AI Product Configuration System for 3D Design Automation
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Solution Overview
Problem
Current methods for designing 3D product configurations with color and material combinations are limited by manual interaction and lack of efficient reuse of past design decisions, relying heavily on human analysis and not effectively combining trend and mood data.
Innovation Solution
A product configuration design system utilizing AI and 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 and manual refinement, capturing demographic data for targeted design decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual interaction is used for configuring 3D product designs, then designers can have full control over design decisions, but the process is time-consuming and limited by human resources
Solution Approach 1:
The system enables self-service by allowing the configurator to automatically generate design configurations based on stored preferences and past decisions, reducing the need for continuous manual intervention while maintaining design quality and exploration speed
Solution Approach 2:
The system implements feedback mechanisms by capturing user selections and past design decisions, storing them in a database, and using this information to automatically generate and refine future design configurations, creating a continuous improvement loop that enhances both productivity and control
2Reliability
If traditional storage methods are used for design decisions, then data can be preserved, but there is no efficient way to reuse and combine past decisions with trend data
Solution Approach 1:
The database system serves multiple functions by not only storing design decisions but also by enabling efficient retrieval, analysis, and combination with external trend data, making the stored information versatile for both historical reference and future design generation
Solution Approach 2:
The system performs preliminary action by pre-processing and storing design decisions in a structured format that facilitates easy reuse and combination with trend data, eliminating the need for manual reanalysis and enabling rapid generation of new design configurations
3Productivity
If AI and ML are used to parse large databases of materials and colors, then design configurations can be generated more efficiently, but the system complexity increases
Solution Approach 1:
The system uses an intermediary database layer that stores pre-processed material and color data, acting as a mediator between the complex AI/ML processing engine and the user interface, thereby managing system complexity while maintaining high productivity in configuration generation
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.