Artificial intelligence-supported centralized metadata and automation system

Abstract

The present invention relates to a system (1) which enables end-to-end automation and orchestration of products owned within organizations and third-party products with synchronous and asynchronous communication support.

Description

[0001] ARTIFICIAL INTELLIGENCE-SUPPORTED CENTRALIZED METADATA AND AUTOMATION SYSTEM

[0002] Technical Field

[0003] The present invention relates to a system which enables end-to-end automation and orchestration of products owned within organizations and third-party products with synchronous and asynchronous communication support.

[0004] Background of the Invention

[0005] Today, while automation processes proceed through services on the SMS (Service Management System), post-retirement SMART (Enterprise Ticket Service) and SCKS (Enterprise Configuration Service) integrations have become a significant challenge for teams. Especially the problems in SMART - L7 (Layer 7) integration are on the agenda of many teams and this situation adversely affects productivity. In addition, the fact that the automations performed by some teams are not in the CI-CD (Continuous Integration and Continuous Delivery) flow causes serious problems in development processes and problem identification, which disrupts workflows.

[0006] For this reason, it is understood that there is a need for a system which enables end-to-end automation and orchestration of products owned within organizations and third-party products with synchronous and asynchronous communication support.

[0007] The Korean patent document no. KR102425731, an application included in the state of the art, discloses a system for process execution of a hyper-automation solution based on artificial intelligence. In the said invention, an agent system for requesting process execution of a hyper-automation solution based on artificial intelligence is described. In the invention, the problems to be solved are described as to establish work automation quickly and reliably in addition to process execution of an artificial intelligence-based hyper-automation solution in which individual automation solutions (RPA, chatbot, OCR, ERP, etc.) are integrated, based on an intelligent workflow for a work process including unstructured work and unstructured data which are continuously increased around medium and large enterprises, and to solve the problem that an unexpected error stops the process during process execution. For example, in the invention, the agent system for requesting process execution comprises: a process execution request reception unit for receiving a request for execution of a process composed of automation solutions including RPA, OCR, e-mail, chatbot, SAP, and Open API from a task requester or a system; a database storing metadata of unit services, which are units constituting the details of an automation process, and a data collection unit each collecting data necessary for execution of the automation solutions from the automation solutions; a system load evaluation control unit for calculating a risk value for an operating system of a computer device based on a linear regression algorithm by using the data collected through the data collection unit, and evaluating a load of the operating system; and a process execution request unit for requesting execution of the automation solutions according to the risk value provided as a result of the load evaluation by the system load evaluation control unit.

[0008] Summary of the Invention

[0009] An object of the present invention is to realize a system developed with the aim of enabling end-to-end automation and orchestration of products owned within organizations and third-party products with synchronous and asynchronous communication support.

[0010] Detailed Description of the Invention “Artificial Intelligence-Supported Centralized Metadata and Automation System” realized to fulfil the objective of the present invention is shown in the figure attached, in which:

[0011] Figure l is a schematic view of the inventive system.

[0012] The components illustrated in the figure are individually numbered, where the numbers refer to the following:

[0013] 1. System

[0014] 2. Application

[0015] 3. Server

[0016] The inventive system (1) developed with the aim of enabling end-to-end automation and orchestration of products owned within organizations and third-party products with synchronous and asynchronous communication support comprises

[0017] at least one application (2) which is configured to enable the authorized user to monitor the automation of the products owned within the organization and third-party products; and

[0018] at least one server (3) which is configured to create a single logical server (cluster) by bringing together multiple servers with the use of various artificial intelligence tools; to keep a record of all messages and data passing through the flow; to perform synchronous and asynchronous communication; to carry out the automation and integration processes of the products owned within organization and third-party products from a single point by centralizing them; and to enable the authorized user to monitor the flow through the application (2) interface. The application (2) included in the inventive system (1) is configured to establish communication and exchange data with the server (3) by using any communication protocol.

[0019] The server (3) included in the inventive system (1) is configured to establish communication and exchange data with the application (2) by using any communication protocol. The server (3) is configured to enable the technical architecture to be run with Asp.net Core 5.0, Oracle and RabbitMQ infrastructure. The server (3) is configured to enable each endpoint to serve as a service independent of each other and to work in an integrated way with each other for different needs. The server (3) is configured to enable end-to-end synchronous communication to be performed by invoking the relevant services through Asp.net Core endpoints. The server (3) is configured to enable asynchronous communication to be performed by certain services communicating through RabbitMQ. The server (3) is configured to access services in the form of Enterprise Ticket Service (SMART), Enterprise Configuration Service (SCKS), databases (all Oracle, MSQL and IBM Db2 databases in the data model), Enterprise Modeling Tool (Power Designer), Enterprise Metadata Management Tool (MetadataHUB), Enterprise Test Data Management Product (EDM), Enterprise Data Dictionary (Datapedia) and Enterprise Versioning Tool (Git) and to enable their integration when necessary. The server (3) is configured to enable end-to-end management of the processes of modeling table metadata, creating the modeling as a request in a ticket system, positioning the data definition language (DDL) in the desired database, creating it as a concept in the enterprise data dictionary, transferring it to the enterprise metadata management tool, versioning the data definition language code in the enterprise versioning tool, and downloading the Prod Environment and Test Environment data that are live environments open to end users with the enterprise test data management product. The server (3) is configured to be run on Azure Al with an artificial intelligence architecture. The server (3) is configured to enable content feeds to be stored on a Blob Storage and Vektor storage databases. The server (3) is configured to comprise a GPT (Generative Pre-trained Transformer) thereon and to comprise the GPT-4o version as the GPT model. The server (3) is configured to enable search indexes defined on content feeds to be run at a specific time interval and new incoming information to be transferred to the GPT. The server (3) is configured to enable GPT and .NET Core versions to be updated when necessary. The server (3) is configured to be run on Enterprise ChatGPT or OnPromise GPT instead of running on Azure Al when necessary. The server (3) is configured to enable models to be drawable with drawing-supported artificial intelligence integrations in the form of Dall-E by expanding the learning cluster when necessary. The server (3) is configured to enable the Mongo platform to be used instead of the Oracle platform used as a repository when necessary.

[0020] Industrial Application of the Invention

[0021] By means of the inventive system (1), end-to-end automation and orchestration of products owned within organizations and third-party products are enabled with synchronous and asynchronous communication support.

[0022] Within these basic concepts; it is possible to develop various embodiments of the inventive “Artificial Intelligence-Supported Centralized Metadata and Automation System (1)”; the invention cannot be limited to examples disclosed herein and it is essentially according to claims.

Claims

CLAIMS1. A system (1) which enables end-to-end automation and orchestration of products owned within organizations and third-party products with synchronous and asynchronous communication support; comprising at least one application (2) which is configured to enable the authorized user to monitor the automation of the products owned within the organization and third-party products; and characterized byat least one server (3) which is configured to create a single logical server (cluster) by bringing together multiple servers with the use of various artificial intelligence tools; to keep a record of all messages and data passing through the flow; to perform synchronous and asynchronous communication; to carry out the automation and integration processes of the products owned within organization and third-party products from a single point by centralizing them; and to enable the authorized user to monitor the flow through the application (2) interface.

2. A system (1) according to Claim 1; characterized by the application (2) which is configured to establish communication and exchange data with the server (3) by using any communication protocol.

3. A system (1) according to Claim 1 or 2; characterized by the server (3) which is configured to establish communication and exchange data with the application (2) by using any communication protocol.

4. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to enable the technical architecture to be run with Asp.net Core 5.0, Oracle and RabbitMQ infrastructure.

5. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to enable each endpoint to serve as a service independent of each other and to work in an integrated way with each other for different needs.

6. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to enable end-to-end synchronous communication to be performed by invoking the relevant services through Asp.net Core endpoints.

7. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to enable asynchronous communication to be performed by certain services communicating through RabbitMQ.

8. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to access services in the form of Enterprise Ticket Service (SMART), Enterprise Configuration Service (SCKS), databases (all Oracle, MSQL and IBM Db2 databases in the data model), Enterprise Modeling Tool (Power Designer), Enterprise Metadata Management Tool (MetadataHUB), Enterprise Test Data Management Product (EDM), Enterprise Data Dictionary (Datapedia) and Enterprise Versioning Tool (Git) and to enable their integration when necessary.

9. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to enable end-to-end management of the processes of modeling table metadata, creating the modeling as a request in a ticket system, positioning the data definition language (DDL) in the desired database, creating it as a concept in the enterprise data dictionary, transferring it to the enterprise metadata management tool, versioning the data definition language code in the enterprise versioningtool, and downloading the Prod Environment and Test Environment data that are live environments open to end users with the enterprise test data management product.

10. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to be run on Azure Al with an artificial intelligence architecture.

11. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to enable content feeds to be stored on a Blob Storage and Vektor storage databases.

12. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to comprise a GPT (Generative Pretrained Transformer) thereon and to comprise the GPT-4o version as the GPT model.

13. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to enable search indexes defined on content feeds to be run at a specific time interval and new incoming information to be transferred to the GPT.

14. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to enable GPT and .NET Core versions to be updated when necessary.

15. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to be run on Enterprise ChatGPT or OnPromise GPT instead of running on Azure Al when necessary.

16. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to enable models to be drawable with drawing-supported artificial intelligence integrations in the form of Dall-E by expanding the learning cluster when necessary.

17. A system (1) according to any one of the preceding claims; characterized by the server (3) which is configured to enable the Mongo platform to be used instead of the Oracle platform used as a repository when necessary.

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