Coordinate-Grid Validation for Machine Learning Workflow Corrections

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Solution Overview

Problem

Current machine learning workflow systems face challenges in accurately predicting and synchronizing data types across multiple branches, leading to redundant operations and runtime errors due to cognitive overload and lack of contextual information in existing graphical user interfaces.

Innovation Solution

A system and method that embeds workflow graphs in a coordinate system, utilizing operator properties and contextual information to validate and correct data type mismatches, redundant operations, and synchronization issues by segmenting workflows into a grid for deterministic validation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If machine learning workflows use multiple branches with data type transformations, then processing flexibility is improved, but data type synchronization between branches deteriorates

Engineering Contradiction:

Improveprocessing flexibilityVSAvoiddata type synchronization

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system performs preliminary validation of data type transformations during workflow prediction by embedding the workflow graph in a coordinate system and segmenting it into a grid. This allows the system to detect potential data type mismatches between branches before execution, preventing runtime errors while maintaining flexible multi-branch processing.

Inventive Principle:

Principle #10Preliminary action

2Ease of operation

If graphical user interfaces provide abstraction for complex workflows, then ease of operation is improved, but cognitive overload worsens due to lack of contextual information

Engineering Contradiction:

Improveinterface usabilityVSAvoidcognitive load

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system provides feedback by validating workflow predictions against the embedded coordinate system model and providing correction assistance. This feedback mechanism informs users about potential issues in their workflow designs without requiring them to manually track complex data type transformations, reducing cognitive load while maintaining ease of operation.

Inventive Principle:

Principle #23Feedback

3Productivity

If workflows are predicted without deterministic validation, then productivity is improved, but manufacturing precision deteriorates due to redundant operations

Engineering Contradiction:

Improveworkflow prediction speedVSAvoidoperation accuracy

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The system segments the workflow graph into a coordinate system grid, allowing for deterministic validation of each segment independently. This segmentation enables the system to quickly identify and correct redundant operations or data type mismatches in specific segments without revalidating the entire workflow, maintaining both productivity and precision.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS12462195B2System and method of providing correction assistance on machine learning workflow predictions

Publication Date: 2025.11.04 GEIGEL ARTURO

AI summary

A system and method of for providing assistance to complete machine learning on workflow engines that deal with machine learning flows comprising operators configured in a coordinate grid. The process analyzes the positions and composition of operators, branches, inconsistencies, collisions and redundancy in the workflow in order to suggest to the user which changes should be made to the workflow.