Istari Digital, Inc.

Deploying neural networks that use unique data sources generated by enterprises as training or input data has significant potential to unlock novel insights and domain-specific AI capabilities. However, data owners are often hesitant to share their proprietary and valuable datasets with third parties for training neural networks, even though doing so could provide significant benefits. Similarly, neural network owners are reluctant to expose their confidential weights and biases, which represent critical intellectual property. The proposed solution is based on both data and neural network parameters being transformed into a secure mathematical space, enabling collaborative training and deployment of neural networks without exposing sensitive information. The solution proposed here resolves data and neural network model privacy issues by allowing data owners and neural network owners to work together securely, ensuring that neither party's assets are compromised, while still enabling effective collaboration.

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 model, a second model representation of a second model, and an intent input. Determining, using an artificial intelligence (AI) model, one or more model endpoints in the first and the second model representations. Generating, using a script-generating AI model, a platform orchestration script that accomplishes the intent input by connecting the first model representation and the second model representation, using the model endpoints. Finally, storing the platform orchestration script as the software-code-defined digital thread. The digital threading of digital models, with and without AI assistance, leads to exponential increases in efficiency of designing, testing, certifying, manufacturing, and operating products, allowing products to be made at digital speeds.

Methods and systems for artificial intelligence (AI) assisted automation of testing of a software functionality related to a user intent on a software platform is provided. In one embodiment, the method includes receiving user action data indicating the user intent related to a software platform and generating a human-readable test scenario. The test scenario, which includes a sequence of human-readable testing steps and an expected outcome, is generated using a scenario machine learning (ML) model. The method includes generating an interpretable test script based on the test scenario using a script ML model. The test script is interpreted to implement testing steps on the software platform, generating a test outcome. A human-readable test report is also generated to evaluate the test scenario on the software platform, based on the test outcome and the expected outcome.

Digital model platform that enables the streamlined creation and management of digital twins and physical twins by leveraging external feedback and artificial intelligence (AI) is disclosed. Methods and systems for a model collaboration platform that facilitate the integration of external feedback, whether from physical, virtual, or human sources, into the streamlined design, validation, verification, certification, assembly, operations, and maintenance processes of complex systems. Updated results are output to those external sources, including physical twins, other digital twins or models and simulations, and/or human users. External feedback includes feedback data from physical prototypes and their environment, virtual prototypes, simulations, and subject-matter experts. Embodiments are directed to integrating external feedback into the assembly and system-level assessment of digital twins, physical twins, digital threads, and the iterative design of constituting digital engineering models, including constructing, maintaining, and improving digital engineering models for the design, validation, verification, certification, operations, and maintenance of complex systems.

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.

A digital documentation system for preparation of engineering documents utilizing one or more artificial intelligence (AI) algorithms is provided. The system includes a user interface for selecting and populating templates with data, and one or more AI algorithms for creating and recommending templates, and preparing documents based on the recommended templates. The system uses natural language processing and semantic analysis algorithms to understand the content of the templates, documents, and associated engineering data, and to generate and recommend relevant templates to the user based on user prompts. The system also uses machine learning and predictive modeling and decision-tree algorithms to assist with the preparation of documents, by generating suggestions for data fields and values based on the user's previous inputs and the overall context of the document and available engineering data, including model data and metadata from digital models accessed in a zero-trust framework.

Digital model platform that enables the streamlined creation and management of digital twins and physical twins by leveraging external feedback and artificial intelligence (AI) is disclosed. Methods and systems for a model collaboration platform that facilitate the integration of external feedback, whether from physical, virtual, or human sources, into the streamlined design, validation, verification, certification, assembly, operations, and maintenance processes of complex systems. Updated results are output to those external sources, including physical twins, other digital twins or models and simulations, and/or human users. External feedback includes feedback data from physical prototypes and their environment, virtual prototypes, simulations, and subject-matter experts. Embodiments are directed to integrating external feedback into the assembly and system-level assessment of digital twins, physical twins, digital threads, and the iterative design of constituting digital engineering models, including constructing, maintaining, and improving digital engineering models for the design, validation, verification, certification, operations, and maintenance of complex systems.

Methods and systems for generating live digital documents are provided. The method includes receiving a static document file including human-readable data. Then, parsing the static document file into a plurality of subunits. Then, generating a sharable document splice of the static document file. The sharable document splice includes access to a subset of the plurality of subunits, and the access is provided through an Application Programming Interface (API) or Software Development Kit (SDK) endpoint for each subunit in the subset. Then, executing a digital thread script to generate the live digital document from the sharable document splice and an input digital model representation, using API or SDK endpoints in the sharable document splice. The live digital document is configured through the digital thread script to reflect changes in the input digital model representation. Finally, adding execution metadata for the execution of the digital thread script to the live digital document.

Methods and systems enabling multimodal inputs for interacting with a live digital object are provided. The system receives the live digital object which includes a digital artifact extracted from a digital model file through a model representation. The system receives a user's security level and determines the user's access permission and modification permission to access and modify the digital artifact. The system accesses a multimodal interface configured to receive a conversational input and a spatial input, outputs the digital artifact to the multimodal interface based on the access permission, receives from the multimodal interface a conversational input and a spatial input from the user, and generates a modified digital artifact from the digital artifact via the digital model representation, based on the modification permission and on the conversational or spatial input. The multimodal interface may include conventional interfaces (GUIs/APIs), conversational interfaces (text/voice), and spatial computing interfaces (VR/AR/MR/gestural).

An artificial intelligence (AI) assisted generative digital task fulfillment process within digital model platforms is provided. Disclosed are methods and systems for carrying out digital tasks through generative AI, including tasks related to the streamlined design, validation, verification, certification, assembly, operations, and maintenance processes of complex systems. The method includes receiving access to a context AI model trained on Internet-scale data, receiving a user prompt indicating the digital task, and generating contextual data based on the user prompt using the context AI model, where the contextual data identifies a syntax AI model. The method includes training the syntax AI model to generate a template script having a placeholder variable for a parameter related to the digital task. The method also includes using a parameter substitution process to generate the orchestration script by substituting the variable with a parameter value.