US 11,875,401 B2
Methods and systems for providing personalized purchasing information
Avid Ghamsari, Austin, TX (US); Staevan Duckworth, Oak Point, TX (US); Geoffrey Dagley, McKinney, TX (US); Micah Price, Plano, TX (US); and Qiaochu Tang, The Colony, TX (US)
Assigned to Capital One Services, LLC, McLean, VA (US)
Filed by Capital One Services, LLC, McLean, VA (US)
Filed on Nov. 1, 2021, as Appl. No. 17/453,058.
Application 17/453,058 is a continuation of application No. 16/695,387, filed on Nov. 26, 2019, granted, now 11,164,246.
Prior Publication US 2022/0051317 A1, Feb. 17, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 40/03 (2023.01); G06Q 20/10 (2012.01); G06Q 40/08 (2012.01); G06Q 30/0601 (2023.01); G06Q 40/04 (2012.01); G06Q 30/02 (2023.01); G06Q 40/02 (2023.01)
CPC G06Q 40/03 (2023.01) [G06Q 20/10 (2013.01); G06Q 30/02 (2013.01); G06Q 30/0601 (2013.01); G06Q 40/04 (2013.01); G06Q 40/08 (2013.01); G06Q 40/02 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for providing personalized purchasing information to a user, the method comprising:
receiving, via one or more processors, data indicative of an interaction between a user and a first engine;
obtaining, via the one or more processors, first identification data of the user from the first engine;
verifying, using the first identification data and via the one or more processors, a registration status of the user with a second engine;
based on the registration status of the user with the second engine, obtaining credit information of the user;
generating the personalized purchasing information of the user by inputting the credit information of the user into a trained machine learning model, wherein:
the trained machine learning model has been trained, based on credit information of customers other than the user and on personalized purchasing information of the customers other than the user, to generate output that includes the personalized purchasing information of the user in response to an input of the credit information of the user;
the personalized purchasing information generated by the machine learning model includes:
a prequalification status of the user based on the credit information of the user; and
a purchasing recommendation for the user based on the prequalification status of the user, the purchasing recommendation including a priced financing option for one or more products, the priced financing option requiring the prequalification status in order to be priced for the user;
generating a web page that is specific to the user and that includes the personalized purchasing information generated by the trained machine learning model; and
causing, via the first engine, a display of a device associated with the user to display the generated web page, such that the personalized purchasing information is provided in response to the interaction between the user and the first engine.