System and method for auto-completion of ICS flow using artificial intelligence/machine learning

Patent metadata

Field	Value
Publication number	US-11556733-B2
Application number	US-201916566490-A
Country	US
Kind code	B2
Filing date	Sep 10, 2019
Priority date	Oct 18, 2018
Publication date	Jan 17, 2023
Grant date	Jan 17, 2023

Abstract

In accordance with an embodiment, described herein are systems and methods for auto-completion of ICS flow using artificial intelligence/machine learning. Next actions prediction is a service that assists users in modeling the flows quickly by predicting and suggesting the next set of actions a user might be thinking of adding. The service also assists the user to follow some of the best practices while creating an integration flow.

First claim

What is claimed is:

1. A system for supporting auto-completion of ICS (integration cloud services) flow using artificial intelligence/machine learning, comprising:
a computer including one or more microprocessors;
an integration platform running on the computer, the integration platform comprising an integration flow designer, the integration flow designer having access to a library comprising a plurality of objects, each object capable of being utilized within an integration flow designed within the integration flow designer;
wherein the computer is configured to perform the following steps:
start a design template for an integration flow within the integration flow designer,
collect user context, the user context relating to the integration flow within the integration flow designer; and
provide a plurality of selectable flow predictions, via a user interface provided by the integration platform, based upon the collected user context, each of the plurality of flow predictions comprising connectors arranged in an order, each connector being associated with an instance of an application external to the integration flow within the integration flow designer.

2. The system of claim 1 , wherein user context takes into account the organization, subsidiary, department, sub-departments, and user information.

3. The system of claim 2 , wherein a hierarchical clustering model of invariant pattern recognition of process models is utilized in providing the plurality of flow predictions.

4. The system of claim 3 , wherein the hierarchical clustering model utilizes stored machine learning knowledge of process design as a hierarchy of clusters.

5. The system of claim 4 , wherein the hierarchical clustering model further utilizes machine learning models based on the collected user context.

6. The system of claim 1 , wherein a ranking generator is used in providing the plurality of flow predictions.

7. The system of claim 6 , wherein the ranking generator utilizes an input pattern to rank a plurality of output patterns matched with the input pattern, the ranking utilizing one or more inputs; and
wherein the input pattern is created in part from the collected user context.

8. A method for supporting auto-completion of ICS (integration cloud services) flow using artificial intelligence/machine learning, comprising:
providing a computer that includes one or more microprocessors;
providing an integration platform running on the computer, the integration platform comprising an integration flow designer, the integration flow designer having access to a library comprising a plurality of objects, each object capable of being utilized within an integration flow designed within the integration flow designer;
starting a design template for an integration flow within the integration flow designer;
collecting user context, the user context relating to the integration flow within the integration flow designer; and
providing, via a user interface provided by the integration platform, a plurality of selectable flow predictions based upon the collected user context, each of the plurality of flow predictions comprising connectors arranged in an order, each connector being associated with an instance of an application external to the integration flow within the integration flow designer.

9. The method of claim 8 , wherein user context takes into account the organization, subsidiary, department, sub-departments, and user information.

10. The method of claim 9 , wherein a hierarchical clustering model of invariant pattern recognition of process models is utilized in providing the next action prediction.

11. The method of claim 10 , wherein the hierarchical clustering model utilizes stored machine learning knowledge of process design as a hierarchy of clusters.

12. The method of claim 11 , wherein the hierarchical clustering model further utilizes machine learning models based on the collected user context.

13. The method of claim 8 , wherein a ranking generator is used in providing the next action prediction.

14. The method of claim 13 , wherein the ranking generator utilizes an input pattern to rank a plurality of output patterns matched with the input pattern, the ranking utilizing one or more inputs; and
wherein the input pattern is created in part from the collected user context.

15. A non-transitory computer readable storage medium, having instructions for supporting auto-completion of ICS (integration cloud services) flow using artificial intelligence/machine learning stored thereon, which when read and executed by one or more computers cause the one or more computers to perform steps comprising:
providing a computer that includes one or more microprocessors;
providing an integration platform running on the computer, the integration platform comprising an integration flow designer, the integration flow designer having access to a library comprising a plurality of objects, each object capable of being utilized within an integration flow designed within the integration flow designer;
starting a design template for an integration flow within the integration flow designer;
collecting user context, the user context relating to the integration flow within the integration flow designer; and
providing, via a user interface provided by the integration platform, a plurality of selectable flow predictions based upon the collected user context, each of the plurality of flow predictions comprising connectors arranged in an order, each connector being associated with an instance of an application external to the integration flow within the integration flow designer.

16. The non-transitory computer readable storage medium of claim 15 , wherein user context takes into account the organization, subsidiary, department, sub-departments, and user information.

17. The non-transitory computer readable storage medium of claim 16 , wherein a hierarchical clustering model of invariant pattern recognition of process models is utilized in providing the next action prediction.

18. The non-transitory computer readable storage medium of claim 17 , wherein the hierarchical clustering model utilizes stored machine learning knowledge of process design as a hierarchy of clusters.

19. The non-transitory computer readable storage medium of claim 18 , wherein the hierarchical clustering model further utilizes machine learning models based on the collected user context.

20. The non-transitory computer readable storage medium of claim 15 ,
wherein a ranking generator is used in providing the next action prediction;
wherein the ranking generator utilizes an input pattern to rank a plurality of output patterns matched with the input pattern, the ranking utilizing one or more inputs; and
wherein the input pattern is created in part from the collected user context.

Assignees

• Oracle Int Corp

Inventors

• Andrabi Syed Najeeb
• Modi Rajan
• Mohanram Venkatesh
• Palanisamy Muthukumar
• Hwang Michael

Classifications

• G06F18/231

  Hierarchical techniques, i.e. dividing or merging pattern sets so as to obtain a dendrogram · CPC title

• G06N20/00

  Machine learning · CPC title

• G06F9/461

  Saving or restoring of program or task context · CPC title

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06F16/908 Primary

  using metadata automatically derived from the content · CPC title