Identifying clusters for service management operations

Patent metadata

Field	Value
Publication number	US-2018322509-A1
Application number	US-201815939602-A
Country	US
Kind code	A1
Filing date	Mar 29, 2018
Priority date	May 5, 2017
Publication date	Nov 8, 2018
Grant date	—

Abstract

Client instance data including a plurality of incidents is obtained, each incident including a plurality of fields. A target field and an evaluation field are selected from among the plural fields. The plurality of incidents are grouped into a plurality of clusters based on a degree of a natural language text similarity of respective target fields in the plurality of incidents. A quality value is determined for each of the plurality of clusters based on the degree of the natural language text similarity of respective target fields in grouped incidents of the cluster from among the plurality of incidents, and based on respective evaluation fields. Each of the plurality of clusters is ranked based on the respective quality value of the cluster and a number of the grouped incidents of the cluster. At least one of the ranked plurality of clusters is identified to perform a service management operation.

First claim

1 . A cluster identification system for service management operations, comprising:
a non-transitory memory; and one or more hardware processors configured to execute instructions from the non-transitory memory to: obtain client instance data including a plurality of incidents, each incident including a plurality of fields; select a first target field and an evaluation field from among the plurality of fields; group the plurality of incidents of the client instance data into a plurality of clusters based on a degree of a first natural language text similarity of respective first target fields in the plurality of incidents; determine, for each of the plurality of clusters, a first quality value based on the degree of the first natural language text similarity of respective first target fields in grouped incidents of the cluster from among the plurality of incidents, and based on respective evaluation fields in the grouped incidents of the cluster; select a second target field from among the plurality of fields, the second target field being different from the first target field; determine, for each of the plurality of clusters, a second quality value based on a degree of a second natural language text similarity of respective second target fields in the grouped incidents of the cluster from among the plurality of incidents, and based on the respective evaluation fields in the grouped incidents of the cluster, rank each of the plurality of clusters of the client instance data based on the respective first quality value and the respective second quality value of the cluster and a respective number of the grouped incidents of the cluster of the client instance data; and identify at least one of the ranked plurality of clusters to perform a service management operation associated with the cluster.

2 . (canceled)

3 . The cluster identification system according to claim 1 , wherein:
the first target field is an incident short description text field; the second target field is an incident resolution detail text field or an incident comments and work notes or close notes text field; and the evaluation field is a validation field including an incident category or an incident assignment group.

4 . The cluster identification system according to claim 1 , wherein the degree of the second natural language text similarity of the respective second target fields in the grouped incidents of the cluster is determined by:
breaking down a natural language text of the respective second target fields in the grouped incidents of the cluster into single words or one or more contiguous sequences of plural words; and determining the degree of the second natural language text similarity based on a comparison of the broken down natural language text across the respective second target fields in the grouped incidents of the cluster.

5 . The cluster identification system according to claim 1 , wherein the one or more hardware processors are further configured to execute instructions to:
display a ranked list of the plurality of clusters by displaying, for each cluster, a description of the respective first and second target fields and the respective number of the grouped incidents included in the cluster; and display a pareto chart that is associated with the description of the respective first target fields of the plurality of clusters and that is based on the respective number of the grouped incidents included in the plurality of clusters.

6 . The cluster identification system according to claim 1 , wherein the service management operation is a virtual agent onboarding operation of creating a conversation tree for the identified at least one cluster and deflecting future incidents associated with the cluster to a virtual agent for automated resolution.

7 . The cluster identification system according to claim 1 , wherein the service management operation is a recommendation operation of making a recommendation associated with the identified at least one cluster to improve a key performance indicator.

8 . The cluster identification system according to claim 1 , wherein the grouping of the plurality of incidents into the plurality of clusters comprises:
breaking down a natural language text of the respective first target fields of the plurality of incidents into single words or one or more contiguous sequences of plural words; and clustering the plurality of incidents into the plurality of clusters based on a comparison of the broken down natural language text of the respective first target fields of the plurality of incidents.

9 . A non-transitory computer-readable recording medium having stored thereon a program for a computer of a cluster identification system for service management operations, the program comprising instructions that when executed, cause the computer to:
obtain client instance data including a plurality of incidents, each incident including a plurality of fields; select a first target field and an evaluation field from among the plurality of fields; group the plurality of incidents of the client instance data into a plurality of clusters based on a degree of a first natural language text similarity of respective first target fields in the plurality of incidents; determine, for each of the plurality of clusters, a first quality value based on the degree of the first natural language text similarity of respective first target fields in grouped incidents of the cluster from among the plurality of incidents, and based on respective evaluation fields in the grouped incidents of the cluster; select a second target field from among the plurality of fields, the second target field being different from the first target field; determine, for each of the plurality of clusters, a second quality value based on a degree of a second natural language text similarity of respective second target fields in the grouped incidents of the cluster from among the plurality of incidents, and based on the respective evaluation fields in the grouped incidents of the cluster; rank each of the plurality of clusters of the client instance data based on the respective first quality value and the respective second quality value of the cluster and a respective number of the grouped incidents of the cluster of the client instance data; and identify at least one of the ranked plurality of clusters to perform a service management operation associated with the cluster.

10 . (canceled)

11 . The computer-readable recording medium according to claim 9 , wherein:
the first target field is an incident short description text field; the second target field is an incident resolution detail text field or an incident comments and work notes or close notes text field; and the evaluation field is a validation field including an incident category or an incident assignment group.

12 . The computer-readable recording medium according to claim 9 , wherein the degree of the second natural language text similarity of the respective second target fields in the grouped incidents of the cluster is determined by:
breaking down a natural language text of the respective second target fields in the grouped incidents of the cluster into single words or one or more contiguous sequences of plural words; and determining the degree of the second natural language text similarity based on a comparison of the broken down natural language text across the respective second target fields in the grouped incidents of the cluster.

13 . The computer-readable recording medium according to claim 9 , wherein the program further includes instructions that when executed, cause the computer to:
display a

Assignees

• Servicenow Inc

Inventors

• Walthers Bruce
• Kulkarni Abhay
• Ramachandran Mukund
• Koohmarey Darius

Classifications

• G06F16/24578

  using ranking · CPC title

• G06F16/285

  Clustering or classification · CPC title

• G06F40/279

  Recognition of textual entities · CPC title

• G06F40/205

  Parsing · CPC title

• G06Q30/016 Primary

  After-sales · CPC title