Patents by Inventor Anh Thien Dinh

Anh Thien Dinh has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11397952
    Abstract: During operation, the system receives a customer request. Next, the system segments the customer request into customer request sentences. The system then encodes each sentence from the customer request with information sequentially collected from the previously observed sentences. Next, the system translates the encodings to sparse probabilities that measure the importance of sentences from the customer request. The system then extracts relevant sentences from the customer request based on the importance. Next, the system forms an extracted-sentence customer request embedding from embeddings for the extracted relevant customer request sentences. The system then uses the extracted-sentence customer request embedding to select an agent response from a set of possible agent responses based on comparisons between the extracted-sentence customer request embedding and embeddings for the set of possible agent responses.
    Type: Grant
    Filed: March 29, 2019
    Date of Patent: July 26, 2022
    Assignee: Zendesk, Inc.
    Inventors: Anh Thien Dinh, Wei Chu, Paul E. Gradie
  • Publication number: 20210326896
    Abstract: We have developed a system that automatically suggests macros to help customer-support agents process customer-support tickets in an online customer-support system. During operation, the system receives a customer-support ticket, which is associated with a request from a customer in the customer-support system, wherein the request relates to a product or a service used by the customer. Next, the system converts text from the customer-support ticket into a ticket embedding in a vector space. The system then feeds the ticket embedding into a macro-suggestion model, which correlates ticket embeddings with macros, wherein each of the macros comprises a sequence of commands that performs an operation to facilitate processing of the customer-support ticket. If the macro-suggestion model produces suggested macros, the system presents the suggested macros to a customer-support agent.
    Type: Application
    Filed: April 21, 2020
    Publication date: October 21, 2021
    Applicant: Zendesk, Inc.
    Inventors: Ai-Lien Tran-Cong, Anh Thien Dinh, Stephanie C. Olding, Christopher J. Hausler, Eleanor B. Stribling, Hing Yip Pak, Pasindu V. Dissanayake, Akhil Naru
  • Patent number: 10699183
    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.
    Type: Grant
    Filed: March 5, 2018
    Date of Patent: June 30, 2020
    Assignee: Zendesk, Inc.
    Inventors: Christopher J. Hausler, Michael G. Mortimer, Soon-Ee Cheah, Shi Yu Zhu, Ai-Lien Tran-Cong, Wai Chee Yau, Hing Yip Pak, Anh Thien Dinh
  • Patent number: 10580012
    Abstract: 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.
    Type: Grant
    Filed: May 26, 2017
    Date of Patent: March 3, 2020
    Assignee: Zendesk, Inc.
    Inventors: Christopher J. Hausler, Michael G. Mortimer, Thomas Pelletier, Arwen Twinkle E. Griffioen, Soon-Ee Cheah, Anh Thien Dinh, Arvind Kunday Anantharaman, Bob Dharmendra Raman, Jason Edward Maynard, Wai Chee Yau, Sean D. Caffery, Jeffrey P. Theobald, Damen Turnbull
  • Publication number: 20190236613
    Abstract: During operation, the system receives a customer request. Next, the system segments the customer request into customer request sentences. The system then encodes each sentence from the customer request with information sequentially collected from the previously observed sentences. Next, the system translates the encodings to sparse probabilities that measure the importance of sentences from the customer request. The system then extracts relevant sentences from the customer request based on the importance. Next, the system forms an extracted-sentence customer request embedding from embeddings for the extracted relevant customer request sentences. The system then uses the extracted-sentence customer request embedding to select an agent response from a set of possible agent responses based on comparisons between the extracted-sentence customer request embedding and embeddings for the set of possible agent responses.
    Type: Application
    Filed: March 29, 2019
    Publication date: August 1, 2019
    Applicant: Zendesk, Inc.
    Inventors: Anh Thien Dinh, Wei Chu, Paul E. Gradie
  • Publication number: 20180197072
    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.
    Type: Application
    Filed: March 5, 2018
    Publication date: July 12, 2018
    Applicant: Zendesk, Inc.
    Inventors: Christopher J. Hausler, Michael G. Mortimer, Soon-Ee Cheah, Shi Yu Zhu, Ai-Lien Tran-Cong, Wai Chee Yau, Hing Yip Pak, Anh Thien Dinh
  • Publication number: 20170286972
    Abstract: 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.
    Type: Application
    Filed: May 26, 2017
    Publication date: October 5, 2017
    Applicant: Zendesk, Inc.
    Inventors: Christopher J. Hausler, Michael G. Mortimer, Thomas Pelletier, Arwen Twinkle E. Griffioen, Soon-Ee Cheah, Anh Thien Dinh, Arvind Kunday Anantharaman, Bob Dharmendra Raman, Jason Edward Maynard, Wai Chee Yau, Sean D. Caffery, Jeffrey P. Theobald, Damen Turnbull
  • Publication number: 20170169438
    Abstract: The disclosed embodiments provide a system that uses a predicted probability of satisfaction for a customer to facilitate a customer-service interaction. During operation, the system obtains information related to an ongoing customer-service interaction involving the customer. The system uses the obtained information to determine a probability that the customer will be satisfied with the customer-service interaction. Next, the system uses the determined probability that the customer will be satisfied to facilitate subsequent interactions whether automated or manual with the customer in furtherance of the customer-service interaction.
    Type: Application
    Filed: December 14, 2015
    Publication date: June 15, 2017
    Applicant: Zendesk, Inc.
    Inventors: Jason Edward Maynard, Michael G. Mortimer, Sean D. Caffery, Christopher J. Hausler, Anh Thien Dinh, Narek Amirbekian, Beau Jonathan Fabry, Jeffrey P. Theobald, Thomas Pelletier