Methods and systems for providing a vehicle recommendation

Patent metadata

Field	Value
Publication number	US-12100034-B2
Application number	US-202217696003-A
Country	US
Kind code	B2
Filing date	Mar 16, 2022
Priority date	Dec 12, 2019
Publication date	Sep 24, 2024
Grant date	Sep 24, 2024

Abstract

A computer-implemented method for determining a reward associated with one or more transactions of a user may comprise obtaining travel data of the user via a device associated with the user, wherein the travel data includes travel dates of the user; obtaining, via one or more processors, exchange rate data based on the travel data of the user; determining, via the one or more processors, a value of the reward associated with the one or more transactions of the user during the travel dates based on the exchange rate data; transmitting, to the user, a notification indicative of the reward associated with the one or more transactions; and causing the reward associated with the one or more transactions to be directed to a financial account associated with the user.

First claim

What is claimed is:

1. A computer-implemented method for providing an item recommendation for a user, the method comprising:
obtaining, via one or more processors, transactional data of the user and prior transactional data of a plurality of users from one or more transactional entities, wherein the transactional data comprises at least one of an income pattern or a spending pattern of the user;
determining, via the one or more processors, a payment goal of the user based on one or more inputs provided via one or more interactive components of a user device, wherein the one or more inputs include a personalized payment amount defined by the user;
determining, via the one or more processors, purchasing power data of the user based on the transactional data and the payment goal, and prior purchasing power data of the plurality of users based on the prior transactional data, wherein the purchasing power data comprises at least an item price range;
determining, via the one or more processors based on the purchasing power data, bidding data including at least a bidding starting price and a bidding increment amount;
obtaining, via the one or more processors, sales data of one or more items, wherein the sales data of the one or more items comprises sale prices of the one or more items;
comparing, via the one or more processors, the bidding data and the sales data during a predetermined bidding period;
determining, via use of a trained machine learning model, the item recommendation based on at least one mapped variable, the trained machine learning model having an input layer to receive the bidding data, an internal layer that upon receipt from the input layer maps the bidding data to at least one variable based on the comparing of the bidding data to the sales data, and an output layer to map the at least one variable to a monetary value of the item recommendation as a function of the internal layer; and
transmitting, to the user device, a notification indicating the item recommendation.

2. The computer-implemented method of claim 1 , wherein the transactional data further includes a debt, a loan, an additional income, or a credit score of the user.

3. The computer-implemented method of claim 1 , wherein the one or more transactional entities include one or more merchants including one or more item vendors, financial services providers, or online resources.

4. The computer-implemented method of claim 3 , wherein the one or more transactional entities include the one or more item vendors, and wherein the sales data of the one or more items further includes one or more vendor identifications associated with the one or more item vendors.

5. The computer-implemented method of claim 3 , wherein the one or more transactional entities include the one or more item vendors, the computer-implemented method further including transmitting, to the one or more item vendors, the notification indicating the item recommendation.

6. The computer-implemented method of claim 1 , wherein the purchasing power data further includes demographic information of the user.

7. The computer-implemented method of claim 1 , wherein mapping the bidding data to the at least one variable includes comparing the item price range of the user, the personalized payment amount of the user, and the sale prices of the one or more items.

8. The computer-implemented method of claim 1 , further including, prior to obtaining the transactional data of the user, authenticating a user identification of the user.

9. A computer system for providing an item recommendation to a user, comprising:
a memory storing instructions; and
one or more processors configured to execute the instructions to perform operations including:
obtaining-transactional data of the user and prior transactional data of a plurality of users from one or more transactional entities, wherein the transactional data comprises at least one of an income pattern or a spending pattern of the user;
determining a payment goal of the user based on one or more inputs provided via one or more interactive components of a user device, wherein the one or more inputs include a personalized payment amount defined by the user;
determining purchasing power data of the user based on the transactional data and the payment goal, and prior purchasing power data of the plurality of users based on the prior transactional data, wherein the purchasing power data comprises at least an item price range;
determining, based on the purchasing power data, bidding data including at least a bidding starting price and a bidding increment amount;
obtaining data of one or more items, wherein the sales data of the one or more items comprises sale prices of the one or more items;
comparing the bidding data and the sales data during a predetermined bidding period;
determining, via use of a trained machine learning model, the item recommendation based on at least one mapped variable, the trained machine learning model having an input layer to receive the bidding data, an internal layer that upon receipt from the input layer maps the bidding data to at least one variable based on the comparing of the bidding data to the sales data, and an output layer to map the at least one variable to a monetary value of the item recommendation as a function of the internal layer; and
transmitting, to the user device, a notification indicating the item recommendation.

10. The computer system of claim 9 , wherein the transactional data further includes a debt, a loan, an additional income, or a credit score of the user.

11. The computer system of claim 9 , wherein the one or more transactional entities include one or more merchants including one or more item vendors, financial services providers, or online resources.

12. The computer system of claim 11 , wherein the one or more transactional entities include the one or more item vendors, and wherein the sales data of the one or more items further includes one or more dealer identifications associated with the one or more item vendors.

13. The computer system of claim 11 , wherein the one or more transactional entities include the one or more item vendors, the operations further including transmitting, to the one or more item vendors, the notification indicating the item recommendation.

14. The computer system of claim 9 , wherein the purchasing power data further includes demographic information of the user.

15. The computer system of claim 9 , wherein mapping the bidding data with the at least one variable includes comparing the item price range of the user, the personalized payment amount of the user, and the sale prices of the one or more items.

16. The computer system of claim 9 , further including, prior to obtaining the transactional data of the user, authenticating a user identification of the user.

17. A computer-implemented method for providing an item recommendation, the method comprising:
obtaining, via one or more processors, transactional data of a user and prior transactional data of a plurality of users from one or more transactional entities, wherein the one or more transactional entities include one or more merchants including one or more item vendors, financial services providers, or online resources, and wherein the transactional data comprises at least one of an income pattern or a spending pattern of the user;
determining, via the one or more processors, a payment goal of the user based on one or more inputs provided via a user device, wherein the one or more inputs include a personalized payment amount defined by the user;
determining,

Assignees

• Capital One Services Llc

Inventors

• Subramanian Ponnazhakan
• Unnikrishnan Sasi Kumar
• Chikkaveerappa Satish

Classifications

• G06Q30/0625

  by formulating product or service queries, e.g. using keywords or predefined options · CPC title

• G06Q30/0611

  Request for offers or quotes · CPC title

• G06Q40/02

  Banking, e.g. interest calculation or account maintenance (credit or loans G06Q40/03) · CPC title

• G06Q30/0631 Primary

  Recommending goods or services · CPC title