827 results about "Workflow process" patented technology

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.

Systems and methods are provided for dynamically selecting, displaying, and synchronizing electronic forms within a conversational AI interface. A chat-based assistant determines the most contextually relevant form or form section using machine learning models and updates the graphical user interface in a split-screen layout. The system supports bidirectional synchronization, such that changes in form fields generate corresponding chat messages, and chat inputs populate structured form fields in real time. Users may pause and resume form workflows across sessions without data loss. The system further supports folder-based organization of structured inputs and document uploads, enabling hierarchical navigation and predictive workflow continuation. These improvements enhance form completion accuracy, user experience, and operational efficiency.

An information technology system for a distributed manufacturing network includes an additive manufacturing management platform configured to manage process and production workflows for a set of distributed manufacturing network entities through design, modeling, printing, and supply chain stages. The information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the set of distributed manufacturing network entities of the distributed manufacturing network to optimize digital production processes and workflows. The information technology system includes a distributed ledger system integrated with a digital thread configured to provide unified views of workflow and transaction information to entities in the distributed manufacturing network.

An AI-driven centralized orchestration system (100) with MCP protocol for real-time compliance and risk data management, including: a. a centralized integration and orchestration platform configured to connect heterogeneous enterprise IT systems through standardized API connections and execute automated workflows; b. a variety of domain-specific artificial intelligence (AI) agents that interface with IT service management (ITSM), vulnerability management, and endpoint and asset management platforms for data collection, analysis, and assessment; c. a Multi-Agent Communication Protocol (MCP) layer that enables structured communication, negotiation and coordination between AI agents in real time; d. a data consolidation module configured to aggregate, standardise and align heterogeneous data into a unified repository using machine learning models; e. a compliance reporting module for dynamically generating and distributing audit-ready compliance reports based on AI-analyzed data and triggered workflows; f. an alerting module configured to deliver context-dependent predictive notifications of compliance violations or risk anomalies through various communication channels; g. a predictive dashboard module capable of displaying compliance status, risk metrics, and proactive insights in real time via AI-driven visualization tools; i.e., where the orchestration platform, AI agents, and MCP layer together enable automated, scalable, and proactive compliance and risk actions.

The invention discloses a multi-agent computer-aided engineering and a computer-aided design method. CAE simulation and design optimization for the design of a complex product are high in threshold and difficulty. According to the method, autonomous planning and automatic execution of CAE simulation and CAD design optimization tasks are realized by using a multi-agent technology based on a large language model. A user interacts with the multi-agent system in the modes of text input, voice, a graphical user interface and the like, the multi-agent system autonomously plans workflow steps according to the requirements of the user, all agents cooperatively execute the working steps in a labor division mode, CAE and CAD software is called to execute simulation and design change, CAE and CAD result data are read and analyzed, the task completion condition is evaluated, and the task completion condition is evaluated. And the task can be re-planned if necessary. The system can automatically process error information, and can notify a user to process when user intervention is needed. The performance of the system can be improved through fine tuning of a large language model and retrieval enhancement generation technology.

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.

A cloud deployment automation system consisting of: a deployment automation device housed in a rack-mountable enclosure, the device comprising: a multi-core orchestration processor configured to execute deployment logic as compiled execution graphs; a storage module operatively coupled to the orchestration processor, the storage storing a set of deployment templates, real-time execution states, telemetry logs, and policy configurations; a secure credential management processing unit embedded in the device, configured to generate, store, and rotate cloud access tokens, API keys, and user-specific credentials, and to provide encrypted access to those credentials during deployment execution; an in-memory workflow execution engine executed by the orchestration processor, configured to analyze a user-defined deployment configuration that includes a declarative specification of infrastructure resources and compile that configuration into a directed acyclic graph (DAG) that represents the resource deployment order, dependency mapping, and rollback relationships, a cloud provider interface subsystem communicatively connected to multiple heterogeneous cloud platforms via appropriate API adapters, the subsystem enabling the orchestration processor to send provisioning requests and receive status events from the platforms; a customizable workflow compiler unit configured to convert graphical workflow definitions or domain-specific language (DSL) scripts into execution sequences that can be used by the workflow execution engine, where the workflow compiler unit supports conditional branching, asynchronous operations, and runtime variable resolution; and A policy enforcement control unit integrated into the deployment automation device, with the policy engine configured to apply organization-specific compliance rules, tagging conventions, security group configurations, and runtime resource limits to all deployment actions in a context-aware manner prior to execution.

Techniques for generating a prebuilt workflow using one or more machine learning models are discussed herein. In some examples, a user may request to generate a workflow configured to automatically perform a series of steps to facilitate the completion of a task(s). In response, the communication platform may present a workflow builder associated a machine learning model(s). In some examples, the machine learning model(s) may receive, as input, a prompt defining a task(s) to be completed and generate, as output, a prebuilt workflow including a suggested series of steps to complete the task(s). The communication platform may receive user input to publish the prebuilt workflow.

An integrated development environment (IDE) leverages a generative AI model to generate industrial control code in accordance with specified functional requirements, which can be provided to the industrial IDE system as intuitive natural language spoken or written text. The industrial IDE can also analyze written code in response to natural language prompts submitted against the code, generate answers to user-submitted questions about the code, and offer recommendations for improving the code in response to specific questions or requests submitted by the user.

A system and a method for generating an optimized power converter design based on one or more multimodal large language models (MLLMs) are disclosed. The system obtains structured design data, one or more user queries, one or more design parameters, and real-time power converter design data. The system performs a similarity matching between one or more embeddings associated with the structured design data, and the one or more user queries and the one or more design parameters to compute an utmost similarity score. The system retrieves one or more similar document chunks. The system processes the one or more user queries and the one or more design parameters to extract one or more parameters. The system determines one or more design workflows based on the one or more parameters. The system executes at least one design workflow. The system visualizes design outcomes, simulation results, and prediction results to a user.

Embodiments manage context repositories in computing environments to enhance automated security analysis. Embodiments obtain context records containing supplemental information associated with security events and integrates them into prompts for large language models (LLMs) to generate severity scores for event classification. Embodiments apply criteria to invalidate outdated or unreliable context records based on age, source reliability, and usage frequency, then modifies prompts and repositories accordingly. Enhanced prompts incorporate context record summaries and entity relationship mappings to improve subsequent event analysis. Embodiments dynamically evaluates context records through quality filters, consolidates duplicates, and maintains audit trails with provenance tracking. User interfaces are dynamically transformed based on telemetry metrics and user feedback to optimize analyst workflows. Embodiments enable organizations to maintain curated, high-quality context repositories that continuously improve AI-assisted security analysis while reducing false positives and enhancing incident response effectiveness.

Recorded sessions of skilled personnel at work are used to train a first machine learning (ML) tool to extract workflow maps. These maps are used to train a second ML tool to emulate at least one workflow. The first ML tool is trained to predict an annotator's output. Either ML tool can be a copilot having a microservice network architecture. Further, complex sets of workflows can be subdivided for efficient support with small ML tools for (1) workflow map extraction and (2) emulation. Based on segment demarcation accompanying a recording, segments are assigned to specialized ML extraction tools for workflow map extraction. Extracted workflow maps are used to train workflow emulator(s). Similarly, specialized ML emulation tools can emulate respective tasks. A distribution microservice can identify a task to be emulated and can invoke the appropriate specialized ML tool. Similar microservice network architectures support both applications.

The invention discloses an AI-based browser user behavior recording intelligent workflow method and system, and relates to the technical field of workflow management.The method comprises the steps that interaction behaviors are recorded in a browser, and normalized event data are generated; the input recording cooperative agent performs user task intention analysis and abstracts the user task intention into a structured workflow; inputting a recording test agent for verification, and adjusting the structured workflow to obtain a verified target workflow; the browser is driven to automatically execute and monitor the execution state, a self-repairing strategy is triggered for adjustment when the execution state is abnormal, and the target workflow is updated when self-repairing succeeds. Through the technical scheme of the invention, standardized modeling and precipitation of the browser event flow are realized, the workload of establishing an automatic process is reduced, the use threshold of establishing the browser automatic process is reduced, the execution success rate and stability of the target workflow are improved, and the user experience is improved. And the robustness and the long-term maintainability of the automatic process are effectively improved.

A personalised artificial-intelligence (AI) case-manager platform provides real-time, policy-constrained clinical decision support. It ingests heterogeneous data from electronic-health records (EHRs), connected medical devices, and clinician inputs; fuses them with a traceable, multilingual reasoning engine; and screens every candidate action through a multi-tier policy-constraint layer that respects patient-consent artefacts, safety grammars, and jurisdictional rules. Dual-factor credential verification and dynamic, role-based access control secure all protected-health-information (PHI) transactions, while a cryptographically chained audit trail—keyed by a global trace identifier—records inputs, rules, overrides, and triggered workflows. Authorised feedback is adjudicated and fed to adaptive learning modules that tune patient-specific and population-level behaviour. A conflict-detection service flags discordant data streams, and a governance-validated workflow engine can issue proactive alerts, automated record updates, or human-in-the-loop escalations. The platform thus delivers explainable, equitable, and continuously learning automation without compromising privacy or clinical accountability.

The present invention relates to a collaborative robot system used in an industrial environment where workers collaborate. The collaborative robot system comprises: a collaborative robot body having a multi-joint structure; a control unit for controlling each joint or auxiliary shaft; one or more sensors for detecting the operational state of the robot in real time; and a status diagnosis and response module for diagnosing the state of the robot on the basis of detected data and controlling the operation of the robot accordingly. Specifically, the system is configured to enable the robot to autonomously perform actions such as deceleration, stopping, and recovery in response to various state changes occurring during work, and to perform self-learning and trajectory optimization on the basis of data accumulated through repetitive tasks. Furthermore, the system includes a graphical user interface (GUI) for intuitive user interaction, which is linked to a digital twin-based virtual simulation environment, enabling presetting and modification of work paths. When multiple collaborative robots are operated together, the system enables task synchronization, path collision avoidance, and sharing of status information among the robots, and may be connected to an external control server or a cloud-based control system to allow integrated management of the entire workflow.

To perform verification with higher consistency with respect to an entire workflow system that realizes coordination with externally provided components whose behavior is uncertain and difficult to be predicted.SOLUTION: A computer 70: observes inputs for a process task block that constitutes a workflow system and calls external software whose outputs fluctuate in response to inputs, and outputs in response to the inputs; creates a test pattern that associates expected outputs with inputs of the workflow system while the process task block is being executed based on the inputs and outputs of the process task block; determines whether or not behavior of the process task block is normal based on a similarity between actual outputs in response to inputs of the workflow system while the process task block is being executed and the expected outputs in response to the inputs of the workflow system registered in the test pattern; and verifies the workflow system from the results of determining the behavior of the process task block.SELECTED DRAWING: Figure 4

An information technology system for a distributed manufacturing network includes an additive manufacturing platform configured to manage workflows for a set of distributed manufacturing network entities associated with the distributed manufacturing network. The information technology system includes a set of digital twins generated by the additive manufacturing platform. The information technology system includes an artificial intelligence system configured to be executed by a data processing system in communication with the additive manufacturing platform. The artificial intelligence system is trained to generate process parameters for the workflows managed by the additive manufacturing platform using data collected from the set of distributed manufacturing network entities. The information technology system includes a control system configured to adjust the process parameters during an additive manufacturing process performed by at least one of the set of distributed manufacturing network entities.

Existing methods for implementing artificial intelligence (AI) models for given training data heavily depend on AI expertise, meaning that organizations need to hire experts to implement the AI model. The present invention discloses a method and process for a platform that eliminates the need to be proficient in AI and coding expertise by automating much of the process. The platform automatically evaluates and selects the best algorithms and methods for each presented problem and dynamically generates tailored AI models and workflows that meet the specific requirements of the particular training data and knowledge domains. The invention may also be configured to learn from experience and continuously improve over multiple sessions.

Systems and methods for orchestrating events in distributed DevOps apparatus leveraging generative AI are disclosed to automate and streamline software development and deployment in distributed DevOps environments using Generative Adversarial Networks (GANs) and other AI techniques. The method involves interpreting UML diagrams, design documents, or the like with generative AI and computer vision to create DevOps tasks, integrating with various DevOps tools for task management, and deploying generated rules for automated event execution. Metadata is generated and processed in order to facilitate AI analysis. The systems and methods reduce manual intervention, increase efficiency, and improve accuracy, scalability, security, and compliance in DevOps workflows.

An AI-powered music registry, collaboration, and workflow management platform that addresses the challenges faced by music industry participants in the digital age. The system comprises a segmentation and hashing subsystem for musical pieces, segments, and isolated elements, enabling the evaluation of uniqueness and the consideration of individual creators' contributions. An artificial intelligence (AI) and machine learning (ML) subsystem is employed for extracting and isolating individual instruments, vocals, and performer contributions, while a component-level tracking module enables enhanced crediting and royalty distribution.

The invention provides a software development system and method and a storage medium. The software development system comprises a process management module and a plurality of software development robots. The process management module is configured to obtain a development design document of to-be-developed target software according to a user instruction; and constructing a file tree according to a logical relationship among a plurality of development tasks recorded in the development design document, and creating a code file and a corresponding prompt word for a function node corresponding to each development task in the file tree. The cue word indicates the function content of the corresponding function node and the logical relationship between the cue word and other associated function nodes; and according to the function content of the at least one function node, determining a matched software development robot so as to enable the matched software development robot to execute the development process of the corresponding code file. The method can be used for autonomously arranging and managing the workflow of software development, so that the multiple robots are used for efficiently and collaboratively developing the software.

Systems and methods for locating target user interface (UI) elements that automation programs intend to interact when performing automation programs. The automation programs can, for example, operate to perform actions as part of a workflow process to complete tasks. Advantageously, when UI elements referenced in automation programs cannot be located based on information recorded about the UI elements during the phase of designing the automation programs. One approach can involve creating candidate fallback element locators (e.g., XPaths) for UI elements during the automation program design phase. If an automation program fails during playback, one or more of these candidate fallback element locators can be used to locate a target UI element. In another approach, when an automation program playback fails, portions of a software application's user interface code can be identified and used as inputs to machine learning model(s), which can generate candidate fallback element locators that can used to locate target UI elements.

A system includes a processor that is configured to analyze position information and environmental information received from a mobile device and a peripheral device using a generation module, to acquire real-time movement data and environmental information utilizing a communication infrastructure, and to generate optimized workflows and movement routes, and to transmit instructions to the mobile device based on the analysis results.

Methods and systems for generating a sharable digital thread orchestration script related to an input digital model on a digital platform are provided. The method includes retrieving a given digital model file of a given digital model; determining characteristic attributes of the given digital model; identifying a predicted digital model similar to the given digital model and a predicted digital thread orchestration script involving the predicted digital model; generating a graph-based representation of the predicted digital thread orchestration script and adding to a training dataset for a machine learning (ML) engine comprising a Graph Neural Network (GNN); receiving a user request indicative of a digital task involving the input digital model; generating, using the ML engine and based on the user request, a graph-based representation of the sharable digital thread orchestration script, wherein the sharable digital thread orchestration script, when executed by the digital platform, implements the digital task.

Described herein are examples of interfaces for designing and / or configuring custom assisted defect recognition (ADR) systems and / or workflows. In some examples, the ADR systems and / or workflows may be designed and / or configured using visual tools, such as, for example, node based processing tools. In some examples, the ADR workflows may be used to analyze two dimensional (2D) and / or three dimensional (3D) image scans, such as might be generated by industrial X-ray scanning systems.

A workflow creation support device and method capable of reducing labor and time required for creating a workflow are proposed. In a workflow creation support method executed by a workflow creation support device that supports creation of a workflow including a plurality of tasks and a workflow creation support method executed by the workflow creation support device, definition contents of a workflow created in the past are managed, and a task candidate of a step to be targeted in the workflow being created is extracted and recommended on the basis of definition contents of a predetermined number of steps of previous and / or subsequent tasks already defined in the workflow being created and definition contents of the existing workflow being managed.

An autonomous customer support Chatbot Agent utilizes a large language model to aid in implementing a workflow to solve a customer issue. A natural language workflow policy may be selected by an admin, along with tools such as API calls. The large language model determines the implementation details for the workflow based on the workflow policy and the selected tools.

The invention discloses an FME-based adjacent pattern spot fusion method and system, and relates to the technical field of adjacent pattern spot fusion, and the method comprises the steps of data processing, parameter setting and rule configuration, adjacent pattern spot fusion execution, result verification and output and feedback and optimization, fusion standard and rule determination, index system unified evaluation, and spatial relationship optimization processing. Manual intervention is reduced through the automatic working process, and the processing efficiency is improved; the invention further provides an adjacent pattern spot fusion system based on FME, the adjacent pattern spot fusion system comprises a data preprocessing module, a data input module, a parameter setting and rule configuration module, a comprehensive correlation index calculation module, a pattern spot fusion module and a result verification and output module, and the adjacent pattern spot fusion method based on FME is executed.

Recorded sessions of skilled personnel at work are used to train a first machine learning (ML) tool to extract workflow maps. These maps are used to train a second ML tool to emulate at least one workflow. The first ML tool is trained to predict an annotator's output. Either ML tool can be a copilot having a microservice network architecture. Further, complex sets of workflows can be subdivided for efficient support with small ML tools for (1) workflow map extraction and (2) emulation. Based on segment demarcation accompanying a recording, segments are assigned to specialized ML extraction tools for workflow map extraction. Extracted workflow maps are used to train workflow emulator(s). Similarly, specialized ML emulation tools can emulate respective tasks. A distribution microservice can identify a task to be emulated and can invoke the appropriate specialized ML tool. Similar microservice network architectures support both applications.

Methods, systems, and apparatus, including medium-encoded computer program products, for secure workflows that enhance data security are described. In one aspect, a digital component request is received. In response to receiving the digital component request, a multi-stage workflow for selecting a digital component is identified, and can include customizable stages. The execution of workflow stages includes: (A) identifying a given customizable stage; (B) for the stage: (i) identifying, a customization specific to the stage that generates an output for use in selecting the digital component; (ii) initiating an isolated execution environment for each customization; (iii) executing, within each isolated execution environment, the customization for which the isolated execution environment was initiated; and (iv) obtaining the output generated by the code of each isolated execution environment; and (C) executing a final stage to select a digital component based on the outputs. The selected digital component is sent to the client device.