# US 2020/0401385 A1 · System and method for metadata-driven external interface generation of application programming interfaces > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2020/0401385 A1 - Document ID: US20200401385A1 - Status: Published application - Kind: A1 - Application number: 17/010,665 - Filed: 2020-09-02 - Published: 2020-12-24 - Earliest claimed: 2016-08-22 - Assignees: Oracle International Corporation - Applicants: Oracle International Corporation - Inventors: Ganesh Seetharaman; Alexander Sasha Stojanovic; Hassan Heidari Namarvar; David Allan - Web page: https://patentgrep.com/patent/US20200401385A1 ## CPC - G06F 8/433: {Dependency analysis; Data or control flow analysis} - G06F 3/0428: {by sensing at the edges of the touch surface the interruption of optical paths, e.g. an illumination plane, parallel to the touch surface which may be virtual (sensing beam interruptions in a planar beam grid of an optical touch-screen G06F3/0421)} - G06F 3/0482: Interaction with lists of selectable items, e.g. menus - G06F 3/0649: {Lifecycle management} - G06F 8/10: Requirements analysis; Specification techniques - G06F 8/34: Graphical or visual programming - G06F 8/4452: {Software pipelining} - G06F 16/144: {Query formulation} - G06F 16/211: {Schema design and management} - G06F 16/2322: {using timestamps} - G06F 16/2358: {Change logging, detection, and notification (replication G06F16/27)} - G06F 16/254: {Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses} - G06F 16/435: Filtering based on additional data, e.g. user or group profiles - G06F 17/18: for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} - G06F 40/30: Semantic analysis - G06N 5/022: Knowledge engineering; Knowledge acquisition - G06N 5/04: Inference or reasoning models - G06N 5/046: Forward inferencing; Production systems - G06N 20/00: Machine learning - G06Q 10/0637: Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals - G06F 9/5061: {Partitioning or combining of resources} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract In accordance with various embodiments, described herein is a system (Data Artificial Intelligence system, Data AI system), for use with a data integration or other computing environment, that leverages machine learning (ML, DataFlow Machine Learning, DFML), for use in managing a flow of data (dataflow, DF), and building complex dataflow software applications (dataflow applications, pipelines). In accordance with an embodiment, the system provides a programmatic interface, referred to herein in some embodiments as a foreign function interface, by which a user or third-party can define a service, functional and business types, semantic actions, and patterns or predefined complex data flows based on functional and business types, in a declarative manner, to extend the functionality of the system. ## Claims ### Claim 1 A method for use with a data integration or other computing environment comprising: receiving, via an interface, one or more definitions of a metadata for use in processing a data; processing the metadata received via the interface, to identify information about the received metadata, including one or more of a classification, semantic action, template defining a pattern, or service defined by the received metadata; storing the metadata received via the interface, in a knowledge source, wherein the knowledge source is updated to include the information about the received metadata and to extend functional capabilities of the system including its supported types, semantic actions, templates, or services; and identifying a pattern for providing a recommendation for a data flow, based on the information updated in the knowledge source via the interface. ### Claim 2 (depends on 1) The method of claim 1, further comprising determining a recommendation for a transformation, for use in a dataflow application, wherein the recommendation is determined on a template identified in the system HUB. ### Claim 3 (depends on 2) The method of claim 2, further comprising performing the transformation on the data flow. ### Claim 4 (depends on 1) The method of claim 1, wherein the knowledge source is a system HUB. ### Claim 5 (depends on 4) The method of claim 4, wherein metadata received through the interface is stored in the system HUB to be accessed by the system for processing a data flow. ### Claim 6 (depends on 5) The method of claim 5, wherein the metadata is used by the system to determine semantic actions permitted for the types of data provided through the interface. ### Claim 7 (depends on 1) The method of claim 1, wherein the method is performed in a cloud or cloud-based computing environment. ### Claim 8 A system for receiving third-party definitions for use with a data integration or other computing environment, comprising: one or more processors operable to: receive, via an interface, one or more definitions of a metadata for use in processing a data; process the metadata received via the interface, to identify information about the received metadata, including one or more of a classification, semantic action, template defining a pattern, or service defined by the received metadata; store the metadata received via the interface, in a knowledge source, wherein the knowledge source is updated to include the information about the received metadata and to extend functional capabilities of the system including its supported types, semantic actions, templates, or services; and identify a pattern for providing a recommendation for a data flow, based on the information updated in the knowledge source via the interface. ### Claim 9 (depends on 8) The system of claim 8, further comprising determining a recommendation for a transformation, for use in a dataflow application, wherein the recommendation is determined on a template identified in the system HUB. ### Claim 10 (depends on 9) The system of claim 9, further comprising performing the transformation on the data flow. ### Claim 11 (depends on 8) The system of claim 8, wherein the knowledge source is a system HUB. ### Claim 12 (depends on 11) The system of claim 11, wherein metadata received through the interface is stored in the system HUB to be accessed by the system for processing a data flow. ### Claim 13 (depends on 12) The system of claim 12, wherein the metadata is used by the system to determine semantic actions permitted for the types of data provided through the interface. ### Claim 14 (depends on 8) The system of claim 8, wherein the system is provided in a cloud or cloud-based computing environment. ### Claim 15 A non-transitory computer readable storage medium, including instructions stored thereon which when read and executed by one or more computers cause the one or more computers to perform a method comprising: receiving, via an interface, one or more definitions of a metadata for use in processing a data; processing the metadata received via the interface, to identify information about the received metadata, including one or more of a classification, semantic action, template defining a pattern, or service defined by the received metadata; storing the metadata received via the interface, in a knowledge source, wherein the knowledge source is updated to include the information about the received metadata and to extend functional capabilities of the system including its supported types, semantic actions, templates, or services; and identifying a pattern for providing a recommendation for a data flow, based on the information updated in the knowledge source via the interface. ### Claim 16 (depends on 15) The non-transitory computer readable storage medium of claim 15, further comprising determining a recommendation for a transformation, for use in a dataflow application, wherein the recommendation is determined on a template identified in the system HUB. ### Claim 17 (depends on 16) The transitory computer readable storage medium of claim 16, further comprising performing the transformation on the data flow. ### Claim 18 (depends on 15) The non-transitory computer readable storage medium of claim 15, wherein the knowledge source is a system HUB. ### Claim 19 (depends on 18) The non-transitory computer readable storage medium of claim 18, wherein metadata received through the interface is stored in the system HUB to be accessed by the system for processing a data flow. ### Claim 20 (depends on 19) The non-transitory computer readable storage medium of claim 19, wherein the metadata is used by the system to determine semantic actions permitted for the types of data provided through the interface.