Servicenow, Inc.

Bruce Walthers

Abhay Narayan Kulkarni

Mukund Ramachandran

Darius Koohmarey

Lingzhu Li

Manjeet Singh

Jeevan Anand Anne

Abhay Kulkarni

Client instance data including a plurality of incidents and a plurality of knowledge elements comprising information relating to resolving one or more of the plurality of incidents is obtained. A validation set is built based on the obtained client instance data, the validation set including fingerprint data of plural fingerprints of known incident-knowledge relationships, each of fingerprint representing a link between one of the incidents and one of the knowledge elements used for resolving the incident. A knowledge element class is predicted from among plural knowledge element classes for each of knowledge element based on the built validation set, the plural knowledge element classes being defined based on respective threshold values indicating a quality of coverage provided by a knowledge element for resolving an incident. Classification data of the plural knowledge elements classified into the plural knowledge element classes is presented with the obtained client instance data.

Knowledge management using machine learning model trained on incident-knowledge relationship fingerprints

A system may include persistent storage containing representations of requests associated with a managed network. The persistent storage may include lists of capabilities associated with agents, and each request may include a textual description of a situation experienced by a user and a resolution of the situation by a particular agent. A computing device may obtain a set of requests from the persistent storage, apply an unsupervised machine learning clustering technique to textual descriptions included in the set of requests, and arrange the requests into groups such that each group contains requests including textual descriptions with at least a threshold degree of similarity to one another. The computing device may perform, for the requests in a particular group, a textual analysis on associated resolutions to identify capabilities used by agents to resolve the requests, and update the lists of capabilities to associate the capabilities with agents that used them.

Intelligent capability extraction and assignment

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.

Identifying clusters for service management operations

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San Jose, CA, United States of America

Bruce Walthers