Servicenow, Inc.

Abhay Narayan Kulkarni

Mukund Ramachandran

Bruce Walthers

Darius Koohmarey

Lingzhu Li

Manjeet Singh

Matthew David Lloyd

Amanjit Singh Johal

Mohammed Abdul Farhan Khan

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

The present disclosure provides systems and methods for classifying incidents based on determining an odds ratio that represents a likelihood of an incident being related to the problem, classifying incidents based on determining a decision tree that forms branches based on whether a feature is present in the incident, and predicting whether a new incident is related to a problem. Features may be extracted from a set of incidents (e.g., that are reported over a certain time period) that include incidents related to a problem and incidents not related to the problem. The incidents related to the problem and a portion of the incidents not related to the problem may be used to train a logistic regression model or generate a decision tree. The trained logistic regression model may be used to determine the odds ratios or predict whether a new incident is related to a problem.

Systems and methods for classifying and predicting the cause of information technology incidents using machine learning

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

An embodiment may involve receiving respective information technology performance data related to managed networks. The embodiment may further involve transmitting a web-based representation of a first graphical user interface. The first graphical user interface may be configurable to display a plurality of performance metrics related to the managed network. The embodiment may further involve receiving an indication to display a detailed representation of a particular performance metric of the plurality of performance metrics. The embodiment may further involve transmitting a web-based representation of a second graphical user interface. The second graphical user interface may be configured to display (i) a textual description of the particular performance metric, (ii) the value of the particular performance metric, (iii) an ordered ranking, (iv) a graph-based representation of the particular performance metric as measured over a time period, and (v) a recommendation of operational modifications to improve the particular performance metric.

Growing community of inventors

Pleasanton, CA, United States of America

Abhay Narayan Kulkarni