degradation resolution

Engineering Contradiction:

Improvedegradation detection accuracyVSAvoidtime to identify and resolve degradation

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical data searching and analysis with an automated electronic system that uses machine learning models to analyze network component data, identify degradations, and determine root causes automatically, eliminating the time-consuming manual process while maintaining or improving detection accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces an intermediary automated analysis system that sits between the raw network data and the operators, using machine learning models to process and interpret data, thereby reducing the time burden on operators while preserving accurate degradation identification

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If multiple network components are monitored manually for outages and degradations, then comprehensive monitoring is achieved, but the time and effort required to identify relevant data increases significantly

Engineering Contradiction:

Improvemonitoring coverageVSAvoiddegradation resolution efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent replaces manual monitoring of multiple network components with an automated electronic system that continuously monitors all components simultaneously using machine learning models, maintaining comprehensive monitoring coverage while dramatically improving resolution efficiency by automatically identifying affected components and root causes

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent performs preliminary automated analysis and identification of degradation patterns before operators need to intervene, using machine learning models to pre-process data from multiple components and prepare root cause assessments, thereby improving both monitoring reliability and resolution productivity

Inventive Principle:

Principle #10Preliminary action

3Speed

If real-time status determination of ORAN components is implemented, then rapid degradation response is achieved, but complex data processing and analysis systems are required

Engineering Contradiction:

Improvestatus determination speedVSAvoiddata processing system complexity

Core Design Contradiction:

SpeedVSDevice complexity

Solution Approach 1:

The patent replaces complex manual data processing with an automated electronic system using machine learning models that can rapidly analyze network component data in real-time, achieving fast status determination while the system complexity is managed through automated algorithms rather than manual procedures

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent enables the system to self-analyze and self-diagnose network component status automatically using machine learning models, reducing the need for complex external analysis tools and simplifying the overall system architecture while maintaining real-time monitoring capabilities

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250310178A1Network Backbone Fault Management

Publication Date: 2025.10.02 BOOST SUBSCRIBERCO LLC

AI summary

A network backbone fault management system (NBFMS), for use with an Open Radio Access Network (ORAN) with a plurality of ORAN components includes a network operations center (NOC) storing computer instructions which instantiate one or more computer engines including a KAFKA® engine, a representational state transfer (REST) engine, a Query engine, a Rule engine, an Insights engine and an API engine. The NOC stores data in a cache and/or in a data lake coupled thereto. The KAFKA engine may monitor a data stream for event data published by an ORAN component. The Rules engine specifies types of event data to be monitored. The Rest engine determine whether relevance of environment data to an event, degradations of an ORAN component and elevations of a degradation. The Query engine retrieves data lake data. The Rule engine performs health checks for ORAN components.