US 11,941,594 B2
User interaction artificial intelligence chat engine for integration of automated machine generated responses
Sudeshna Banerjee, Waxhaw, NC (US); and Paul Gerard Mistor, Winston-Salem, NC (US)
Assigned to TRUIST BANK, Charlotte, NC (US)
Filed by Truist Bank, Charlotte, NC (US)
Filed on May 13, 2022, as Appl. No. 17/663,370.
Application 17/663,370 is a continuation of application No. 17/661,388, filed on Apr. 29, 2022.
Prior Publication US 2023/0351352 A1, Nov. 2, 2023
Int. Cl. H04L 51/02 (2022.01); G06F 16/35 (2019.01); G06F 40/20 (2020.01); G06F 40/30 (2020.01); G06N 3/04 (2023.01); G06N 3/045 (2023.01); G06Q 20/10 (2012.01); G10L 15/22 (2006.01); H04L 51/046 (2022.01); H04M 3/493 (2006.01); H04M 3/51 (2006.01); H04M 3/22 (2006.01)
CPC G06Q 20/108 (2013.01) [G06F 16/35 (2019.01); G06F 40/20 (2020.01); G06F 40/30 (2020.01); G06N 3/04 (2013.01); G06N 3/045 (2023.01); G10L 15/22 (2013.01); H04L 51/02 (2013.01); H04L 51/046 (2013.01); H04M 3/493 (2013.01); H04M 3/5166 (2013.01); G10L 2015/223 (2013.01); H04M 3/2281 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A system for training a machine learning algorithm configured to guide a user interaction during a phone call or a chat session based on clusters in data contained in a database, comprising:
a database housing data;
a processor operably coupled with the database;
a memory device storing computer-executable instructions that when executed cause the processor to:
collect a set of interaction data from the database;
create a first training set comprising the set of interaction data, a set of correct responses, and a set of incorrect responses;
train the machine learning algorithm in a first stage using the set of interaction data, the set of correct responses, and the set of incorrect responses;
create a second training set for a second stage of training comprising the first training set and a set of incorrect responses that are incorrectly identified as correct responses after the first stage of training; and
train the machine learning algorithm in a second stage using the second training set;
and further comprising:
a computer with one or more processor and memory, where the computer executes the machine learning algorithm configured to guide dialog and actions during a phone call or a chat session with a user concerning a user matter; and
a network connection operatively connecting the user via a user device to the computer,
where the machine learning algorithm is configured to perform steps including:
asking an initial question of the user and receiving a response from the user;
determining a next step based on the response, where the next step is determined by the machine learning algorithm based on probability of a most rapid resolution of the user matter, and where the next step is selected from a group consisting of asking another question of the user and performing an action;
determining a next question when the next step is asking another question of the user;
determining a next action when the next step is performing an action, where the next action is selected from a group consisting of authenticating an identity of the user, providing account information to the user, performing an account transaction for the user, providing assistance to the user in logging into an electronic data system, and connecting a live agent;
connecting a live agent into the phone call or the chat session when connecting the live agent is determined as the next action, where connecting the live agent is determined as the next action for reasons including ambiguity in the response from the user and complexity of the user matter;
presenting the next question or the next action to the user;
receiving a user response to the next question or next action which was presented;
determining if the user matter has been resolved based on the user response;
returning to determining a next step when the user matter has not been resolved; and
ending the phone call or the chat session and storing data describing the user interaction when the user matter has been resolved.