US 12,260,424 B2
Techniques to predict and implement an amortized bill payment system
Austin Grant Walters, Savoy, IL (US); Vincent Pham, Champaign, IL (US); Reza Farivar, Champaign, IL (US); and Jeremy Edward Goodsitt, Champaign, IL (US)
Assigned to Capital One Services, LLC., McLean, VA (US)
Filed by Capital One Services, LLC, McLean, VA (US)
Filed on Jul. 6, 2023, as Appl. No. 18/218,949.
Application 18/218,949 is a continuation of application No. 16/657,597, filed on Oct. 18, 2019, granted, now 11,734,705.
Prior Publication US 2023/0351426 A1, Nov. 2, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 30/0201 (2023.01); G06N 3/08 (2023.01); G06Q 20/08 (2012.01); G06Q 20/10 (2012.01); G06Q 30/0204 (2023.01)
CPC G06Q 30/0206 (2013.01) [G06N 3/08 (2013.01); G06Q 20/0855 (2013.01); G06Q 20/102 (2013.01); G06Q 30/0205 (2013.01)] 20 Claims
OG exemplary drawing
 
1. An apparatus, comprising:
a communication interface;
a processor circuit coupled with the communication interface; and
a memory coupled with the processor circuit and the communication interface, the memory storing instructions which when executed by the processor circuit, cause the processor circuit to:
receive, via the communication interface coupled with the processor circuit, service billing data for a plurality of customers, the service billing data comprising a plurality of attributes for each of the plurality of customers;
identify a group of customers based on a cluster operation using at least two attributes of the plurality of attributes;
predict, with a plurality of recurrent neural networks (RNNs), predicted periodic bill amounts for a future period of time for the group of customers, the prediction based on bill information for the group of customers received during a predetermined duration, wherein each of the RNNs is associated with a particular customer in the group of customers and trained with particular bill information for the particular customer to predict periodic bill amounts for the future period of time for the particular customer;
retrain each of the plurality of RNNs with the predicted periodic bill amounts for the future period of time from all of the plurality of customers;
predict, for the particular customer with an associated RNN, the periodic bill amounts for the future period of time, wherein the associated RNN is trained with the bill information for the particular customer and retrained with the predicted periodic bill amounts for the plurality of customers; and
display the periodic bill amounts for the future period of time for the particular customer.