EP-4735994-A2 - ARTIFICIAL INTELLIGENCE (AI) ASSISTED INTEGRATION OF NEW DIGITAL MODEL TYPES AND TOOLS INTO INTEGRATED DIGITAL MODEL PLATFORM

EP4735994A2EP 4735994 A2EP4735994 A2EP 4735994A2EP-4735994-A2

Abstract

Methods and systems for generating a digital model splicer are provided. The method comprises training a first Al model and a second Al model on an interconnected digital model platform (IDMP) platfonn resource-capability mapping; generating input and output schemas for a target digital model tool using the first Al model, wherein the first Al model is prompted based on an identifier for the target digital tool; generating a plurality of function scripts executable by the IDMP using a second Al model, wherein the second Al model is prompted based on the input and output schemas, and wherein at least one function script calls Application Programming Interface (API) or Software Development Kit (SDK) functions associated with the target digital tool, using the input and output schemas; and storing the input and output schemas and the plurality of function scripts as the digital model splicer for the target digital tool.

Inventors

Roper, Jr., William
BENSON, Christopher, Lee
KRISHNAN, SRIRAM
KENDYUKHOV, Vadim
RMAILECH, Najem, Aldeen Abu
AL-GHANIM, Malik, Suliman Ghanim

Assignees

Istari Digital, Inc.

Dates

Publication Date: 20260506
Application Date: 20240627

Claims (20)

1. A non-transitory physical storage medium storing program code, the program code executable by a hardware processor to cause the hardware processor to execute a computer-implemented process for artificial intelligence (Al)-assisted generation of a digital model splicer on an interconnected digital model platform (IDMP), comprising program code to: train a first Al model and a second Al model on an IDMP platform resource-capability mapping; generate input and output schemas for a target digital model tool using the first Al model, wherein the first Al model is prompted based on an identifier for the target digital tool; generate a plurality of function scripts executable by the IDMP using the second Al model, wherein the second Al model is prompted based on the input and output schemas, and wherein at least one function script calls Application Programming Interface (API) or Software Development Kit (SDK) functions associated with the target digital tool, using the input and output schemas; and store the input and output schemas and the plurality of function scripts as the digital model splicer for the target digital tool.
2. The non-transitory physical storage medium of claim 1. further comprising program code to align the input and output schemas to a standardized IDMP data schema, wherein at least one variable within the input and output schemas is of a standard IDMP variable type;
3. The non-transitory physical storage medium of claim 1, wherein the first Al model is prompted further based on input and output schema examples for a template digital tool associated with a template digital model type.
4. The non-transitory physical storage medium of claim 1, wherein the IDMP platform has an IDMP platform API, wherein the IDMP platform API is universal across at least two different digital tools for a common digital model type category, and wherein tire IDMP platform resource-capability mapping comprises the IDMP platform API.
5. The non-transitory physical storage medium of claim 1, further comprising program code to: receive a user intent input, wherein the second Al model is a recommender engine, wherein the second Al model is prompted further based on the user intent input, and wherein the plurality of function scripts accomplish the user intent input.
6. The non-transitory physical storage medium of claim 1, wherein the first Al model is prompted further based on a target digital model type associated with the target digital tool.
7. Tire non-transitory physical storage medium of claim 6, wherein a category of the target digital model type is previously integrated into the IDMP and described in the resource-capability mapping, and wherein the input and output schemas overlap with an existing input and output schema for an existing digital tool with the target digital model type category.
8. Tire non -transitory physical storage medium of claim 1, further comprising program code to: extract tool API information from API documentations of the target digital tool, wherein the second Al model is prompted further based on the tool API information.
9. The non-transiton' physical storage medium of claim 1, further comprising program code to: fine-tune the first Al model and the second Al model on API documentations of the target digital tool.
10. The non-transitory’ physical storage medium of claim 1, further comprising program code to: generate a design mockup for the digital model splicer, wherein tire second Al model is prompted further based on a portion of the design mockup.
11. Tire non-transitory' physical storage medium of claim 10, wherein the program code to generate the design mockup comprises program code to: receive a digital task involving the digital model type: upload the input and output schemas into a design mockup took generate, using the design mockup tool, a base design mockup of the digital model splicer, with a standardized data schema; for each variable in the standardized schema, search for example text or image in a database to update the base design mockup; generate the design mockup by adding tire example texts or images to the base design mockup, based on the input and output schema and the digital task; receive user feedback on the design mockup; and finalize the design mockup based on the user feedback.
12. The non-transitory physical storage medium of claim 1, wherein the first Al model or the second Al model is a generative Al model .
13. Tire non-transitory physical storage medium of claim 1, wherein the first Al model or the second Al model is a transformer-based Large Language Model (LLM) or a Small Language Model (SLM)
14. The non-transitory physical storage medium of claim 1, wherein the second Al model is pre-trained on existing script functions from the IDMP.
15. Tire non-transitory physical storage medium of claim 1, wherein the first Al model or the second Al model is prompted further based on human user input.
16. The non-transitory physical storage medium of claim 1, wherein the first Al model or the second Al model is prompted further based on a digital task.
17. The non-transitory physical storage medium of claim 1, wherein at least one function script calls API functions associated with a digital tool different from the target digital tool.
18. A computer-implemented method executable by a hardware processor for generating a digital model splicer on an interconnected digital model platform (IDMP), comprising: training a first Al model and a second Al model on an IDMP platform resource-capability mapping; generating input and output schemas for a target digital model tool using the first Al model, wherein the first Al model is prompted based on an identifier for the target digital tool; generating a plurality of function scripts executable by the IDMP using the second Al model, wherein the second Al model is prompted based on the input and output schemas, and wherein at least one function script calls Application Programming Interface (API) or Software Development Kit (SDK) functions associated with the target digital tool, using the input and output schemas; and storing the input and output schemas and the plurality of function scripts as the digital model splicer for the target digital tool.
19. The method of claim 18, further comprising: aligning tire input and output schemas to a standardized IDMP data schema, wherein at least one variable within the input and output schemas is of a standard IDMP variable type.
20. A system for generating a digital model splicer on an interconnected digital model platform (IDMP), comprising: at least one hardware processor; and at least one non-transitory physical storage medium storing program code, the program code executable by a hardware processor to cause the hardware processor to execute a computer-implemented process for artificial intelligence (Al)-assisted generation of the digital model splicer on the IDMP, comprising program code to: train a first Al model and a second Al model on an IDMP platform resource-capability mapping; generate input and output schemas for a target digital model tool using the first Al model, wherein the first Al model is prompted based on an identifier for the target digital tool; generate a plurality of function scripts executable by the IDMP using the second Al model, wherein the second Al model is prompted based on the input and output schemas, and wherein at least one function script calls Application Programming Interface (API) or Software Development Kit (SDK) functions associated with the target digital tool, using the input and output schemas; and store the input and output schemas and the plurality of function scripts as the digital model splicer for the target digital tool.

Description

Artificial Intelligence (Al) Assisted Integration of New Digital Model Types and Tools into Interconnected Digital Model Platform Reference to Related Applications If an Application Data Sheet (“ADS”) or PCT Request Form (“Request”) has been filed on the filing date of this application, it is incorporated by reference herein. Any applications claimed on the ADS or Request for priority under 35 U.S.C. §§119, 120, 121. or 365(c), and any and all parent, grandparent, great-grandparent, etc. applications of such applications, are also incorporated by reference, including any priority claims made in those applications and any material incorporated by reference, to the extent such subject matter is not inconsistent herewith. Furthermore, this application is related to the U.S. patent applications listed below, which are incorporated by reference in their entireties herein, as if fully set forth herein: • PCT patent application No. PCT/US24/ 19297 (Docket No. IST-01.002PCT), fded on March 10, 2024, entitled "Sofiware- 'ode-Defined Digital Threads in Digital Engineering Systems with Artificial Intelligence (Al) Assistance,” describes Al-assisted digital threads for digital engineering platforms. • PCT patent application No. PCT/US24/18278 (Docket No. IST-02.001PCT), fded on March 3, 2024, entitled “Secure and Scalable Model Splicing of Digital Engineering Models for Software-Code-Defined Digital Threads,” describes model splicing for digital engineering platforms. • PCT patent application No. PCT/US24/ 14030 (Docket No. IST-01.001PCT), fded on February 1, 2024, entitled “Artificial Intelligence (Al) Assisted Digital Documentation for Digital Engineering,” describes Al-assisted documentation for digital engineering platforms. • U.S. provisional patent application No. 63/442.659 (Docket No. IST-01.001P), fded on February I. 2023, entitled “AI-Assisted Digital Documentation for Digital Engineering with Supporting Systems and Methods,” describes Al-assistance tools for digital engineering (DE), including modeling and simulation applications, and the certification of digitally engineered products. • U.S. provisional patent application No. 63/451,545 (Docket No. IST-01.002P), fded on March 10, 2023, entitled “Digital Threads in Digital Engineering Systems, and Supporting AI-Assisted Digital Thread Generation,” describes model splicer and digital threading technology. • U.S. provisional patent application No. 63/451,577 (Docket No. IST-02.001P1), fded on March I I, 2023, entitled “Model Splicer and Microservice Architecture for Digital Engineering,” describes model splicer technology. • U.S. provisional patent application No. 63/462,988 (Docket No. IST-02.001P2), filed on April 29, 2023, also entitled "Model Splicer and Microservice Architecture for Digital Engineeringf describes model splicer technology. • U.S. provisional patent application No. 63/511.583 (Docket No. IST-02.002P), filed on June 30, 2023, entitled "AI-Assisied Model Splicer Generation for Digital Engineeringf describes model splicer technology with Al-assistance. • U.S. provisional patent application No. 63/516,624 (Docket No. IST-02.003P), filed on July 31, 2023, entitled "Document and Model Splicing for Digital Engineeringf describes document splicer technology. • U.S. provisional patent application No. 63/520,643 (Docket No. IST-02.004P), filed on August 20, 2023, entitled “Artificial Intelligence (Al)-Assisted Automation of Testing in a Software Environment describes software testing with Al-assistance. • U.S. provisional patent application No. 63/590,420 (Docket No. IST-02.005P), filed on October 14, 2023, entitled “Commenting and Collaboration Capability within Digital Engineering Platform,” describes collaborative capabilities. • U.S. provisional patent application No. 63/586,384 (Docket No. IST-02.006P), filed on September 28, 2023. entitled “Artificial Intelligence (AI)-Assisted Streamlined Model Splice Generation. Unit Testing, and Documentation,” describes streamlined model splicing, testing and documentation with Al-assistance. • U.S. provisional patent application No. 63/470,870 (Docket No. IST-03.001P), filed on June 3, 2023, entitled “Digital Twin and Physical Twin Management with Integrated External Feedback within a Digital Engineering Platform describes digital and physical twin management and the integration of external feedback within a DE platfonn. • U.S. provisional patent application No. 63/515,071 (Docket No. IST-03.002P), filed on July 21, 2023, entitled “Generative Artificial Intelligence (Al) for Digital Engineeringf describes an Al-enabled digital engineering task fulfillment process within a DE software platform. • U.S. provisional patent application No. 63/517,136 (Docket No. IST-03.003P), filed on August 2, 2023, entitled “Machine Learning Engine for Workflow Enhancement in Digital Engineeringf describes a machine learning engine for model splicing and DE script generation. • U.S. provisional patent application No. 63/516