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:
Apr. 20, 2021

Filed:

Feb. 01, 2017
Applicant:

[24]7.ai, Inc., San Jose, CA (US);

Inventors:

Dinesh Ajmera, Bangalore, IN;

Debashish Panda, Bangalore, IN;

Pankaj Ghanshani, Delhi, IN;

Sumit Kumar, Bangalore, IN;

Ravi Vijayaraghavan, Bangalore, IN;

Mathangi Sri Ramachandran, Bangalore, IN;

Assignee:

[24]7.ai, Inc., San Jose, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06N 5/04 (2006.01); G06N 20/00 (2019.01); H04L 12/58 (2006.01); G06Q 10/10 (2012.01); H04L 12/18 (2006.01); G06Q 30/02 (2012.01); G06N 3/00 (2006.01); G06Q 30/00 (2012.01); H04M 3/51 (2006.01);
U.S. Cl.
CPC ...
G06N 5/04 (2013.01); G06N 3/006 (2013.01); G06N 20/00 (2019.01); G06Q 10/10 (2013.01); G06Q 30/016 (2013.01); G06Q 30/0202 (2013.01); H04L 12/1827 (2013.01); H04L 51/02 (2013.01); H04L 51/04 (2013.01); H04M 3/5183 (2013.01);
Abstract

A mechanism for facilitating customer interactions within a customer service environment provides prompt and accurate answers to customer questions. A smart chat facility for use in a customer service environment to predict a customer problem examines a customer chat transcript to identify customer statements that set forth a customer issue and, responsive to this, can route the customer to an agent, an appropriate FAQ, or can implement a problem specific widget in the customer UI. Customer queries are matched with most correct responses and accumulated knowledge is used to predict a best response to future customer queries. The iterative system thus learns from each customer interaction and can adapt to customer responses over time to improve the accuracy of problem prediction.


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