Product Image Attribute Estimation for Personalized Product Design
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Solution Overview
Problem
Existing online retail platforms limit user experience to preexisting products, failing to provide personalized and distinctive items, and traditional custom product creation is costly and time-consuming.
Innovation Solution
An AI-driven platform that generates images of conceptual products based on user input, allowing users to create unique products with distinct physical attributes, incorporating manufacturability constraints and user feedback, and facilitates rapid production and delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional custom product creation is used, then product uniqueness and personalization are improved, but time consumption and cost increase
Solution Approach 1:
The patent uses AI models to generate synthetic product images that replicate real product appearances and characteristics. The system creates virtual copies of products with customized attributes (colors, materials, designs) without physically manufacturing them first, allowing rapid exploration of personalized product options before actual production.
Solution Approach 2:
The system performs preliminary actions by generating and evaluating multiple product concept images before final product creation. Users can visualize and provide feedback on customized product designs in advance, and the system can pre-determine manufacturability constraints and optimize designs before actual manufacturing begins, reducing overall development time.
2Adaptability or versatility
If traditional custom product creation is used, then product uniqueness and personalization are improved, but cost increases
Solution Approach 1:
The patent creates virtual copies of products through AI-generated images, allowing multiple customization options to be explored and evaluated digitally before physical production. This eliminates the need to create physical prototypes for each variation, significantly reducing material costs and manufacturing setup expenses while maintaining product personalization.
Solution Approach 2:
The system incorporates feedback loops where users evaluate AI-generated product images and provide guidance on preferences. This feedback is used to iteratively refine designs and automatically adjust manufacturing parameters, optimizing production efficiency and reducing costs by aligning final products with user expectations before manufacturing begins.
3Productivity
If AI-generated product images are used, then design speed and productivity are improved, but manufacturing complexity increases
Solution Approach 1:
The system uses feedback mechanisms where users review and rate AI-generated product images. This feedback is automatically processed to refine future generations and to identify manufacturability issues. The system can detect when generated designs violate manufacturing constraints and automatically adjust parameters, reducing the complexity burden on manual manufacturing processes.
Solution Approach 2:
The patent employs parameter changes by adjusting AI model inputs (such as color palettes, material properties, design styles) to generate variations that inherently satisfy manufacturability constraints. By modifying generation parameters based on identified manufacturing capabilities, the system produces designs that are both rapid and manufacturable, reducing the gap between design speed and manufacturing complexity.
Data Source
AI summary
The disclosed technology includes a computer-implemented technique for predicting or estimating physical attributes of products depicted in images is disclosed. The method includes receiving one or more images—either photographs of real-world products or synthetic images generated from user-provided descriptions—processing the images using one or more machine learning (ML) models trained to detect and measure physical characteristics, and predicting or estimating measures such as volume, weight, and dimensions. The system can identify product categories, select appropriate ML models, and generate natural language descriptions of predicted attributes for user display. Training datasets may include both real and synthetic images with known attributes, and user feedback can be incorporated to improve model accuracy.