Granted Patent B2

US 12,314,675 · App. 17/741,423 · Granted May 27, 2025

Inventors: Sreevishnu Somasekharan Nair (Frisco, TX); Karthik Venkatesh (Frisco, TX)

Assignee: WALDEN UNIVERSITY, LLC

G06F40/35G06F16/353G06N5/04H04L51/02

Abstract

Claims (47)

1. A computer-implemented method comprising:

at a server having one or more processors and memory storing one or more programs for execution by the one or more processors so as to perform the method:

communicating electronically with a chatbot service to receive a digital request from a user interface;

identifying, by the chatbot service, an intent from the digital request;

transmitting, by the chatbot service, the intent to a webhook for interacting with a conversation engine for conversing with a user;

receiving, at the webhook, the intent from the chatbot service and orchestrating a communication with a backend microservices namespace to provide a response to the intent; and

determining, by the backend microservices namespace, the intent as one of a chit chat or a question; and

wherein the orchestrated communication with the backend microservices namespace comprises:

invoking at least one AI-based microservice that performs real-time processing of the digital request by transforming the digital request into a vector representation using a transformer encoder;

comparing the vector to a set of previously embedded vectors stored in a Facebook AI Similarity Search (FAISS) based similarity search database; and

returning a contextually relevant response to the chatbot service for presentation to the user.

2. The method of claim 1 , wherein the digital request is a voice message or a text transmitted using a web or mobile device interface.

3. The method of claim 1 , further comprising:

generating, by the backend microservices namespace, a response to the intent using a chit chat generation microservice if the intent is chit chat; and

transmitting the response to the user.

4. The method of claim 1 , further comprising:

generating, by the backend microservices namespace, a response to the intent directly to the user if the intent is a question; and

transmitting the response to the user; and

wherein the response is generated from a repository of well documented responses to questions.

5. The method of claim 1 , further comprising detecting, using a frequently asked question (FAQ) similarity microservice of the chatbot service, similarities between the intent and a previously asked and answered intent.

6. The method of claim 5 , wherein the previously asked and answered request is learned content stored in a knowledge base accessible by the chatbot service.

7. The method of claim 5 , wherein the previously asked and answered request is stored locally in cache to immediately provide the response to the user in the form of a text.

8. The method of claim 5 , wherein the frequently asked question (FAQ) similarity microservice of the chatbot service identifies the response to the user based on a context of the intent.

9. The method of claim 1 , wherein the conversation engine is trained automatically trained by sending articles, frequently asked questions (FAQs), or documents on specific topics to the conversation engine.

10. The method of claim 1 , further comprising:

converting the digital request, using a frequently asked question (FAQ) similarity microservice, into a vector by transmitting the digital request through a transformer encoder; and

determining a most similar vector using an AI Similarity Search Database Microservice to find a most similar vector.

11. The method of claim 10 , further comprising performing a vector space search for the encoded digital request to find an answer to the intent.

12. A computer-implemented method comprising:

at a server having one or more processors and memory storing one or more programs for execution by the one or more processors so as to perform the method:

communicating electronically with a chatbot service to receive a digital request from a user interface;

identifying, by the chatbot service, an intent from the digital request;

transmitting, by the chatbot service, the intent to a webhook for interacting with a conversation engine for conversing with a user;

receiving, at the webhook, the intent from the chatbot service and orchestrating a communication with a backend microservices namespace to provide a response to the intent;

determining, by the backend microservices namespace, the intent as one of a chit chat or a question;

converting the digital request, using a frequently asked question (FAQ) similarity microservice, into a vector by transmitting the digital request through a transformer encoder;

determining a most similar vector using an AI Similarity Search Database Microservice to find a most similar vector;

performing a vector space search for the encoded digital request to find an answer to the intent; and

wherein the vector space search and similarity matching are performed using a Facebook AI Similarity Search (FAISS) based similarity database and transformer-based encoder to identify and return a contextually relevant response in real-time.

13. The method of claim 12 , wherein the digital request is a voice message or a text transmitted using a web or mobile device interface.

14. The method of claim 12 , further comprising:

generating, by the backend microservices namespace, a response to the intent using a chit chat generation microservice if the intent is chit chat; and

transmitting the response to the user.

15. The method of claim 12 , further comprising:

generating, by the backend microservices namespace, a response to the intent directly to the user if the intent is a question; and

transmitting the response to the user; and

wherein the response is generated from a repository of well documented responses to questions.

Assignments (2)

Continuity (2)

References Cited (46)