SYSTEMS AND METHODS FOR PROVING A FINANCIAL PROGRAM FOR BUYING A VEHICLE

• US 20200134690A1
• Filed: 12/26/2019
• Published: 04/30/2020
• Est. Priority Date: 06/29/2017
• Status: Abandoned Application

• Current Assignee: Beijing Didi Infinity Technology And Development Co.Ltd.
• Original Assignee: Beijing Didi Infinity Technology And Development Co.Ltd.
• Inventors: WANG, Yu, WANG, Chunliang, YE, Zhou, GUO, Rui, ZHANG, Duokun
• Application Number:
  US16/727,013
• Publication Number:
  US 20200134690A1
• Time in Patent Office: Days
• CPC Class Codes: G06Q 10/20 Administration of product repair or maintenance
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0207 Discounts or incentives, e.g. coupons or rebates
• CPC Class Codes: G06Q 30/0627 using item specifications
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083

Abstract

A system may include at least one computer-readable storage medium including a set of instructions for providing a driver registered in an online platform with a financial program for buying a vehicle, and at least one processor in communication with the computer-readable storage medium, wherein when executing the set of instructions, the at least one processor is directed to: receive data of a plurality of drivers registered in the online platform from an input device, the data including usage history of a plurality of vehicles associated with the plurality of drivers; identify from the plurality of drivers a first group of candidate drivers based on the usage history, each candidate driver is associated with a purchase intention higher than a threshold value; and save a first structured data in the storage medium to identify the first group of candidate drivers.

Claims

1. A system for providing a driver registered in an online computer platform with a financial program for buying a vehicle, comprising:

at least one computer-readable storage medium including a set of instructions for providing a driver registered in an online computer platform with a financial program for buying a vehicle; and

at least one processor in communication with the computer-readable storage medium, wherein when executing the set of instructions, the at least one processor is directed to;

receive data of a plurality of drivers registered in the online computer platform, the data including usage history of a plurality of vehicles associated with the plurality of drivers;

identify, from the plurality of drivers, a first group of candidate drivers based on the usage history of the plurality of vehicles, each candidate driver is associated with a purchase intention higher than a threshold value; and

save a first structured data in the storage medium to identify the first group of candidate drivers.

2. The system of claim 1, wherein the usage history includes at least one of:

driving routes of a vehicle of the plurality of vehicles;

driving duration of the plurality of vehicles over the driving routes;

active duration of the plurality of drivers in the plurality of vehicles;

fueling history of the plurality of vehicles;

maintenance history of the plurality of vehicles;

or

online browsing history relating to vehicle purchasing.

3. The system of claim 1, wherein to identify the first group of candidate drivers, the at least one processor is further directed to:

identify, from the plurality of drivers, a second group of buyer drivers having actual vehicle purchasing history;

for a driver of the plurality of drivers,

determine an overall similarity between the driver and the second group of buyer drivers based on a hyper-parameter and the usage history of a vehicle associated with the driver;

determine a purchase intention of the driver based on the overall similarity; and

save a second structured data in the storage medium to;

identify the driver as a candidate driver when the purchase intention is greater than the threshold value; and

include the purchase intention of the driver in a purchase intention data set.

4. The system of claim 3, wherein to identify the second group of buyer drivers having actual vehicle purchasing history, the at least one processor is further directed to:

access the storage medium of the online computer platform to obtain driver information of the plurality of drivers and vehicle information associated with the plurality of drivers;

access the storage medium of the online computer platform to obtain online browsing history relating to vehicle purchasing of the plurality of drivers;

determine the second group of buyer drivers having actual vehicle purchasing history based on the driver information, the vehicle information and the online browsing history; and

save a third structured data in the storage medium to identify the second group of buyer drivers.

5. The system of claim 3, wherein the at least one processor is further directed to:

access the storage medium of the online computer platform to obtain from the purchase intention data set target purchase intention data associated with a target driver among the plurality of drivers;

execute a purchasing capacity prediction model to generate target purchasing capacity data of the target driver based on the target purchase intention data;

access the storage medium to read a database of financial programs;

determine a financial program from the database of financial programs based on the target purchasing capacity data of the target driver; and

save a fourth structured data in the storage medium, the structured data associated the target driver with the target financial program.

6. The system of claim 5, wherein the at least one processor is further directed to:

access the storage medium of the online computer platform to obtain vehicle types that the second group of buyer drivers have bought and the corresponding fair market prices;

access the storage medium to obtain usage history of the vehicles associated with the second group of buyer drivers;

determine a purchasing capacity prediction model based on the vehicle types, the corresponding fair market prices and the usage history of the vehicles associated with the second group of buyer drivers; and

save a fifth structured data in the storage medium to identify the purchasing capacity prediction model.

7. The system of claim 3, wherein the at least one processor is further directed to:

access the storage medium of the online computer platform to obtain, from the purchase intention data set, target purchase intention data associated with a target driver among the plurality of drivers;

access the storage medium to obtain a database including information of a plurality of on-sale-vehicles;

select, from the plurality of on-sale-vehicles, a target vehicle based on the usage history of the vehicle associated with the target driver; and

save a sixth structured data in the storage medium, the structured data associated the target driver with the target vehicle.

8. A method for providing a driver registered in an online computer platform with a financial program for buying a vehicle, comprising:

receiving data of a plurality of drivers registered in the online computer platform, the data including usage history of a plurality of vehicles associated with the plurality of drivers;

identifying from the plurality of drivers a first group of candidate drivers based on the usage history of the plurality of vehicles, each candidate driver is associated with a purchase intention higher than a threshold value; and

saving a first structured data in a storage medium to identify the first group of candidate drivers.

9. The method of claim 8, wherein the usage history includes at least one of:

driving routes of a vehicle of the plurality of vehicles;

driving duration of the plurality of vehicles over the driving routes;

active duration of the plurality of drivers in the plurality of vehicles;

fueling history of the plurality of vehicles;

maintenance history of the plurality of vehicles;

or

online browsing history relating to vehicle purchasing.

10. The method of claim 8, wherein the identifying the first group of candidate drivers comprising:

identifying from the plurality of drivers a second group of buyer drivers having actual vehicle purchasing history;

for a driver of the plurality of drivers,

determining an overall similarity between the driver and the second group of buyer drivers based on a hyper-parameter and the usage history of a vehicle associated with the driver;

determining a purchase intention of the driver based on the overall similarity; and

saving a second structured data in the storage medium to;

identify the driver as a candidate driver when the purchase intention is greater than the threshold value; and

include the purchase intention of the driver in a purchase intention data set.

11. The method of claim 10, wherein the identifying the second group of buyer drivers having actual vehicle purchasing history comprising:

accessing the storage medium of the online computer platform to obtain driver information of the plurality of drivers and vehicle information associated with the plurality of drivers;

accessing the storage medium of the online computer platform to obtain online browsing history relating to vehicle purchasing of the plurality of drivers;

determining the second group of buyer drivers having actual vehicle purchasing history based on the driver information, the vehicle information and the online browsing history; and

saving a third structured data in the storage medium to identify the second group of buyer drivers.

12. The method of claim 10 further comprising:

accessing the storage medium of the online computer platform to obtain from the purchase intention data set target purchase intention data associated with a target driver in the plurality of drivers;

executing a purchasing capacity prediction model to generate target purchasing capacity data of the target driver based on the target purchase intention data;

accessing the storage medium to read a database of financial programs;

determining a financial program from the database of financial programs based on the target purchasing capacity data of the target driver; and

saving a fourth structured data in the storage medium, the structured data associated the target driver with the target financial program.

13. The method of claim 12 further comprising:

accessing the storage medium of the online computer platform to obtain vehicle types that the second group of buyer drivers have bought and the corresponding fair market prices;

accessing the storage medium to obtain usage history of the vehicles associated with the second group of buyer drivers;

determining a purchasing capacity prediction model based on the vehicle types, the corresponding fair market prices and the usage history of the vehicles associated with the second group of buyer drivers; and

saving a fifth structured data in the storage medium to identify the purchasing capacity prediction model.

14. The method of claim 10 further comprising:

accessing the storage medium of the online computer platform to obtain, from the purchase intention data set, target purchase intention data associated with a target driver among the plurality of drivers;

accessing the storage medium to obtain a database including information of a plurality of on-sale-vehicles;

selecting, from the plurality of on-sale-vehicles;

a target vehicle based on the usage history of the vehicle associated with the target driver; and

saving a sixth structured data in the storage medium, the structured data associated the target driver with the target vehicle.

15. A non-transitory computer readable medium, comprising at least one set of instructions for providing a driver registered in an online computer platform with a financial program for buying a vehicle, wherein when executed by at least one processor of a computer server, the at least one set of instructions directs the at least one processor to perform acts of:

receiving data of a plurality of drivers registered in the online computer platform, the data including usage history of a plurality of vehicles associated with the plurality of drivers;

identifying from the plurality of drivers a first group of candidate drivers based on the usage history of the plurality of vehicles, each candidate driver is associated with a purchase intention higher than a threshold value; and

saving a first structured data in a storage medium to identify the first group of candidate drivers.

16. The non-transitory computer readable medium of claim 15, wherein the usage history includes at least one of:

driving routes of a vehicle of the plurality of vehicles;

driving duration of the plurality of vehicles over the driving routes;

active duration of the plurality of drivers in the plurality of vehicles;

fueling history of the plurality of vehicles;

maintenance history of the plurality of vehicles;

or

online browsing history relating to vehicle purchasing.

17. The non-transitory computer readable medium of claim 15, wherein the identifying the first group of candidate drivers includes:

identifying from the plurality of drivers a second group of buyer drivers having actual vehicle purchasing history;

for a driver of the plurality of drivers,

determining an overall similarity between the driver and the second group of buyer drivers based on a hyper-parameter and the usage history of a vehicle associated with the driver;

determining a purchase intention of the driver based on the overall similarity; and

saving a second structured data in the storage medium to;

identify the driver as a candidate driver when the purchase intention is greater than the threshold value; and

include the purchase intention of the driver in a purchase intention data set.

18. The non-transitory computer readable medium of claim 17, the at least one set of instructions further directs the at least one processor to perform acts of:

accessing the storage medium of the online computer platform to obtain from the purchase intention data set target purchase intention data associated with a target driver among the plurality of drivers;

executing a purchasing capacity prediction model to generate target purchasing capacity data of the target driver based on the target purchase intention data;

accessing the storage medium to read a database of financial programs;

determining a financial program from the database of financial programs based on the target purchasing capacity data of the target driver; and

saving a fourth structured data in the storage medium, the structured data associated the target driver with the target financial program.

19. The non-transitory computer readable medium of claim 18, the at least one set of instructions further directs the at least one processor to perform acts of:

accessing the storage medium of the online computer platform to obtain vehicle types that the second group of buyer drivers have bought and the corresponding fair market prices;

accessing the storage medium to obtain usage history of the vehicles associated with the second group of buyer drivers;

determining a purchasing capacity prediction model based on the vehicle types, the corresponding fair market prices and the usage history of the vehicles associated with the second group of buyer drivers; and

saving a fifth structured data in the storage medium to identify the purchasing capacity prediction model.

20. The non-transitory computer readable medium of claim 17, the at least one set of instructions further directs the at least one processor to perform acts of:

accessing the storage medium of the online computer platform to obtain, from the purchase intention data set, target purchase intention data associated with a target driver among the plurality of drivers;

accessing the storage medium to obtain a database including information of a plurality of on-sale-vehicles;

selecting, from the plurality of on-sale-vehicles, a target vehicle based on the usage history of the vehicle associated with the target driver; and

saving a sixth structured data in the storage medium, the structured data associated the target driver with the target vehicle.