Systems and methods for navigating vehicle inventory

Patent metadata

Field	Value
Publication number	US-2022046308-A1
Application number	US-202117509515-A
Country	US
Kind code	A1
Filing date	Oct 25, 2021
Priority date	Oct 18, 2019
Publication date	Feb 10, 2022
Grant date	—

Abstract

Embodiments of the present disclosure provide systems, methods, and devices for utilizing an application to alert a user to detected changes to inventory data. Example embodiments relate to a predictive model and development of a predictive model using machine learning techniques. Example embodiments of systems and methods may utilize web-based applications and plug-ins, extensions, or add-ons thereof for facilitating communication and presenting dynamic information to a user.

First claim

1 - 20 . (canceled)

21 . A method performed by an application, the method comprising:
recording, using an activity plugin, user interface activity on a user interface displayed on a user device; determining suggested vehicle selection terms based on the user interface activity; receiving vehicle inventory data from a data storage server; modifying vehicle selection criteria based on the suggested vehicle selection terms and the vehicle inventory data; monitoring, using an inventory plugin, vehicle inventory data stored by the data storage server for changes associated with the modified vehicle selection criteria; determining, using the inventory plugin, a change to the vehicle inventory data which is associated with the modified vehicle selection criteria; and transmitting an alert to the user device.

22 . The method of claim 21 , wherein the application is configured to operate in the background until the determination of the change to the vehicle inventory data by the inventory plugin.

23 . The method of claim 21 , wherein:
determining suggested vehicle terms based on the user interface activity comprises applying a predictive model to the user interface activity, and the predictive model is developed by machine learning using at least one algorithm selected from the group of gradient boosting machine, logistic regression, and neural networks.

24 . The method of claim 23 , wherein the predictive model is developed using historical data associated with at least one selected from the group of a vehicle, vehicle inventory data, user information, user activity, user credit information, vehicle selection terms, and vehicle sales information.

25 . The method of clam 23 , further comprising:
receiving credit information via the user interface; applying the predictive model to the received credit information and the modified vehicle selection criteria to determine whether to present a pre-approved offer of credit; and displaying pricing information associated with the selected vehicle upon determining to present the pre-approved offer of credit.

26 . The method of clam 23 , further comprising using the predictive model to determine an event for the inventory plugin to determine the alert.

27 . The method of claim 23 , further comprising determining payment criteria based on the user interface activity by applying the predictive model to the user interface activity.

28 . The method of claim 23 , further comprising:
storing user interface activity using the activity plugin, wherein the user interface activity includes activity performed on a website.

29 . The method of claim 21 , wherein:
the vehicle inventory data comprises information associated with a vehicle, and the inventory plugin is configured to determine the change to the vehicle inventory data by accessing an application programming interface.

30 . The method of claim 21 , wherein the suggested vehicle selection terms comprise at least one selected from the group of vehicle make, vehicle model, vehicle year, vehicle features, price, and location.

31 . A system, comprising:
a user interface displayed on a user device; and an application communicatively connected to the user interface and the data storage server, wherein the application is configured to:
record, using an activity plugin, user interface activity on a user interface displayed on a user device;
determine suggested vehicle selection terms based on the user interface activity;
receive vehicle inventory data from a data storage server;
modify vehicle selection criteria based on the suggested vehicle selection terms and the vehicle inventory data;
monitor, using an inventory plugin, vehicle inventory data stored by the data storage server for changes associated with the modified vehicle selection criteria;
determine, using the inventory plugin, a change to the vehicle inventory data which is associated with the modified vehicle selection criteria; and
transmit an alert to the user device.

32 . The system of claim 31 , wherein the application is further configured to operate in the background until the determination of the change to the vehicle inventory data by the inventory plugin.

33 . The system of claim 31 , wherein:
the user interface is configured to receive vehicle selection terms via an input device operably connected to the user device, and the vehicle selection terms comprise at least one selected from the group of vehicle make, vehicle model, vehicle year, vehicle features, price, and location.

34 . The system of claim 31 , wherein:
the application is further configured to determine vehicle selection terms based on the user interface activity by applying a predictive model to the user interface activity, and the predictive model is developed by machine learning using at least one algorithm selected from the group of gradient boosting machine, logistic regression, and neural networks.

35 . The system of claim 34 , wherein the predictive model is developed using historical data associated with at least one selected from the group of a vehicle, vehicle inventory data, user information, user activity, user credit information, vehicle selection terms, and vehicle sales information.

36 . The system of claim 34 , wherein the application uses the predictive model to determine an event for the inventory plugin to determine the alert.

37 . The system of claim 36 , wherein the event comprises at least one selected from the group of the announcement of a price for a new vehicle, a change in price of a vehicle, a change in availability of a vehicle, a change in expected availability of a vehicle, a change in financing options of a vehicle, a change in location of a vehicle, and a change in trade-in status of a vehicle.

38 . The system of claim 34 , wherein:
the user interface is configured to receive payment criteria, and the application is further configured to:
determine suggested payment criteria based on the user interface activity by applying the predictive model to the user interface activity, and
modify the payment criteria based on the suggested payment criteria.

39 . The system of claim 31 , wherein the application is configured to automatically execute an application update without input from a user.

40 . A non-transitory computer-readable medium containing instructions that, when executed by a processor, configure the processor to perform procedures comprising:
recording, using an activity plugin, user interface activity on a user interface displayed on a user device; determining suggested vehicle selection terms based on the user interface activity; receiving vehicle inventory data from a data storage server; modifying vehicle selection criteria based on the suggested vehicle selection terms and the vehicle inventory data; monitoring, using an inventory plugin, vehicle inventory data stored by the data storage server for changes associated with the modified vehicle selection criteria; determining, using the inventory plugin, a change to the vehicle inventory data which is associated with the modified vehicle selection criteria; and transmitting an alert to the user device.

Assignees

• Capital One Services Llc

Inventors

• Deshpande Amit
• Mathews Liju
• Chikkaveerappa Satish
• Venuraju Mithra Kosur

Classifications

• G06Q30/06

  Buying, selling or leasing transactions · CPC title

• H04N21/251

  Learning process for intelligent management, e.g. learning user preferences for recommending movies (details of learning user preferences for the retrieval of video data in a video database G06F16/739; computer systems using learning methods G06N3/08) · CPC title

• G06F16/9535

  Search customisation based on user profiles and personalisation · CPC title

• H04N21/41422 Primary

  located in transportation means, e.g. personal vehicle (arrangements specially adapted for transportation systems in broadcast systems H04H20/62) · CPC title

• G07C5/008

  communicating information to a remotely located station (transmission systems for measured values G08C) · CPC title