Vehicle Image Overlay for Feature-Level Listing Information
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Solution Overview
Problem
Potential buyers of vehicles face the challenge of finding relevant information such as vehicle features, specifications, and title history, which is scattered across different parts of a web interface or third-party services, making it cumbersome to gather for purchase decisions.
Innovation Solution
A method and system that uses machine learning models to classify vehicle images, identify features, and overlay relevant information directly on the images, utilizing a web plugin to retrieve and display data from databases based on image classification and individual identifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If vehicle information is displayed in text portions or through third-party services, then information completeness is improved, but information accessibility and ease of gathering deteriorate
Solution Approach 1:
The patent combines multiple information sources (vehicle features, specifications, title history, mileage, accident reports, recalls) that were previously scattered across different text portions and third-party services into a single integrated overlay display on the vehicle image. This merging allows buyers to access all relevant information in one unified location, simultaneously achieving information completeness and ease of gathering.
Solution Approach 2:
The patent transitions information display from a traditional linear text-based interface to a spatial overlay dimension on top of the vehicle image. By placing information directly on the image at feature locations, the system creates a new dimensional layer that combines visual imagery with informational content, making information more accessible without losing completeness.
2Device complexity
If information is scattered across different parts of the web interface, then information organization is improved, but information retrieval time increases
Solution Approach 1:
The system performs preliminary classification of the vehicle image to identify feature locations and retrieve corresponding overlay information in advance. By pre-processing the image to detect features and pre-fetching the associated information, the system eliminates the need for buyers to search through scattered text portions, significantly reducing information retrieval time while maintaining organized presentation.
3Ease of operation
If overlay information is displayed at feature locations in the image, then information accessibility is improved, but system complexity increases
Solution Approach 1:
The system employs machine learning models that automatically classify vehicle images, identify features, and determine where to place overlay information without requiring manual configuration. The models self-service the complex tasks of image analysis, feature detection, and information positioning, thereby improving information accessibility while minimizing the need for complex manual system configuration and maintenance.
Data Source
AI summary
An image information overlay system retrieves an image associated with a vehicle listing and uses machine learning models to classify the image, generating identification data that may comprise a vehicle make and model, a feature or part of the vehicle present in the image, and a location of the vehicle feature or part. The identification data or an individual identifier of the vehicle, such as a Vehicle Identification Number (VIN), may be used to retrieve overlay information related to the vehicle make and model, such as recalls or known maintenance issues or information specific to the vehicle, such as mileage, accident reports, or ownership history. The overlay information is displayed on the image as an overlay at the location of the vehicle feature or part corresponding to the overlay information.