Vehicle Image Matching for Real-Time Auto Financing Quotes
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Solution Overview
Problem
Current auto financing methods are lengthy, manual, and often provide inaccurate quotes, limiting customers' ability to change vehicle choices during the purchasing process, and do not offer real-time financing options.
Innovation Solution
A system using machine learning algorithms to identify vehicles from images and generate real-time quotes by matching vehicle images with a database, incorporating convolutional neural networks for image recognition and augmented reality for quote display on user devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual credit application processes are used, then financing approval can be obtained, but the process is lengthy and time-consuming
Solution Approach 1:
The system performs preliminary actions by capturing vehicle images and generating financing quotes before the customer actually visits the dealership or completes traditional applications. The machine learning model pre-identifies vehicles in images and pre-calculates financing terms, so when the customer is ready to purchase, the financing is already approved or near-approved, eliminating the lengthy on-site process.
Solution Approach 2:
The patent replaces the mechanical manual process of credit applications, document verification, and human review with an automated machine learning system. The ML model automatically processes vehicle images, identifies vehicles, retrieves pricing data, and generates financing quotes without human intervention, substituting the entire manual workflow with an automated digital system that operates in seconds rather than days.
2Productivity
If pre-qualification is done before vehicle selection, then financing estimate is provided, but the quotes are inaccurate
Solution Approach 1:
The system performs preliminary vehicle identification and financing calculation based on actual vehicle images captured by the customer, rather than generic pre-qualification forms. By using real photos of the specific vehicle the customer is interested in, the system can retrieve exact pricing, inventory status, and financing terms for that particular vehicle, providing accurate quotes before the customer even selects it at the dealership.
Solution Approach 2:
The system uses photographic copies (images) of the actual vehicle as the basis for identification and financing calculation, rather than relying on customer-provided information or generic vehicle specifications. The machine learning model processes these image copies to identify the vehicle and match it with exact pricing data, ensuring the quote reflects the actual vehicle being considered.
3Measurement precision
If vehicle selection is made before financing, then specific vehicle pricing can be obtained, but the customer loses flexibility to change vehicle choices
Solution Approach 1:
The system performs preliminary financing calculations for multiple potential vehicles simultaneously by processing images of vehicles on the dealership lot. The customer can capture photos of several different vehicles and receive financing quotes for each one before making a selection, allowing them to compare financing terms across multiple vehicles without being locked into a single choice early in the process.
Solution Approach 2:
The machine learning system is designed to handle multiple vehicles and multiple financing scenarios universally. The same image processing and financing calculation engine can evaluate any vehicle in the dealership inventory, allowing customers to flexibly switch between different vehicle options while maintaining accurate financing quotes for each, rather than being restricted to a single pre-selected vehicle.
4Reliability
If manual credit applications are used at the dealership, then financing can be approved, but the process requires multiple steps and customer effort
Solution Approach 1:
The system enables self-service by allowing customers to initiate and complete the financing process independently using their own mobile devices. Customers simply capture photos of vehicles they're interested in, and the machine learning system automatically handles vehicle identification, pricing retrieval, and financing calculation without requiring customers to fill out forms, visit lenders, or undergo manual credit checks. The entire process is customer-driven and requires minimal effort.
Solution Approach 2:
The patent replaces the complex manual mechanics of credit applications, document collection, human review, and approval workflows with an automated machine learning system that processes everything digitally. The ML model substitutes for human underwriters, document reviewers, and loan officers, automatically making financing decisions based on the processed vehicle images and customer information, thereby simplifying the process to a few straightforward steps for the customer.
Data Source
AI summary
The present disclosure relates to systems, methods, and computer readable media for processing an image including a vehicle using machine learning. The systems can include determining a location of the client device. The systems can further include receiving a first image of a vehicle from an image sensor of the client device and matching, using machine learning, the first image to one or more images of vehicles in a vehicle database to identify the vehicle. The vehicle database can list vehicles located at the determined location of the client device and images of the vehicles. The systems can include retrieving vehicle information from the vehicle database, based on the identified vehicle, and obtaining comparison information based at least in part on the vehicle information. The systems can include estimating a quote for the vehicle based on the comparison information and transmitting the estimated quote for display on the client device.