161 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.

Systems and methods for receiving an enterprise resource dataset associated with a customer from an enterprise application associated with the customer, the enterprise resource dataset including a plurality of financial inputs captured on or before a first date, a plurality of customer recommendations, and a plurality of recommendation replies, receiving, from a first machine learning model, a predicted customer state including one or more predicted financial outputs corresponding with the predicted customer state at a second date subsequent to the first date, providing the plurality of financial inputs to a second machine learning model trained to predict a customer recommendations, determining, a customer recommendation corresponding to an action to be performed at a third date subsequent to the first date, wherein performing the action causes the first machine learning model to predict an updated customer state differing from the predicted customer state.

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.

Systems and methods for receiving an enterprise resource dataset includes a plurality of financial inputs captured associated with a customer from an enterprise application associated with the customer, providing the plurality of financial inputs to a machine learning model trained to predict a customer state using a plurality of training financial inputs and a plurality of training financial outputs, each of the plurality of training financial inputs corresponding to financial data and each of the plurality of training financial outputs corresponding with financial data, determining, responsive to providing the plurality of financial inputs to the machine learning model, a future customer state of the customer, including a predicted financial output, at a fourth date, and generating one or more financial recommendations based at least partially on the determined future customer state of the customer.

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.

A system and method for enabling fine-grained access to enterprise applications in an enterprise network. The method includes receiving, via a discovery application running on a computing device, user input from a user, where the user input identifies at least one of an application name, a server name, or an Internet Protocol (IP) address associated with a computing resource for accessing an enterprise application; comparing, via the discovery application, the user input to application information stored in a database accessible by the discovery application; sending, via the discovery application, a message to the user in response to determining a match between the user input and the application information, where the message identifies a membership group containing the computing resource for accessing the enterprise application; and enabling the user to access the membership group using the computing resource when the user is a member of the membership group.

Solutions for integration and ongoing management of enterprise applications operating in a networked environment are disclosed. An AI transformer extracts, transforms, and manages integration point information from various sources, including documents, UMLs, and other system diagrams. Instead of relying on traditional documentation, the invention utilizes blockchain-based NFTs (non-fungible tokens) to store and manage integration point information. NFTs are created using smart contracts, which define the rules and conditions for the NFT. A metadata engine prepares metadata for generating the Integration Point NFTs. An NFT manager creates the integration point NFTs. A change analyzer determines the impact of changes to integration points on different applications and functionalities. A release compliance component is integrated with DevOps and is responsible for ensuring that integration point changes are incorporated into all impacted applications according to the release defined in the NFT.

The present invention provides linkedchain, control tower and blockchain for one or more enterprise applications. The system includes a linked chain within a control tower configured to connect with blockchain and non-blockchain based entity systems.

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.

Quantum computers pose significant threat to modern day cryptography. To mitigate this risk, migration of enterprise applications to a quantum safe state is needed. Crypto inventory, an automatic process to discover all crypto application programming interfaces usage in an enterprise application, is a first problem to be addressed in the migration journey. Embodiments of the present disclosure provide a method and system for a dynamic generation of rules. An input program statement includes an unseen crypto application programming interface (API) is received which represents a method invocation. The invoked method is mapped by the input program statement with a rules database to infer and generate new rules for an unseen crypto application programming interface (API) belonging to (a) a same library by a weighted distance approach, (b) a library implementing known contracts by a graph traversal approach, and (c) a library implementing unknown contracts by a pattern matching approach respectively.

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.

A system for integrating an enterprise asset management (EAM) system with enterprise applications implements a dataflow studio and an endpoint catalog. The dataflow studio provides a no-code graphical user interface for building and configuring customized business-specific data and process flows between the EAM system and enterprise applications, wherein the dataflow studio integrates with the EAM system via an API for sending records to the EAM system and for receiving records from the EAM system. The endpoint catalog provides a centralized registry of enterprise applications that are available to the dataflow studio as endpoints for creating and managing application connections for data integrations between the EAM system and the enterprise applications. A 360 transaction view component may be included to integrate data events from the dataflow studio into a user- comprehensible flow to provide visibility into data and process flows both within the EAM system and within the dataflow studio.

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.

Systems, methods, and computer readable medium are provided that relate to, among other things, establishing data structures that quantify test-determined parameters related to identifying and improving core decision making skills, implementing adapters that interface with enterprise application services (and data) to identify signals relevant to the parameters from the user activity within the enterprise service, implementing in conjunction with these feature a private online workspace, and providing training through the workspace based on the signals determined from the (other) enterprise services. Signals can be automatically determined by interfaces to other enterprise application services such as email, employee feedback, employee evaluation, and other systems.

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.