Cable Network Signature Detection for Legacy Upgrade Barriers

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Legacy network components such as filters and amplifiers in cable networks impede the installation and provisioning of new services, particularly those requiring higher frequency splits, creating impediments to upgrades like DOCSIS 3.1 and 4.0.

Innovation Solution

A method and system for detecting and mitigating incompatible network components by obtaining data from first components, classifying signatures, and facilitating mitigation using machine learning and rules-based analysis to identify and address these components.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If legacy network components (filters, amplifiers) are retained in the network, then existing service provisioning is maintained, but new services requiring higher frequency splits cannot be installed or provisioned

Engineering Contradiction:

Improvenetwork service compatibilityVSAvoidservice provisioning impediment

Core Design Contradiction:

Adaptability or versatilityVSObject-generated harmful factors

Solution Approach 1:

The system performs preliminary detection and classification of legacy network components before new service deployment. By identifying incompatible components (filters, amplifiers, diplexers) in advance through data collection and machine learning classification, the network operator can proactively mitigate their presence, preventing service provisioning issues rather than reacting to them after deployment attempts fail

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces an intermediary detection and classification system between the legacy network infrastructure and new service deployment. This intermediary system collects data from network components, classifies them using machine learning models, and provides actionable information to operators, enabling informed decisions about which legacy components need mitigation for successful high-split service deployment

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If network components are detected and mitigated using traditional methods, then service compatibility is improved, but detection precision and automated identification capability remain insufficient

Engineering Contradiction:

Improvecomponent detection accuracyVSAvoiddetection system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces traditional manual or rule-based detection methods with machine learning-based automated classification systems. Instead of relying on simple presence/absence checks or manual inspection, the system uses trained machine learning models to classify network components based on collected data, significantly improving detection precision and automated identification capability while managing complexity through software-based solutions

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system changes the detection approach by collecting multiple parameters from network components (signal characteristics, frequency responses, operational data) rather than relying on single binary states. By analyzing multiple parameters simultaneously through machine learning classification, the system achieves higher detection accuracy and can distinguish between different types of legacy components with varying degrees of incompatibility

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20260059350A1Method and systems to detect network components in networks

Publication Date: 2026.02.26 CHARTER COMM OPERATING LLC

AI summary

Obtaining data from a plurality of first network components in a network. The data includes at least one detectable signature characterizing presence of at least one second network component that is incompatible with a modification of the network. Classify the obtained data to identify the at least one detectable signature. Identify the at least one second network component implicated by the classification results. Facilitate mitigation of the at least one second network component.