130 results about "Enterprise application" patented technology

The present invention provides a large language model-based system and method for data processing in application developed by codeless platform. The invention includes identification of intent of a user to process procurement, supply chain, application integration, application restructuring or development scenarios.

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 an automatic resource planning method, system and device for hybrid clouds and a medium, and the method specifically comprises the steps: determining a business change trend through employing a time sequence analysis algorithm, and obtaining a demand prediction model; obtaining dynamic resource demand parameters from the demand prediction model, judging a resource allocation proportion of the private cloud and the public cloud, and determining an initial configuration scheme of a cross-cloud resource pool; based on the initial configuration scheme, a reinforcement learning algorithm is adopted to optimize resource scheduling dynamics, and a real-time scheduling plan is obtained; specific parameters of resource allocation are extracted from the real-time scheduling plan, whether the requirements for safety and flexibility are met or not is judged, and a final scheduling instruction is determined; and based on the final scheduling instruction, generating a configuration script and an automatic deployment file for the cross-cloud environment by adopting a preset template engine. According to the method, the hybrid cloud resource delivery efficiency and the resource utilization rate are improved, and deepening and breakthrough of the hybrid cloud technology in enterprise application are promoted.

The embodiment of the invention provides an enterprise-oriented document question and answer model training method and system, and the method comprises the steps: obtaining general supervision fine tuning data, general document question and answer data and enterprise internal data; mixing the general supervision fine tuning data, the general document question and answer data and the enterprise internal data according to a predetermined proportion to obtain training data; and on the basis of the training data, performing supervised fine tuning training on a basic large language model according to a unified document question and answer prompt word template to obtain a document question and answer model. According to the embodiment of the invention, the accuracy, controllability and rejection capability of the document question and answer model are remarkably improved, the defects of an existing RAG system in an enterprise application scene are overcome, and the method is more suitable for enterprise-level knowledge question and answer tasks.

The invention discloses a large model and RAG-based file generation method and device, and the method comprises the steps: carrying out the deep learning and semantic analysis of an uploaded bidding document file through a large model LLM, extracting core project information, and judging the business field and industry category; combining the information, and screening a matching template by referring to an industry knowledge graph and a set bidding document template database to generate a bidding document directory outline; then, according to the outline, the LLM extracts data from a set material library for filling, and a first draft of the bidding book is generated; then, the NLP technology is used for auditing the first draft, the problem of the consistency between the waste bidding factors and the terms is checked, correction suggestions are given, and the correction suggestions are corrected by editors to obtain a corrected bidding book; and finally, adjusting the format of the revised version, optimizing typesetting, performing manual review, and outputting a final bidding book manuscript. The bidding document compiling efficiency and quality can be improved, compliance is guaranteed, the enterprise application threshold is reduced, and enterprises are assisted to obtain advantages in bidding.

The embodiment of the invention discloses a method and a system for realizing enterprise collaborative decision-making based on an AI algorithm and a government-enterprise API (Application Program Interface). The method comprises the following steps: receiving input data interacted by using a natural language; converting the input data into standard format data; determining a service type related to the input data by using an AI model; the service type comprises public service and enterprise application service; the public service and the enterprise application service are both associated with one or more subtasks; determining a thinking chain providing the service; the sub-tasks contained in different thinking chains are different, or the execution sequences of the sub-tasks of different thinking chains are different, or the execution proportions of the sub-tasks contained in the thinking chains are different; planning an optimal thinking chain; the optimal thinking chain corresponds to an optimal execution path; the optimal execution path comprises one or more execution nodes which are arranged in order; and calling the API according to the optimal execution path, so that the called API provides the service required by the user based on the standard format data.

The invention provides an enterprise-level application rapid development and deployment method and system based on model driving, and relates to the technical field of data processing, the method comprises the following steps: obtaining structured modeling data, carrying out semantic disassembly and relationship classification, and constructing an intermediate model structure; performing traversal analysis on the flow path to generate an execution mapping configuration table; extracting a form type, a data field definition and subsequent flow path information corresponding to each flow node, and matching and generating a front-end rendering component file and a rear-end interface description file in combination with a preset front-end template library and a service interface template; automatically splicing the front-end page code and the rear-end controller code by using a template engine to generate a target deployment structure; executing code construction and dependent compilation operation, and outputting a deployment package file conforming to enterprise-level application specifications; according to the method, the autonomy and the accuracy of rapid development and deployment of enterprise-level applications are improved.

The present invention provides a data network, a data ingestion system and a method of data ingestion in the data network for a supply chain management enterprise application. The data network includes one or more data objects of different data types received from different data sources structured on multiple distinct architecture, connected to each other for executing multiple functions in the enterprise application.

Techniques for leveraging the MASQUE protocol to provide remote clients with full application access to private enterprise resources are described herein. One or more network nodes may be configured to execute a MASQUE proxy service to provide a remote client device with full access to an enterprise / private application resource executing on an application node and hosted in an enterprise / application network, behind the MASQUE proxy service. In some examples, the MASQUE proxy service may execute on a single proxy node hosted at an edge of a cloud network or at an edge of an enterprise network. Additionally, or alternatively, a first instance of the MASQUE proxy service may execute on a first proxy node hosted at an edge of a cloud network (e.g., an ingress proxy node) and a second instance of the MASQUE proxy service may execute on a second proxy node hosted at an edge of the enterprise network.

The disclosure comprises systems and methods for migrating an application between platforms, the method comprising identifying a legacy application for transfer from a legacy mainframe to an enterprise platform. The method can include determining legacy data associated with the legacy application. The method can include receiving the legacy data and the legacy application into the enterprise platform. The method can include determining a classification of the legacy application. The method can include implementing an emulator application wherein the emulator application is configured to generate at least one integration application associated with the classification of the legacy application. The method can further include generating an enterprise application on the enterprise platform based on the legacy data, the legacy application, and the classification of the legacy application.

A computer-implemented method for management of an application on an enterprise network which accesses external networks via a web proxy. The method comprises obtaining enriched metadata concerning an application executed on the enterprise network, the enriched metadata including at least source code information and ownership information, identifying application traffic on the enterprise network based on proxy log data, source IP and destination URL, generating an access control list (ACL) based on the enriched metadata and identified application traffic, the ACL including a source address of the application and a list of allowed destination addresses, converting the ACL into a proxy policy that can be processed by a web proxy to permit access by the application to the destination addresses in the ACL, and establishing data communication between the application and an external network based on the proxy policy.

Techniques are provided for an isolated test environment for ransomware analysis. One or more enterprise applications are assigned to one or more server systems to be provisioned in an isolated test environment. An enterprise replica is generated in the isolated test environment by provisioning the one or more server systems and deploying the one or more enterprise applications on the one or more server systems. A kernel monitoring component is deployed on the one or more server systems in the isolated test environment. The kernel monitoring component is configured to generate kernel telemetry data for a plurality of system calls initiated by processes executing on the one or more server systems. A selected ransomware variant is deployed in the isolated test environment. An effect of the selected ransomware variant on the enterprise replica is determined based on analyzing the kernel telemetry data.

The present invention provides a system and method for multi AI agent driven data processing in enterprise application developed by codeless platform. The invention includes one or more AI agents configured for processing one or more input received on conversational assistant interface. The invention includes triggering contextual processing of received input by orchestration agent interacting with multi-AI agents for executing a task identified from the received input.

Systems and methods are disclosed for dual, simultaneous, single-session, proximity-based, secure authentication of a Digital Assisted Shopping (DAS) representative and a customer in an unsecured remote location. The method includes installing a mobile banking application on the customer's device and an enterprise application on the DAS representative's device, both with biometric verification. Proximity detection using Bluetooth Low Energy (BLE) initiates a secure session via push notifications. A secure communication channel is established through a secure local handshake, involving encryption key exchange and mutual authentication. The system exchanges data related to customer profiles and financial accounts, continuously monitors geolocation using GPS, Wi-Fi, and cellular data, and performs periodic background biometric re-verifications. AI / ML algorithms analyze customer data to propose financial products and services, which are securely shared with the customer for review and selection. The system facilitates real-time enrollment and transaction processing, terminating the session upon detecting security breaches.

The present invention provides a large language model (LLM) based development application framework and a method for codeless development of one or more enterprise application. The invention includes a layered platform architecture for supporting and executing development of SCM applications. The development framework includes one or more LLM agents for executing various operations in the enterprise application.

The present invention discloses an enterprise human resources comprehensive management platform, which relates to the field of data management technology and includes: a resource management system determination module for determining a resource management system; a management logic chain determination module for determining a management logic chain; a task management strategy determination module for determining a task management strategy; a sensitive management mode setting module for setting a sensitive management mode for sensitive data classes; a sensitive management strategy determination module for determining a sensitive management strategy; and a pre-management task control module for executing and controlling pre-management tasks. The platform solves the technical problems of the existing enterprise human resources management, such as the difficulty in coping with complex and changing enterprise management needs, the lack of integration of multiple enterprise application systems, and the risks of sensitive data management, which lead to the inaccuracy and security of enterprise human resources management. The platform intelligently realizes the comprehensive integration and efficient use of human resources information, achieving the technical effect of improving the accuracy and security of enterprise management.

A generative AI framework for an enterprise may utilize a cloud-based generative AI operational environment to execute LLMs. A mini-platform library data store contains electronic records associated with a plurality of potential generative AI mini-platforms, and a workflow function library data store contains functions usable to customize managed workflows. A plurality of active enterprise mini-platforms may each be based on a potential generative AI mini-platform and have a customized managed workflow for an enterprise use case. An enterprise application integration component coupled to the cloud-based generative AI operational environment and the active enterprise mini-platforms facilitates model routing and orchestration to support the LLMs. The integration component may also interface between the cloud-based generative AI operational environment and the active enterprise mini-platforms to provide access to enterprise data that is processed via customized managed workflows.

The invention relates to the field of enterprise application integration, in particular to a supply chain finance-oriented multi-protocol conversion and semantic verification system and method, which comprises a protocol adaptation layer, a core processing layer, a management support layer and a distributed data storage layer, the protocol adaptation layer adopts a plug-in and metadata driving design, and a protocol plug-in is an independent JAR packet and supports multi-protocol encoding and decoding and hot updating; the core processing layer realizes data standardization and financial-level semantic verification through a general message model, a dynamic routing engine, a visual data conversion module and a rule engine; and the management support layer provides a low-code portal and supports rule configuration, simulation testing and full-link monitoring. According to the technical scheme, through the seven steps of configuration, decoding, routing, conversion, verification, coding and auditing, the method adapts to a multi-specification heterogeneous system, data intercommunication accuracy and compliance are improved, docking cost is reduced, and supply chain financial service requirements are met.

A configuration information management system is provided for secure update of dynamic configuration data in a containerized execution environment. A configuration container may be maintained within the environment separately from an application container hosting a secure enterprise application. The secure enterprise application may require secure configuration information for interaction via a secured connection with one or more remote endpoints from within the application container. The configuration container hosts a service that is configured to maintain the secure configuration information on behalf of the secure enterprise application, allowing the secure enterprise application to not persist secure configuration information within the application container.

The invention discloses a data integration method, system and device, a storage medium and a program product, and relates to the technical field of enterprise application integration, and the method comprises the steps: receiving a data access interface and a debugging command defined by a user based on an SQL template through an integration platform of an SaaS end, and the debugging command is issued to a front service client of an enterprise local system through a secure communication channel. And the front service client constructs an actual SQL statement according to the debugging command in combination with the input and output parameters in the SQL template, and executes and returns debugging result information to the integrated platform for display when accessing the database. And after the user confirms that the SQL interface information is correct, the platform issues the SQL interface information to the front service client for analysis and storage. According to the scheme, efficient and safe data integration between the old enterprise system and the SaaS application is achieved through an integrated architecture based on cooperation of a remote debugging mechanism of an SQL template and a safe data channel.

The present invention provides a data processing system and method for managing rules in one or more enterprise applications developed by codeless platform. The invention includes large language model (LLM) based intent detection for rule identification, creations, updating or deletion. Further, the invention determines conflicts in execution of rules by identifying the impact of execution of the rule on one or more functions of the application.

The described technology relates to integrating events electronically scheduled in enterprise web applications and other event applications. A capability is provided for events created by an enterprise web application and events from other external event streams to be presented in a consolidated calendar in the enterprise web application. Capabilities are also provided for sharing the calendar among enterprise users and non-enterprise users, and for efficiently generating the shared calendar.

This application discloses a dynamic form construction method, device, and medium, relating to the fields of software engineering and enterprise application development technology. The method includes: receiving a form request containing a view model identifier and request type based on a preset view parsing and execution engine; parsing a predefined view model according to the identifier to obtain its metadata, which defines the target business entity, view data object structure, field set, and filtering conditions; for data query requests, dynamically constructing and executing SQL statements based on the aforementioned metadata, obtaining raw data, performing aggregation and transformation, and generating structured view data that meets the front-end rendering requirements for return; for data submission requests, converting the data submitted from the front-end back into domain model data according to the same view model, and calling the business entity engine service to complete business logic verification and persistent storage, thereby achieving dynamic generation of the form view, decoupling of front-end and back-end logic, and ensuring business consistency.

A Real-Time Enterprise Conversation Analyzer and Decision Enabler (RECA&DE) that enhances the capabilities of enterprise applications, transforming them from transactional or operational support systems into decision support systems. In one aspect, the RECA&DE is capable of performing the following: generating a first graphical user interface for implementing a decision support module within an enterprise application, obtaining data for a discussion event from the enterprise application based on input received via the first graphical user interface, transferring event data to a discussion service, receiving feedback data from an artificial-intelligence platform, the feedback data comprises sentiment data derived by the artificial-intelligence platform based on transcript data generated from conversations of the participants using the discussion service, analyzing the feedback data, and rendering one or more dashboards in a second graphical user interface based on analyzing of the feedback data.

A method, apparatus and computer program product provides for zero configuration service registration in association with a network-accessible infrastructure that hosts services on behalf of an enterprise. A service is required to be registered with an authority before being online. The technique is implemented in a network-based automated bootstrap mechanism. In operation, and responsive to a determination that a new service instance is required to be brought online, a determination is made whether a peer service instance has an existing registration with the authority and is active. If so, and without requiring manual intervention, that existing registration is then leveraged on behalf of the new service instance to automatically register that instance with the authority. After automatically register the new service instance, that instance is then executed in the network-accessible infrastructure. Several automated bootstrap mechanisms are described. In one embodiment, the service instance is a connector to an enterprise application.

A system for AI-powered employee engagement with real-time integration into enterprise applications; the system includes: a signal acquisition module configured to capture structured and unstructured employee-related data from enterprise platforms, including human resources information systems, communication platforms, and project management tools; a preprocessing unit configured to anonymize, normalize and embed the ingested data by applying field-level encryption, irreversible hashing of identifiers and transformer-based vectorization of text inputs into high-dimensional embeddings combined with standardized tabular metrics; an inference engine comprising a transformer-based sentiment model, a graph neural network model, and an attention-based temporal predictor, wherein the inference engine is configured to compute a dynamic engagement score and provide explainable attributions for engagement fluctuations; an action recommendation engine configured to map the engagement score and attribution vectors to a library of intervention policies, the action recommendation engine further comprising a reinforcement learning module configured to refine intervention strategies based on closed-loop feedback; a data protection controller configured with federated learning aggregation, differential privacy noise injection, and consent management services to ensure compliance with legal frameworks while enabling distributed model updates; and a visualization dashboard that offers role-aware organizational analyses, drill-downs to root causes, and predictive simulations for managers.

The invention discloses a method for matching a service scene and a fine tuning data set in a vertical field. The method comprises the following steps: determining service scene characteristics of the vertical field; selecting a fine tuning data set type according to business scene characteristics; matching a logical reasoning structure according to the fine tuning data set type; and configuring question-answer pair types according to the logical reasoning structure. According to the method, a four-layer technical matching framework of'business scene characteristics, data set types, logical reasoning structures and question and answer pair types' is constructed, so that data set construction is always performed around business requirements, the integrating degree of the data sets and business targets can be remarkably improved, the enterprise AI application threshold is effectively reduced, and the trial and error cost is controlled. Therefore, the method has important engineering practical significance and wide engineering application prospects.

The present invention provides a platform architecture, a development application framework and a method for codeless development of one or more Supply chain management (SCM) enterprise application. The invention includes a layered platform architecture for supporting and executing development of SCM applications. The platform and method provide interaction of an SCM application user, a citizen developer user and a platform developer user with one or more layers of the platform architecture for codeless development of the SCM applications.

A method, computer program product, and computer system for monitoring a plurality of interdependent tasks, wherein the plurality of interdependent tasks includes tasks from different software applications including, but not limited to, client applications and enterprise applications. A workflow of the plurality of interdependent tasks may be controlled to. A workflow of the plurality of interdependent tasks may be controlled to ensure the plurality of interdependent tasks run in a correct sequence. It may be determined that a task among the plurality of interdependent tasks has failed to complete. An action may be executed based upon, at least in part, determining that the task among the plurality of interdependent tasks has failed to complete.

The application discloses a data blood relationship mining method and device of an advanced enterprise application programming language, and the data blood relationship mining method comprises the following steps: performing word segmentation processing on the code of the advanced enterprise application programming language to obtain a mark list; wherein the word segmentation processing is performed in the minimum unit of words and symbols; the mark list is parsed according to the grammar rules of the Backus Normal Form to obtain a semantic expression list, and the semantic expression list comprises grammar keywords and grammar contents; the semantic expression list is formatted according to the grammar rules of the Backus Normal Form to obtain a semantic expression dictionary list; the semantic expression dictionary list is parsed, nodes are created, the blood relationship between different nodes is found and saved. The ABAP program code is subjected to fine-grained word segmentation processing, and semantic analysis is performed on the basis of the fine-grained word segmentation processing, so that the association relationship between data is finally obtained, and the blood relationship between nodes is determined.