IP Library Granted Patent US 12,681,469
Granted Patent B2
US 12,681,469 · App. 18/462,661 · Granted Jul 14, 2026

Generative AI for industrial automation design environment

Inventors: Anthony Carrara (Strongsville, OH); Michael J. Ohlsen (Chesterland, OH); Ashish Anand (Aurora, OH); Matthew T. Masarik (Cleveland, OH); Adam J. Gregory (Oak Creek, WI); Justin Wengatz (North Ridgeville, OH); Daniel T. Richter (Hudson, OH); Omar A. Bahader (Solon, OH); Lorenzo Majewski (Waukesha, WI); Elie Nader (Montreal, CA); Fabiano Fernandes (Solon, OH); Srdjan Josipovic (Pompano Beach, FL)
Assignee: ROCKWELL AUTOMATION TECHNOLOGIES, INC.
G05B19/41885G05B19/4183
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Quick Facts
Patent No.
US 12,681,469
App. No.
18/462,661
Filed
Sep 7, 2023
Granted
Jul 14, 2026
Kind
B2
Examiner
VO, TED T
Art Unit
2191
USPC
717/123
Abstract

An integrated development environment (IDE) for designing, programming, and configuring aspects of an industrial automation system uses a generative artificial intelligence (AI) model and associated neural networks to generate portions of an industrial automation project in accordance with functional requirements provided to the industrial IDE system in intuitive formats, such as spoken or written plain language text. The system uses generative AI to translate plain language requests or functional specifications into industrial control code, human-machine interface (HMI) applications, device configuration settings, or other aspects of an industrial control project.

Claims (48)

1 . A system, comprising:

a memory that stores executable components and a generative artificial intelligence (AI) model that has been trained using training data comprising at least industrial safety standards and at least one of industrial control code samples, industrial standards data, or industrial protocol data; and

a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising:

a user interface component configured to render a chat interface configured to receive industrial design input data;

a project generation component configured to generate industrial control code based on the industrial design input data; and

a generative AI component configured to

generate, based on application of generative AI analysis to the industrial control code using the generative AI model, one or more plain language comments describing functionalities of respective sections of the industrial control code,

embed the one or more plain language comments into the control code at the respective sections, and

generate, based on the application of the generative AI analysis, safety documentation for an industrial control system for which the industrial control code is designed.

2 . The system of claim 1 , wherein the one or more comments comprise at least one comment assigned to a rung or line of the industrial control code.

3 . The system of claim 1 , wherein

the generative AI component is further configured to generate, based on the application of the generative AI analysis to the industrial control code using the generative AI model, one or more plain language labels for respective elements of the industrial control code, and to assign the one or more labels to the respective elements, and

the one or more elements comprise at least one of an output coil, an input contact, or a data tag.

4 . The system of claim 1 , wherein the generative AI component is configured to generate a comment, of the one or more plain language comments, based on a variable name extracted from a section of the industrial control code to which the comment is to be assigned.

5 . The system of claim 1 , wherein the generative AI component is configured to, infer a control function that a section of the industrial control code is designed to perform based on the application of the generative AI analysis, and generate, for the section, a plain language comment describing the control function.

6 . The system of claim 1 , wherein the generative AI component is further configured to generate, based on the application of the generative AI analysis on the industrial control code and the industrial design input data, development documentation for the industrial control system.

7 . The system of claim 1 , wherein the generative AI component is configured to generate the safety documentation based on at least one a determination of an industrial vertical for which the industrial control code was developed, a determination of a type of industrial application that the industrial control code is designed to perform, a determination of a control function that a portion of the industrial control code is designed to perform, or a determination of an industrial device of an automation system to be monitored and controlled by the industrial control code.

8 . The system of claim 1 , wherein the safety documentation defines validation tests to be performed on an automation system that the industrial control code is designed to monitor and control.

9 . The system of claim 6 , wherein the development documentation comprises at least one of control code development documentation or engineering drawings for an automation system being designed by the industrial design input data.

10 . The system of claim 1 , wherein the project generation component is configured to generate the industrial control code formatted as an industrial domain-specific language.

11 . A method, comprising:

rendering, by an industrial integrated development environment (IDE) system comprising a processor, a chat interface configured to receive industrial design input data;

generating, by the industrial IDE system, industrial control code based on the industrial design input data;

generating, by the industrial IDE system based on application of generative artificial intelligence (AI) analysis to the industrial control code using a generative AI model, one or more plain language comments describing functionalities of respective sections of the industrial control code, wherein the generative AI model is trained using training data comprising at least industrial safety standards and at least one of industrial control code samples, industrial standards data, or industrial protocol data;

embedding, by the industrial IDE system, the one or more plain language comments into the control code at the respective sections; and

generating, by the industrial IDE system based on the application of the generative AI analysis, safety documentation for an industrial control system for which the industrial control code is designed.

12 . The method of claim 11 , wherein the generating of the one or more plain language comments comprises generating at least one comment assigned to a rung or line of the industrial control code.

13 . The method of claim 11 , further comprising:

generating, by the industrial IDE system based on the application of the generative AI analysis, one or more plain language labels for respective elements of the industrial control code, and

assigning, by the industrial IDE system, the one or more labels to the respective elements,

wherein the one or more elements comprise at least one of an output coil, an input contact, or a data tag.

14 . The method of claim 11 , wherein the generating of the one or more plain language comments comprises generating a comment based on a variable name extracted from a section of the industrial control code to which the comment is to be assigned.

15 . The method of claim 11 , wherein the generating of the one or more plain language comments comprises

inferring a control function that a section of the industrial control code is designed to perform based on the application of the generative AI analysis, and

generating, for the section, a plain language comment describing the control function.

16 . The method of claim 11 , further comprising generating, by the industrial IDE system based on the application of the generative AI analysis, development documentation for the industrial control system.

17 . The method of claim 11 , wherein the generating of the safety documentation comprises generating the safety documentation based on at least one a determination of an industrial vertical for which the industrial control code was developed, a determination of a type of industrial application that the industrial control code is designed to perform, a determination of a control function that a portion of the industrial control code is designed to perform, or a determination of an industrial device of an automation system to be monitored and controlled by the industrial control code.

18 . A non-transitory computer-readable medium having stored thereon instructions that, in response to execution, cause an industrial integrated development environment (IDE) system comprising a processor to perform operations, the operations comprising:

rendering a chat interface configured to receive industrial design input data;

generating industrial control code based on the industrial design input data;

generating, based on application of generative artificial intelligence (AI) analysis to the industrial control code using a generative AI model, one or more plain language comments describing functionalities of respective sections of the industrial control code, wherein the generative AI model is trained using training data comprising at least industrial safety standards and at least one of industrial control code samples, industrial standards data, or industrial protocol data;

embedding the one or more plain language comments into the control code at the respective sections; and

generating, based on the application of the generative AI analysis, safety documentation for an industrial control system for which the industrial control code is designed.

19 . The non-transitory computer-readable medium of claim 18 , wherein the generating comprises generating at least one comment assigned to a rung or line of the industrial control code.

20 . The non-transitory computer-readable medium of claim 18 , wherein the operations further comprise:

generating, based on the application of the generative AI analysis, one or more plain language labels for respective elements of the industrial control code, and

assigning the one or more labels to the respective elements,

wherein the one or more elements comprise at least one of an output coil, an input contact, or a data tag.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 7, 2023
From: CARRARA, ANTHONY; OHLSEN, MICHAEL J.; ANAND, ASHISH; MASARIK, MATTHEW T.; GREGORY, ADAM J.; WENGATZ, JUSTIN; RICHTER, DANIEL T.; BAHADER, OMAR A.; MAJEWSKI, LORENZO; NADER, ELIE; FERNANDES, FABIANO; JOSIPOVIC, SRDJAN
To: ROCKWELL AUTOMATION TECHNOLOGIES, INC.
Reel/Frame 064827/0813 →
Continuity (1)
Related Publication 20250085700A1 · Mar 13, 2025
References Cited (74)
US 7324856B1 · Bromley · 2008 [cited by applicant]
US 7334216B2 · Molina-Moreno et al. · 2008 [cited by applicant]
US 7835805B2 · Hood et al. · 2010 [cited by applicant]
US 7912560B2 · Hood et al. · 2011 [cited by applicant]
US 8191005B2 · Baier et al. · 2012 [cited by applicant]
US 10204311B2 · Standing · 2019 [cited by examiner]
US 10942710B1 · Dunn et al. · 2021 [cited by applicant]
US 11169507B2 · Chao · 2021 [cited by examiner]
US 11314493B1 · Stump et al. · 2022 [cited by applicant]
US 11567737B1 · Stump et al. · 2023 [cited by applicant]
US 11604626B1 · Sawant et al. · 2023 [cited by applicant]
US 11640566B2 · Stump et al. · 2023 [cited by applicant]
US 11733687B2 · Stump et al. · 2023 [cited by applicant]
US 20020191023A1 · Chandhoke et al. · 2002 [cited by applicant]
US 20040073404A1 · Brooks et al. · 2004 [cited by applicant]
US 20070073750A1 · Chand et al. · 2007 [cited by applicant]
US 20080022259A1 · MacKlem et al. · 2008 [cited by applicant]
US 20080082185A1 · Hood et al. · 2008 [cited by applicant]
US 20080082186A1 · Hood et al. · 2008 [cited by applicant]
US 20080307400A1 · Dalal · 2008 [cited by applicant]
US 20090089709A1 · Baier et al. · 2009 [cited by applicant]
US 20100042376A1 · Weatherhead · 2010 [cited by applicant]
US 20100049335A1 · Assarsson et al. · 2010 [cited by applicant]
US 20100050097A1 · McGreevy et al. · 2010 [cited by applicant]
US 20120095575A1 · Meinherz et al. · 2012 [cited by applicant]
US 20130342546A1 · Baier et al. · 2013 [cited by applicant]
US 20170346768A1 · Wise · 2017 [cited by applicant]
US 20180129181A1 · Kratzer, III · 2018 [cited by examiner]
US 20180231954A1 · Booker · 2018 [cited by examiner]
US 20190101900A1 · Miller et al. · 2019 [cited by applicant]
US 20200103894A1 · Cella · 2020 [cited by examiner]
US 20200285907A1 · Mehr et al. · 2020 [cited by applicant]
US 20210089276A1 · Dunn · 2021 [cited by applicant]
US 20210096824A1 · Stump et al. · 2021 [cited by applicant]
US 20210096827A1 · Stump · 2021 [cited by applicant]
US 20210141614A1 · Dunn et al. · 2021 [cited by applicant]
US 20210294279A1 · Stump · 2021 [cited by applicant]
US 20210397166A1 · Sayyarrodsari · 2021 [cited by applicant]
US 20220083318A1 · Mabote · 2022 [cited by applicant]
US 20220129251A1 · Dunn et al. · 2022 [cited by applicant]
US 20220198261A1 · Blagodurov et al. · 2022 [cited by applicant]
US 20220292457A1 · Stump et al. · 2022 [cited by applicant]
US 20230091697A1 · Subbunarayanan et al. · 2023 [cited by applicant]
US 20230161838A1 · Shriram et al. · 2023 [cited by applicant]
US 20230259882A1 · Stump et al. · 2023 [cited by applicant]
US 20230280992A1 · Stump et al. · 2023 [cited by applicant]
US 20230324894A1 · Stump et al. · 2023 [cited by applicant]
US 20240281218A1 · Masad et al. · 2024 [cited by applicant]
US 20240303184A1 · Liu et al. · 2024 [cited by applicant]
US 20250004915A1 · Rudenko et al. · 2025 [cited by applicant]
US 20250021663A1 · Katz et al. · 2025 [cited by applicant]
US 20250055277A1 · Fakhar et al. · 2025 [cited by applicant]
US 20250077238A1 · Obando Chacon et al. · 2025 [cited by applicant]
Stephen St. Michael, “Ladder Logic in Programmable Logic Controllers (PLCs)”, 2019, Technical Article, EETech Group, LLC., 7 pages. (Year: 2019). [cited by examiner]
Shuster et al., “BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage”, 2022, arXiv, 38 pages. (Year: 2022). [cited by examiner]
Michael Levanduski, “Can ChatGPT Be Used For Control System Programming?”, 2023, Technical Article, EETech Media, LLC., 9 pages. (Year: 2023). [cited by examiner]
Final office action received for U.S. Appl. No. 18/462,674 dated Jun. 4, 2025, 6 pages. [cited by applicant]
Notice of Allowance received for U.S. Appl. No. 18/462,674 dated Jul. 24, 2025, 8 pages. [cited by applicant]
Non-Final Office Action for U.S. Appl. No. 18/462,674, dated Feb. 26, 2025. [cited by applicant]
Non-final office action received for U.S. Appl. No. 18/459,881 dated Sep. 16, 2025, 40 pages. [cited by applicant]
Notice of Allowance received for U.S. Appl. No. 18/459,822 dated Dec. 22, 2025, 19 pages. [cited by applicant]
Regenwetter et al., “Deep Generative Models in Engineering Design: A Review”, Journal of Mechanical Design, vol. 144, Jul. 2022, 15 pages. [cited by applicant]
Zhang, Feng, “Design and Implementation of Industrial Design and Transformation System Based on Artificial Intelligence Technology”, Hindawi Mathematical Problems in Engineering, vol. 2022, Article ID 9342691, Mar. 29, … [cited by applicant]
Tang et al., “AI Bench Training: Balanced Industry-Standard AI Training Benchmarking”, 2021 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), 2021, pp. 24-35. [cited by applicant]
Liukkonen, Sami, “Utilizing Generative Artificial Intelligence in Streamlining Cyber-Physical Systems Software Development”, Lappeenranta-Lahti University of Technology LUT Bachelor's Program in Industrial Engineering a… [cited by applicant]
Hughes et al., “Generative Adversarial Networks-Enabled Human-Artificial Intelligence Collaborative Applications for Creative and Design”, Frontiers in Artificial Intelligence, Systematic Review, vol. 4, Article No. 604… [cited by applicant]
Non-Final office action received for U.S. Appl. No. 18/459,847 dated Jan. 15, 2026, 17 pages. [cited by applicant]
Codee, “Many Ways to Speed up Your Program”, Waybackmachine, Online available at URL: https://www.codee.com/many-ways-to-speed-up-your-program/, Aug. 8, 2022, 4 pages. [cited by applicant]
Reddit, message thread, “Why Isn't My Hello World Code Working?,” published on Dec. 16, 2022, 1 page. [cited by applicant]
Notice of Allowance received for U.S. Appl. No. 18/462,626 dated Dec. 17, 2025, 16 pages. [cited by applicant]
Leung et al., “The Generative Artificial Intelligence Large Language Product Design Multi-model Framework for Manufacturing Operations”, Journal of the Operational Research Society, Oct. 17, 2025, 28 pages. [cited by applicant]
Yucekule et al., “Proposal for a Product Classification Strategy for the AI-Assisted Generative Design Approach in Industrial Design Process”, Gazi University Journal of Science Part B: Art, Humanities, Design and Plann… [cited by applicant]
Suner-Pla-Cerda et al., “Designer Experiences and Perspectives on the Role of Generative AI in Industrial Design”, AI & Society, Jul. 24, 2025, 24 pages. [cited by applicant]
Notice of Allowance received for U.S. Appl. No. 18/459,881 dated Mar. 13, 2026, 12 pages. [cited by applicant]