568 results about "Workflow process" patented technology

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 Embodiments apply criteria to invalidate outdated or unreliable context records based on age, source reliability, and usage frequency, then modifies prompts and repositories accordingly.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.

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.

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.

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.

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.

A system for event-driven distributed data network analysis, the system includes: a multitude of domain data nodes, each comprising a stream storage, a multitude of transformation pods, and a product API, with each domain data node ingesting heterogeneous event streams, validating schema compliance, performing enrichments, and making data products available under versioned schema contracts; each domain data node further comprises a hardware-based edge intelligence appliance comprising an enclosure, a compute module with heterogeneous processing units selected from CPUs, GPUs, and TPUs, a secure enclave module for confidential execution of transformation code on encrypted data, a protocol-structured stream storage medium supporting multi-level retention, and industrial I / O interfaces configured for direct interaction with sensors, programmable logic controllers, and cloud-native services; an event mesh backbone processor configured to connect the multitude of domain data nodes via a publish-subscribe architecture, with the backbone guaranteeing exactly one-time delivery, partition-level sorting, and geo-replicated durability; a Contextual Intelligence Engine coupled with the Event Mesh Backbone processor, the engine comprising a semantic graph memory and a Streaming Graph Reasoner configured to map raw events into an ontology, perform graph joins in real time, and recognize composite causal patterns across multiple domain data nodes; a governance and policy control unit integrated into both the data and control layers. This layer enforces zero-trust security, role-based access control, real-time provenance tracking, and regulatory compliance; and An actuation interface coupled with the contextual intelligence engine and the governance layer. The actuation interface is configured to generate control commands for operational technology devices and digital workflows, thus closing the loop from data acquisition to autonomous decision-making.

Methods and systems are disclosed for database management with controlling documents. Tasks addressed include: identification of requirements in the documents, tracking changes as documents evolve, mapping documents or requirements to database entries, identifying gaps between documents and the database, and proposing database updates. Disclosed embodiments address these tasks using a combination of sequential program logic, machine-learning tools, and client interaction. Workflows address one or more tasks. Examples pertaining to regulatory documents are presented. Variations are disclosed.

The invention provides an end-to-end software development automation platform and method, and the platform comprises an intelligent design module which automatically generates a structured design document through multi-Agent cooperation and builds a traceable link; the intelligent development module is used for analyzing the UI design draft through computer vision to generate a front-end code and automatically generating a rear-end code based on the design document; the intelligent test module automatically generates a test case and drives a test closed loop; the workflow engine configures a flow through a visual interface and monitors a task through an intelligent Agent; and the RAG knowledge base and the Agent base disk are used for dynamically slicing the document and storing the document in a vectorization manner, and support multi-role Agent collaboration. According to the platform, the time consumed by manual design can be reduced by 50%-70%, and the design period is shortened by 67%; the front-end development efficiency is improved by three times; the automatic coverage rate of back-end codes reaches more than 90%, and the labor cost is reduced by 60%; the test case generation efficiency is improved by three times, and the test period is shortened by 50%.

In one aspect, a computerized method for document extraction workflow for unstructured documents includes the steps of implementing a text mining operation on a set of digital documents the incoming documents. This is done by defining a document type of each digital document. Based on the document type, the method defines a set of data dictionaries to extract any data from each digital document. The method uses the defined set of data dictionaries to extract any data from each digital document.

The invention relates to an application that utilizes artificial intelligence to enhance project management in construction and repair industries. The system includes a user computing device, an application server, and a database server. The application server processes project data, including blueprints, to generate materials lists, integrates with online marketplaces to facilitate purchasing, and employs machine learning for product recommendations. The system also uses predictive analytics to forecast project deliveries and prevent backorders, while optimizing resource allocation through efficient supplier and material selection. The software application features an AI-powered chat, job planner, marketplace, and cart, providing a comprehensive platform for real-time collaboration among contractors, suppliers, and manufacturers. This innovation streamlines project workflows, reduces errors, and improves overall efficiency in managing construction and repair projects.

The invention discloses an intelligent workflow generation method and device, equipment and a storage medium, and the method comprises the steps: obtaining multi-modal demand information inputted by a user, carrying out the semantic understanding of the multi-modal demand information, and obtaining a semantic tag set and a workflow node link; according to the semantic tag set and the workflow node link, screening a basic template matched with the multi-modal demand information from a plurality of workflow generation templates, and expanding nodes in the basic template to obtain a target template; and acquiring hardware resource information of the user equipment, adjusting the node configuration information in the target template, and generating a target workflow file according to the adjusted target template. According to the technical scheme provided by the embodiment of the invention, the technical difficulty of a workflow generation scheme can be reduced, the generation efficiency of the workflow and the quality of a workflow generation result are improved, the problem that the template cannot normally operate due to the fact that the hardware of the user equipment is not supported or the plug-in is missing is avoided, and the workflow can smoothly fall to the ground.

The invention provides an industrial operation area multi-view video collaborative analysis method and system based on artificial intelligence. The method comprises the following steps: firstly, acquiring multi-view video data; secondly, based on the working area and the equipment layout, deducing a semantic association relationship among the multi-view video data; according to the semantic association relationship, dynamically adjusting the analysis priority of the multi-view video data through an artificial intelligence model to determine an analysis priority sequence; and according to the determined analysis priority sequence, executing interactive co-processing of the multi-view video data, and finally generating a co-analysis result. According to the technical scheme provided by the invention, the physical layout and the workflow of the industrial site are converted into the semantic association relationship, and the dynamic scheduling analysis resources and the interactive collaborative analysis are performed according to the semantic association relationship, so that the spanning from simple monitoring of a plurality of isolated video streams to intelligent and coherent understanding of the whole operation flow is realized; and the intelligent level and decision support capability of monitoring analysis are improved.

The invention provides a state machine management method based on a hierarchical architecture and a dynamic trigger mechanism, which adopts a four-layer nested state hierarchical architecture and comprises the following steps: acquiring an equipment initial state signal and starting a state machine working process; switching from an initial state to a task state according to a trigger mechanism, and executing a task action; determining a state transition path according to a task state execution result, switching to a stable state and executing a resource synchronization operation if task execution succeeds, and switching to a fault state and executing an exception handling action if task execution fails; and determining whether to recover to a task state or enter a safety mode according to a fault state processing result. According to the technical scheme, the reliability and the operation efficiency of the electric energy meter equipment are effectively improved, and the fault recovery capability and the resource management capability of the system are enhanced.

Leveraging information gathered using static analysis for remediating detected issues in a monitored deployment, including: inspecting, using static code analysis, a non-executable representation of an application to identify one or more vulnerabilities in the application; detecting one more conditions in the monitored deployment; and initiating, based on information describing the one or more vulnerabilities in the application that were identified using static code analysis, a remediation workflow.

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.

Monitoring the usage of an application by a user, including: determining, for the user and based on data describing usage of the application by the user, an access pattern of the application; and restricting, without modifying the one or more access policies that control access to the application for additional users, access to the application by the user, including implementing an approval workflow for accessing the application one or more steps that, if completed, allows access to the application by the user, wherein the approval workflow is implemented by an intermediary between a user device and the application.

The invention discloses a scenario generation method and system based on a large language model, and relates to the technical field of computers. By designing the scenario generation system comprising a scenario generation subsystem, a scenario setting subsystem, a scenario deduction subsystem and a scenario management subsystem, based on a large language model, a process of quickly generating a simulation scenario in a manner of retrieval enhancement generation or combat scenario text conversion is realized; moreover, the system has the functions of scenario editing, scenario deduction, scenario management and the like, achieves a workflow closed loop of intelligent generation, editing management, deduction inspection and modular storage, and improves the scenario generation efficiency and the intelligent level.

The invention discloses a workflow analysis linkage verification method and device based on log analysis, equipment and a medium, and relates to the technical field of workflow verification, and the method comprises the following steps: collecting operation log data, and carrying out time sequence reconstruction and event extraction to obtain a structured log record; performing semantic analysis on the structured log record, extracting a log semantic feature vector of each event, and constructing a semantic feature matrix; based on the semantic feature matrix, performing semantic modeling on nodes in a preset process model to obtain a process node semantic vector; matching the log semantic feature vector with the flow node semantic vector, and constructing and optimizing an initial mapping relation matrix according to a matching result; executing flow rule compliance and rationality joint verification according to an optimization result; according to the method, deep coupling of the log, the process model and the business rule is realized, so that the problems that log analysis and rule coupling are insufficient and process compliance and rationality verification are difficult to accurately carry out are solved.

In an industrial processes, a properly instrumented line facilitates capture of data including detected steps, application input, and execution graph transitions, that permit the creation of empirical models of process timing. In this context, a process controlled by individual applications, e.g., at manufacturing workstations, provides a proxy for overall process timing by dividing a workflow into a number of discrete steps completed at each workstation, and further into any number of sub-steps, each controlled by a user and explicitly completed, e.g., by user interactions with widgets or other controls of the application. These applications provide a useful framework for modeling execution timing by providing an initial, implicit model for workflow (based on application control logic) that also facilitates automated detection of process sub-steps based on execution flow, as well as detection and measurement of the contributions of individual widgets and / or combinations of widgets to the process timing. By gathering data in this manner, mixed statistical distributions can be applied based on individual timing data for each possible sub-step, widget, process step, and the like performed with each application.

Embodiments of the present disclosure generally relate to methods for autonomous orchestration of a data labeling workflow. Embodiments include generating, using a generative machine learning model, a natural language description of input data. Embodiments include identifying candidate labels that are semantically similar to the natural language description of the input data. Embodiments include providing natural language descriptions of the candidate labels and the natural language description of the input data to a language processing machine learning model. Embodiments include receiving an output from the language processing machine learning model in response to the natural language descriptions of the candidate labels and the natural language description of the input data, wherein the output indicates a selected label from the candidate labels. Embodiments include validating the output based on an alternative label determination technique. Embodiments include associating the selected label with the input data based on the validating.

An embodiment may involve receiving a textual description of a workflow; determining, based on the textual description and operational characteristics associated with a computing system, a workflow outline that represents respective steps of the workflow; for each of the respective steps of the workflow, identifying a respective portion of the operational characteristics based on determining that the respective portion satisfies a relevance criterion; obtaining, from a natural language model and based on the respective portions of the operational characteristics, a respective programmatic representation for each of the respective steps of the workflow; and updating the workflow outline based on the respective programmatic representations.

The embodiment of the invention relates to the technical field of security assessment, and provides a security assessment method and device for a retrieval enhancement generation system, and the method comprises the steps: collecting target data used by the retrieval enhancement generation system; based on the target data, aiming at a plurality of attack types faced by the retrieval enhancement generation system, respectively creating poisoning data corresponding to each attack type; uploading the poisoning data to a knowledge base of a retrieval enhancement generation system to interfere with a working process of the retrieval enhancement generation system; performing multiple rounds of testing on the retrieval enhancement generation system by using the standard test question set to obtain a test result; determining the influence degree of the poisoning data corresponding to each attack type on the retrieval enhancement generation system based on the test result; the vulnerability and attacked features of the retrieval enhancement generation system are evaluated based on the degree of influence. Therefore, poisoning attacks of the retrieval enhancement generation system are carried out under stricter conditions, meanwhile, the concealment of existing attacks is improved, and the safety problem in the retrieval enhancement generation system is relieved.

The invention discloses a smart course personalized resource label classification and recommendation system based on teaching practical experience, and relates to the technical field of smart education, and the system comprises a knowledge graph construction module, a resource labeling module, a user capability modeling module, a recommendation core module, a system interaction module and a parameter optimization module. The working process of the system is as follows: extracting a knowledge point set from a course standard, defining a relationship type between knowledge points, constructing a weighted directed graph, and calculating a shortest path distance between the knowledge points; marking a knowledge point set which is directly explained, a knowledge point set which needs to be mastered in advance, cognitive difficulty and a resource type for resources in the resource library; initializing a user knowledge state matrix, updating the mastery degree according to a test result, and calculating a current capability threshold value of the user; preliminarily screening a candidate resource set, calculating a knowledge migration intensity score, and grouping and sequencing according to resource types to generate a final recommendation list; and adjusting core parameters of a knowledge migration intensity algorithm according to user behavior data generated by a recommendation result.

The invention discloses a construction method of a single-cell multi-omics data analysis system based on a containerization technology, and the construction method of the single-cell multi-omics data analysis system based on the containerization technology comprises the following steps: constructing a single-cell multi-omics analysis modular toolkit; a production mirror image is constructed on the high-performance cluster through a Single container engine, and deployment of a production environment is completed; a single-cell multi-omics automatic analysis platform is built through a snakemake process engine; and a JupyterLab interactive analysis platform for exploratory analysis of the single-cell multi-omics data is constructed. According to the single-cell multi-omics data analysis system construction method based on the containerization technology, the data acquisition link in the analysis process is accelerated, an automatic working process is formed to solve the problem of complex process, and the actual requirements of multiple users on a high-performance computing cluster for scientific exploration and analysis of single-cell data are met.

One or more non-transitory machine-readable media to store data and instructions executable by one or more processors. The instructions are programmed to analyze workflow data for a given phase of a plurality of phases of an ongoing electrophysiology (EP) workflow implemented using a first computing apparatus. The instructions can also determine at least one support event based on the analysis of the workflow data and send a request to at least one remote support specialist responsive to determining the at least one support event. The instructions further can establish a communication link between the first computing apparatus and a second computing apparatus, which is associated with a respective remote support specialist, and enable user interaction with and control of the machine-readable EP workflow instructions on the first computing apparatus responsive to a user input by the respective remote support specialist at the second computing apparatus.