Assisting entities in responding to a request of a user

• US 10,387,888 B2
• Filed: 09/01/2016
• Issued: 08/20/2019
• Est. Priority Date: 07/08/2016
• Status: Active Grant

• Current Assignee: Asapp, Inc.
• Original Assignee: Asapp, Inc.
• Inventors: Sapoznik, Gustavo, Dai, Hui, Hackman, Joseph
• Primary Examiner(s): Rahman, Sm A
• Application Number:
  US15/254,101
• Publication Number:
  US 20180013699A1
• Time in Patent Office: 1,083 Days
• Field of Search: 709206
• CPC Class Codes: G06F 16/2237 Vectors, bitmaps or matrices
• CPC Class Codes: G06F 16/245 Query processing
• CPC Class Codes: G06F 16/3329 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 16/3344 using natural language analysis
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 16/90332 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 40/205 Parsing
• CPC Class Codes: G06F 40/211 Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
• CPC Class Codes: G06F 40/216 using statistical methods
• CPC Class Codes: G06F 40/35 Discourse or dialogue representation
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/044 Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/047 Probabilistic or stochastic networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 3/088 Non-supervised learning, e.g. competitive learning
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06N 5/022 : Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06Q 10/06311 : Scheduling, planning or task assignment for a person or group
• CPC Class Codes: G06Q 10/1053 : Employment or hiring
• CPC Class Codes: G06Q 10/107 : Computer-aided management of electronic mailing [e-mailing]
• CPC Class Codes: G06Q 30/01 : Customer relationship services
• CPC Class Codes: G06Q 30/016 : After-sales
• CPC Class Codes: G10L 15/26 : Speech to text systems G10L15/08 takes precedence
• CPC Class Codes: H04L 41/04 : Network management architectures or arrangements
• CPC Class Codes: H04L 51/02 : using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
• CPC Class Codes: H04L 51/52 : for supporting social networking services
• CPC Class Codes: H04L 67/02 : based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/306 : User profiles
• CPC Class Codes: H04L 67/53 : using third party service providers
• CPC Class Codes: H04M 3/42382 : Text-based messaging services in telephone networks such as PSTN/ISDN, e.g. User-to-User Signalling or Short Message Service for fixed networks
• CPC Class Codes: H04M 3/5141 : Details of processing calls and other types of contacts in an unified manner unified messaging in packet-switching networks H04L51/56
• CPC Class Codes: H04M 3/5183 : Call or contact centers with computer-telephony arrangements

Abstract

A third-party service may be used to assist entities in responding to requests of users. A third-party service may receive, directly or indirectly, a request of a first user for assistance from a first entity. The third-party service may request information about the first user by sending a request to a computer of the first entity. The third-party service may use the request of the first user and the information about the first user to automatically generate a response to the request of the first user. The third-party service may then transmit, directly or indirectly, the response to the first user.

Claims

1. A computer-implemented method for assisting corporate entities in responding to customer support requests and implemented by a third-party service provider, the method comprising:

receiving, at the third-party service provider, a first customer support request from a first customer, wherein the first customer support request seeks assistance from a first corporate entity;

obtaining a first text corresponding to the first customer support request;

transmitting a first request for first information from a server of the third-party service provider to a first server of the first corporate entity;

receiving the first information from the first corporate entity;

automatically generating a first response to the first customer support request using semantic processing of the first text and using the first information, wherein automatically generating the first response comprises selecting a first node from a first action graph;

transmitting the first response to the first customer;

receiving, at the third-party service provider, a second customer support request from a second customer, wherein the second customer support request seeks assistance from a second corporate entity and wherein the second corporate entity is different from the first corporate entity;

obtaining second text corresponding to the second customer support request;

transmitting a second request for second information from a server of the third-party service provider to a second server of the second corporate entity;

receiving the second information from the second corporate entity;

automatically generating a second response to the second customer support request using semantic processing of the second text and using the second information, wherein automatically generating the second response comprises selecting a second node from a second action graph; and

transmitting the second response to the second customer.

2. The computer-implemented method of claim 1, wherein the first request comprises information identifying the first customer.

3. The computer-implemented method of claim 1, wherein the first information comprises information about the first customer.

4. The computer-implemented method of claim 1, wherein the first customer is a customer of the first corporate entity and the second customer is a customer of the second corporate entity.

5. The computer-implemented method of claim 1, wherein automatically generating the first response comprises processing the first text with a neural network.

6. The computer-implemented method of claim 1, wherein the first request is received from a first device of the first customer and the first response is transmitted to the first device of the first customer.

18. The computer-implemented method of claim 1, wherein automatically generating the first response using semantic processing comprises:

determining a text response by processing the first text with a classifier to understand a meaning of the first text;

or

generating a completion to the first text by processing the first text with a neural network.

7. A system for assisting corporate entities in responding to customers, 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 a first text from a first customer seeking assistance from a first corporate entity;

transmit a first request for first information to a first server of the first corporate entity;

receive the first information from the first corporate entity;

automatically generate a first response to the first text using semantic processing of the first text, using the first information, and by selecting a first node from a first action graph;

transmit the first response to the first customer;

receive second text from a second customer seeking assistance from a second corporate entity and wherein the second corporate entity is different from the first corporate entity;

transmit a second request for second information to a second server of the second corporate entity;

receive the second information from the second corporate entity;

automatically generate a second response to the second text using semantic processing of the second text, using the second information, and by selecting a second node from a second action graph; and

transmit the second response to the second customer.

8. The system of claim 7, wherein the first information comprises information about the first customer.

9. The system of claim 7, wherein the at least one server computer is configured to automatically generate the first response by processing the first text with a neural network.

10. The system of claim 7, wherein the at least one server computer is configured to automatically generate the first response using a first classifier.

11. The system of claim 10, wherein the first classifier comprises a logistic regression classifier.

12. The system of claim 7, wherein the first request is received from the first corporate entity and the first response is transmitted to the first corporate entity.

13. 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 a first text from a first user seeking assistance from a first corporate entity;

transmitting a first request for first information to a first server of the first corporate entity;

receiving the first information from the first corporate entity;

automatically generating a first response to the first text using semantic processing of the first text and using the first information, wherein automatically generating the first response comprises selecting a first node from a first action graph;

transmitting the first response to the first user;

receiving second text from a second user seeking assistance from a second corporate entity and wherein the second corporate entity is different from the first corporate entity;

transmitting a second request for second information to a second server of the second corporate entity;

receiving the second information from the second corporate entity;

automatically generating a second response to the second text using the second text and the second information, wherein automatically generating the second response comprises selecting a second node from a second action graph; and

transmitting the second response to the second user.

14. The one or more non-transitory computer-readable media of claim 13, wherein the first information comprises information about the first user.

15. The one or more non-transitory computer-readable media of claim 13, wherein automatically generating the first response comprises processing the first text with a neural network.

16. The one or more non-transitory computer-readable media of claim 13, wherein automatically generating the first response comprises using a first classifier.

17. The one or more non-transitory computer-readable media of claim 16, wherein the first classifier comprises a logistic regression classifier.