METHOD AND SYSTEM FOR DETERMINING AUTOMATION SEQUENCES FOR RESOLUTION OF AN INCIDENT TICKET

• US 20180032601A1
• Filed: 09/28/2016
• Published: 02/01/2018
• Est. Priority Date: 07/30/2016
• Status: Active Grant

• Current Assignee: Wipro Limited
• Original Assignee: Wipro Limited
• Inventors: VENKATARAMAN, Arthi, SINGH, Amit, PRAJAPATI, Rahul
• Granted Patent: US 10,459,951 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/285 Clustering or classification

Abstract

The present disclosure relates to a method and system for determining automation sequences for resolution of an incident ticket by an automation system. The automation system retrieves data associated with plurality of incident tickets received from a ticketing system during predefined time duration and groups the plurality of incident tickets into one or more clusters based on the data. The automation system receives a plurality of user actions associated with the plurality of incident tickets performed across a plurality of user devices and identifies similarity among sequences of the plurality of user actions for each ticket cluster. Based on the similarity, the automation system groups the sequences of the plurality of user actions into one or more bucket and determines automation sequences for resolution of the incident ticket by correlating the data associated with plurality of incident tickets with one or more buckets of the sequences.

Claims

1. A method for determining automation sequences for resolution of an incident ticket, the method comprising;

retrieving, by an automation system, data associated with plurality of incident tickets received from a ticketing system during a predefined time duration;

grouping, by the automation system, the plurality of incident tickets into one or more clusters based on the data;

receiving, by the automation system, a plurality of user actions associated with the plurality of incident tickets performed across a plurality of user devices;

identifying, by the automation system, similarity among sequences of the plurality of user actions for each ticket cluster;

grouping, by the automation system, the sequences of the plurality of user actions into one or more buckets based on the similarity; and

determining, by the automation system, automation sequences for resolution of the incident ticket by correlating the data associated with plurality of incident tickets with one or more buckets of the sequences.

2. The method as claimed in claim 1, wherein the data associated with the plurality of incident tickets comprises description of issues associated with each of the tickets.

3. The method as claimed in claim 1, wherein the plurality of user actions comprise information about at least one of launching an application, mouse and cursor movement, types of keystrokes, touch screen data, web portal access data, remote device access data, navigations across different applications, command and data entered and field data.

4. The method as claimed in claim 3, wherein the field data comprise application name, keys pressed by the users, location data of the key pressed, timestamps and user details performing the user actions.

5. The method as claimed in claim 1, wherein identifying similarity among sequences of the plurality of user actions comprises;

assigning, by the automation system, a unique identifier to each type of action of the plurality of user actions;

identifying, by the automation system, one or more parameterized variables between each pair of sequences based on greatest common sub-sequence between each pair of sequences; and

assigning, by the automation system, a score to each pair of sequences based on the one or more parameterized variables and one or more parameters, wherein the score indicates similarity among sequences of the plurality of the user actions.

6. The method as claimed in claim 5, wherein the one or more parameters comprise at least one of length of matched patterns between the pair of sequences, number of the one or more parameterized variables in the pair of sequences, sequences matching at end points and in between in the pair of the sequences.

7. The method as claimed in claim 1 further comprising eliminating the one or more buckets based on minimum number of sequences in the corresponding bucket.

8. An automation system for determining automation sequences for resolution of an incident ticket comprising:

a processor; and

a memory communicatively coupled to the processor, wherein the memory stores processor instructions, which, on execution, causes the processor to;

retrieve data associated with plurality of incident tickets received from a ticketing system during a predefined time duration;

group the plurality of incident tickets into one or more clusters based on the data;

receive a plurality of user actions associated with the plurality of incident tickets performed across a plurality of user devices;

identify similarity among sequences of the plurality of user action for each ticket cluster;

group the sequences of the plurality of user actions into one or more buckets based on the similarity; and

determine automation sequences for resolution of the incident ticket by correlating the data associated with the plurality of incident tickets with one or more buckets of the sequences.

9. The automation system as claimed in claim 8, wherein the data associated with the plurality of incident tickets comprises description of issues associated with each of the tickets.

10. The automation system as claimed in claim 8, wherein the plurality of user actions comprise information about at least one of launching an application, mouse and cursor movement, types of keystrokes, touch screen data, web portal data, remote device access data, navigations across different applications, command and data entered and field data.

11. The automation system as claimed in claim 10, wherein the field data comprise application name, keys pressed by the users, location data of the key pressed, timestamps and user details performing the user actions.

12. The automation system as claimed in claim 8, wherein the processor identifies similarity among sequences of the plurality of user actions by:

assigning a unique identifier to each type of action of the plurality of user actions;

identifying one or more parameterized variables between each pair of sequences based on greatest common sub-sequence between each pair of sequences; and

assigning a score to the each pair of sequences based on the one or more parameterized variables and one or more parameters, wherein the score indicates similarity among sequences of the plurality of the user action.

13. The automation system as claimed in claim 12, wherein the one or more parameters comprise at least one of length of matched patterns between the pair of sequences, number of the one or more parameterized variables in the pair of sequences, sequences matching at end points and in between in the pair of the sequences.

14. The automation system as claimed in claim 8, wherein the processor eliminates the one or more buckets based on minimum number of sequences in the corresponding bucket.

15. A non-transitory computer readable medium including instruction stored thereon that when processed by at least one processor cause an automation system to perform operation comprising:

retrieving data associated with plurality of incident tickets received from a ticketing system during a predefined time duration;

grouping the plurality of incident tickets into one or more clusters based on the data;

receiving a plurality of user actions associated with the plurality of incident tickets performed across a plurality of user devices;

identifying similarity among sequences of the plurality of user actions for each ticket cluster;

grouping the sequences of the plurality of user actions into one or more buckets based on the similarity; and

determining automation sequences for resolution of the incident ticket by correlating the data associated with plurality of incident tickets with one or more buckets of the sequences.

16. The medium as claimed in claim 15, wherein the data associated with the plurality of incident tickets comprises description of issues associated with each of the tickets.

17. The medium as claimed in claim 15, wherein the plurality of user actions comprises information about at least one of launching an application, mouse and cursor movement, types of keystrokes, touch screen data, web portal data, remote device access data, navigations across different applications, command and data entered and field data.

18. The medium as claimed in claim 17, wherein the field data comprise application name, keys pressed by the users, location data of the key pressed, timestamps and user details performing the user actions.

19. The medium as claimed in claim 15, wherein the instruction causes the processor to identify similarity among sequences of the plurality of user actions by:

assigning a unique identifier to each type of action of the plurality of user actions;

identifying one or more parameterized variables between each pair of sequences based on greatest common sub-sequence between each pair of sequences; and

assigning a score to each pair of sequences based on the one or more parameterized variables and one or more parameters, wherein the score indicates similarity among sequences of the plurality of the user action.

20. The medium as claimed in claim 19, wherein the one or more parameters comprise at least one of length of matched patterns between the pair of sequences, number of the one or more parameterized variables in the pair of sequences, sequences matching at end points and in between in the pair of the sequences.