Servicenow, Inc.

Moveworks, Inc.

Mukund Ramachandran

Abhay Narayan Kulkarni

Bruce Walthers

Darius Koohmarey

Jiang Chen

Jing Chen

Varun Singh

Bhavin Nicholas Shah

Vaibhav Nivargi

Lingzhu Li

Nishit Asnani

Desmond Wing-Yin Chan

Nick Naixuan Guo

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

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

The content of a knowledge datastore is evaluated and improved. In a first aspect, the content effectiveness of individual snippets is evaluated and a content creator is requested to improve snippets with a low content effectiveness. In a second aspect, the supply of and demand for content in each content topic is evaluated, and a content creator is requested to create articles for content topics for which the demand exceeds the supply. In a third aspect, the message responsiveness and content effectiveness of content topics is evaluated and a content creator is requested to create articles for content topics with a low message responsiveness and/or content effectiveness. In a fourth aspect, the content utilization and content effectiveness of individual snippets is monitored and snippets with a high content effectiveness and a low content utilization are promoted, whereas snippets with a low content effectiveness and a low content utilization are deprecated.

Methods and systems for evaluating and improving the content of a knowledge datastore

A multi-domain chatbot is used to service a message of a user. An automated agent of the multi-domain chatbot may act as an intermediary between the user and a plurality of domain-specific modules of the multi-domain chatbot. The automated agent may receive the message from the user, determine an intent of the message, and based on the intent, determine a group of the domain-specific modules that should be investigated. The automated agent may then investigate the group of domain-specific modules by sending the user message to and receiving responses from the domain-specific modules within the group. Based on the received responses, the automated agent may determine whether to provide, to the user, one of the domain-specific responses or a null response, in the event that none of the domain-specific responses is aligned with the intent of the message.

Multi-domain chatbot

Growing community of inventors

Mountain View, CA, United States of America

Mukund Ramachandran