The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 30, 2020

Filed:

Mar. 05, 2018
Applicant:

Zendesk, Inc., San Francisco, CA (US);

Inventors:

Christopher J. Hausler, Coburg, AU;

Michael G. Mortimer, Croydon Hills, AU;

Soon-Ee Cheah, Bentleigh East, AU;

Shi Yu Zhu, South Bank, AU;

Ai-Lien Tran-Cong, East Melbourne, AU;

Wai Chee Yau, Caulfield, AU;

Hing Yip Pak, Brunswick, AU;

Anh Thien Dinh, Pascoe Vale, AU;

Assignee:

Zendesk, Inc., San Francisco, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 30/00 (2012.01); G06Q 10/10 (2012.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); G06F 16/35 (2019.01); G06F 16/93 (2019.01); G06F 40/279 (2020.01); G06F 40/30 (2020.01); G06Q 10/02 (2012.01);
U.S. Cl.
CPC ...
G06N 3/0427 (2013.01); G06F 16/35 (2019.01); G06F 16/93 (2019.01); G06F 40/279 (2020.01); G06N 3/0445 (2013.01); G06N 3/0454 (2013.01); G06N 3/084 (2013.01); G06Q 10/10 (2013.01); G06Q 30/016 (2013.01); G06F 40/30 (2020.01); G06N 3/08 (2013.01); G06Q 10/02 (2013.01);
Abstract

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.


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