2539 results about "Workflow" patented technology

A computer system implements a unified framework integrating an adaptive elastic funnel (AEF) with a convergent intelligence fabric (CIF) for multi-agent AI collaboration. The system provides a universal multi-modal key-value subsystem for sharing partial computations, implements hybrid placement strategies for dynamic memory management, and incorporates quantum-resistant secure enclaves. The architecture integrates hardware acceleration through GPU-FPGA hybrid caching and neuromorphic processors, applies adaptive energy and thermal management across hardware generations, and implements autonomous flash resource orchestration with multi-dimensional wear management. The system orchestrates tensor workflows using hierarchical scheduling, enables cross-agent collaboration with privacy preservation, and supports continuous learning without catastrophic forgetting. This integration delivers unprecedented computational efficiency and security in high-dimensional decision-making environments while supporting incremental adoption through modular interfaces.

Systems and methods for integrating generative artificial intelligence (AI) within Software-as-a-Service (SaaS) platforms to automate data operations, synchronize cross-platform workflows, and enable intent-based interactions. A platform displays table structures of items and characteristics linked to a common objective, provides input interfaces, and enrolls AI agents as credentialed users with read / write privileges. The system prompts agents with column types, structural relations, and role profiles to generate and execute editing instructions that progress workflow objectives, detect missing or inconsistent data, and notify users or request information as needed. Hierarchical access schemes permit multiple agent instances with inherited privileges and resource limits managed through an AI center. Agents can operate as autonomous team members, analyze outputs, and support natural-language explanation sessions. Additional embodiments coordinate inter-service updates, maintain deviation detection tools, and construct tailored products and platform elements. These capabilities improve robust automation, decision support, and operational efficiency in complex SaaS environments.

The invention discloses a structured decision-making method based on multi-agent collaborative decision-making and reinforcement learning, and relates to the technical field of natural language processing, knowledge engineering and agent collaboration, and the method comprises the steps: receiving an original rule document, analyzing the document type, complexity and constraint conditions, and defining a task target and a success standard; and according to the task target, matching and scheduling the intelligent agent from the registered intelligent agent library, and further analyzing the capacity configuration of the intelligent agent for standby. Through the multi-agent cooperation and reinforcement learning technology, full-process automation of rule documents from input to structured analysis is realized, document types, complexity evaluation and constraint condition analysis can be automatically identified, and a clear task target and a success standard are generated; and the large language model generates a structured workflow according to task requirements and agent capabilities, so that the performability is ensured through logic verification, manual intervention is greatly reduced, and the processing efficiency and the system intelligence degree are improved.

Systems and methods disclosed herein automatically evaluate, select, and coordinate artificial intelligence (AI)-based agents for collaborative distributed task execution based on dynamic, multi-attribute scoring and resource allocation models. The system obtains a task specification request defining a computational requirement set, a performance metric set, and an available resource set for one or more tasks to be executed by a network of AI-based agents. A first AI model set generates domain-specific test datasets and validates prospective agents by comparing agent-generated fingerprints against predetermined hash values stored on a distributed or federated ledger. A second AI model set constructs a multi-dimensional scoring data structure for each agent by using historical performance metrics to compute weighted composite scores. The system selects a subset of AI-based agents, ranks the agents, and allocates resources proportional to each agent's composite score. A third AI model set coordinates and executes distributed computer-executable workflows across the selected agents.

The invention relates to the technical field of data processing and storage management, and discloses a multi-source index data intelligent storage management method and system, and the method comprises the steps: building a unified data integration and processing channel, and forming a standardized index data set; meanwhile, an enterprise-level index unified management system is established, and a credible index data set is generated; the credible index data set is stored through an optimized storage strategy and a data organization model, a data consanguinity association map is constructed based on data full-link processing, and visual and traceable index data assets are obtained; according to the method, a real-time quality evaluation engine is used for carrying out continuous monitoring and intelligent warning on all links of synchronization, processing, storage and service of an index data set so as to construct a data anomaly detection and repair mechanism, and quality controllable supervision is carried out on the whole processing process of the index data set through a data quality billboard and an anomaly processing workflow; therefore, the intelligent management level and the data quality guarantee capability of multi-source index data processing and storage can be improved.

Systems and methods for generating industry-specific solutions using collaborative Artificial Intelligence (AI) agents are disclosed. In an aspect, input data corresponding to an industry-specific problem is received. A goal context for the industry-specific problem is then identified. Further, an industry-specific process workflow corresponding to the industry-specific problem is selected based on the goal context. Furthermore, an agentic context, historical intelligence data, group dynamics data for agent compatibility, appropriate agent character data, and historical user feedback data corresponding to the industry-specific workflow are retrieved. Moreover, AI agents and agent compatibility rules to execute user goals are selected and the rules are assigned to each AI agent. An agentic process workflow for the industry-specific problem is then generated. A candidate solution is then generated by executing the generated agentic process workflow. The candidate solution, agentic process workflow and agent compatibility rules are then outputted on a user interface of a user device.

Systems and methods for integrating generative artificial intelligence (AI) within Software-as-a-Service (SaaS) platforms to automate data operations, synchronize cross-platform workflows, and enable intent-based interactions. A platform displays table structures of items and characteristics linked to a common objective, provides input interfaces, and enrolls AI agents as credentialed users with read / write privileges. The system prompts agents with column types, structural relations, and role profiles to generate and execute editing instructions that progress workflow objectives, detect missing or inconsistent data, and notify users or request information as needed. Hierarchical access schemes permit multiple agent instances with inherited privileges and resource limits managed through an AI center. Agents can operate as autonomous team members, analyze outputs, and support natural-language explanation sessions. Additional embodiments coordinate inter-service updates, maintain deviation detection tools, and construct tailored products and platform elements. These capabilities improve robust automation, decision support, and operational efficiency in complex SaaS environments.

The invention relates to the technical field of informatization processing, and provides an IT asset intelligent management system and an implementation method thereof, and the system comprises an asset discovery subsystem which outputs a standardized asset list through a feature comparison and deduplication fusion program; the unified modeling subsystem forms an asset topological graph by processing association and dependence between information technology assets; the intelligent analysis and prediction subsystem outputs insight results and prediction indexes about resource capacity prediction, anomaly detection, cost optimization suggestions, risk trends and the like; the life cycle management subsystem drives an automatic operation and maintenance workflow through a strategy engine to realize closed-loop management of the information technology asset state; the security and compliance subsystem generates an alarm, a repair suggestion or executes an automated repair policy. According to the invention, intelligence, automation and safety compliance of information technology asset management are realized, and the efficiency and reliability of information technology operation, maintenance and management are significantly improved.

A computer-implemented system for automated document generation receives user input and processes it through artificial intelligence models to create professional documents. The system combines user-provided information with specialized knowledge databases containing domain-specific requirements and formatting standards to generate enhanced processing instructions. Trained machine learning models process these instructions to produce contextually appropriate content that addresses specific technical and legal requirements. The generated content is formatted using configurable templates to create completed documents that meet professional standards. The system provides adaptive user interfaces that adjust functionality based on user expertise levels, supports multiple operational modes including guided and automated generation, learns from example documents to improve output quality, and enables comprehensive workflow management through network-accessible services. Applications include patent applications, legal responses, technical documentation, and other specialized professional documents requiring domain expertise and regulatory compliance.

Systems and methods are described for dynamic execution of artificial intelligence (“AI”) agents. A server can receive, from a client device, an input associated with an AI agent. Based on a manifest file or user profile, the server can identify a management policy that applies to the AI agent. The server then dynamically configures access to the agent objects based on applying the management policy. The management policy is applied to a device status of the client device, a user profile of a user of the client device, and / or a network configuration of the client device. The server then executes a modified workflow based on the dynamically configured access, wherein the modified workflow bypasses or changes operation of at least one of the agent objects. Based on the modified workflow, the server transmits an output to the client device.

The invention discloses an intelligent file service system generation method based on a low-code development mode. The method comprises the following steps: constructing a file service core data model, defining file metadata fields, a storage structure and an association relationship, and forming a standardized data architecture. According to the method, the core data model, the visual page component library and the drag-and-drop workflow template are constructed, linkage binding of data-component-process is achieved in combination with a low-code engine, and the efficiency bottleneck of a traditional development mode is broken through; through a three-way real-time synchronization mechanism, real-time hot repair and dynamic branch judgment, page layout and flow rule adjustment can take effect immediately through visual configuration, and the problem of dynamic demand response lag is solved; through the intelligent resource scheduling logic compiled through environment adaptation, a differentiation scheme is generated for different deployment environments, the configuration difference influence under a private cloud and server mixed deployment scene is remarkably reduced, the deployment failure rate is greatly reduced, and the system online stability is improved.

The invention discloses an enterprise production management method based on digital twinning and workflow simulation, and particularly relates to the technical field of industrial internet and intelligent manufacturing, and the method comprises the steps: constructing a physical production system digital twinning body and business process workflow model, and carrying out the dynamic association through a model fusion engine to form an integrated digital twinning model; real-time event driving is used for deducing and simulating a future production process, and bottleneck and conflict prediction is output; based on the prediction result, utilizing a multi-objective optimization engine to generate a plurality of alternative scheduling schemes; performing parallel simulation quantitative evaluation on the KPI of each scheme, selecting an optimal scheme, analyzing the optimal scheme into a control instruction, and issuing and executing the control instruction; and model self-correction and closed-loop optimization are realized through real-time monitoring and feedback. According to the method, the problem of service and physical state disjunction caused by digital twinning and workflow independence is solved, the whole-process closed-loop management from prediction to execution is realized, and the production self-adaption and intelligent level is improved.

The invention provides an engineering drawing intelligent processing system based on a ComfyUI, and the system comprises an algorithm service module which integrates a plurality of drawing processing algorithm assemblies, such as drawing layout analysis, OCR recognition, drawing direction detection, semantic understanding, drawing frame disassembly, and axis net recognition, and provides services for the outside through a standardized interface; the workflow arrangement module is used for packaging the algorithm components into parameterized drawing processing nodes based on a visual node configuration framework of a ComfyUI, and supporting flexible combination, real-time preview and batch execution of drawing processing flows; and the workflow management module is used for importing, analyzing and publishing the workflow configuration file in the ComfyUI format, and realizing metadata management, publishing verification, API document generation, interface registration and call authority control of the workflow. The method supports multi-type input of PDF and CAD drawings, is suitable for engineering drawing processing tasks such as drawing label extraction, sub-drawing recognition, drawing frame segmentation and drawing filing, and is high in intelligent degree, flexible in process configuration, easy to integrate and high in expansibility.

Systems and methods for resolving code vulnerabilities through collaborative agents which may include accessing a code base of an identified vulnerability; configuring a plurality of autonomous agents, each comprising a predefined agent role associated with application security remediation process; executing a directed workflow of the plurality of agents, wherein the workflow is a conditional sequence of agent-driven processing steps for generating a proposed resolution to the identified vulnerability; and outputting a candidate resolution for the vulnerability based on results produced by the workflow.

Artificially intelligent systems and methods for financial coaching provide personalized, fiduciary-compliant financial guidance through advanced machine learning architectures with measurable performance criteria. The systems implement privacy-preserving processing pipelines that detect personally identifiable information using multi-layered pattern recognition including regular expressions for formatted data sequences, named entity recognition with confidence thresholds above 0.85, and contextual analysis algorithms. A multi-step artificial intelligence processing workflow includes automated language detection, emotional tone classification with confidence scoring, financial profile transformation using predefined templates, context-aware question rephrasing, and semantic similarity matching employing vector embeddings with financial domain vocabulary weighting applying multiplier values between 1.3-2.0. Specialized training methodologies expand datasets through mathematical transformation functions utilizing statistical standard deviations with incremental variations between 0.5-2.0. Mood-based escalation logic automatically transfers users to human advisors when emotional indicators exceed confidence thresholds above 0.8. The systems maintain response times below 5 seconds while providing regulatory compliance through curated content sources and predefined fiduciary instruction parameters.

Systems and methods are described for dynamic execution of artificial intelligence (“AI”) agents. A server can receive, from a client device, an input associated with an AI agent. Based on a manifest file or user profile, the server can identify a management policy that applies to the AI agent. The server then dynamically configures access to the agent objects based on applying the management policy. The management policy is applied to a device status of the client device, a user profile of a user of the client device, and / or a network configuration of the client device. The server then executes a modified workflow based on the dynamically configured access, wherein the modified workflow bypasses or changes operation of at least one of the agent objects. Based on the modified workflow, the server transmits an output to the client device.

A computerized system for dynamic cybersecurity policy using AI-based contextual adaptive learning includes an AI system that evaluates business contexts, risk tolerance, and productivity impact to generate threat intelligence assessments. The system includes a Contextual Adaptive Learning module that dynamically adjusts cybersecurity policies based on threat assessments to create security workflows. A Cybersecurity Mesh Development module that integrates policies across security frameworks. A Dynamic Scenario Catalog module that updates policy adjustments based on threat intelligence. An Automated Workflow Orchestration module that creates and refines security workflows for optimal efficiency. A Policy Recommendation and Automation module that generates prioritized security recommendations and automates policy changes based on organizational risk profiles and current security controls. This system harmonizes security policies while considering business context, risk, and productivity impacts.

Systems, methods, and software are disclosed herein for resolving coding issues via generative AI integrations in various implementations. In an implementation, in a debugging session, a computing apparatus receives a user query relating to an exception in source code. The computing apparatus elicits a response from a generative AI model which is tasked with identifying an interaction pattern for resolving the user query. The computing apparatus mediates the debugging session according to the interaction pattern identified by the generative AI model.

A computer system implements a unified framework integrating an adaptive elastic funnel (AEF) with a convergent intelligence fabric (CIF) for flexible and contextualized multi-agent AI and human collaboration at scale. The system provides a universal multi-modal key-value subsystem for sharing partial computations across agents, implements a hybrid greedy / non-greedy placement strategy for dynamic memory management, orchestrates dynamic computational workflows and tensor workflows using hierarchical tensor-fragment scheduling, enables cross-agent orchestration with policy-based privacy preservation, and incorporates quantum-resistant secure memory enclaves. The architecture supports continuous learning without catastrophic forgetting, compositional reasoning across modalities, and secure task execution in distributed environments. This integration enables unprecedented computational efficiency, secure collaboration, and adaptive intelligence in high-dimensional decision-making environments while supporting incremental adoption through modular interfaces.

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.

The invention provides a multi-agent collaborative decision-making operation and maintenance platform for an on-cloud electric power information system, which is suitable for integrated operation and maintenance of the on-cloud electric power information system, the operation and maintenance platform is in butt joint with a plurality of different on-cloud electric power information systems, and each on-cloud electric power information system is deployed with a plurality of agents responsible for different basic tasks. The operation and maintenance platform is composed of an operation and maintenance task making module, an agent calling module, a workflow generation module, a collaborative decision execution module and an operation and maintenance report distribution module. According to the operation and maintenance platform, multi-agent collaborative decision-making is realized, so that an operation and maintenance response mechanism is converted from passive discovery to active early warning, an execution mode is upgraded from manual driving to intelligent driving, knowledge management is evolved from scattered storage to structured reasoning, and the operation toughness, adaptability and intelligent level of a cloud electric power information system are comprehensively improved.

The invention relates to a BI intelligent question answering system and method based on business rule retrieval and AI workflow, and belongs to the field of intelligent data processing. The system comprises a user interaction module, a knowledge base module and an AI workflow module. The method comprises the following steps: (1) business rule retrieval: taking a BI analysis problem written by a user interaction module as a target, retrieving business data table information from a knowledge base module, judging a business data table of the user problem in combination with a big language model, obtaining business indexes contained in the table according to the business table, retrieving BI business information related to the user problem, and performing business rule retrieval; and (2) AI workflow execution: taking a BI analysis problem as input, gradually scheduling workflow nodes of business rule retrieval, cue word construction, large language model interaction and database interaction, realizing an accurate Text-to-SQL function and a data visualization function, and further realizing a question-answer ability of converting the BI analysis problem input by a user into a BI analysis result.

The invention relates to the technical field of genome data analysis, and discloses an intelligent system, method and equipment for assisting multi-step genome data analysis, and the system comprises a dialogue agent which is used for generating a corresponding answer according to a question of a user, or reading an analysis plan file generated by a workflow agent, generating an analysis interpretation text for the analysis plan file; the workflow agent is used for generating a structured task execution plan according to the to-be-executed analysis task and executing the to-be-executed analysis task; and the modeling analysis agent is used for generating a configuration file and a script based on the user request, constructing a model and generating an analysis result corresponding to the user request in combination with the workflow agent. Through multi-agent cooperation, task division and cooperative scheduling are realized, each agent independently completes task planning, execution control, model analysis and other functions, the bottleneck problem of processing of a traditional single model in a complex process is avoided, error accumulation is reduced, and the execution efficiency and stability of the whole process are improved.

The invention discloses an AI agent construction system based on a vector knowledge base and large model workflow, and belongs to the technical field of process management. The problem that the whole process of workflow scheduling and quality control cannot be managed smoothly due to the fact that an existing AI system cannot assemble heterogeneous and unstructured domain knowledge and business rules dispersed in internal documents, databases, APIs and expert experience through a standardized and low-code platform in a rapid and high-quality mode is solved. Comprising a project initialization module, a knowledge base configuration module, a model service module, a workflow arrangement module, a test verification module and a deployment monitoring module. According to the method, data, knowledge, reasoning and quality control are communicated through a unified vector knowledge base and a low-code drag workflow, a dynamic hypergraph is driven by using computable information potential energy / cognitive entropy, automatic topology operation cutting circulation and shortcut establishment are performed before deadlock outbreak, and predictable hard constraint management of knowledge fusion, process arrangement and cost process is realized.

The invention discloses a multi-modal large model automatic evaluation method and system based on Agentic Workflow, relates to the technical field of artificial intelligence model evaluation, and solves the technical problems that an existing large model evaluation method lacks image-text evaluation, is relatively high in subjectivity and is easy to cause illusion. The method comprises the following steps that S1, an input task is analyzed, and a structured task template is generated; s2, generating a multi-round multi-modal task flow chart based on the task template generated in the step S1; s3, based on the flow chart in the step S2, distributing the multi-modal large model component to each task node, executing a task and obtaining an intermediate result; s4, performing multi-dimensional scoring on the intermediate and final results in the step S3 by a plurality of scoring Agents, and fusing to generate an evaluation result; and S5, generating a feedback report based on the process data in the steps S1 to S4, identifying a model short plate, and providing an optimization suggestion. The method has the advantages of comprehensive evaluation of multiple modes, real-time feedback optimization and the like.

The invention relates to the technical field of natural language processing, and discloses a workflow calling method based on a large language model. The method comprises the steps of receiving an initial task description text of a user, and decomposing the initial task description text into a discrete intention unit set through a semantic analysis engine; inputting a pre-trained large language model to carry out context association analysis, and generating a task node topological graph containing a hierarchical relationship; detecting a data transmission dependency relationship between nodes, and marking a strong association cluster with bidirectional data streams; according to cluster dynamic load parameters, automatically dividing parallel execution domains and distributing independent data transmission channels; and monitoring channel throughput fluctuation in real time, and triggering a channel switching protocol when the channel throughput is blocked. According to the method, the complex task execution logic and the data flow path are straightened out, the problems of dependency conflicts and resource allocation are solved, the orderliness and stability of workflow execution are guaranteed, and the automatic task scheduling requirements under various complex scenes are met.

Systems and methods disclosed herein automatically register, monitor, and authenticate distributed artificial intelligence (AI) agents and their operational contexts using a distributed or federated ledger-based agent knowledge registry. The system obtains a registration or query request (e.g., from an AI agent, orchestrator, or user interface) to identify or store operational context linked to each AI-based agent. The system determines a feature set of agent metadata and operational parameters using a first AI model set, and dynamically generates a cryptographically verifiable registry record set using a second AI model set (same as or different from the first AI model set) based on the operational feature set, to be stored in a distributed ledger database. The system automatically executes registry updating, agent selection, or operational verification workflows using a third AI model set (same as or different from the first and / or second AI model sets) to maintain records that trace inter-agent interactions.

Systems and methods involving various registration and authentication workflows are disclosed herein. A user may be authenticated without the use of static usernames or passwords. In some embodiments, an authentication identifier may be generated that is associated with an authentication request for a user to access a protected resource (e.g., a web app). An authentication code may be generated based on the authentication identifier. The authentication code may be sent to a computing device to be provided to the user, who may provide the authentication code to an application on their mobile device. The mobile device may send a payload containing the authentication identifier, credentials saved on the user device from a previous registration step, and a digital signature. The digital signature may be authenticated using contents of the payload before validating the authentication identifier.

The invention discloses an operation and maintenance workflow cooperation system and method, and relates to the technical field of business process.The method comprises the steps that after a natural language operation and maintenance requirement is received, a subtask set containing task attributes is extracted through a semantic model built based on a pre-training operation and maintenance field language model; inputting the sub-tasks into a causal mining model, capturing an implicit dependency relationship between the tasks through an attention mechanism which takes task types and resource demands as weight regulation factors, and generating an operation and maintenance relationship graph which contains dependency confidence coefficients and dependency types and does not have cyclic conflicts; splitting the atlas into a task chain set and a free task point set by adopting a causal-oriented greedy pruning algorithm based on a directed edge association subtask maximum aggregation and task chain set scale minimization principle; task chains are distributed through a weighted matching algorithm in combination with the chain overlap ratio and the to-be-handled task amount of the intelligent agent, remaining free task points are distributed according to the balance principle, and accurate disassembly and efficient cooperation of operation and maintenance tasks are achieved.

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.