Generative AI Machine Automation Design for Component Compatibility

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Solution Overview

Problem

Conventional machine automation design processes are cumbersome due to manual component selection from multiple suppliers with varying specifications and complex integration of components with different communication standards, leading to inefficiencies and integration errors.

Innovation Solution

A computer-implemented method using generative artificial intelligence (AI) models to automatically select compatible components and generate software code for machine automation designs based on user inputs, such as text prompts or electrical schematics, ensuring interoperability and reducing manual effort.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual component selection is performed from multiple suppliers, then compatibility and interoperability can be ensured, but the design process becomes time-consuming and complex

Engineering Contradiction:

Improvecomponent compatibilityVSAvoiddesign process time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical selection processes with an automated AI-based system. The generative AI model automatically selects compatible components from multiple suppliers by analyzing specifications and compatibility requirements, eliminating the need for manual review of supplier documentation while ensuring compatibility constraints are met.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service component selection through the generative AI model, which autonomously performs component compatibility checking and selection without requiring expert intervention. The AI model independently analyzes component specifications, identifies compatible components, and generates selections that satisfy design requirements.

Inventive Principle:

Principle #25Self-service

2Manufacturing precision

If expert knowledge is used for component integration, then integration accuracy improves, but the complexity of the design process increases

Engineering Contradiction:

Improveintegration accuracyVSAvoiddesign process complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The patent substitutes expert human knowledge with an AI-based knowledge system. The generative AI model has been trained on extensive industrial automation data, enabling it to perform integration tasks that previously required expert knowledge. The system automatically handles protocol matching, communication interface configuration, and integration validation without requiring human experts to manually apply their knowledge.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The AI-based integration system provides universal functionality that handles multiple integration scenarios simultaneously. It can manage different communication protocols (Modbus, Profibus, EtherCAT), various component types, and multiple supplier standards through a single unified platform, eliminating the need for separate expert processes for each integration case.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Measurement precision

If comprehensive component specifications are reviewed, then selection accuracy improves, but the difficulty of the selection process increases

Engineering Contradiction:

Improveselection accuracyVSAvoidselection process difficulty

Core Design Contradiction:

Measurement precisionVSDifficulty of detecting and measuring

Solution Approach 1:

The patent replaces manual specification review with automated AI-based analysis. The generative AI model automatically parses and analyzes comprehensive component specifications from multiple suppliers, extracting relevant parameters and comparing them against design requirements. This automated analysis achieves high selection accuracy without requiring manual review of extensive technical documentation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system introduces an intermediary AI layer between component specifications and selection decisions. The generative AI model acts as a mediator that processes raw specification data, interprets technical parameters, and translates them into compatibility assessments and selection recommendations, simplifying the selection process while maintaining accuracy.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250298945A1Techniques for implementing machine automation designs using generative artificial intelligence

Publication Date: 2025.09.25 XABA INC

AI summary

One embodiment sets forth a technique for generating solutions for machine automation designs. According to some embodiments, the technique includes the steps of receiving an input for a machine automation design; generating, via at least one generative artificial intelligence (AI) model, a design approach for the machine automation design based on the input, where the design approach includes a list of required components for implementing the machine automation design; generating, via the at least one generative AI model, one or more solutions for the machine automation design based on the design approach; displaying, via at least one user interface, information associated with the one or more solutions; receiving or performing a selection of a solution included in the one or more solutions; and performing at least one action in response to receiving the selection of the solution.