1915 results about "Workflow process" patented technology

Systems, methods, and computer-readable media for workflow construction operations include displaying a canvas for enabling placement and interconnection of workflow blocks with some of the workflow blocks being associated with data sources, enabling placement of a workflow block on the canvas and associating the at least one workflow block with at least one of the plurality of data sources, enabling placement of a switch block on the canvas at a downstream position of the workflow block and placement of a plurality of additional workflow blocks at locations downstream of the switch block and arranged in a plurality of alternative downstream branches. The switch block is configured to direct trigger action flow to the plurality of alternative downstream branches. The switch block is configured with a plurality of differing queries associated with the plurality of alternative downstream branches, at least one of the differing queries being associated with the data sources.

Systems and methods for searching, summarizing, and automating workflows for financial related documents, and a user interface for displaying the same, are disclosed. The summaries are generated in response to a user query or through natural-language, conversational chat, and they provide the user with underlying supporting documentation to allow the user to follow-up and / or confirm as necessary. An AI-powered document search is performed to identify relevant documents and relevant snippets within the documents. The relevant snippets and / or documents are then summarized by one or more AI models. The models use metadata associated with the documents from the document search to rank the snippets for summarization. AI agents further automate the workflow.

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 method may include receiving a sustainability model indicative of expected sustainability parameters associated with implementing action plans that correspond to enterprise operations of an enterprise over a period of time, such that the enterprise operations correspond to production data performed in a hydrocarbon production system, facility data corresponding to buildings associated with the enterprise, or both. The method may also include receiving an indication to optimize one sustainability parameter and identifying an engineering workflow system that improves a first sustainability parameter associated with the one sustainability parameter. The method may involve sending the sustainability model to the engineering workflow system and receiving an action plan to improve the sustainability parameter, such that commands may be sent to devices based on the action plan to cause the one or more devices to adjust one or more respective operations.

An intelligent search agent that can accept search request input in various ways to initiate a search of databases, websites, documents, PDF files, photos, and other digital data. Search requests can be initiated manually, through automated scheduling, and other ways and can be initiated in multiple ways including voice input, text input, form filling, large language model (LLM) processes, AI agents, selecting from dashboard menus, computer instructions and other methods. Applications of machine learning (ML) and natural language processing (NLP) and others are used to enhance the search agent's capabilities while minimizing user effort requirements. The Search Agent works in conjunction with a knowledge base function to dynamically form and manage one or more knowledge bases using search results and to automatically integrate search results into workflow processes.

Provided is a process, including: executing, with the computer system, tasks in the workflow with AI agents; detecting, with the computer system, that a human is needed to assist with a given task among the tasks in the workflow after one of the AI agents attempts the given task; selecting, with the computer system, the human from among a set of humans based on a profile of the human and the given task and sending a message that causes the selected human to perform the given task; and receiving, with the computer system, the selected human's completion of the given task and inputting the human completion of the given task, or input, to one of the AI agents performing a downstream task in the workflow; and storing, with the computer system, a result of completing the workflow in memory.

A digital content management system may include a set of one or more memory components and a set of one or more processing devices coupled to the set of one or more memory components. The set of one or more processing devices to perform operations comprising: obtaining one or more electronic signatures on a digital document to yield an electronically signed digital document, storing the electronically signed digital document at an off-chain address, using a trained machine learning model to create a smart contract, the smart contract comprising a reference to the off-chain address; and deploying the smart contract to a blockchain.

A method may include receiving production data and facility data associated with an enterprise, such that the production data includes operational tasks performed in a hydrocarbon production system, and the facility data includes one or more utility operations within one or more buildings associated with the enterprise. The method may also include receiving sustainability parameter data associated with one or more operations corresponding to the enterprise based on the production data and the facility data, generating a sustainability report representative of sustainability parameters associated with the operations of the enterprise based on the sustainability parameter data, and sending the sustainability report to one or more engineering workflow systems to determine action plans associated with improving the sustainability parameters. After receiving the action plans, the method includes sending commands to cause devices to adjust their respective operations.

A method includes receiving datasets comprising image data, marketplace data, third-party data, internet-of-things (IoT) data, corporate data, or any combination thereof associated with enterprise operations corresponding to production data for operational tasks performed in a hydrocarbon production system, facility data for utility operations within buildings associated with the enterprise, or both. The method involves detecting a change in sustainability parameter data associated with the enterprise operations based on the datasets and a sustainability model representative of current sustainability parameters associated with the enterprise operations, updating the sustainability model based on the datasets, and sending the updated sustainability model to engineering workflow systems to determine action plans associated with improving the sustainability parameters or the additional sustainability parameters. The method may then involve sending commands to devices associated with the hydrocarbon production system, the buildings, or both based on the action plans to cause the devices to adjust their respective operations.

A property management software system (PMSS) receives, from a client device connected to the PMSS, a request associated with a property management task, identifies a workflow corresponding to the property management task, the workflow comprising a sequence of operations, generates a prompt based on the request, the workflow, and additional contextual data from the PMSS, and provides the prompt as an input to a generative AI model agent, the generative AI model agent to execute the sequence of operations to perform the property management task.

An AI-powered agile project management system for automatic risk assessment and mitigation, including: a risk identification module configured to extract project data from agile project management tools, including but not limited to Jira, Trello, Asana, and Monday.com, and identify potential risks using machine learning and NLP; a risk analysis module configured to assign risk scores based on probability, impact, and severity using predictive analytics, where the module uses historical data, real-time project status, and external dependencies to determine the severity and impact of risks; a real-time monitoring module configured to continuously track project conditions, detect anomalies, and generate immediate notifications via email, mobile apps, or messaging platforms when high-risk events are identified; a risk mitigation module configured to dynamically recommend and execute proactive corrective actions such as task reassignment, resource optimization, schedule adjustments, and implementation of alternative strategies using AI-driven decision-making, where the actions can be automated through predefined workflows or manually adjusted based on AI recommendations; an AI-driven task prioritization module configured to dynamically prioritize tasks based on risk factors, project deadlines, and team workload distribution; an integration module configured to ensure seamless compatibility with existing Agile project management frameworks through API-based synchronization; a learning and continuous improvement module configured to refine risk prediction models over time using machine learning and historical risk mitigation results, with AI-driven models continuously updating risk assessment mechanisms based on evolving project conditions, industry-specific risk factors, and historical performance metrics; a compliance and audit reporting module configured to automatically generate risk reports, audit logs, and compliance checklists; a customizable risk assessment configuration module configured to allow project managers to define custom risk parameters, probability thresholds, and mitigation preferences; an interactive dashboard module configured to visualize data in real time, including risk trends, effectiveness of remedial actions, and analysis of project progress; and a scalability feature configured to be used in industries such as IT, healthcare, finance, construction, and manufacturing.

A method for using a large language model to generate executable code for workflow execution in a manner that integrates user feedback and adjusts the workflow as needed while preserving data privacy is provided. The method includes: receiving first information that relates to a workflow context, second information that relates to at least one application programming interface (API), and third information that relates to a code generation request; using the received information to generate a lecture, and transmitting the lecture to a language model; receiving a user query that relates to performing a task, and transmitting the query to the language model; receiving a workflow that is automatically generated by the language model based on the lecture and the query; and executing the workflow in order to generate an output that is responsive to the query.

A method may include receiving a sustainability model indicative of a plurality of sustainability parameters associated with enterprise operations corresponding to production data of a hydrocarbon production system or facility data of buildings associated with the enterprise. The method includes simulating implementing action plans via a plurality of devices that correspond to the enterprise operations over time to determine an amount of greenhouse gas (GHG) emissions associated with the enterprise, identifying at least one of a plurality of engineering workflow systems to reduce the amount of GHG emissions based on the GHG emissions, such that the plurality of engineering workflow systems determines maintenance or replacement operations for equipment in the enterprise. The method includes sending commands to devices of the devices based on the action plan, such that the commands are cause the devices to go offline at a schedule time period to perform maintenance or replacement of the devices.

Systems, methods, and computer-readable media for workflow management operations are disclosed. A template selection is received for a predefined workflow to be implemented in an existing user's account having at least one internal resource, and a selection of a set of resources is received from a plurality of resources to implement the functionality of the predefined workflow. The selected set includes at least one internal resource of the existing user's account and at least one external resource accessible via an API. A secure storage area is queried to determine that credentials are non-existent for the at least one external resource, an authentication flow is initiated with the at least one external resource to obtain a token from an external resource, and the token is received. The token is stored in the secure storage area, and a configuration of the predefined workflow initiates functionality of the predefined workflow.

A system is provided for processing user queries by using an automated agent and a workflow. The system comprises reusable components that include states, tools, and / or data sources. Based on analysis of a query's content and goals, the system generates a workflow comprising a sequence of states, each state optimized for a subtask and dynamically bound to a selected tool(s) for that specific query. The workflow can provide a structured high-level control, while allowing for flexible selection of the tool(s) for each state of the workflow for that given query. The system produces a result using the structured workflow and selected tools, answering a user's original query.

Disclosed embodiments provide techniques for cybersecurity AI-driven workflow generation using policies. A set of cybersecurity threat protection applications is accessed and managed by a security orchestration, automation, and response (SOAR) platform. The cybersecurity threat protection applications are deployed across a managed cybersecurity network. One or more cybersecurity network compliance requirements are assimilated into the SOAR platform by translating the compliance requirements into one or more cybersecurity application policies and work processes. The assimilation is accomplished using an AI user interface with natural language processing. The cybersecurity application policies provide conformity with the compliance requirements. The application policies generate one or more cybersecurity application workflows for the managed cybersecurity network. The SOAR platform executes the cybersecurity workflow. The workflow is enabled by an embedded universal data layer that maps the cybersecurity threat protection application inputs and outputs to the SOAR platform.

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.

Embodiments of the present invention provide a computer-implemented method for Artificial Intelligence (AI) based provision of management consulting. The computer-implemented method includes dividing the provision of management consulting into a plurality of tasks, the plurality of tasks defined as a plurality of respective workflows. Furthermore, the computer-implemented method includes assigning a plurality of Artificial Intelligence (AI) agents to the plurality of tasks with each one of the plurality of tasks assigned at least one AI agent. The computer-implemented method further includes providing the plurality of AI agents with characteristic reference data obtained from a plurality of data sources. Furthermore, the computer-implemented method includes providing a plurality of interfaces to the plurality of AI agents for enabling exchanges of data amongst the plurality of AI agents. The computer-implemented method also includes generating a plurality of deliverables in a plurality of user-readable formats, the plurality of deliverables constituting management consulting.

An AI chatbot responds to the intent of a customer question by triggering an automatic workflow appropriate for the intention of the question. An information retrieval pipeline may be initiated to response to question corresponding to an information request. A Large Language Model may be initiated to generate a workflow to respond to other types of questions. The Large Language Model is provided with policies, tools and prompts to implement workflows. An evaluation engine evaluates factualness and helpfulness of responses to information questions and workflow intent accuracy and workflow appropriateness. Overall conversation resolution verification ay also be performed.

Disclosed embodiments provide techniques for cybersecurity AI-driven workflow modifications. A security orchestration, automation, and response (SOAR) platform used to manage a plurality of cybersecurity threat protection applications deployed across a cybersecurity network is accessed. A cybersecurity workflow is executed using the SOAR platform and one or more cybersecurity actions related to the workflow are captured and analyzed for workflow relevance. The cybersecurity actions can include steps taken by security operations center staff and automated cybersecurity threat protection applications. The analysis can be performed by machine learning, and can include evaluations of repeated cybersecurity incidents, operation regression exercises, and suggested remedial steps. The workflow analysis can include identifying recidivistic security operations responses. Based on the analysis, the cybersecurity workflow is updated to improve workflow quality. The updating can include reordering the workflow steps, automating responses of operations staff, or executing actions recommended by separate AI cybersecurity systems.

An artificial intelligence (AI) / machine learning (ML) recommendation engine for robotic process automation (RPA) is disclosed. An AI / ML model may be trained to provide recommendations for a next activity, a next sequence of activities, and / or modifications to parameters for one or more existing activities to include during RPA workflow development. The recommendations may be based on the context of where the user is in the RPA workflow. For user interface (UI) automations, the AI / ML model may be linked to an object repository and trained to make recommendations therefrom. The AI / ML model may also be trained to generate new UI descriptors for the object repository.

Systems and methods for adherence and compliance of contact center agents, and more specifically to systems and methods of monitoring, identifying, and correcting deviations of contact center agents from ideal workflows and checklists. In one aspect, the system monitors agent-customer interactions and assesses adherence to compliance guidelines through analysis of agent and customer utterances, identifying non-compliant situations and suggesting actions for correction. Each contact center agent is provided compliance assistance through a visual progress indicator or a checklist feature. A supervisor system provides monitoring of a set of contact center agents and notice of non-compliant situations.

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 online system improves the development and deployment of LLM-based applications providing offering an application workflow user interface to developers of these applications. The application workflow UI is a user interface that includes different sections for the efficient design of a workflow and logic for an application operating on the online system. The application workflow UI allows the user to generate UI elements for stages within the overall application workflow and connect those stages through a simple to use interface. The application workflow UI improves the development process of LLM-based applications by allowing the user to set parameters for stages that involve prompting an LLM through the same interface. Specifically, the application workflow UI includes a parameters section that allows the user to specify parameters for prompting the LLM.

Systems and techniques for are described herein. Natural language workflow descriptions are received from expert users, the descriptions are processed using language models to identify action sequences, and directed acyclic graphs (DAGs) are generated that represent workflows by mapping actions to automated agents. These DAGs are stored as composite action recipes in a vector database. End users can then submit natural language queries, which the system matches to stored recipes. Selected recipes are executed in a sandboxed environment, with the system sequentially invoking automated agents according to the DAG structure. The systems and techniques described herein provide real-time feedback during execution and support features such as graphical workflow validation, dynamic parameter collection, role-based access control, and dry run simulations. This approach enables efficient creation and reuse of complex workflows based on natural language inputs.

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.

A collaborative cloud-based digital music composition, performance and production studio system network employing machine-intelligent digital audio workstation (DAW) systems that are supported by cloud-based automated music composition, performance, production and publishing services. Such services enable improved workflows and enhanced productivity, while ensuring that the music IP rights of all parties involved in the AI-assisted music creation process are respected and responsibly managed in the best economic interests of individual artists, performers, producers, publishers and consumers alike. The digital music studio system network can be realized in various ways supporting the DAW systems in ways suited to different user preferences and application environments.

An AI-powered system for optimizing industrial workflows and predictive maintenance, which includes the following: a network of sensors configured to collect real-time data from industrial plants, including temperature, pressure, vibration, and energy consumption metrics; a central control unit configured to process and analyze the collected data using machine learning algorithms that continuously improve by learning from both historical and real-time data to increase prediction accuracy; a predictive maintenance module configured to analyze data to predict potential equipment failures, schedule proactive maintenance tasks, and prioritize maintenance activities based on the predicted impact of the failure and the criticality of the equipment; a workflow optimization module configured to dynamically adjust operating parameters to optimize the process flow, including the detection of inefficiencies and the suggestion of improvements in real time; an integration module configured to connect and interact with existing industrial control systems and ERP (Enterprise Resource Planning) software to ensure a seamless flow of data between the platforms; a user interface configured to visualize data in real time, provide alerts and customizable dashboards, and is accessible via desktop and mobile devices for remote monitoring and management; an alarm and notification module configured to deliver critical alarms and maintenance notifications via email, SMS and in-app notifications to ensure a timely response; a cloud-based architecture configured to facilitate scalable data storage, remote access, and management across multiple industrial sites; a reporting module configured to generate comprehensive reports on plant performance, maintenance history, and workflow efficiency for analysis and decision-making; A security protocol to protect data integrity and ensure secure communication between all system components.

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.

System and methods herein are configured to receive and ingest a set of course materials, receive user requests from teachers or students, tokenize and generate vector embeddings based on the user requests, and compare generated input vector embeddings vector embeddings associated with workflows to find a matching workflow. The systems and methods are further configured to obtain a subset of the course materials corresponding to the matching workflow, generate a prompt, and submit the prompt to a language model. The systems and methods are further configured to provide a response to the prompt back to the user.