Intent-Based Network Management Using AI Digital Twins

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Solution Overview

Problem

Intent-based networking (IBN) faces challenges in translating high-level intents into low-level actions due to complexity, ambiguity, scalability, compatibility, and security issues arising from diverse network components, unclear intent specifications, dynamically changing conditions, and potential misconfigurations, along with conflicts between multiple intents.

Innovation Solution

Integration of generative AI and digital twins to interpret high-level intents, perform conflict resolution, and test configurations in a virtual environment before deployment, using large language models (LLMs) to translate intents into actionable commands and digital twins to simulate network changes, ensuring accurate and secure network management.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If high-level intents are translated into low-level actions using traditional automation methods, then network management can be simplified for non-experts, but complexity and ambiguity arise from diverse network components and unclear intent specifications

Engineering Contradiction:

Improvenetwork management simplicityVSAvoidtranslation complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The patent introduces digital twins as intermediary entities that bridge high-level network intents and low-level device configurations. The digital twin serves as a virtual model that translates abstract intent specifications into concrete device-level actions, resolving the complexity mismatch between user-friendly intent definition and complex network implementation

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces traditional rule-based automation mechanisms with generative AI models that can interpret and translate intents dynamically. The large language model substitutes rigid mechanical translation rules with flexible semantic understanding, enabling ambiguous intent specifications to be resolved through AI-generated translation rather than predefined mapping rules

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If automation translates intents into configurations across diverse network infrastructure, then operational efficiency improves, but security risks and misconfigurations increase

Engineering Contradiction:

Improveautomation efficiencyVSAvoidconfiguration accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent implements a preliminary validation phase where the digital twin simulates and tests configuration changes before they are applied to the actual network infrastructure. This preliminary action in the virtual environment allows verification of configuration accuracy and security compliance before automated deployment, preventing misconfigurations from reaching production systems

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent creates and maintains digital twin copies of network devices and configurations. These copies serve as virtual replicas that can be manipulated and validated without affecting the real infrastructure, allowing thorough testing and verification of automated translations before applying changes to actual network devices

Inventive Principle:

Principle #26Copying

3Speed

If multiple intents are executed simultaneously in a dynamic network environment, then network responsiveness improves, but conflicts between intents and misconfigurations arise

Engineering Contradiction:

Improvenetwork responsivenessVSAvoidintent consistency

Core Design Contradiction:

SpeedVSStability of the object's composition

Solution Approach 1:

The patent implements feedback mechanisms where the digital twin continuously monitors the state of multiple executing intents and provides real-time information about potential conflicts. This feedback loop enables the system to detect intent inconsistencies during execution and trigger resolution processes, maintaining stability while allowing rapid parallel intent processing

Inventive Principle:

Principle #23Feedback

4Device complexity

If traditional network management approaches are used, then system simplicity is maintained, but human error and misconfigurations increase

Engineering Contradiction:

Improvesystem simplicityVSAvoidhuman error

Core Design Contradiction:

Device complexityVSObject-affected harmful factors

Solution Approach 1:

The patent enables the network management system to self-validate and self-correct configurations through the digital twin and generative AI. The system automatically checks for errors, validates intent translations, and resolves conflicts without human intervention, eliminating human error while maintaining interface simplicity for end users

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12598111B2Enabling intent-based network management with generative ai and digital twins

Publication Date: 2026.04.07 AT&T INTELLECTUAL PROPERTY I L P

AI summary

Aspects of the subject disclosure may include, for example, an intent-based network (IBN) management system that effects changes in a communication network based on high-level intents. High-level intents are translated into operator-level intents by a large language model (LLM). Conflicts between operator-level intents are resolved, and the operator-level intents are mapped to intent functions. The intent functions are then mapped to policies that may effect changes in the network in accordance with the high-level intents. Other embodiments are disclosed.