Network Topology Visualization via Natural Language Query Generation

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Solution Overview

Problem

Existing communication network operations require significant manual effort and expertise in complex query languages to construct queries and generate network topology visualizations, which is time-consuming and inefficient for users without specialized knowledge.

Innovation Solution

A generative model-based approach that generates network topology visualizations from natural language requests, automatically converting user prompts into queries and processing them to produce visualizations, reducing the need for manual query construction and simplifying the process for users.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual query construction is used to generate network topology visualizations, then query precision and control are improved, but user complexity and time consumption increase significantly

Engineering Contradiction:

Improvequery precisionVSAvoiduser complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent introduces a generative AI model as an intermediary between the user's natural language request and the complex database query system. The AI model translates simple user prompts into precise, formatted queries that can be executed against the network database, thereby maintaining query precision while eliminating the need for users to directly construct complex queries.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces the mechanical process of manual query construction with an automated generative AI system. Instead of requiring users to manually write and format database queries, the system uses AI to automatically generate the necessary query structure from natural language inputs, significantly reducing user complexity and effort.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If manual query construction is used to generate network topology visualizations, then query control and customization are improved, but time consumption increases

Engineering Contradiction:

Improvequery controlVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary processing by pre-training the generative AI model on network topology data and query patterns. This preliminary action enables the model to quickly generate accurate queries in real-time without requiring users to spend time understanding or constructing complex query syntax themselves.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces the time-consuming manual query construction process with an automated AI system that instantly generates formatted queries from natural language prompts, significantly reducing the time required to obtain network topology visualizations while maintaining full query control capabilities.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If specialized query language knowledge is required, then query accuracy is improved, but ease of operation deteriorates

Engineering Contradiction:

Improvequery accuracyVSAvoidease of operation

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The generative AI model serves as an intermediary layer that translates user-friendly natural language requests into accurate, properly formatted database queries. This eliminates the need for users to learn specialized query languages while maintaining query accuracy through the AI's sophisticated translation capabilities.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system changes the input parameter format from complex query syntax to simple natural language. The generative AI model handles the transformation, converting everyday language into the precise query parameters needed for accurate network topology visualization while keeping the user interface simple and easy to operate.

Inventive Principle:

Principle #35Parameter changes

4Ease of operation

If automated query generation is implemented, then ease of operation is improved, but system complexity increases

Engineering Contradiction:

Improveease of operationVSAvoidsystem complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The patent introduces a generative AI model as an intermediary component that handles the complexity of query generation. This intermediary absorbs the system complexity by providing a simple natural language interface, thereby improving ease of operation for users while the AI system manages the underlying complexity of query formatting and execution.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20260050590A1Communication network topology visualization

Publication Date: 2026.02.19 AT&T INTELLECTUAL PROPERTY I L P

AI summary

A processing system including at least one processor may obtain a natural language request for a network topology visualization associated with a communication network. The processing system may next generate a prompt based upon the natural language request in accordance with a prompt mapping function, apply the prompt as an input to a generative model to generate a query, and apply the query to a communication network database system to obtain a query result. The processing system may generate the network topology visualization from the query result. The processing system may then present the network topology visualization via at least one display device.