US 11,810,042 B1
Disclosure quality assurance
Rachel Elizabeth Csabi, Frisco, TX (US); Hollie Ilene King, Frisco, TX (US); Victor Kwak, Frisco, TX (US); Zachery C. Lake, Aubrey, TX (US); Yogen Rai, Plano, TX (US); Samantha Elizabeth Taylor, Frisco, TX (US); and Nicholas C. Wheeler, The Colony, TX (US)
Assigned to United Services Automobile Association, San Antonio, TX (US)
Filed by UIPCO, LLC, San Antonio, TX (US)
Filed on Oct. 30, 2020, as Appl. No. 17/085,447.
Claims priority of provisional application 62/929,319, filed on Nov. 1, 2019.
Int. Cl. G06Q 10/0639 (2023.01); G06Q 30/016 (2023.01); G06F 3/14 (2006.01); G06N 20/00 (2019.01); G06F 3/16 (2006.01); G06Q 10/107 (2023.01)
CPC G06Q 10/06398 (2013.01) [G06F 3/14 (2013.01); G06F 3/16 (2013.01); G06N 20/00 (2019.01); G06Q 10/107 (2013.01); G06Q 30/016 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A customer service system, comprising:
an agent device associated with an agent; and
a monitoring system, comprising:
a memory configured to store instructions; and
a processor configured to execute the instructions, wherein the instructions, when executed by the processor, cause the monitoring system to:
monitor a conversation between a customer device associated with a customer and the agent device to identify a context of the conversation;
identify, via a first machine learning algorithm, a disclosure notice based at least in part on the context of the conversation, wherein the first machine learning algorithm comprises a first neural network, a first Hidden Markov Model, or a combination thereof, wherein the first machine learning algorithm determines a first pattern, a first threshold number of keywords, or a combination thereof, to identify the disclosure notice as associated with the context of the conversation, wherein the first machine learning algorithm uses a first mathematical model based on training data, a first statistical model based on training data, or a combination thereof, wherein the disclosure notice comprises an indication of a disclosure to be read in light of the context of the conversation;
iteratively train the first machine learning algorithm using the training data;
provide a first indication of the disclosure notice to the agent device, wherein the agent device is configured to provide the first indication to the agent, by:
receiving the first indication from the monitoring system; and
rendering a graphical indication of the disclosure notice in a first graphical user interface (GUI) of the agent device based on the first indication;
provide the disclosure notice to a second machine learning algorithm;
monitor and evaluate, via the second machine learning algorithm, a disclosure provision of the disclosure notice from the agent to the customer, wherein the second machine learning algorithm uses past disclosure readings to determine a second pattern, a second threshold number of keywords, or a combination thereof, to assign a score to the disclosure provision; and
provide a second indication of a disclosure provision feedback regarding evaluation of the disclosure provision to the agent, wherein the agent device is configured to provide the second indication, by:
receiving the second indication from the monitoring system; and
rendering the disclosure provision feedback in a second GUI of the agent device based on the second indication.