Natural Language Query Generation for Network Data Visualization

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Solution Overview

Problem

Existing communication network operations require significant time and effort for users to manually construct structured queries and visualize data, especially for those without expertise in Structured Query Language (SQL) or similar languages, leading to inefficiencies in data retrieval and analysis.

Innovation Solution

Implementing a generative artificial intelligence (AI) and machine learning (ML) system that processes natural language requests to automatically generate structured queries and visualize query results, allowing users to interact with databases using human language prompts, thereby simplifying network operations and reducing the need for manual query construction.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If users manually construct structured queries to access database systems, then query accuracy and control are improved, but time consumption and operational complexity increase significantly

Engineering Contradiction:

Improvequery accuracyVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent introduces a natural language processing intermediary layer between the user and the database system. This intermediary automatically converts user-friendly natural language queries into structured query statements, eliminating the need for users to manually construct complex queries while maintaining query accuracy through intelligent translation and validation mechanisms.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system enables self-service query generation by automatically analyzing user intent, selecting appropriate database tables and fields, and constructing optimized query statements without human intervention. The system autonomously handles query parsing, validation, and execution, significantly reducing time consumption while preserving query precision.

Inventive Principle:

Principle #25Self-service

2Ease of operation

If users manually construct structured queries without SQL expertise, then accessibility is improved, but query accuracy and reliability deteriorate

Engineering Contradiction:

Improveuser accessibilityVSAvoidquery reliability

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The natural language processing intermediary acts as an intelligent mediator that bridges the gap between user intent and database requirements. It automatically translates casual user queries into reliable structured statements, performing semantic analysis, schema matching, and query optimization to ensure high reliability without requiring user expertise in query languages.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system dynamically adjusts query parameters and transformation rules based on the specific database schema, data types, and relationships. By automatically adapting query construction parameters to match the target database structure, the system maintains high reliability across different user skill levels and database configurations.

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If expert-level knowledge is required for data retrieval and visualization, then query precision is improved, but ease of operation deteriorates

Engineering Contradiction:

Improvedata retrieval precisionVSAvoidoperational simplicity

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system performs self-service by automatically analyzing user intent, inferring data retrieval requirements, and generating precise structured queries without requiring user expertise. It autonomously handles schema resolution, field selection, and query optimization, delivering precision comparable to expert-level queries while maintaining operational simplicity for all users.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical process of manual query construction with an intelligent automated system. Instead of requiring users to mechanically assemble query statements according to syntax rules, the system uses natural language processing and machine learning to automatically generate precise queries, substituting expert knowledge with automated intelligence.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Productivity

If automated query generation is implemented, then productivity is improved, but system complexity increases

Engineering Contradiction:

Improvedata access efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The natural language processing intermediary serves as a modular component that handles the complexity of query generation internally. By encapsulating the complex transformation logic within a dedicated intermediary layer, the system achieves high productivity while isolating complexity from both users and the core database system, allowing independent optimization and maintenance.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20260003859A1Communication network data management and visualization using generative large language model-based query statement generation

Publication Date: 2026.01.01 AT&T INTELLECTUAL PROPERTY I L P

AI summary

A processing system including at least one processor may obtain a natural language request associated with a database system and may generate a prompt based upon the natural language request in accordance with a prompt mapping function. The processing may next apply the prompt as an input to a generative model to generate a structured query and may further apply the structured query to the database system to obtain a query result. The processing system may then present at least one visualization of the query result.