Generation of graphics for vehicle items

Patent metadata

Field	Value
Publication number	US-2023153896-A1
Application number	US-202117455101-A
Country	US
Kind code	A1
Filing date	Nov 16, 2021
Priority date	Nov 16, 2021
Publication date	May 18, 2023
Grant date	—

Abstract

A computer-implemented method of generating a graphic for a vehicle item may include: causing a user device to display a user interface indicative of one or more vehicles; receiving, from the user device, vehicle selection information, the vehicle selection information indicative of a vehicle selected by a user; obtaining, from a database, user data corresponding to the user; generating, using a machine learning model, a first score corresponding to a first vehicle item based on the user data; determining whether the first score exceeds a first predetermined score threshold; generating, in response to a determination that the first score exceeds the first predetermined score threshold, a first graphic indicative of the first vehicle item; and causing the user device to display the first graphic via the user interface.

First claim

What is claimed is:

1 . A computer-implemented method of generating a graphic for a vehicle item, the method comprising:
causing a user device to display a user interface indicative of one or more vehicles; receiving, from the user device, vehicle selection information, the vehicle selection information indicative of a vehicle selected by a user; obtaining, from a database, user data corresponding to the user, the user data including one or more of (1) a set of user interaction data and (2) prior vehicle information for one or more prior vehicles associated with the user; generating, using a machine learning model, a first score corresponding to a first vehicle item based on the user data, wherein the machine learning model is:
trained to learn associations between at least (i) a set of user population data and (ii) a set of vehicle item selections, wherein each of the vehicle item selections correspond to a subset of the user population data; and
configured to generate the first score based on the first vehicle item using the learned associations;
determining whether the first score exceeds a first predetermined score threshold; generating, in response to a determination that the first score exceeds the first predetermined score threshold, a first graphic indicative of the first vehicle item; and causing the user device to display the first graphic via the user interface.

2 . The computer-implemented method of claim 1 , further comprising:
receiving, from the user device, first vehicle item selection information indicating selection of the first vehicle item by the user.

3 . The computer-implemented method of claim 2 , further comprising:
updating the machine learning model based on the user data and the first vehicle item selection information.

4 . The computer-implemented method of claim 1 , further comprising:
generating, using the machine learning model, a second score corresponding to a second vehicle item based on the user data; determining whether the second score exceeds a second predetermined score threshold; generating, in response to a determination that the second score exceeds the second predetermined score threshold, a second graphic indicative of the second vehicle item; and causing the user device to display the second graphic concurrently with the first graphic via the user interface.

5 . The computer-implemented method of claim 4 , further comprising:
receiving, from the user device, vehicle item selection information indicating selection of the first vehicle item and the second vehicle item by the user; and updating the machine learning model based on the user data and the vehicle item selection information.

6 . The computer-implemented method of claim 1 , further comprising:
generating, using the machine learning model, a second score corresponding to a second vehicle item based on the user data; determining whether the second score exceeds a second predetermined score threshold; and causing, in response to a determination that the second score does not exceed the second predetermined score threshold, the user device to continue to display the first graphic via the user interface without adding a second graphic indicative of the second vehicle item.

7 . The computer-implemented method of claim 1 , wherein the set of user interaction data comprises at least one of: an identification of interactions, a location corresponding to each of the interactions, or a time corresponding to each of the interactions.

8 . The computer-implemented method of claim 1 , wherein the prior vehicle information is indicative of one or more prior vehicle items previously selected by the user for the one or more prior vehicles.

9 . The computer-implemented method of claim 1 , wherein the prior vehicle information is indicative of an accident involving at least one of the prior vehicles.

10 . The computer-implemented method of claim 1 , wherein the user data includes both of (1) the set of user interaction data and (2) the prior vehicle information; and
the prior vehicle information is indicative of a repair made to at least one of the prior vehicles.

11 . The computer-implemented method of claim 1 , further comprising:
receiving threshold adjustment information indicative of a request to adjust the first predetermined score threshold; and adjusting the first predetermined score threshold.

12 . The computer-implemented method of claim 1 , wherein the first graphic is indicative of the first score.

13 . The computer-implemented method of claim 1 , wherein the machine learning model is a logistic regression model.

14 . A computer-implemented method of generating a graphic for a vehicle item, the method comprising:
causing a user device to display a user interface indicative of one or more vehicles; receiving, from the user device, vehicle selection information, the vehicle selection information being indicative of a vehicle selected by a user; obtaining, from a database, user data corresponding to the user, the user data including (1) prior vehicle information for one or more prior vehicles associated with the user and (2) prior vehicle item information for one or more prior vehicle items associated with the one or more prior vehicles; determining, based on the user data, whether a first vehicle item matches at least one of the prior vehicle items; generating, in response to a determination that the first vehicle item matches at least one of the prior vehicle items, a first graphic indicative of the first vehicle item; and causing the user device to display the first graphic via the user interface.

15 . The computer-implemented method of claim 14 , further comprising:
determining, based on the user data, whether a second vehicle item matches at least one of the prior vehicle items; and generating, in response to a determination that the second vehicle item matches at least one of the prior vehicle items, a second graphic indicative of the second vehicle item and causing the user device to display the second graphic concurrently with the first graphic via the user interface.

16 . The computer-implemented method of claim 15 , further comprising:
receiving, from the user device, vehicle item selection information indicating selection by the user of at least one of the first vehicle item and the second vehicle item.

17 . The computer-implemented method of claim 14 , further comprising,
determining, based on the user data, whether a second vehicle item matches at least one of the prior vehicle items; and causing, in response to a determination that the second vehicle item does not match at least one of the prior vehicle items, the user device to continue displaying the first graphic without adding a second graphic indicative of the second vehicle item.

18 . The computer-implemented method of claim 14 , wherein prior the vehicle item information is indicative of one or more prior vehicle items selected by the user for the one or more prior vehicles.

19 . A system for generating a graphic for a vehicle item comprising:
one or more memories storing instructions and a machine learning model, wherein the machine learning model is:
trained to learn associations between at least (i) a set of user population data and (ii) a set of vehicle item selections, each of the vehicle item selections corresponding to a subset of the user population data; and
configured to generate scores based on vehicle items using the learned associations; and
one or more processors

Assignees

• Capital One Services Llc

Inventors

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

Classifications

• G06Q40/08

  Insurance · CPC title

• G06Q30/0623 Primary

  by investigating goods or services · CPC title

• G06Q30/0643 Primary

  graphically representing goods, e.g. 3D product representation · CPC title

• G06F9/451

  Execution arrangements for user interfaces · CPC title

• G06N5/022

  Knowledge engineering; Knowledge acquisition · CPC title