US 12,190,068 B2
Systems and methods for generating real-time dynamic conversational responses during conversational interactions using machine learning models
Md Arafat Hossain Khan, Dallas, TX (US); Isha Chaturvedi, Mountain View, CA (US); Arturo Hernandez Zeledon, Arlington, VA (US); and Mohammad Sorower, McLean, VA (US)
Assigned to Capital One Services, LLC, McLean, VA (US)
Filed by Capital One Services, LLC, McLean, VA (US)
Filed on Jun. 27, 2022, as Appl. No. 17/850,282.
Prior Publication US 2023/0419046 A1, Dec. 28, 2023
Int. Cl. G06F 40/35 (2020.01); G06F 18/214 (2023.01); G06N 20/00 (2019.01)
CPC G06F 40/35 (2020.01) [G06F 18/2155 (2023.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A system for generating real-time dynamic conversational responses during conversational interactions with messaging applications using machine learning models based on historic intents for a plurality of users and user-specific interactions, the system comprising:
storage circuitry configured to store:
a first machine learning model, wherein the first machine learning model comprises a neural network trained to select a first intent from a plurality of intents based on historic data accumulated prior to conversational interactions; and
a second machine learning model, wherein the second machine learning model is based on the first machine learning model as augmented with a neural network, and wherein the neural network is trained to select a first interaction-specific intent from a plurality of interaction-specific intents based on interaction-specific data for a first user accumulated during the conversational interactions;
control circuitry configured to:
receive, from the first user, a first user action during a conversational interaction with a user interface of a messaging application, wherein the conversational interaction comprises an interactive exchange of text messages between the first user and the messaging application;
determining a static feature, wherein the static feature remains constant during the conversational interaction;
generate a first feature input based on the first user action;
input the first feature input into the first machine learning model to determine the first intent;
receive, from the first user, a second user action during the conversational interaction;
determine a temporal relationship between the first user action and the second user action;
generate a second feature input based on the first user action, the second user action, the static feature, the temporal relationship, and the first intent;
input the second feature input into the second machine learning model to determine the first interaction-specific intent; and
input/output circuitry configured to:
generate for display, at the user interface during the conversational interaction, a first dynamic conversational response based on the first intent, wherein the first dynamic conversational response comprises a first text message; and
generate for display, at the user interface during the conversational interaction, a second dynamic conversational response based on the first interaction-specific intent, wherein the second dynamic conversational response comprises a second text message.