Istari Digital, Inc.

Baha Aldeen E A Abunojaim

Sriram Krishnan

William Roper, Jr

Christopher Benson

Peter Galvin

Danne Stayskal Huffaker

Ellie Marie Daw

Omar Valverde

Najem Aldeen Abu Rmaileh

Long N Phan

Pranav Sumanth Doijode

Mohammad M Y Zahra

Abdullah M S Al-Hatem

Joshua Adam Marks

Methods and systems for generating digital threads that connect models and software tools for an interconnected digital engineering and certification ecosystem are provided. In one embodiment, a process for generating a software-code-defined digital thread includes receiving a first model representation of a first engineering model, a second model representation of a second engineering model, and an intent input. Determining, using a machine learning (ML) model, one or more model endpoints in the first and the second model representations. Generating, using a script-generating ML model, a platform orchestration script that accomplishes the intent input by connecting the first model representation and the second model representation, using the one or more model endpoints. Finally, storing the platform orchestration script as the software-code-defined digital thread. The digital threading of digital engineering models, with and without AI assistance, leads to exponential increases in efficiency of designing, testing, certifying, manufacturing, and operating physical products, allowing physical products to be made at digital speeds.

Software-code-defined digital threads in digital engineering systems with artificial intelligence (AI) assistance

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for quantifying avoided carbon emissions. The approach includes receiving, from a client device in communication with a digital platform, a request to execute a task by the digital platform. A token is assigned by the digital platform, the token is configured to uniquely identify the received request to execute the task using the digital platform. The digital platform receives a first value representing a baseline execution cost associated with executing the task. The digital platform determines, using the assigned token and based on execution of the task using the digital platform, a second value representing a reduction in execution cost attained by using the digital platform. The digital platform generates, based on a comparison of the second value to the first value, an attained execution reduction cost associated with the execution of the task by the digital platform.

Architecture for quantifying avoided carbon emissions in a digital engineering platform

Methods and systems for generating a sharable script related to an input digital model on a digital platform are provided. The method includes receiving a user request indicative of a digital task involving an input digital model, and retrieving a corresponding input digital model file. Then, determining characteristic attributes of the input digital model, where the characteristic attributes include digital artifacts generated from the input digital model file. Then, selecting from a collection of templates, using a machine learning (ML) engine, a template matching the characteristic attributes of the input digital model. The ML engine may be trained on documentations of digital tools integrated into the digital platform, a resource-capability mapping of the digital platform, and sample digital thread orchestration scripts collected through past uses of the digital platform. Finally, the method includes generating the sharable script that implements the digital task, based on the selected template.

Machine learning engine for workflow enhancement in digital workflows

Methods and systems for generating a sharable model splice of a digital engineering (DE) model are provided. The method comprises receiving a DE model file in a native file format; extracting model data; storing the model data in a storage area; generating external, commonly-accessible splice functions that enable external access to digital artifacts derived from the model data; and generating the sharable model splice, which comprises access to a selective portion of the digital artifacts, and access to at least one of the splice functions. The splice functions provide addressable API or SDK endpoints that enable access by third-party applications and users to the digital artifacts without access to an entirety of the DE model file and without requiring direct engagement with a DE tool associated with the DE model type. These endpoints also provide a unified programming interface to sharable model splices generated from DE models.

Secure and scalable model splicing of digital engineering models for software-code-defined digital threads

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for securely certifying products in an ecosystem. In some implementations, a digital platform receives a user request to access digital models. The digital platform determines whether the user is authorized to access the digital models. In response, the digital platform generates a transaction request to send to a location of the digital models, wherein the transaction request comprises data identifying operations to perform using the digital models. The digital platform transmits, to the location of the digital models, the generated transaction request that causes execution of the operations. The digital platform receives data representative of a result of the operations performed. The digital platform provides, to a user interface of the user device, the data representative of the result. The digital platform audits the data related to the transaction request and the data representative of the result.

Security architecture for interconnected digital engineering and certification ecosystem

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Roslindale, MA, United States of America

Baha Aldeen E A Abunojaim