Universal Data Translator for Semantic Harmonization

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Solution Overview

Problem

Entities face inefficiencies and errors in harmonizing semantic discrepancies in data due to manual and laborious processes, leading to redundant data elements and inconsistent identifiers, which affect data analysis accuracy and organizational bloat.

Innovation Solution

A universal data language (UDL) translator using artificial intelligence and machine learning algorithms to automatically map and harmonize inconsistent data elements, reducing redundancy and improving data quality by translating data into a standardized ontology.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual processes are used to harmonize semantic discrepancies in data, then data elements can be standardized, but the process becomes laborious and error-prone

Engineering Contradiction:

Improvedata harmonization accuracyVSAvoiddata processing efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent replaces manual mechanical processes with an automated natural language processing system. The NLP-based translator automatically identifies and harmonizes semantic discrepancies in data elements, replacing the manual review and standardization process while maintaining high accuracy through intelligent algorithmic analysis.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces a natural language processing translator as an intermediary between disparate data systems. This intermediary automatically translates and harmonizes semantic differences in data elements, enabling seamless integration without requiring manual intervention from data practitioners.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Stability of the object's composition

If manual semantic integration is performed, then data consistency can be maintained, but dedicated resources are required and processes go out of date without ongoing effort

Engineering Contradiction:

Improvesemantic consistencyVSAvoidresource requirements

Core Design Contradiction:

Stability of the object's compositionVSDevice complexity

Solution Approach 1:

The patent implements a self-service automated system that continuously maintains semantic consistency without requiring dedicated human resources. The NLP translator autonomously processes data elements, identifies inconsistencies, and applies harmonization rules, eliminating the need for ongoing manual intervention while maintaining stable data composition.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent performs preliminary automated harmonization of semantic discrepancies before data integration issues arise. By proactively translating and standardizing data elements using NLP techniques, the system prevents inconsistencies from developing, eliminating the need for corrective manual intervention later.

Inventive Principle:

Principle #10Preliminary action

3Adaptability or versatility

If different identifiers are used for the same data element, then regulatory compliance can be met, but organizational bloat and inefficiencies occur

Engineering Contradiction:

Improveregulatory compliance flexibilityVSAvoiddata redundancy

Core Design Contradiction:

Adaptability or versatilityVSQuantity of substance

Solution Approach 1:

The patent creates a universal translation layer that handles multiple regulatory identifier systems simultaneously. The NLP-based translator can map different regulatory identifiers to their corresponding data elements, allowing the system to comply with multiple regulations while maintaining a single source of truth and eliminating redundant data storage.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Productivity

If automated translation is implemented, then productivity increases, but system complexity increases

Engineering Contradiction:

Improvedata processing speedVSAvoidtranslation system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent replaces complex manual semantic analysis with automated natural language processing algorithms. The NLP translator handles the complexity of understanding and harmonizing semantic differences in data elements, providing automated translation at scale while managing system complexity through established language processing techniques.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12524629B2Universal data language translator

Publication Date: 2026.01.13 COLLIBRA BELGIUM BV

AI summary

The present disclosure is directed to a universal data language (UDL) translator. Specifically, the systems and methods disclosed enable input data from a variety of sources to be translated into a UDL that can be consistently analyzed and compared against other sources of data. For example, an entity may upload input data that has a plurality of data terms and definitions (e.g., header column in a spreadsheet). These terms may be duplicative and/or inaccurate with respect to the underlying data. If the entity wishes to compare and transact data within a data marketplace, the entity may not fully comprehend what data it is missing and/or what data another entity may have to offer for trade. To remedy this problem of business semantic management, the present invention discloses steps for creating a UDL and a UDL translator so that any input data can be translated to UDL.