# US 2018/0322509 A1 · Identifying clusters for service management operations > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2018/0322509 A1 - Document ID: US20180322509A1 - Status: Published application - Kind: A1 - Application number: 15/939,602 - Filed: 2018-03-29 - 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/US20180322509A1 ## CPC - G06Q 30/016: After-sales - G06F 16/24578: {using ranking} - G06F 16/285: {Clustering or classification} - G06F 40/205: Parsing - 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 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. ### Claim 2 (canceled) ### Claim 3 (depends on 1) 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. ### Claim 4 (depends on 1) 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. ### Claim 5 (depends on 1) 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. ### Claim 6 (depends on 1) 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. ### Claim 7 (depends on 1) 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. ### Claim 8 (depends on 1) 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. ### Claim 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. ### Claim 10 (canceled) ### Claim 11 (depends on 9) 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. ### Claim 12 (depends on 9) 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. ### Claim 13 (depends on 9) The computer-readable recording medium according to claim 9, 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. ### Claim 14 (depends on 9) The computer-readable recording medium according to claim 9, 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. ### Claim 15 (depends on 9) The computer-readable recording medium according to claim 9, 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. ### Claim 16 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; and identifying, by the one or more hardware processors, at least one of the ranked plurality of clusters to perform a service management operation associated with the cluster. ### Claim 17 (canceled) ### Claim 18 (depends on 16) The cluster identification method according to claim 16, 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 19 (depends on 16) The cluster identification method according to claim 16, 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. ### Claim 20 (depends on 16) The cluster identification method according to claim 16, 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.