Assisting entities in responding to a request of a user

• US 10,733,614 B2
• Filed: 06/21/2019
• Issued: 08/04/2020
• 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:
  US16/448,532
• Publication Number:
  US 20190311375A1
• Time in Patent Office: 410 Days
• 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 users and implemented by a third-party service provider, the method comprising:

receiving, at the third-party service provider, a first request from a first user, wherein the first request relates to a first corporate entity;

obtaining first text corresponding to the first request;

transmitting a first application program interface (API) 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;

computing first natural language processing (NLP) features from the first text;

automatically generating first semantic processing results by processing the first NLP features and the first information with a neural network;

transmitting the first semantic processing results to the first user;

receiving, at the third-party service provider, a second request from a second user, wherein the second request relates to a second corporate entity and wherein the second corporate entity is different from the first corporate entity;

obtaining second text corresponding to the second request;

transmitting a second API 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;

computing second NLP features from the second text;

automatically generating second semantic processing results by processing the second NLP features and the second information with the neural network;

transmitting the second semantic processing results to the second user.

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

3. The computer-implemented method of claim 1, wherein obtaining the first text comprises performing automated speech recognition on audio of the first request.

4. The computer-implemented method of claim 1, wherein computing the first NLP features from the first text comprises one or more of computing a bag of words vector from the first text, computing a term frequency inverse document frequency vector from the first text, or obtaining word embeddings corresponding to the first text.

5. The computer-implemented method of claim 1, wherein automatically generating the first semantic processing results comprises selecting a node from an action graph.

6. The computer-implemented method of claim 1, wherein automatically generating the first semantic processing results comprises:

determining a text response to the first text;

or

generating a completion to the first text.

7. The computer-implemented method of claim 1, wherein the first request is received from a first device of the first user and the first semantic processing results are transmitted to the first device of the first user.

8. A system for assisting corporate entities in responding to users, 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 a third-party service provider, a first request from a first user, wherein the first request relates to a first corporate entity;

obtain a first text corresponding to the first request;

transmit a first application program interface (API) request for first information from a server of the third-party service provider to a first server of the first corporate entity;

receive the first information from the first corporate entity;

compute first natural language processing (NLP) features from the first text;

automatically generate first semantic processing results by processing the first NLP features and the first information with a neural network;

transmit the first semantic processing results to the first user;

receive, at the third-party service provider, a second request from a second user, wherein the second request relates to a second corporate entity and wherein the second corporate entity is different from the first corporate entity;

obtain second text corresponding to the second request;

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

receive the second information from the second corporate entity;

compute second NLP features from the second text;

automatically generate second semantic processing results by processing the second NLP features and the second information with the neural network;

transmit the second semantic processing results to the second user.

9. The system of claim 8, wherein the first information comprises information about the first user.

10. The system of claim 8, wherein the at least one server computer is configured to compute the first NLP features using a topic model, a latent semantic indexing model, a latent Dirichlet allocation model, or a second neural network.

11. The system of claim 8, wherein the at least one server computer is configured to automatically generate the first semantic processing results by:

determining a text response to the first text;

or

generating a completion to the first text.

12. The system of claim 8, wherein the neural network comprises a classifier.

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

14. The system of claim 8, wherein the first request is received via the first corporate entity and the first semantic processing results are transmitted via the first corporate entity.

15. 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 third-party service provider, a first request from a first user, wherein the first request relates to a first corporate entity;

obtaining a first text corresponding to the first request;

transmitting a first application program interface (API) 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;

computing first natural language processing (NLP) features from the first text;

automatically generating first semantic processing results by processing the first NLP features and the first information with a neural network;

transmitting the first semantic processing results to the first user;

receiving, at the third-party service provider, a second request from a second user, wherein the second request relates to a second corporate entity and wherein the second corporate entity is different from the first corporate entity;

obtaining second text corresponding to the second request;

transmitting a second API 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;

computing second NLP features from the second text;

automatically generating second semantic processing results by processing the second NLP features and the second information with the neural network;

transmitting the second semantic processing results to the second user.

16. The one or more non-transitory computer-readable media of claim 15, wherein the first request comprises a customer support request.

17. The one or more non-transitory computer-readable media of claim 15, wherein automatically generating the first semantic processing results comprises selecting a node from an action graph.

18. The one or more non-transitory computer-readable media of claim 15, wherein automatically generating the first semantic processing results comprises:

determining a text response to the first text;

or

generating a completion to the first text.

19. The one or more non-transitory computer-readable media of claim 15, wherein the neural network comprises a classifier.

20. The one or more non-transitory computer-readable media of claim 19, wherein the classifier comprises a logistic regression classifier.