4507results about "Visual/graphical programming" patented technology

An example operation may include one or more of ingesting data from one or more websites via one or more application programming interfaces (APIs) and storing the data within a data store of a host platform, generating one or more prompts and displaying the one or more prompts on a user interface of a user profile page within a software application hosted by the host platform, receiving one or more responses to the one or more prompts, generating a design of a structure based on execution of an artificial intelligence (AI) model on the data from the one or more websites, the one or more prompts, and the one or more responses, and displaying the design of the structure via the user interface of the user profile page of the software application.

Systems and methods for integrating generative artificial intelligence (AI) capabilities within Software as a Service (SaaS) platforms. One of the computer-implemented methods are for querying a generative AI model about structured data in a SaaS environment, enabling users to interact with and manipulate data through AI-assisted interfaces. One of the systems maintains a generative AI agent configured to interact with SaaS platform data as a virtual team member, capable of understanding context and nuances of project data. Also described are methods for color-context aware data analysis, generation of interactive elements in messaging sessions, and cross-application generative AI agent interactions triggered by user mentions. Also described is facilitating the creation of custom SaaS platform products by combining functionalities from existing products using generative AI. The systems and methods represent advancement in AI-driven SaaS customization and data analysis.

The invention relates to methods and systems for integrating generative artificial intelligence (AI) capabilities into Software as a Service (SaaS) platforms. It comprises maintaining AI agents with varying credentials, enabling their interaction with alphanumeric data in table structures, and implementing a hierarchical access control scheme. The system displays table structures, provides interfaces for user inputs, and allows AI agents to be added as platform users. The generative AI agents can analyze data, identify actions, and perform tasks autonomously. The invention also includes methods for proactive information gathering, interactive analysis of AI outputs, and management of AI resources as limited assets. This approach enhances SaaS functionality by enabling AI-driven task completion, data analysis, and decision-making while maintaining data security and user-specific access controls.

A system for creating functionality modules for deployment in a workflow for use in visual programming including a configuration server with a processing element operable to implement the functionality modules and workflow, at least one large language model, a customizable functionality module in a workflow including at least one interface defining one or more customizable properties, and wherein the workflow executes a first operational environment different from a second operational environment executed by the large language models.

The invention discloses a multi-modal visual arrangement recommendation method, belongs to the technical field of artificial intelligence and data visualization crossing, and realizes visual arrangement recommendation based on multi-modal input analysis, a dynamic mixed recommendation model and an intelligent optimization algorithm. Comprising the following steps: multi-modal intention analysis: realizing intelligent analysis of multi-modal input through combined use of a base model and a fine tuning model, realizing high-precision intention classification in combination with a pre-training language model and a domain adaptation fine tuning technology, and triggering dynamic prompt word recommendation; performing intelligent layout generation: performing global optimization of component space allocation by adopting a genetic algorithm, performing business rule adaptation by combining a constraint solver, and modeling an interaction relationship between components by utilizing a graph neural network; and dynamic mixed recommendation: constructing a three-level recommendation architecture including collaborative filtering, content matching and reinforcement learning. According to the method, a closed-loop recommendation process of user intention-intelligent recommendation-feedback optimization is realized, and the intelligent level of visual arrangement and the user experience are remarkably improved.

The invention discloses an intelligent low-code development method and system based on deep learning model optimization, and relates to the technical field of deep learning. A multi-dimensional domain knowledge graph is constructed, a three-dimensional space-time fusion training sample set is constructed based on the domain knowledge graph, and cross-modal feature alignment is performed on the training sample set, so that the multi-dimensional domain knowledge graph is constructed; the method comprises the following steps: generating an executable logic flow template, encoding the executable logic flow template into a Markov decision process, and performing joint strategy optimization on an optimization target of logic flow by integrating a feature importance index generated by a gradient back propagation path and a multi-target reinforcement learning framework of a Pareto leading edge analysis module. Extracting a strategy parameterization sequence after joint strategy optimization, injecting the strategy parameterization sequence into a dynamic verification sandbox environment, and performing abnormal mode detection and feedback type parameter distillation iteration on an execution track through an online variational auto-encoder to complete dynamic adjustment of the strategy; and the elasticity, the stability and the expandability of the low-code platform are improved.

The invention provides a rules engine that evaluates, modifies, and dynamically routes inputs to AI models based on provider restrictions and management rules. Provider restrictions are stored. Based on management rules and the detected provider restrictions, the input can be modified with contextual placeholders and prompts can be injected for input into the destination model. Reversal of the modifications can occur to the outputs. The system can notify the user, an administrator, and a supervisor regarding the security evaluation and remedial actions. The evaluations can be logged for auditing purposes.

The present invention provides a system and method for data processing in procurement and supply chain application developed by codeless platform. The invention includes one or more large language model (LLM) agents configured for processing one or more input received on an electronic user interface. The invention includes selecting a tool selection agent from a tool repository and invoking the selected tool by a tool execution agent for processing at least one task to be executed.

Methods and systems for generating digital threads that connect models and software tools for an interconnected digital engineering and certification ecosystem are provided. In one embodiment, a process for generating a software-code-defined digital thread includes receiving a first model representation of a first engineering model, a second model representation of a second engineering model, and an intent input. Determining, using a machine learning (ML) model, one or more model endpoints in the first and the second model representations. Generating, using a script-generating ML model, a platform orchestration script that accomplishes the intent input by connecting the first model representation and the second model representation, using the one or more model endpoints. Finally, storing the platform orchestration script as the software-code-defined digital thread. The digital threading of digital engineering models, with and without AI assistance, leads to exponential increases in efficiency of designing, testing, certifying, manufacturing, and operating physical products, allowing physical products to be made at digital speeds.

The invention provides a low-code dynamic configuration method based on a natural language, and relates to the technical field of natural language processing, and the method comprises the steps: carrying out semantic analysis on a natural language description input by a user through an intelligent agent, extracting a page type, a field attribute, a verification rule and interaction logic, and generating structured configuration data; dynamically generating JSON metadata for describing page layout, field components, verification rules and interaction logic according to a preset template on the basis of the structured configuration data; according to page function requirements, interface metadata matched with the JSON metadata is generated, and the interface metadata comprises a request address, a parameter list and corresponding SQL (Structured Query Language) operation logic; and analyzing the JSON metadata by utilizing a low-code configuration rendering engine, dynamically generating an interactive management function page, and modifying the JSON metadata in real time and re-rendering the page through natural language interaction between a user and an intelligent agent.

The invention provides a business process construction method combining a workflow engine and a dynamic form, which relates to the technical field of low-code platforms, and comprises the following steps of: establishing a data table according to a business, selecting and importing the data table in a code generation module, generating a basic code package, re-operating a main program compiling code package and loading the code package to a JVM (Java Virtual Machine) operating environment; creating a dynamic form on the basis of the entity class information of the code package through a visual form designer of the dynamic form module, and outputting dynamic form configuration in a JSON (JavaScript Object Notation) format; according to the method, the dynamic form and the process engine are combined, highly flexible business process design can be realized, a user can dynamically design the form and the process model as required to meet different business requirements, and the user-defined component can be designed according to a business data structure and common functions, so that the form can better adapt to a specific business scene, and the user experience is improved. And the user experience and the working efficiency are improved.

The invention provides an interactive integration method and system for an AI large model and a low-code platform, and relates to the technical field of interaction of the AI large model and the low-code platform, and the method comprises the steps: obtaining a task demand input by a user, analyzing the task demand into a structured task instruction, automatically matching an optimal AI model, and dynamically generating an adaptive API call parameter, according to the method, the AI large model and the low-code platform can be deeply integrated, the technical problems of integration complexity, interaction mode solidification, updating lag and the like are solved, and the method has the advantages that the method is simple and convenient to operate, and the method is suitable for popularization and application. According to the method, a user can call a complex model without AI professional knowledge, the low-code development efficiency is improved by more than 50%, and the security in a data processing process is enhanced by introducing a security verification mechanism, so that the integration efficiency of a low-code platform and an AI large model is greatly improved.

The invention discloses an automatic control code generation and verification method and device, equipment and a storage medium, and relates to the technical field of automatic control, the method is applied to a large language model, and a natural language command is received; performing matching retrieval on the vector database according to the natural language command to obtain an example code snippet; obtaining API structured information corresponding to the example code snippets from a knowledge graph database; generating an initial control code based on the example code snippet and the API structured information; and performing multi-stage virtual operation verification on the initial control code in the software motion control system, and generating a target control code according to a multi-stage verification result confirmed by a user for multiple times and the initial control code. According to the method, the initial control code is automatically generated through double-database retrieval based on the large language model, then multi-stage code verification is performed through the software motion control system, the target control code is generated in combination with verification results confirmed by a user for multiple times, and the code generation speed and reliability are improved.

Systems, methods, and computer-readable storage mediums for generating a user interface and user experience (UI / UX). The method comprises receiving user interaction data from multiple sources and creating one or more UI / UX elements and associated content based on the received user interaction data. The method also comprises receiving user response to the one or more UI / UX elements and the associated content; modifying at least one of the one or more UI / UX elements based on the received user response; and generating the UI / UX based on the modification.

The invention provides a multi-agent-based low-code component development method and system and electronic equipment, and the method comprises the steps: obtaining a user demand, and converting the user demand into an initial business model through a first agent; obtaining a prefabricated component library of the low-code platform, and automatically generating a demand component by using a second agent according to the initial business model and the prefabricated component library; automatically adapting and integrating a cross-platform data source by using a preset cross-domain data integration mechanism, and performing intelligent scheduling on the automatically generated demand component through a third agent; and establishing an automatic service arrangement mechanism based on the micro-service architecture, automatically identifying each service dependency relationship, and dynamically adjusting the execution sequence and resource allocation of each demand component according to the service dependency relationship. Through modular component design, the low-code platform has high flexibility and expandability, efficient collaboration of the components in the complex industry is guaranteed, and resource allocation and use efficiency is optimized.

The invention discloses an embedded system construction method and device, and the method comprises the steps: loading a configuration file which is a. Config file generated based on configuration parameters selected by a user through a visual configuration interface; when the CONFIGCONANAUTOUPDATE flag bit is detected to be in an activated state, the Conan warehouse data center is accessed, the latest compatible version of each Conan software package dependency item is inquired, and a semantic version constraint analysis algorithm is adopted to update a version identifier in the configuration file into the latest compatible version; generating a description file containing a dependency item list based on the updated configuration file, and generating a dependency lock file through a Hash locking mechanism; and importing the pre-compiled binary package into the construction environment, calling the corresponding pre-compiled binary package according to the version information and the Hash check value in the dependency lock file in the firmware compiling process, and executing hierarchical compiling operation.

Unified artificial intelligence (AI) agent, robotic process automation (RPA) robot, and agentic orchestration process (AOP) development applications are disclosed. In such embodiments, AI agents “coexist” in tandem with RPA robots that execute RPA automations. Automation developers can provide a natural language description of a process intended to be implemented by an AI agent, as well as prompts for a user. Developers can also select grounding context, service integration tools, RPA robots, and escalation attributes for the AI agent. When executed, the AI agent will implement a dynamic flow and attempt to perform its logic autonomously. If this is not successful, the AI agent can escalate to a human-in-the-loop, and the human resolution may be saved in memory for self-healing purposes. Developers can also develop AOPs that can utilize AI agents and RPA robots via a conductor application as well.

Techniques for leveraging a large language model (LLM) in software development are described. A description of a software development task is received from a user. Data associated with the software system is obtained from a data source. An LLM is prompted to identify at least one aspect of the task which requires clarification from the user, at least partly by providing the obtained data to the LLM and asking the LLM to identify a question for the user which remains unanswered by the obtained data. The question is presented to the user. An answer to the question is received from the user. The LLM is prompted to respond to propose an implementation of the task at least partly based on the data associated with the software system and the answer received from the user. The proposed implementation is received from the LLM and caused to be displayed to the user.

The system provides a permissions engine derives permissions for responses from artificial intelligence (AI) agents. The permissions are derived based on the access permissions of chunks used to generate the response, and the response is cached for reuse with users meeting the permissions requirements. The system can also derive an access permission level for a user attempting to access the cached result or other content. This can be based on a managed access profile that includes user behavior criteria and device criteria. Likewise, the system can validate and derive sensitivity levels of documents that are ingested for use in AI agents and discriminate between chunks to use in synthesizing results based on the derived permissions of the chunks and their relationship to the user.

A visual programming platform can leverage a machine learning-based coding system to generate an initial set of programming-language code for further graphical editing by a human user. As an example, the visual programming platform can obtain a natural language description of a task to be performed by a computational pipeline. The visual programming platform can process the natural language description of the task with a machine learning coding system that includes one or more machine-learned models to generate, as an output of the machine learning coding system, a set of pseudocode that describes performance of the task. The platform can process the set of pseudocode that describes performance of the task with a compiler to generate a set of programming-language code that defines the computational pipeline for performing the task. The visual programming platform can generate a graphical visualization of the computational pipeline defined by the set of programming-language code.

Systems and methods are described for building artificial intelligence (“AI”) agents. A server can provide a user interface (UI) that includes options to select and connect various agent objects. The agent objects can include prompt objects, dataset objects, model objects, and one or more code objects. A subset of the agent objects can be identified as available for selection based on evaluation of at least one management policy associated with an administrative user. A manifest file is generated based on the connected agent objects, and the manifest is validated against dependency rules to ensure that the stages of the agent meet prerequisites for the stages. Then, the server performs a simulated execution of an agent that corresponds to the validated manifest file, including an identification of at least one execution metric associated with the simulated execution.

The invention discloses a multi-person collaborative programming method and system, electronic equipment and a medium, and the method comprises the steps: providing a canvas task designer which supports the visual design of a plurality of canvases, and enabling each canvas to correspond to a subtask; sub-tasks designed in one or more canvases are stored as independent module files, and each module file can be developed by different developers; persistently storing the developed module file locally, and creating a module ID for the module file; and importing each module file into a unified project, and editing a data reference relationship and execution logic among the module files. According to the multi-person collaborative programming method and system, the electronic equipment and the medium provided by the invention, the whole project is divided into a plurality of independent sub-tasks through the canvas task designer, and each sub-task is stored in the form of the module file and can be independently developed by different developers.

The invention discloses a visual arrangement method for model tool calling based on an MCP, and relates to the technical field of artificial intelligence, the method is applied to a model context protocol MCP server, and the method comprises the following steps: receiving a first tool calling request sent by a client, the first tool calling request being generated by a large language model deployed on the client according to the MCP; the first tool calling request is analyzed through a preset unified agent tool and preset tool configuration information to construct a second tool calling request, and the tool configuration information is generated according to configuration operation of a preset visual arrangement interface on the MCP server; sending the second tool calling request to the external interface service, and receiving response information generated by the external interface service according to the second tool calling request; and constructing a request result according to the MCP and the response information, and sending the request result to the large language model. According to the method, the model tool calling efficiency is improved.

The invention discloses an intelligent access API generation method, system and device and a medium, and the method specifically comprises the steps: generating a dynamic dragging interface based on user role information, and obtaining function access parameters customized by a user through the dynamic dragging interface; according to semantic features and operation sequences in historical user interaction data, user intentions are analyzed, and target data topic logic is generated in combination with function access parameters; converting the subject logic of the target data into an executable query statement by utilizing an SQL splicing tool, and recommending an index optimization scheme to a database engine based on a high-frequency condition and field combination in a historical query mode; and distributing the optimized executable query statements to Serverless workflow nodes adapted to different query engines for execution, and packaging returned data into a standardized API (Application Program Interface) for output. According to the method and the device, full-process automation from parameter customization by a user to standardized API output is realized, and the efficiency and the flexibility of data acquisition are remarkably improved.

An integrated development environment (IDE) for designing, programming, and configuring aspects of an industrial automation system uses a generative artificial intelligence (AI) model and associated neural networks to generate portions of an industrial automation project in accordance with functional requirements provided to the industrial IDE system in intuitive formats, such as spoken or written plain language text. The system uses generative AI to translate plain language requests or functional specifications into industrial control code, human-machine interface (HMI) applications, device configuration settings, or other aspects of an industrial control project.

The invention provides a cross-platform low-code application development system based on a Flutter framework. Comprising a login registration module, a project management module, an interface design module, a state management module, a route management module, a team management module, a help document module, an AI page design module, a development document generation module, a hardware integration module, a code generation module, a multi-user collaborative development module, a communication module, an online simulator module, an AI generation application module and an interface management module. An asset management module, a developer forum module, an application market module and an application packaging module. The communication module supports various communication protocols and secure transmission modes, and ensures that data exchange applied to cross-platform and cross-system environments is secure and reliable. Through component dragging and graphical configuration, the low-code platform automatically analyzes configuration data to generate an App, a development document and a source code. And meanwhile, multi-person collaborative development is supported, so that the development threshold is reduced and the development speed is increased.

The invention provides a conflict resolution method and system for realizing multi-person collaborative editing based on WebSocket and JSON-PATCH, and relates to the technical field of I T and software development, the method comprises the following steps: obtaining an editing operation of a user at a client of a low-code platform, and converting the editing operation into operation information in a JSON format; the method comprises the following steps: receiving operation information in a JSON format sent by a plurality of clients through a WebSocket server; storing the received operation information in the JSON format in a database according to a user account; based on the operation type defined by the JSON-PATCH, conflict detection is carried out on the concurrent operation of the multiple users; according to a preset priority rule, carrying out merging processing on the detected conflict operations; a result obtained after combination processing is broadcasted to all the clients through a WebSocket server; and the client receives the broadcast combination result and updates the low-code platform interface. According to the method, the user operation is formatted by using the JSON-PATCH, so that operation conversion is realized to solve conflicts.

In various examples, an interactive agent platform that hosts development and / or deployment of interactive agent may include an interpreter that generates interaction modeling API events specifying commands to make agent (e.g., bot) expressions, poses, gestures, or other interactions or movements, which may be translated into corresponding agent animations. The interpreter may generate an interaction modeling API event using a standardized interaction categorization schema, which an action server implementing an animation service may use to identify a corresponding supported animation. The animation service may implement an action state machine and action stack for all events related to a particular interaction modality (e.g., bot gestures), connect with an animation graph that implements a state machine of animation states and transitions between animations, and instruct the animation graph to set a corresponding state variable based on a command to change the state of an agent movement represented by an interaction modeling API event.

The invention provides a semantic fusion graphical software development management method and system. The method comprises the following steps: acquiring a multi-user interaction behavior data flow containing an operation track sequence and an interface control triggering time sequence; according to operation track sequences of different users and a time-space association mode of an interface control triggering time sequence, constructing a dynamic semantic map; inputting a hierarchical mapping relationship in the dynamic semantic map into a preset edge computing node, and generating a real-time computing power distribution scheme matched with a logic dependency chain in the dynamic semantic map; analyzing a mapping relationship between a control triggering timestamp and a control type in the space-time association mode, and generating an interface control rendering priority queue; the synchronous regulation and control interface control renders the priority queue and an operation instruction response sequence corresponding to the logic dependency chain, and generates a graphical software development process synchronous instruction set; according to the method, the hierarchy and the dependency chain are fused to form the dynamic semantic graph, the hierarchical operation structure and the logic dependency relationship are constructed, and the high efficiency and the consistency of the graphical development process are ensured.

Currently, there is no means for a user to build an integration process using natural language. Accordingly, in an embodiment, a user submits a user request via natural language (e.g., via interaction with a screen-based or voice-based chatbot). Embodiments wrap the user request with a contextual wrapper to generate a prompt, submit the prompt to a generative AI model (e.g., large language model) to produce an actionable set of objectives, and then programmatically and recursively generate software instances of each necessary component, including the final integration process, based on the set of objectives and using the generative AI model.