Image analysis and identification using machine learning with output estimation
Systems and methods for generating realtime quotes using machine learning algorithms are provided. The system includes a processor in communication with a client device, and a storage medium storing instructions that, when executed, cause the processor to perform operations including: receiving an image of a vehicle from the client device, extracting one or more features from the image, and identifying one or more attributes of the vehicle. The operations also include: based on the identified attributes, determining a make and a model of the vehicle, obtaining comparison information based at least in part on the determined make and model, estimating and transmitting a quote for the vehicle for display on the client device, based on the comparison information.
1. A system for processing an image including a vehicle using machine learning, comprising:
a processor in communication with a client device; and
a storage medium storing instructions that, when executed, configure the processor to perform operations comprising:
authenticating a user of the client device;
receiving an image of a vehicle from the client device;
inputting the image to an input layer of a first convolutional neural network;
extracting one or more features from an output layer of the first convolutional neural network;
inputting the extracted features to an input layer of a second convolutional neural network;
identifying an attribute of the vehicle from an output layer of the second convolutional network;
retrieving account information of the user based on the authentication, wherein the account information includes information from a pre-qualification vehicle financing application;
estimating a purchase quote or a lease quote for the vehicle based on the account information and the identified attribute of the vehicle; and
transmitting the estimated purchase or lease quote for display on the client device.
2. The system of claim 1 , wherein the attribute of the vehicle includes a trim of the vehicle.
3. The system of claim 1 , wherein the operations further comprise:
obtaining comparison information based on the identified attribute of the vehicle; and
estimating the purchase quote or the lease quote is further based on the comparison information.
4. The system of claim 3 , wherein the comparison information comprises at least one of pricing information, vehicle-related information, or personal information.
5. The system of claim 4 , wherein the pricing information comprises at least one of vehicle price information, monthly payment amount information, APR information, term length information, or warranty product information.
6. The system of claim 4 , wherein the vehicle-related information comprises at least one of vehicle specification information, dealership information, or geographic area information.
7. The system of claim 4 , wherein the personal information comprises at least one of demographic information, financial information, residence information, or prior behavior information.
8. The system of claim 1 , wherein the operations further comprise:
determining that the image is unacceptable; and
transmitting information for display on the client device, the information including an error message or user guidance.
9. The system of claim 8 , wherein the user guidance comprises a guiding line for obtaining a second image of the vehicle.
10. The system of claim 1 , wherein the image comprises digital information stored in a static format or a dynamic format.
11. A computer-implemented method for processing an image including a vehicle using machine learning, the method comprising:
authenticating a user of a client device;
receiving an image of a vehicle from the client device;
inputting the image to an input layer of a first convolutional neural network;
extracting one or more features from an output layer of the first convolutional neural network;
inputting the extracted features to an input layer of a second convolutional neural network;
identifying an attribute of the vehicle from an output layer of the second convolutional network;
retrieving account information of the user based on the authentication, wherein the account information includes information from a pre-qualification vehicle financing application;
estimating a purchase quote or a lease quote for the vehicle based on the account information and the identified attribute of the vehicle; and
transmitting the estimated purchase or lease quote for display on the client device.
12. The method of claim 11 , wherein the attribute of the vehicle includes a trim of the vehicle.
13. The method of claim 11 further comprising:
obtaining comparison information based on the identified attribute of the vehicle, the comparison information comprising at least one of pricing information, vehicle-related information, or personal information; and
estimating the purchase quote or the lease quote is further based on the comparison information.
14. The method of claim 11 further comprising:
determining that the image is unacceptable; and
transmitting information for display on the client device, the information including an error message or user guidance.
15. The method of claim 14 , wherein the user guidance comprises a guiding line for obtaining a second image of the vehicle.
16. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to operate a computer system for processing an image including a vehicle using machine learning, comprising:
authenticating a user of a client device;
receiving an image of a vehicle from the client device;
inputting the image to an input layer of a first convolutional neural network;
extracting one or more features from an output layer of the first convolutional neural network;
inputting the extracted features to an input layer of a second convolutional neural network;
identifying an attribute of the vehicle from an output layer of the second convolutional network;
retrieving account information of the user based on the authentication, wherein the account information includes information from a pre-qualification vehicle financing application;
estimating a purchase quote or a lease quote for the vehicle based on the account information and the identified attribute of the vehicle; and
transmitting the estimated purchase or lease quote for display on the client device.
17. The computer-readable medium of claim 16 , wherein the attribute of the vehicle includes one of a make, a model, a trim, a year, or a color of the vehicle.
18. The computer-readable medium of claim 16 , wherein the instructions further comprise:
obtaining comparison information based on the identified attribute of the vehicle, the comparison information comprising at least one of pricing information, vehicle-related information, or personal information; and
estimating the purchase quote or the lease quote is further based on the comparison information.
19. The computer-readable medium of claim 16 , wherein the instructions further comprise:
determining that the image is unacceptable; and
transmitting information for display on the client device, the information including an error message or user guidance.
20. The computer-readable medium of claim 19 , wherein the user guidance comprises a guiding line for obtaining a second image of the vehicle.