Grammar-Based Data Integration for Wireless Networks

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Solution Overview

Problem

Integrating new databases into existing ones, especially in large wireless telecommunication networks, is a slow, error-prone, and laborious process due to compatibility issues, different relationships, and ontologies, as well as the presence of corrupt data.

Innovation Solution

A system and method that create a grammar representing multiple concepts and relationships based on a corpus of data, using triples to extract records and relationships, and generating grammars to identify correct triples and improve data accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional database integration methods are used, then data compatibility issues can be addressed, but the process becomes slow, laborious, and error-prone

Engineering Contradiction:

Improvedata integration accuracyVSAvoidintegration time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces manual, mechanical database integration processes with an automated natural language processing system. The system uses AI models to automatically extract entities, relationships, and concepts from unstructured data, generating grammars and knowledge graphs without human intervention, thereby eliminating the slow and laborious manual process while maintaining high accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces an intermediary natural language processing layer between the raw data and the database integration process. This intermediary system translates unstructured data into structured grammars and knowledge graphs, facilitating seamless integration between databases with different schemas and relationships without direct manual mapping.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Ease of manufacture

If manual database merging is performed, then compatibility between different databases can be achieved, but the process is laborious and error-prone

Engineering Contradiction:

Improveease of database integrationVSAvoidintegration accuracy

Core Design Contradiction:

Ease of manufactureVSReliability

Solution Approach 1:

The patent replaces manual database merging operations with an automated NLP-based system that extracts relationships and generates grammars automatically. This substitution eliminates human error while maintaining ease of integration, as the system handles the complexity of merging databases with different schemas and relationships through automated entity recognition and relationship extraction.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent enables the database integration process to be self-service through automated grammar generation and knowledge graph construction. The system automatically processes unstructured data, identifies entities and relationships, and integrates databases without requiring manual intervention, thereby improving both ease of integration and reliability simultaneously.

Inventive Principle:

Principle #25Self-service

3Loss of information

If comprehensive data analysis is performed to discover latent relationships, then valuable insights can be found, but the process becomes slow and complex

Engineering Contradiction:

Improvediscovery of latent relationshipsVSAvoidanalysis process complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent replaces complex manual data analysis processes with automated NLP and AI-based relationship extraction. The system uses pre-trained language models to automatically identify latent relationships in unstructured data, generating grammars that capture these relationships without requiring complex manual analysis procedures, thereby reducing process complexity while maintaining comprehensive relationship discovery.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent performs preliminary action by pre-processing unstructured data through entity recognition, relationship extraction, and grammar generation before the actual analysis phase. This preliminary structuring of data through automated NLP processes simplifies subsequent analysis by organizing information into structured grammars and knowledge graphs, making latent relationship discovery more efficient and less complex.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250028903A1Creating a grammar representing multiple concepts and multiple relationships based on a corpus of data associated with a wireless telecommunication network

Publication Date: 2025.01.23 T MOBILE US INC

AI summary

The system obtains a corpus of data, and extracts triples from the corpus. A first element in a triple indicates a first record in the corpus, a second element in the triple indicates a second record in the corpus, and a third element in the triple indicates a relationship between the first and second records. The system generates grammars representing the triples. A grammar includes concepts and relationships. The concepts include a first and a second concept representing the first and the second record, respectively. The relationships represent the relationship between the first and second records. The system applies each grammar to the triples to obtain an indication of whether each triple is correct. Based on the indication of whether each triple is correct, the system determines an accuracy of each grammar. Based on the accuracy of each grammar, the system selects a grammar having the highest accuracy.