Synthetic Product Image Preprocessing for Manufacturable Personalization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing online retail platforms limit users to choosing from preexisting products, lacking the ability to create personalized and distinctive items, and traditional custom product design methods are costly and time-consuming.
Innovation Solution
An AI-driven platform that generates images of synthetic products based on user input, allowing users to create unique products with distinct physical attributes, incorporates manufacturability constraints, and supports manufacturing processes, enabling rapid production and pricing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional custom product design methods are used, then products can be personalized, but the process is costly and time-consuming
Solution Approach 1:
The patent replaces manual mechanical design processes with an AI-based system that automatically generates product images and designs. The system uses machine learning models to interpret user preferences and generate customized product visualizations, eliminating the need for manual designers and significantly reducing design time while maintaining personalization capabilities
Solution Approach 2:
The system enables users to directly interact with AI models to generate their own customized product designs without requiring professional design services. Users can input their preferences and the system autonomously creates product images, allowing customers to serve their own design needs and reducing dependency on expensive external design resources
2Adaptability or versatility
If traditional custom product design methods are used, then products can be personalized, but the process is costly
Solution Approach 1:
The patent replaces expensive manual design services with automated AI-based generation. The system uses machine learning models trained on existing product data to automatically create customized product images and designs, eliminating the need for paid designers and significantly reducing the cost of personalized product creation
Solution Approach 2:
The system creates digital copies and variations of existing products through AI generation rather than requiring physical prototyping and manual redesign. The machine learning models generate virtual product images and designs that can be replicated and modified efficiently, reducing material and labor costs associated with traditional custom design processes
3Adaptability or versatility
If users can create unique products, then product variety increases, but manufacturing complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-processing user inputs and pre-generating product images through AI models before actual manufacturing occurs. The machine learning models anticipate manufacturing constraints and generate designs that are inherently manufacturable, reducing the complexity of subsequent production processes while maintaining high product variety
Data Source
AI summary
A computer-implemented technique for preprocessing user input to generate images of synthetic products representing conceptual products includes receiving user input, including text and/or images, indicative of a conceptual product, and selecting a machine learning (ML) model from a set of models based on characteristics such as product category or maker. Image inputs can be converted to text-based descriptions, combined with text inputs, and configured as a prompt instruction for the selected ML model, incorporating constraints (e.g., material, production, cost) and user feedback. The ML model can generate one or more images of a synthetic product, which can be refined iteratively based on further feedback. The system supports recognition of known and unknown objects in images and can adapt prompt instructions accordingly. The generated images include synthetic products that are producible as physical products, enabling efficient conceptual product visualization and refinement.