Vehicle Image Classification and Sequencing for Consistent Listings

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Solution Overview

Problem

Existing image capture and display systems for vehicles lack consistency and efficiency, requiring costly professional photography and manual intervention for background replacement, foreground relocation, and image sequencing, which is time-consuming and complex.

Innovation Solution

A system utilizing artificial intelligence and machine learning to automatically classify, modify, and sequence images by identifying objects, adding metadata, and standardizing backgrounds, while cropping, scaling, and adding overlays.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If professional photographers are employed to capture and manually edit vehicle photographs, then image quality and consistency are improved, but cost and time consumption increase significantly

Engineering Contradiction:

Improveimage consistencyVSAvoidtime consumption

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical processes (professional photographers capturing and editing photos) with an automated computer-based system that uses machine learning models to capture, process, and standardize vehicle images automatically, eliminating the need for human intervention while maintaining consistency

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service automation where the computer automatically performs image capture, processing, and standardization without requiring professional photographers or manual editing, making the process independent and efficient

Inventive Principle:

Principle #25Self-service

2Productivity

If sellers use their own cameras to take photographs of vehicles, then cost and time are reduced, but image consistency and quality deteriorate

Engineering Contradiction:

Improvecost effectivenessVSAvoidimage consistency

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The patent replaces inconsistent manual photography with an automated system that standardizes image capture and processing, maintaining cost-effectiveness while ensuring consistent quality through algorithmic control rather than human operation

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system changes the parameters of image processing by automatically applying standardized transformations including background replacement, vehicle relocation, scaling, and cropping to ensure consistent presentation across all vehicle images

Inventive Principle:

Principle #35Parameter changes

3Manufacturing precision

If manual processes are used for background replacement and foreground relocation, then image consistency is improved, but cost and time consumption increase

Engineering Contradiction:

Improvebackground consistencyVSAvoidprocess complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The patent replaces complex manual background replacement and foreground relocation processes with automated computer-based algorithms that use machine learning to identify vehicles, remove backgrounds, and reposition vehicles consistently across images

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system extracts the vehicle foreground from the original background using automated image processing, then relocates it to a standardized position with a consistent background, eliminating the need for manual manipulation

Inventive Principle:

Principle #2Taking out (Extraction)

4Measurement precision

If manual intervention is used for sorting, labelling, and sequencing images, then accuracy is improved, but time consumption and cost increase

Engineering Contradiction:

Improveimage classification accuracyVSAvoidprocessing speed

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces manual image sorting, labelling, and sequencing with automated machine learning models that analyze image content, generate appropriate labels, and sequence images based on predetermined criteria, achieving both accuracy and speed

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs self-service automation in image classification and sequencing, using trained machine learning models to automatically identify image characteristics, assign labels, and determine optimal display sequences without human intervention

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12536566B2Artificial intelligence machine learning system for classifying images and producing a predetermined visual output

Publication Date: 2026.01.27 FREDDY TECHNOLOGIES LLC

AI summary

The present disclosure is directed to automatically receiving, modifying, sequencing, and displaying vehicle images for marketing to prospective buyers. A seller uploads images of a vehicle to the system, which system uses artificial intelligence and machine learning to classify the images, identify objects within the images, add backgrounds, banners, and hotspots, and generate metadata associating the results of the foregoing processes with the particular images. The system uses the stored metadata to sequence the images in a format predetermined by the seller and upload the sequenced images for display to a prospective buyer.