Issue-resolution automation

Patent metadata

Field	Value
Publication number	US-11269717-B2
Application number	US-201916580318-A
Country	US
Kind code	B2
Filing date	Sep 24, 2019
Priority date	Sep 24, 2019
Publication date	Mar 8, 2022
Grant date	Mar 8, 2022

Abstract

Implementations of the present disclosure include methods, systems, and computer-readable storage mediums for issue resolution based on actual use of configuration parameters. Actions include receiving, from a monitoring system, an incident report including a description of an issue of a process and a context of the issue, retrieving features associated with the issue based on the context of the issue, processing the features to extract a set of solutions that were executed to resolve associated issues, processing the set of solutions to generate a solution for the issue, comparing an accuracy of the solution with a solution implementation threshold, and implementing the solution to resolve the issue.

First claim

What is claimed is:

1. A computer-implemented method for issue resolution, the method being executed using one or more processors and comprising: receiving, by the one or more processors and from a monitoring system, an incident report comprising a description of an issue of a process and a context of the issue; retrieving, by the one or more processors, features describing the process associated with the issue based on the context of the issue, wherein the features are organized in groups of features; processing, by the one or more processors, the features to extract a set of solutions corresponding to the groups of features that were executed to resolve associated issues; processing, by the one or more processors, using machine learning, the set of solutions to generate a solution for the issue; determining, by the one or more processors, whether an accuracy of the solution exceeds a solution implementation threshold; and in response to determining that the accuracy of the solution exceeds the solution implementation threshold, implementing, by the one or more processors, the solution to resolve the issue by updating a software parameter or a hardware parameter before restarting the process.

2. The computer-implemented method of claim 1 , wherein retrieving the features associated with the issue comprises performing a pattern-matching algorithm based on the context of the issue.

3. The computer-implemented method of claim 1 , wherein implementing the solution to resolve the issue comprises scheduling the solution for automation by a development system.

4. The computer-implemented method of claim 1 , further comprising retrieving a set of parameters of a processing system executing the process.

5. The computer-implemented method of claim 4 , wherein retrieving the set of parameters is based on annotations of the set of solutions.

6. The computer-implemented method of claim 4 , wherein implementing the solution to resolve the issue comprises updating the set of parameters of the processing system to correctly perform the process.

7. The computer-implemented method of claim 1 , wherein implementing the solution to resolve the issue comprises updating the process.

8. A non-transitory computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for issue resolution, the operations comprising: receiving, from a monitoring system, an incident report comprising a description of an issue of a process and a context of the issue; retrieving features describing the process associated with the issue based on the context of the issue, wherein the features are organized in groups of features; processing the features to extract a set of solutions corresponding to the groups of features that were executed to resolve associated issues; processing, using machine learning, the set of solutions to generate a solution for the issue; determining whether an accuracy of the solution exceeds a solution implementation threshold; and in response to determining that the accuracy of the solution exceeds the solution implementation threshold, implementing the solution to resolve the issue by updating a software parameter or a hardware parameter before restarting the process.

9. The non-transitory computer-readable storage medium of claim 8 , wherein retrieving the features associated with the issue comprises performing a pattern-matching algorithm based on the context of the issue.

10. The non-transitory computer-readable storage medium of claim 8 , wherein implementing the solution to resolve the issue comprises scheduling the solution for automation by a development system.

11. The non-transitory computer-readable storage medium of claim 8 , wherein the operations further comprise retrieving a set of parameters of a processing system executing the process.

12. The non-transitory computer-readable storage medium of claim 11 , wherein retrieving the set of parameters is based on annotations of the set of solutions.

13. The non-transitory computer-readable storage medium of claim 11 , wherein implementing the solution to resolve the issue comprises updating the set of parameters of the processing system to correctly perform the process.

14. The non-transitory computer-readable storage medium of claim 8 , wherein implementing the solution to resolve the issue comprises updating the process.

15. A computer-implemented system, comprising: a client-side computing device; and a computer-readable storage device coupled to the client-side computing device and having instructions stored thereon which, when executed by the client-side computing device, cause the client-side computing device to perform operations for issue resolution, the operations comprising: receiving, from a monitoring system, an incident report comprising a description of an issue of a process and a context of the issue; retrieving features describing the process associated with the issue based on the context of the issue, wherein the features are organized in groups of features; processing the features to extract a set of solutions corresponding to the groups of features that were executed to resolve associated issues; processing, using machine learning, the set of solutions to generate a solution for the issue; determining whether an accuracy of the solution exceeds a solution implementation threshold; and in response to determining that the accuracy of the solution exceeds the solution implementation threshold, implementing the solution to resolve the issue by updating a software parameter or a hardware parameter before restarting the process.

16. The computer-implemented system of claim 15 , wherein retrieving the features associated with the issue comprises performing a pattern-matching algorithm based on the context of the issue.

17. The computer-implemented system of claim 15 , wherein implementing the solution to resolve the issue comprises scheduling the solution for automation by a development system.

18. The computer-implemented system of claim 15 , wherein the operations further comprise retrieving a set of parameters of a processing system executing the process based on annotations of the set of solutions.

19. The computer-implemented system of claim 18 , wherein implementing the solution to resolve the issue comprises updating the set of parameters of the processing system to correctly perform the process.

20. The computer-implemented system of claim 15 , wherein implementing the solution to resolve the issue comprises updating the process.

Assignees

• Sap Se

Inventors

• Eberlein Peter
• Driesen Volker

Classifications

• G06F11/0709 Primary

  in a distributed system consisting of a plurality of standalone computer nodes, e.g. clusters, client-server systems · CPC title

• G06F11/0793 Primary

  Remedial or corrective actions (recovery from an exception in an instruction pipeline G06F9/3861; by retry G06F11/1402; for recovering from a failure of a protocol instance or entity H04L69/40) · CPC title

• G06F18/22

  Matching criteria, e.g. proximity measures · CPC title

• G06F11/302

  where the computing system component is a software system · CPC title

• G06F11/3006

  where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems (multiprogramming arrangements G06F9/46; allocation of resources G06F9/50) · CPC title