Generative Adversarial Network Product Design Generator with Feedback
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Solution Overview
Problem
Current product design technologies lack the ability to effectively evaluate features related to visual content and style, and fail to incorporate feedback from designers and manufacturers, leading to ineffective new product design generation.
Innovation Solution
A system that combines Generative Adversarial Networks (GANs) with reinforcement learning to generate new product designs based on pre-existing designs, market data, and human input, incorporating feedback to improve design quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Generative Adversarial Networks are used to generate new product designs, then the creativity and variety of design options are improved, but the ability to ensure design feasibility and satisfy manufacturing constraints deteriorates
Solution Approach 1:
The system implements feedback loops where generated designs are evaluated against manufacturing constraints and design requirements. The evaluation results feed back into the GAN training process, allowing the model to learn from mistakes and improve its ability to generate feasible designs while maintaining creativity.
Solution Approach 2:
The patent introduces intermediary components such as constraint satisfaction modules and evaluation functions that act as mediators between the creative GAN generation process and the practical manufacturing requirements. These intermediaries translate creative designs into manufacturable specifications without losing the innovative essence.
2Reliability
If the system incorporates multiple data sources including market data and human feedback, then the market relevance and quality of designs are improved, but the system complexity increases
Solution Approach 1:
The system merges multiple data sources including market data, consumer feedback, and expert evaluations into a unified training framework. By combining these diverse inputs, the system achieves higher design quality and market relevance while managing complexity through integrated processing pipelines.
Solution Approach 2:
The patent creates a multi-functional system that can process various types of inputs (market data, images, text feedback) and generate comprehensive design solutions. This universal approach allows a single system to handle multiple functions rather than requiring separate specialized systems for each data type.
3Measurement precision
If the system generates photo-realistic designs with high visual quality, then the aesthetic appeal is improved, but the computational resources and training time required increase
Solution Approach 1:
The system performs preliminary actions by pre-processing training data, pre-training on lower-resolution images, and establishing base models before generating high-resolution photo-realistic designs. This staged approach reduces the computational burden during final high-quality generation while maintaining visual fidelity.
Solution Approach 2:
The patent implements dynamic training strategies where the system adapts its computational resources based on the generation stage. Early stages use fewer resources for structural learning, while later stages allocate more resources to detailed visual quality, optimizing the balance between training time and output quality.
Data Source
AI summary
An embodiment establishes a product design database based at least in part on product design data received from a product image repository, wherein the product design data is representative of a plurality of product designs. The embodiment ranks each product design based on a ranking metric derived from market data corresponding to existing retail products. The embodiment constructs a product design ranking based on a relative ranking between each of the product designs that have been ranked. The embodiment generates a new product design using a machine learning model based on the plurality of product designs and the product design ranking. The embodiment evaluates whether the new product design satisfies a design constraint. The embodiment, upon an evaluation that the new product design satisfies the design constraint, stores the new product design in the product design database. The embodiment displays the new product design via an interface.