# US 2018/0322508 A1 · Identifying clusters for service management operations

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- Number: US 2018/0322508 A1
- Document ID: US20180322508A1
- Status: Published application
- Kind: A1
- Application number: 15/723,731
- Filed: 2017-10-03
- Published: 2018-11-08
- Earliest claimed: 2017-05-05
- Assignees: ServiceNow, Inc.
- Applicants: ServiceNow, Inc.
- Inventors: Bruce Walthers; Abhay Kulkarni; Mukund Ramachandran; Darius Koohmarey
- Web page: https://patentgrep.com/patent/US20180322508A1

## CPC

- G06Q 30/016: After-sales
- G06F 16/24578: {using ranking}
- G06F 16/285: {Clustering or classification}
- G06F 40/186: Templates
- G06F 40/279: Recognition of textual entities
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## 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.

## Claims

### Claim at position 1

1-20. (canceled)

### Claim 21

A cluster identification method, comprising:
extracting historical incident report data for a plurality of incidents from one or more databases related to usage of client instances, wherein the plurality of incidents are closed incidents that have gone through an incident management life cycle;
grouping the plurality of incidents into a plurality of clusters based on a degree of natural language text similarity of respective target fields in the plurality of incidents having been completed in natural language;
ranking the plurality of clusters based at least on respective numbers of grouped incidents in individual clusters of the plurality of clusters;
identifying at least one of the ranked plurality of clusters to perform a service management operation associated with the cluster; and
deflecting future incidents associated with the at least one of the ranked plurality of clusters to a virtual agent for automated resolution.

### Claim 22 (depends on 21)

The method of claim 21, wherein each incident of the plurality of incidents includes a plurality of fields, and the method further comprising:
selecting an evaluation field from among the plurality of fields; and
determining, for each of the plurality of clusters, a quality value 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 in the grouped incidents of the cluster.

### Claim 23 (depends on 22)

The method of claim 22, wherein ranking the plurality of clusters further comprises:
ranking each of the plurality of clusters of the client instance data based on a respective quality value of the cluster.

### Claim 24 (depends on 23)

The method of claim 23, wherein the target field is a first target field and the quality value is a first quality value, and wherein the method further comprises:
selecting a second target field from among the plurality of fields, the second target field being different from the first target field; and
determining, for each of the plurality of clusters, a second quality value based on a degree of a 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;
wherein the ranking of each of the plurality of clusters is based on the respective first and second quality values of the cluster and the respective number of the grouped incidents of the cluster.

### Claim 25 (depends on 24)

The method of claim 24, 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.

### Claim 26 (depends on 24)

The method of claim 24, wherein the degree of the 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 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.

### Claim 27 (depends on 24)

The method of claim 24, further comprising:
displaying 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
displaying 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.

### Claim 28 (depends on 21)

The method of claim 21, further comprising:
determining respective quality values for clusters of the plurality of clusters; and
determining whether or not to regroup the plurality of incidents based on the determined quality values.

### Claim 29 (depends on 21)

The method of claim 21, further comprising:
creating issue-specific templates; and
populating template text and field values with information from the respective target fields of the at least one ranked plurality of clusters.

### Claim 30

A non-transitory computer-readable recording medium having stored thereon a program for a computer of a cluster identification system, the program comprising instructions that when executed, cause the computer to:
extract historical incident report data for a plurality of incidents from one or more databases related to usage of client instances, wherein the plurality of incidents are closed incidents that have gone through an incident management life cycle;
group the plurality of incidents into a plurality of clusters based on a degree of natural language text similarity of respective target fields in the plurality of incidents having been completed in natural language;
rank the plurality of clusters based at least on respective numbers of grouped incidents in individual clusters of the plurality of clusters;
identify at least one of the ranked plurality of clusters to perform a service management operation associated with the cluster; and
deflect future incidents associated with the at least one of the ranked plurality of clusters to a virtual agent for automated resolution.

### Claim 31 (depends on 30)

The computer-readable recording medium according to claim 30, wherein each incident of the plurality of incidents includes a plurality of fields, and wherein the program further includes instructions that when executed, cause the computer to:
select an evaluation field from among the plurality of fields; and
determine, for each of the plurality of clusters, a quality value 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 in the grouped incidents of the cluster.

### Claim 32 (depends on 31)

The computer-readable recording medium according to claim 31, wherein the program further includes instructions that when executed, cause the computer to:
rank each of the plurality of clusters of the client instance data based on a respective quality value of the cluster.

### Claim 33 (depends on 31)

The computer-readable recording medium according to claim 31, wherein the target field is a first target field and the quality value is a first quality value, and wherein the program further includes instructions that when executed, cause the computer to:
select a second target field from among the plurality of fields, the second target field being different from the first target field; and
determine, for each of the plurality of clusters, a second quality value based on a degree of a 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;
wherein the ranking of each of the plurality of clusters is based on the respective first and second quality values of the cluster and the respective number of the grouped incidents of the cluster.

### Claim 34 (depends on 33)

The computer-readable recording medium according to claim 33, 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.

### Claim 35 (depends on 33)

The computer-readable recording medium according to claim 33, wherein the program further includes instructions that when executed, cause the computer 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.

### Claim 36 (depends on 30)

The computer-readable recording medium according to claim 30, wherein the program further includes instructions that when executed, cause the computer to:
determine respective quality values for clusters of the plurality of clusters; and
determine whether or not to regroup the plurality of incidents based on the determined quality values.

### Claim 37 (depends on 30)

The computer-readable recording medium according to claim 30, and wherein the program further includes instructions that when executed, cause the computer to:
create issue-specific templates; and
populate template text and field values with information from the respective target fields of the at least one ranked plurality of clusters.

### Claim 38

A system comprising:
one or more processors; and
memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform functions comprising:
extract historical incident report data for a plurality of incidents from one or more databases related to usage of client instances, wherein the plurality of incidents are closed incidents that have gone through an incident management life cycle;
group the plurality of incidents into a plurality of clusters based on a degree of natural language text similarity of respective target fields in the plurality of incidents having been completed in natural language;
rank the plurality of clusters based at least on respective numbers of grouped incidents in individual clusters of the plurality of clusters;
identify at least one of the ranked plurality of clusters to perform a service management operation associated with the cluster; and
deflect future incidents associated with the at least one of the ranked plurality of clusters to a virtual agent for automated resolution.

### Claim 39 (depends on 38)

The system of claim 38, wherein the one or more processors are further configured to execute instructions to:
determine respective quality values for clusters of the plurality of clusters; and
determine whether or not to regroup the plurality of incidents based on the determined quality values.

### Claim 40 (depends on 38)

The system of claim 38, wherein the one or more processors are further configured to execute instructions to:
create issue-specific templates; and
populate template text and field values with information from the respective target fields of the at least one ranked plurality of clusters.