Methods and systems for providing a recommendation

Patent metadata

Field	Value
Publication number	US-2021398186-A1
Application number	US-202016905192-A
Country	US
Kind code	A1
Filing date	Jun 18, 2020
Priority date	Jun 18, 2020
Publication date	Dec 23, 2021
Grant date	—

Abstract

A computer-implemented method for providing a vehicle recommendation to a user may include: obtaining one or more vehicle images via a device associated with the user; identifying one or more user-selected images of the one or more vehicle images based on user interaction with the one or more vehicle images performed by the user via a user interface; identifying one or more first-level attributes from the one or more user-selected images; obtaining one or more vehicle identifications from the one or more user-selected images; determining one or more second-level attributes based on the one or more vehicle identifications; determining a value of each of the one or more first-level attributes and the one or more second-level attributes; determining the vehicle recommendation based on the value; and transmitting, to the device associated with the user, a notification indicating the vehicle recommendation.

First claim

What is claimed is:

1 . A computer-implemented method for providing a vehicle recommendation to a user, the method comprising:
obtaining, via one or more processors, one or more vehicle images via a device associated with the user; identifying, via the one or more processors, one or more user-selected images of the one or more vehicle images based on user interaction with the one or more vehicle images performed by the user via a user interface displayed on the device associated with the user; identifying, via the one or more processors, one or more first-level attributes from the one or more user-selected images, wherein the one or more first-level attributes comprise at least one of a make, a model, a color, a door count, or a seat count of one or more vehicles indicated via the one or more user-selected images; obtaining, via the one or more processors, one or more vehicle identifications from the one or more user-selected images; determining, via the one or more processors, one or more second-level attributes based on the one or more vehicle identifications, wherein the one or more second-level attributes comprise at least one of an engine type, a manufacturing region, or a manufacturing year of the one or more vehicles indicated via the one or more user-selected images; determining, via the one or more processors, a value of each of the one or more first-level attributes and the one or more second-level attributes via one or more algorithms; determining, via the one or more processors, the vehicle recommendation based on the value of each of the one or more first-level attributes and the one or more second-level attributes; and transmitting, to the device associated with the user, a notification indicating the vehicle recommendation.

2 . The computer-implemented method of claim 1 , wherein a given vehicle image of the one or more vehicle images includes at least a picture of a vehicle.

3 . The computer-implemented method of claim 1 , further including updating, via the one or more processors, the one or more user-selected images based on the value of each of the one or more first-level attributes and the one or more second-level attributes via one or more algorithms and a predetermined threshold value.

4 . The computer-implemented method of claim 3 , further including determining, via the one or more processors, the vehicle recommendation based on one or more updated user-selected images.

5 . The computer-implemented method of claim 3 , wherein the predetermined threshold value is set by the user or the one or more algorithms.

6 . The computer-implemented method of claim 1 , wherein determining the vehicle recommendation includes determining the vehicle recommendation via a trained machine learning algorithm.

7 . The computer-implemented method of claim 1 , wherein the one or more first-level attributes further include at least one of a weight, a mileage, or a height of the one or more vehicles indicated via the one or more user-selected images.

8 . The computer-implemented method of claim 1 , wherein the one or more second-level attributes further include at least a vehicle price of the one or more vehicles indicated via the one or more user-selected images.

9 . The computer-implemented method of claim 1 , wherein the one or more vehicle identifications include one or more vehicle identification numbers (VINs).

10 . The computer-implemented method of claim 1 , wherein the notification indicating the vehicle recommendation includes an image of a recommended vehicle.

11 . The computer-implemented method of claim 6 , wherein the trained machine learning model is configured to utilize principal component analysis.

12 . The computer-implemented method of claim 1 , wherein the notification is configured to be displayed on the user interface of the device associated with the user.

13 . The computer-implemented method of claim 1 , wherein the notification includes an interactive feature configured to enable the user to accept or reject the vehicle recommendation.

14 . A computer-implemented method for providing a vehicle recommendation to a user, the method comprising:
obtaining, via one or more processors, one or more vehicle images via a device associated with the user; identifying, via the one or more processors, one or more user-selected images of the one or more vehicle images based on user interaction with the one or more vehicle images performed by the user via a user interface displayed on the device associated with the user; identifying, via the one or more processors, one or more first-level attributes from the one or more user-selected images, wherein the one or more first-level attributes comprise at least one of a make, a model, a color, a door count, or a seat count of one or more vehicles indicated via the one or more user-selected images; obtaining, via the one or more processors, one or more vehicle identifications from the one or more user-selected images; determining, via the one or more processors, one or more second-level attributes based on the one or more vehicle identifications, wherein the one or more second-level attributes comprise at least one of an engine type, a manufacturing region, or a manufacturing year of the one or more vehicles indicated via the one or more user-selected images; determining, via the one or more processors, a value of each of the one or more first-level attributes and the one or more second-level attributes via one or more algorithms; generating, via the one or more processors, user preference data based on the value of each of the one or more first-level attributes and the one or more second-level attributes via one or more algorithms and a predetermined threshold value; determining, via the one or more processors, the vehicle recommendation based on the user preference data via a trained machine learning algorithm; and transmitting, to the device associated with the user, a notification indicating the vehicle recommendation, wherein the notification includes an interactive feature configured to enable the user to accept or reject the vehicle recommendation, wherein the notification is configured to be displayed on the user interface of the device associated with the user.

15 . The computer-implemented method of claim 14 , wherein a given vehicle image of the one or more vehicle images includes at least a picture of a vehicle.

16 . The computer-implemented method of claim 14 , further including updating, via one or more processors, the one or more user-selected images based on the user preference data.

17 . The computer-implemented method of claim 16 , further including determining, via the one or more processors, the vehicle recommendation based on one or more updated user-selected images.

18 . The computer-implemented method of claim 14 , wherein the predetermined threshold value is set by the user or the one or more algorithms.

19 . The computer-implemented method of claim 14 , wherein the notification indicating the vehicle recommendation includes an image of a recommended vehicle.

20 . A computer system for providing a vehicle recommendation to a user, comprising:
a memory storing instructions; and one or more processors configured to execute the instructions to perform operations including:
obtaining one or more vehicle images via a device associated with the user;
identifying one or more user-selected images of the one or more vehicle images based on user interaction with the one or more vehicle images perf

Assignees

• Capital One Service Llc
• Capital One Services Llc

Inventors

• Tang Qiaochu
• Emamdie Michelle
• Guo Jiaxin
• Huang Jeremy
• Bumpas Derek

Classifications

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/08

  Learning methods · CPC title