87 results about "Enterprise application" patented technology

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 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 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 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.

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.

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.

Embodiments described herein are generally related to data analytics environments, and are particularly directed to a system and method for enterprise application decision capture and learning. The described approach can be used to capture information about decisions made within an organization, and provide feedback to machine learning or other processes, for use in forming an understanding of the decisions, or generating recommendations for future actions. Decision objects can be used to capture and store decision records in a decision store, which indicates the fact that a decision has been made, together with a decision context. The decision store can include information descriptive of decisions made within various analytic applications, such as for example, ERP, HCM, HR, or SCM applications, or within a larger enterprise ecosystem, to provide a bottom-up approach in learning from the decisions and actions that users take within the organization.

The invention provides an intelligent management monitoring method for an enterprise application tutoring process, and the method comprises the steps: uploading a project approval book to a tutoring process monitoring system, obtaining a project name keyword change record, a technical route description text of each version, and an inter-version modification time interval, and obtaining a version evolution record of the project approval book; according to the version evolution record, identifying item name revision times, keyword change frequency, technical route change times, and assessment item positioning swing frequency and direction deviation times; analyzing the corresponding relationship between the item name revision times and the technical route change times, identifying the synchronization degree of positioning adjustment and scheme change, and determining an item maturity grading list in combination with the swing frequency and the direction deviation times; and comparing the reasonable period of project direction adjustment with a preset research and development period, evaluating conformity difference and technical argumentation missing items, and determining a priority ranking scheme with perfect technical description and innovation point extraction.

Described herein are exemplary devices, apparatuses, systems, methods, and non-transitory storage media for providing an application framework. The application framework can provide various machine-learning models to perform a variety of analysis tasks to analyze enterprise data such as communications between one or more employees of an organization and one or more clients of the organization and provide intelligence and insights for a user in the organization. The insights and intelligence can include a recommendation or an observation related to a client or customer of the organization. The recommendation or observation can be provided, for example, in a communication platform, a chatbot, or a variety of other interfaces. Advantageously, to perform an analysis task, the application framework automatically provides to the machine-learning model(s) information in accordance with the enterprise's data sharing and access control requirements to prevent inappropriate access and use of sensitive information.

This invention provides a universal interface and application integration method based on multiple factories and applications, belonging to the field of enterprise application integration technology. This universal interface addresses problems such as inconsistent protocols, inconsistent data standards, poor scalability, and low synchronization efficiency in the integration of heterogeneous application systems between the headquarters and multiple factory sites of a group enterprise. This invention constructs a three-layer integration architecture, establishes a standardized master data system, designs a universal interface gateway, and configures flexible data interaction rules to achieve efficient data sharing and business collaboration across levels and systems. Through standardized encapsulation, multi-protocol adaptation, intelligent routing, and dynamic synchronization strategies, this invention reduces the development and maintenance costs of system integration, improves integration flexibility and data transmission efficiency, and ensures data security and consistency, making it suitable for the application integration needs of group enterprises in multiple scenarios.

The invention discloses an authority management method and device based on Next.js, equipment and a medium, and relates to the technical field of front-end development, and the method comprises the steps that middleware obtains an access request of a target user side to a target server; analyzing the access request to obtain a corresponding analysis result, and determining whether the target user side has a corresponding access permission or not based on the analysis result through the permission policy engine; and when the target user side has the access permission, acquiring a page permission corresponding to the access request from the target server, and injecting the page permission into a page component of the target user side to obtain an injected component, so that the target user side determines a target high-order component based on the injected component, and sends the target high-order component to the target server. And completing a preset page interaction operation on the injected component based on the target high-order component. According to the invention, an efficient authority management scheme suitable for enterprise Web applications can be realized.

The invention provides an enterprise application component building method and system based on a large language model, and the method comprises the steps: S1, obtaining a user component demand, carrying out the analysis, generating a structured demand document, and determining whether to enter a step S2 or not according to a user demand confirmation result; s2, generating a core function module of a component through a component generation engine based on the structured demand document confirmed by the user; s3, matching a corresponding interface template from a preset universal interface library according to an adaptive system list in the structured demand document, and generating an interface adaptive code; and S4, deploying the core function module and the interface adaptation code to a preview environment for a user to preview, and selecting to automatically package and generate a component installation package or re-executing the steps S1, S2 or S3 according to a user preview confirmation result. According to the method, a full-link automation mechanism of'natural language demand-component generation-interface adaptation 'is constructed, so that the component building threshold is reduced, and rapid adaptation of the component and the existing system is realized.

Various embodiments of the teachings herein include a method for enabling an enterprise communication system to contact a device application of a device with a transceiver connected to a 5G communication network. An example includes: requesting a PDU session from the 5G communication network; creating the requested session; adding information about the new session to a data base; informing the enterprise application about the new session; inferring the device identifier from the information provided; if the identifier matches a list of identifiers or application types, contacting the device application using the information; if the identifier is an IPv6 address provided to the wireless transceiver by the device application or a device middleware, the IPv6 address containing the device application identifier to ensure a one-to-one relation between the IPv6 address and the device application identifier.

This invention describes methods and systems for utilizing blockchain-verified sparse language models (SLMs) in enterprise applications. Sparse models address the limitations of large language models (LLMs) by reducing overfitting, bias, and complexity, while blockchain integration ensures result verification and maintains immutable records of model operations. The system employs smart contracts for automated validation and distributed consensus mechanisms to verify model outputs. This comprehensive approach leads to improved transparency, trust, and cost-efficiency through both model sparsification and blockchain-based accountability, making these verified SLMs particularly suitable for high-risk applications. The invention's combination of sparse modeling techniques with blockchain verification creates a robust framework for deploying trustworthy and efficient language models in critical enterprise environments.

The present invention provides a system and method for managing one or more dynamically changing domain model of one or more applications developed by codeless platform. The invention includes API configured to adjust to dynamic changes in the domain model based on modification of the one or more applications.

A method for training a machine learning model using positive and negative synthetic data is implemented via a computing system including a processor. The method includes generating synthetic data using a generative pre-trained transformer bidirectional language model and self-supervising the generated synthetic data based on positive traits including rule-based criteria and / or model-based criteria. The method also includes generating a set of positive synthetic data labels with gradient scale rating based on the self-supervised synthetic data, synthesizing a set of negative synthetic data labels by self-supervising the positive synthetic data labels, and training a machine learning model using the set of positive synthetic data labels and the set of negative synthetic data labels. Another method further includes utilizing the trained machine learning model to generate Objectives and Key Results (OKRs) within the context of an enterprise application.

The present disclosure generally relates to systems and methods for intelligently predicting which tests to run on modified source code of an enterprise application. More specifically, the present disclosure relates to systems and methods that build a model using machine-learning algorithms to predict which tests to run on modified source code. The prediction may be based on the modification made to the application code of the enterprise application.