AI Product Configuration System for 3D Design
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current product design processes for 3D objects lack efficient methods for reusing past design decisions and combining trend and mood data, relying heavily on human interaction and manual analysis, which limits the ability to create targeted and innovative material and color configurations.
Innovation Solution
A product configuration design system utilizing Artificial Intelligence (AI) and Machine Learning (ML) to parse large databases of materials and colors based on personal and public image collections, allowing designers to interactively configure 3D objects with AI-driven suggestions, user feedback, and demographic analysis for targeted design decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual interaction and human analysis are used to analyze design data and translate it into design proposals, then design decisions can be made with human judgment, but the process is limited by the amount of time and resources available and lacks efficiency
Solution Approach 1:
The patent replaces manual human analysis and interaction with an automated machine learning system that processes design data, trends, and mood boards to generate design proposals. The ML model automatically analyzes image collections, extracts design patterns, and generates material and color configurations without requiring manual human intervention for each design iteration, thereby dramatically improving productivity while reducing time loss.
Solution Approach 2:
The system enables self-service design exploration where the machine learning model autonomously processes design data, generates configurations, and iterates on design proposals without continuous human guidance. The system serves itself by automatically learning from feedback and improving its design generation capabilities over time, freeing designers from manual analysis tasks.
2Adaptability or versatility
If traditional methods are used to store and reference past design decisions, then design references are preserved, but there is no smart way of reusing past design decisions and combining this efficiently with trend and mood data
Solution Approach 1:
The patent merges multiple data sources including past design decisions, trend data, and mood boards into a unified machine learning training dataset. The system combines structured design decision data with unstructured image collections from social media and personal photo libraries, creating a comprehensive training corpus that enables the ML model to learn from both historical designs and current trends simultaneously.
Solution Approach 2:
The machine learning system serves multiple functions: it stores design decisions, analyzes trends, generates design proposals, and iterates based on feedback. The same ML infrastructure handles diverse data types including images, text descriptions, and structured design parameters, providing a universal platform that replaces multiple separate tools and processes.
3Measurement precision
If configurators are used to present design visualizations, then photorealistic representations can be achieved, but the user experience is lacking when it comes to setting up configurations and the process is largely driven by manual interaction
Solution Approach 1:
The system performs preliminary action by automatically generating design configurations and photorealistic visualizations before the designer even begins manual configuration. The ML model pre-processes design data, extracts material and color preferences, and generates initial design proposals that are ready for review, eliminating the need for designers to manually set up configurations from scratch.
Solution Approach 2:
The patent replaces manual configuration setup with automated machine learning generation. Instead of requiring designers to manually select materials, colors, and design parameters through traditional configurator interfaces, the system automatically generates multiple design proposals with photorealistic visualizations, allowing designers to review and select from pre-generated options rather than creating configurations manually.
Data Source
AI summary
A product configuration design system, includes a product configuration design server, including a processor, a non-transitory memory, an input/output, a product storage, a configuration library, and a machine learner; and a product configuration design device, which enables a user to select a three-dimensional object representation, a collection, and an inspiration source, such that the product configuration design server generates a plurality of product configurations as an output from a machine learning calculation on a configuration generation model, which takes as input the three-dimensional object representation, the collection, and the inspiration source. Also disclosed is a method of selecting a three-dimensional object representation, a collection, and an inspirations source; and generating product configurations.