Automatically responding to a request of a user

Patent metadata

Field	Value
Publication number	US-9715496-B1
Application number	US-201615254008-A
Country	US
Kind code	B1
Filing date	Sep 1, 2016
Priority date	Jul 8, 2016
Publication date	Jul 25, 2017
Grant date	Jul 25, 2017

Abstract

A user may request assistance or information using natural language, such as by speaking or entering a text. A response may be automatically determined to the request of the user be performing semantic processing to understand the request and formulate an appropriate response. A response may be automatically determined, be computing features from the request, using the features to select a node of a graph corresponding to the request, and then causing an action to be performed to provide a response to the user. For example, the action may include providing information to the user, requesting further information from the user, or connecting the user with a customer service representative.

First claim

What is claimed is:

1. A computer-implemented method for automatically responding to a request of a customer, the method comprising:
receiving, at a server computer, text of a message corresponding to a customer request, wherein the message is received from a computing device of the customer;
computing, by a server computer, a plurality of features from text of the message;
obtaining, by a server computer, a graph comprising a plurality of nodes, wherein each node of the graph corresponds to a node selector classifier and an action, wherein each node selector classifier receives the plurality of features as inputs and processes the inputs to output scores, and wherein each node of the graph corresponds to a different type of a customer request;
obtaining, by a server computer, a first node selector classifier corresponding to a first node of the graph;
processing, by a server computer, the plurality of features with the first node selector classifier to generate a first plurality of scores comprising a score for traversing to one or more child nodes of the first node;
selecting, by a server computer, a second node of the graph using the first plurality of scores, wherein the second node is a child node of the first node;
obtaining, by a server computer, a second node selector classifier corresponding to the second node, wherein the second node selector classifier is different from the first node selector classifier;
processing, by a server computer, the plurality of features with the second node selector classifier to generate a second plurality of scores comprising (i) a score indicating a match between the customer request and the second node and (ii) a score for traversing to one or more child nodes of the second node;
selecting, by a server computer, the second node using the second plurality of scores; and
causing, by a server computer, an action corresponding to the selected second node to be performed, wherein the action comprises responding to the customer request by sending a computer-generated message to the customer or electronically connecting the customer with a customer service representative.

2. The computer-implemented method of claim 1 , wherein the score indicating a match between the customer request and the second node is a largest score of the second plurality of scores.

3. The computer-implemented method of claim 1 , wherein the first node selector classifier is independent of the second node selector classifier.

4. The computer-implemented method of claim 1 , wherein receiving text of a message corresponding to a customer request comprises performing speech recognition on an audio signal comprising speech of the customer.

5. The computer-implemented method of claim 1 , wherein the message is received via a text message, electronic mail, a web server, or an application running on the computing device of the customer.

6. The computer-implemented method of claim 1 , wherein selecting the second node comprises using a greedy search algorithm.

7. The computer-implemented method of claim 1 , wherein computing the first plurality of scores comprises using information about the customer.

8. The computer-implemented method of claim 1 , wherein the first plurality of scores is not used in generating the second plurality of scores.

9. The computer-implemented method of claim 1 , comprising:
receiving an identifier of the customer;
obtaining data about the customer from a data store comprising data about a plurality of customers; and
wherein the first classifier is configured to generate the first plurality of scores by processing the features and the data about the customer.

10. The computer-implemented method of claim 9 , wherein the second classifier does not process the data about the customer.

11. The computer-implemented method of claim 9 , wherein the data about the customer comprises a customer profile, a location of the customer, billing data, payment data, or services provided to the customer.

12. A system for automatically responding to a request of a customer, the system comprising:
at least one server computer comprising at least one processor and at least one memory, the at least one server computer configured to:
receive, at the at least one server computer, text of a message corresponding to a customer request, wherein the message is received from a computing device of the customer;
compute, by the at least one server computer, a plurality of features from text of the message;
obtain, by the at least one server computer, a graph comprising a plurality of nodes, wherein each node of the graph corresponds to a node selector classifier and an action, wherein each node selector classifier receives the plurality of features as inputs and processes the inputs to output scores, and wherein each node of the graph corresponds to a different type of a customer request;
obtain, by the at least one server computer, a first node selector classifier corresponding to a first node of the graph;
process, by the at least one server computer, the plurality of features with the first node selector classifier to generate a first plurality of scores comprising a score for traversing to one or more child nodes of the first node;
select, by the at least one server computer, a second node of the graph using the first plurality of scores, wherein the second node is a child node of the first node;
obtain, by the at least one server computer, a second node selector classifier corresponding to the second node, wherein the second node selector classifier is different from the first node selector classifier;
process, by the at least one server computer, the plurality of features with the second node selector classifier to generate a second plurality of scores comprising (i) a score indicating a match between the customer request and the second node and (ii) a score for traversing to one or more child nodes of the second node;
select, by the at least one server computer, the second node using the second plurality of scores; and
cause, by the at least one server computer, an action corresponding to the selected second node to be performed, wherein the action comprises responding to the customer request by sending a computer-generated message to the customer or electronically connecting the customer with a customer service representative.

13. The system of claim 12 , wherein the second node is associated with a plurality of actions, and wherein the at least one server computer is configured to select an action from the plurality of actions.

14. The system of claim 13 , wherein the at least one server computer is configured to select the action using information about the customer.

15. The system of claim 12 , wherein the graph is a directed acyclic graph.

16. The system of claim 12 , wherein the at least one server computer is configured to select the second node using a beam search algorithm.

17. The system of claim 12 , wherein the at least one server computer is configured to compute the second plurality of scores using information.

18. The system of claim 12 , wherein the first classifier comprises a logistic regression classifier.

19. One or more non-transitory computer-readable media comprising computer executable instructions that, when executed, cause at least one processor to perform actions comprising:
receiving, at a server computer, text of a message corresponding to a customer request, wherein the message is received from a computing device of the customer;
computing, by a server computer, a plurality of fe

Assignees

• Asapp Inc
• Asapp Inc

Inventors

• Sapoznik Gustavo
• Dai Hui
• Hackman Joseph
• Henry Shawn

Classifications

• G06N3/08

  Learning methods · CPC title

• G06N3/04

  Architecture, e.g. interconnection topology · CPC title

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N3/047

  Probabilistic or stochastic networks · CPC title