Agnostic Data Modeling Platform for Scalable Transfers

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Solution Overview

Problem

Conventional data modeling approaches are resource-intensive and not scalable due to the need for unique conversion tables for each logical and physical data model pair, and they are limited by proprietary solutions that are not compatible with non-proprietary systems, leading to inefficiencies in data storage and transfer.

Innovation Solution

A programming language-agnostic data modeling platform using a supplemental data structure with logical data modeling metadata that maps standardized modeling languages to common programming languages, allowing for scalable and compatible data transfers and analytics across diverse systems.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If unique conversion tables are created for each logical and physical data model pair, then data mapping accuracy is improved, but resource consumption and system complexity increase significantly

Engineering Contradiction:

Improvedata mapping accuracyVSAvoidsystem complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The patent creates a universal conversion table structure that can serve multiple logical and physical data model pairs. Instead of creating unique conversion tables for each model pair, a single conversion table design is established that can be reused across different data model combinations, thereby reducing system complexity while maintaining mapping accuracy through the standardized universal structure

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent performs preliminary action by pre-defining the conversion table structure and mapping relationships in advance. The conversion tables are created and configured beforehand as a reusable framework, eliminating the need to create unique conversion tables for each logical and physical data model pair when they are needed, thus reducing both complexity and resource consumption

Inventive Principle:

Principle #10Preliminary action

2Ease of manufacture

If proprietary data modeling languages are used, then data modeling capability is improved, but system compatibility and accessibility deteriorate

Engineering Contradiction:

Improvedata modeling capabilityVSAvoidsystem compatibility

Core Design Contradiction:

Ease of manufactureVSAdaptability or versatility

Solution Approach 1:

The patent introduces an intermediary layer that translates between proprietary data modeling languages and standardized languages. This intermediary conversion mechanism allows the system to leverage the advanced capabilities of proprietary languages while maintaining compatibility with non-proprietary systems, effectively bridging the gap between specialized functionality and broad accessibility

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent applies parameter changes by transforming data models between different language paradigms. The system converts data models from proprietary languages with advanced features to standardized languages with broader compatibility, and vice versa, by changing the linguistic parameters while preserving the essential data modeling semantics and relationships

Inventive Principle:

Principle #35Parameter changes

3Manufacturing precision

If manual conversion table creation is performed, then mapping precision is improved, but productivity and scalability worsen

Engineering Contradiction:

Improvemapping precisionVSAvoiddata transfer efficiency

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The patent implements self-service by enabling the system to automatically generate and maintain conversion tables without requiring manual intervention for each data transfer operation. The conversion framework is designed to self-configure and adapt to different data model pairs, preserving mapping precision through automated processes while dramatically improving productivity and scalability

Inventive Principle:

Principle #25Self-service

4Ease of operation

If metadata is transmitted separately using ad hoc processes, then transmission flexibility is improved, but metadata completeness and richness deteriorate

Engineering Contradiction:

Improvetransmission flexibilityVSAvoidmetadata richness

Core Design Contradiction:

Ease of operationVSLoss of information

Solution Approach 1:

The patent merges the metadata transmission process with the data transfer operation itself. Instead of transmitting metadata separately through ad hoc processes, the system combines metadata with the primary data transfer stream, ensuring that all metadata richness and completeness is preserved while maintaining transmission flexibility through the unified approach

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS12182090B1Systems and methods for generating data transfers using programming language-agnostic data modeling platforms

Publication Date: 2024.12.31 CITIBANK N A

AI summary

Systems and methods for a programming language-agnostic data modeling platform that is both less resource intensive and scalable. Additionally, the programming language-agnostic data modeling platform allows for advanced analytics to be run on descriptions of the known logical data models, to generate data offerings describing underlying data, and to easily format data for compatibility with artificial intelligence systems. The systems and methods use a supplemental data structure that comprises logical data modeling metadata, in which the logical data modeling metadata describes the logical data model in a common, standardized language. For example, the logical data modeling metadata may comprise a transformer lineage of the logical data model.