AI Data Transformation Workflow Reuse for Industrial Iteration

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Solution Overview

Problem

Existing data transformation solutions in industrial settings face challenges such as inefficiencies, re-inventing the wheel, high skill and licensing requirements, and complex customization, with limited sharing of successes and failures, leading to tedious manual handling and slow development-to-execution transitions.

Innovation Solution

A method utilizing artificial intelligence (AI) and machine learning (ML) tools to iteratively refine data transformation sets, comparing components across iterations, proposing solutions, and validating convergence based on user feedback and historical data, with neural networks and Latin Square algorithms to identify key variables.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If conventional data transformation solutions are implemented by individual engineers, then specific data transformation tasks can be completed, but the same solutions are re-invented multiple times across the company leading to inefficiencies

Engineering Contradiction:

Improvedata transformation efficiencyVSAvoidtime spent re-inventing solutions

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent merges individual engineer solutions into a centralized platform that stores, shares, and reuses data transformation solutions across the organization. This eliminates redundant work by allowing engineers to access and adapt existing solutions rather than re-inventing them for each new task.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The platform creates universal data transformation solutions that can serve multiple purposes and different users across the company. A single transformation solution can be reused by multiple engineers for different projects, making the solution universally applicable and eliminating the need for duplicate efforts.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Adaptability or versatility

If engineers customize data transformation solutions to meet specific requirements, then the solutions fit particular needs, but the customization process becomes complex and time-consuming

Engineering Contradiction:

Improvesolution customization capabilityVSAvoidcustomization process complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent segments data transformation solutions into modular, reusable components that can be independently configured and combined. This allows users to customize solutions by selecting and configuring specific modules rather than modifying entire complex systems, reducing customization complexity while maintaining adaptability.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The platform provides dynamic customization capabilities where solutions can be adapted to specific needs through configurable parameters and settings rather than hard-coded modifications. Users can dynamically adjust solution behavior to meet particular requirements without undergoing complex customization processes.

Inventive Principle:

Principle #15Dynamics

3Productivity

If developers release software programs with fixed source code, then the software can be distributed and used, but users cannot modify the code to meet their specific needs

Engineering Contradiction:

Improvesoftware distribution efficiencyVSAvoiduser ability to modify software

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The patent prepares software solutions in advance with built-in configurability and adaptation mechanisms. Rather than requiring users to modify source code, the solutions are pre-configured to allow users to adjust parameters and settings to meet their specific needs, maintaining both distribution efficiency and user adaptability.

Inventive Principle:

Principle #10Preliminary action

4Reliability

If developers recompile software programs each time source code changes, then the software reflects latest modifications, but the development-to-execution transition becomes tedious and slow

Engineering Contradiction:

Improvesoftware code accuracyVSAvoidrecompilation time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces the mechanical recompilation process with an automated interpretation or just-in-time compilation system. The platform can directly execute or interpret modified code without requiring full recompilation, maintaining code accuracy through automated validation while dramatically reducing the time required for development-to-execution transitions.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12517703B2Artificial intelligence approach to solving industrial data transformation problems

Publication Date: 2026.01.06 SCHNEIDER ELECTRIC SYSTEMS USA INC

AI summary

A method of using artificial intelligence (AI) for solving industrial data transformation problems, including receiving in a first iteration an initial transformation set and in at least one respective subsequent iteration a trained transformation set, each of the initial and trained transformation sets having components that include input and target datasets and a process flow for transforming the input dataset into the target dataset, wherein the trained transformation set uses training data generated using AI tools that modifies at least one of the components of a transformation set from a previous iteration. For each iteration the components of the trained transformation set are compared to corresponding components of trained transformation sets of previous iterations and to intersecting data stored from executions using the corresponding components. A trained transformation set for solving the industrial data transformation problem is selected if a suitable match is found based on the comparison.