Systems and methods for automated trade-in with limited human interaction

Patent metadata

Field	Value
Publication number	US-12073442-B2
Application number	US-202217974104-A
Country	US
Kind code	B2
Filing date	Oct 26, 2022
Priority date	Oct 22, 2019
Publication date	Aug 27, 2024
Grant date	Aug 27, 2024

Abstract

Aspects described herein may facilitate an automated trade-in of a vehicle with limited human interaction. A server may receive a request to begin a value determination of a vehicle associated with the user. The server may receive first data comprising: vehicle-specific identifying information, and multimedia content showing a first aspect of the vehicle. The user may be directed to place the vehicle within a predetermined staging area. The server may receive, from one or more image sensors associated with the staging area, second data comprising multimedia content showing a second aspect of the vehicle. The server may create a feature vector comprising the first data and the second data. The feature vector may be inputted into a machine learning algorithm corresponding to the vehicle-specific identifying information of the vehicle. Based on the machine learning algorithm, the server may determine a value of the vehicle.

First claim

What is claimed is:

1. A method comprising:
receiving a first indication that a use period of a vehicle associated with a user has started;
receiving, based on the first indication, preliminary data for the vehicle associated with the user;
receiving, from a device associated with the user, a second indication that the use period has ended;
receiving, in response to the second indication and from one or more image sensors, first data comprising:
vehicle-specific identifying information of the vehicle, and
multimedia content showing a first aspect of the vehicle;

creating a feature vector comprising the first data;
inputting the feature vector into a machine learning algorithm corresponding to the vehicle-specific identifying information of the vehicle associated with the user;
determining, using the machine learning algorithm, an instance of damage to the vehicle associated with the user based on the first data and the preliminary data; and
sending, to a mobile device associated with the user, an indication of the instance of damage to the vehicle associated with the user.

2. The method of claim 1 , further comprising:
receiving, from the mobile device associated with the user, a request to begin a determination of the instance of damage to vehicle associated with the user;
receiving, from the mobile device associated with the user, a second data comprising multimedia content showing a second aspect of the vehicle associated with the user; and
determining an initial damage estimate for the vehicle associated with the user.

3. The method of claim 2 , wherein the feature vector further comprises the second data.

4. The method of claim 1 , further comprising:
prior to the inputting the feature vector into the machine learning algorithm, identifying the machine learning algorithm based on the vehicle-specific identifying information of the vehicle associated with the user.

5. The method of claim 1 , further comprising,
prior to the use period, training the machine learning algorithm using reference vehicle-specific identifying information and reference data of one or more aspects of a plurality of reference vehicles that are not associated with the user.

6. The method of claim 5 , wherein the training the machine learning algorithm further comprises:
receiving, for each of the plurality of reference vehicles that are not associated with the user, reference vehicle-specific identifying information and reference data of the first aspect of a given reference vehicle of the plurality of reference vehicles;
receiving, for each of the plurality of reference vehicles, an actual value of the given reference vehicle;
creating, for each of the plurality of reference vehicles, a reference feature vector comprising the reference vehicle-specific identifying information and the reference data;
associating, for each of the plurality of reference vehicles, the reference feature vector to the actual value of the given reference vehicle; and
training the machine learning algorithm using the associated reference feature vectors to predict the actual value of the vehicle associated with the user based on the vehicle-specific identifying information of the vehicle and the first data.

7. The method of claim 6 , further comprising,
determining a predictability for each of the one or more aspects of the reference vehicle for estimating the actual value of the given reference vehicle; and
assigning, based on the determined predictability, a first weight to the first data.

8. The method of claim 1 , wherein the one or more image sensors are calibrated to produce the multimedia content based on a degree of illumination or a time within a diurnal cycle.

9. A computing device, comprising:
one or more processors;
one or more image sensors; and
memory storing instructions that, when executed by the one or more processors, cause the computing device to:
receive a first indication that a use period of a vehicle associated with a user has started;
receive, based on the first indication, preliminary data for the vehicle associated with the user;
receive, from a device associated with the user, a second indication that the use period has ended;
receive, from one or more image sensors, first data comprising:
vehicle-specific identifying information of the vehicle associated with the user, and
multimedia content showing a first aspect of the vehicle associated with the user;

create a feature vector comprising the first data;
input the feature vector into a machine learning algorithm corresponding to the vehicle-specific identifying information of the vehicle associated with the user;
determine, using the machine learning algorithm, an instance of damage to the vehicle associated with the user based on the first data and the preliminary data; and
send, to a mobile device associated with the user, an indication of the instance of damage to the vehicle associated with the user.

10. The computing device of claim 9 , wherein the instructions when executed by the one or more processors, cause the computing device to:
receive, from the mobile device associated with the user, a request to begin a determination of the instance of damage to vehicle associated with the user;
receive, from the mobile device associated with the user, a second data comprising multimedia content showing a second aspect of the vehicle associated with the user; and
determine an initial damage estimate for the vehicle associated with the user.

11. The computing device of claim 10 , wherein the feature vector further comprises the second data.

12. The computing device of claim 9 , wherein the instructions when executed by the one or more processors, cause the computing device to:
prior to the inputting the feature vector into the machine learning algorithm, identifying the machine learning algorithm based on the vehicle-specific identifying information of the vehicle associated with the user.

13. The computing device of claim 9 , wherein the instructions when executed by the one or more processors, cause the computing device to:
prior to the use period, training the machine learning algorithm using reference vehicle-specific identifying information and reference data of one or more aspects of a plurality of reference vehicles that are not associated with the user.

14. The computing device of claim 13 , wherein the instructions when executed by the one or more processors, cause the computing device to train the machine learning algorithm by:
receiving, for each of the plurality of reference vehicles that are not associated with the user, reference vehicle-specific identifying information and reference data of the first aspect of a given reference vehicle of the plurality of reference vehicles;
receiving, for each of the plurality of reference vehicles, an actual value of the given reference vehicle;
creating, for each of the plurality of reference vehicles, a reference feature vector comprising the reference vehicle-specific identifying information and the reference data;
associating, for each of the plurality of reference vehicles, the reference feature vector to the actual value of the given reference vehicle; and
training the machine learning algorithm using the associated reference feature vectors to predict the actual value of the vehicle associated with the user based on the vehicle-specific identifying information of the vehicle and the first data.

15. The computing device of claim 14 , wherein the instructions when executed by the one or more processors, cause the computing device to:
determine a predictability f

Assignees

• Capital One Services Llc

Inventors

• Price Micah
• Ghamsari Avid
• Dagley Geoffrey
• Tang Qiaochu
• Hoover Jason

Classifications

• G06V10/40

  Extraction of image or video features · CPC title

• G06V10/462

  Salient features, e.g. scale invariant feature transforms [SIFT] · CPC title

• G06V10/764

  using classification, e.g. of video objects · CPC title

• G06F18/24

  Classification techniques · CPC title

• G06V20/00

  Scenes; Scene-specific elements (control of digital cameras H04N23/60) · CPC title