Identifying clusters for service management operations

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;

receive client instance data associated with a new incident; and

automatically resolve the new incident with a virtual agent associated with at least one of the ranked plurality of clusters.

2. 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.

3. 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.

4. 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.

5. The cluster identification system according to claim 1 , wherein the grouping of the plurality of incidents into the plurality of clusters comprises applying a data clustering algorithm to the obtained client instance data to group the plurality of incidents into the plurality of clusters.

6. 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;

receive client instance data associated with a new incident; and

automatically resolve the new incident with a virtual agent associated with at least one of the ranked plurality of clusters.

7. The computer-readable recording medium according to claim 6 , 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.

8. The computer-readable recording medium according to claim 6 , 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.

9. The computer-readable recording medium according to claim 6 , 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 respective first target fields of the plurality of clusters and that is based on the respective number of the grouped incidents included in the ranked clusters.

10. A cluster identification method for service management operations, comprising:

obtaining, by one or more hardware processors, client instance data including a plurality of incidents, each incident including a plurality of fields;

selecting, by the one or more hardware processors, a first target field and an evaluation field from among the plurality of fields;

grouping, by the one or more hardware processors, 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;

determining, by the one or more hardware processors and 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;

selecting, by the one or more hardware processors, a second target field from among the plurality of fields, the second target field being different from the first target field;

determining, by the one or more hardware processors and 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;

ranking, by the one or more hardware processors, each of the plurality of clusters of the client instance data based on the respective first 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;

receiving, by the one or more hardware processors, client instance data associated with the new incident; and

automatically resolving the new incident with a virtual agent associated with at least one of the ranked plurality of clusters.

11. The cluster identification method according to claim 10 , 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.