Detection and correction of robotic process automation failures

Patent metadata

Field	Value
Publication number	US-11635752-B2
Application number	US-202117314354-A
Country	US
Kind code	B2
Filing date	May 7, 2021
Priority date	May 7, 2021
Publication date	Apr 25, 2023
Grant date	Apr 25, 2023

Abstract

An example embodiment involves rules related to repairing software programs, wherein the rules associate indications of software program failures with repair applications that are configured to correct the software program failures. One or more processors are configured to: (i) receive, by a predictive model, a representation of an execution history of a particular software program, wherein the predictive model has been trained on a corpus of execution histories of the software programs; (ii) generate, by the predictive model and from the execution history, a failure prediction for the particular software program; (iii) receive, by an automated repair controller application, the failure prediction from the predictive model; (iv) based on applying the rules to the failure prediction, determine, by the automated repair controller application, a repair application from the repair applications; and (v) cause, by the automated repair controller application, the repair application to be executed within the network.

First claim

What is claimed is:

1. A system comprising:
persistent storage containing rules related to repairing software programs in a network, wherein the rules associate indications of software program failures with repair applications implemented by an automated repair controller according to domain knowledge of the network, wherein the repair applications are configured to correct corresponding software program failures; and
one or more processors configured to:
receive, by a predictive model, a representation of an execution history of a particular software program of the software programs, wherein the predictive model has been trained on a corpus of execution histories of the software programs in order to be able to estimate root causes of software program failures;
generate, by the predictive model and from the execution history, a failure prediction for the particular software program, the failure prediction including an estimated root cause;
receive, by an automated repair controller application, the failure prediction from the predictive model;
based on applying the rules to the failure prediction, determine, by the automated repair controller application, a repair application from the repair applications that is configured to correct the estimated root cause; and
cause, by the automated repair controller application, the repair application to be executed within the network.

2. The system of claim 1 , wherein the one or more processors are further configured to:
receive, by the predictive model, a second representation of a second execution history of a second particular software program of the software programs;
generate, by the predictive model and from the second execution history, a second failure prediction for the second particular software program, the second failure prediction including a second estimated root cause;
receive, by the automated repair controller application, the second failure prediction from the predictive model;
based on applying the rules to the second failure prediction, determine, by the automated repair controller application, that the rules do not specify a matching repair application; and
cause, by the automated repair controller application, an alert regarding the second failure prediction to be transmitted to a human agent.

3. The system of claim 2 , wherein the one or more processors are further configured to:
after causing the alert to be transmitted to the human agent, receiving, from the human agent, an update to the rules that specifies the matching repair application for the second failure prediction.

4. The system of claim 1 , wherein the rules each include: (i) a repair application reference, and (ii) one or more of a software program name, a software program failure indication, or a network address.

5. The system of claim 4 , wherein the failure prediction includes at least one of the software program name, the software program failure indication, or the network address.

6. The system of claim 5 , wherein determining the repair application that is configured to correct the estimated root cause comprises:
selecting a particular rule from the rules, wherein the particular rule matches one or more of the software program name, the software program failure indication, or the network address included in the failure prediction, and wherein the particular rule contains a particular repair application reference to the repair application.

7. The system of claim 4 , wherein the network address is of a computing device on which the particular software program executes or with which the particular software program attempts to communicate.

8. The system of claim 1 , wherein the estimated root cause is one or more of: missing authentication credentials, or a server or application being unreachable or unresponsive.

9. The system of claim 1 , wherein execution of the repair application causes the particular software program to be restarted or reconfigured.

10. The system of claim 1 , wherein execution of the repair application causes an application, device, or service used by the particular software program to be restarted or reconfigured.

11. The system of claim 1 , wherein the execution history of the particular software program includes at least part of a log file generated by the particular software program.

12. The system of claim 1 , wherein the predictive model was either trained on the corpus of execution histories using unsupervised machine learning, or trained on labelled entries of the corpus of execution histories using supervised machine learning.

13. The system of claim 1 , wherein the predictive model and the automated repair controller application are disposed within a computational instance of a remote network management platform that is configured to manage the network, and wherein receiving the representation of the execution history and causing the repair application to be executed within the network occur by way of a proxy server disposed within the network.

14. A computer-implemented method comprising:
receiving, by a predictive model, a representation of an execution history of a particular software program, wherein the predictive model has been trained on a corpus of execution histories of software programs in order to be able to estimate root causes of software program failures, wherein persistent storage contains rules related to repairing software programs in a network, and wherein the rules associate indications of software program failures with repair applications implemented by an automated repair controller according to domain knowledge of the network, wherein the repair applications are configured to correct corresponding software program failures;
generating, by the predictive model and from the execution history, a failure prediction for the particular software program, the failure prediction including an estimated root cause;
receiving, by an automated repair controller application, the failure prediction from the predictive model;
based on applying the rules to the failure prediction, determining, by the automated repair controller application, a repair application from the repair applications that is configured to correct the estimated root cause; and
causing, by the automated repair controller application, the repair application to be executed within the network.

15. The computer-implemented method of claim 14 , wherein the rules each include: (i) a repair application reference, and (ii) one or more of a software program name, a software program failure indication, or a network address.

16. The computer-implemented method of claim 15 , wherein the failure prediction includes at least one of the software program name the software program failure indication, or the network address.

17. The computer-implemented method of claim 16 , wherein determining the repair application that is configured to correct the estimated root cause comprises:
selecting a particular rule from the rules, wherein the particular rule matches one or more of the software program name, the software program failure indication, or the network address included in the failure prediction, and wherein the particular rule contains a particular repair application reference to the repair application.

18. The computer-implemented method of claim 15 , wherein the network address is of a computing device on which the particular software program executes or with which the particular software program attempts to communicate.

19. The computer-implemented method of claim 14 , wherein execution of the rep

Assignees

• Servicenow Inc

Inventors

• Crane Jonathan
• Singh Manjeet
• Damodaran Praveen Minnikaran
• Schroeder Eric
• Bhatnagar Binny

Classifications

• G06N20/00

  Machine learning · CPC title

• G05B23/0283 Primary

  Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL] (preventive maintenance, i.e. planning maintenance according to the available resources without monitoring the system G06Q10/06) · CPC title

• G06F17/40

  Data acquisition and logging (for input to computer G06F3/00) · CPC title

• G05B17/02

  electric · CPC title

• G05B23/0281

  Quantitative, e.g. mathematical distance; Clustering; Neural networks; Statistical analysis · CPC title