Zendesk, Inc.

Soon-Ee Cheah

Anh Thien Dinh

Michael G Mortimer

Christopher J Hausler

Ai-Lien Tran-Cong

Wai Chee Yau

Arwen Twinkle E Griffioen

Hing Yip Pak

Thomas Pelletier

Arvind Kunday Anantharaman

Shi Yu Zhu

Bob Dharmendra Raman

Jason Edward Maynard

Sean D Caffery

Jeffrey P Theobald

Damen Turnbull

The system obtains a set of tickets representing customer requests for a customer-support ticketing system. Next, the system produces a set of request vectors by feeding words from each ticket through a model to generate a request vector for the ticket, wherein the set of request vectors is represented as a set of points in a vector space. The system then performs a clustering operation on the set of points to form clusters representing support topics, wherein the clustering operation creates a new point for a new ticket in the vector space when the new ticket is received, and incrementally updates existing clusters to accommodate the new point. Finally, the system presents a user interface to a customer-support agent, wherein the user interface uses the support topics to organize the customer requests, and enables the customer-support agent to perform a customer-support operation in response to a customer request.

Automatically clustering customer-support requests to form customer-support topics

The system obtains a set of tickets representing customer requests generated by a customer-support ticketing system. Next, the system feeds words from each ticket through a model to generate a request vector for the ticket, wherein the request vector comprises numerical values representing words in the ticket. The system then embeds the request vectors in a vector space. If help center articles already exist, the system embeds article vectors for the existing help center articles in the vector space. Next, the system identifies clusters of request vectors, which are within a pre-specified distance of each other in the vector space. If an identified cluster is more than a pre-specified distance away from a closest article vector in the vector space, the system notifies a content creator that a new article needs to be written, or an existing article needs to be updated, to cover the identified cluster.

Automated system for proposing help center articles to be written to facilitate resolving customer-service requests

The disclosed embodiments relate to a system that suggests helpful articles to resolve a customer request. During operation, the system receives the customer request, wherein the customer request is associated with a product or a service used by the customer. Next, the system feeds a set of words from the customer request through a model to generate a request vector comprising numerical values representing words in the customer request. The system then compares the request vector against article vectors representing articles in a set of help center articles to determine whether the customer request matches one or more help center articles. If the customer request matches one or more help center articles, the system presents the one or more help center articles to the customer to facilitate automatically resolving the customer request.

Article-suggestion system for automatically resolving customer-service requests

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Bentleigh East, Australia

Soon-Ee Cheah