AI-Generated 3D Design Objects with Editable Parametric Features
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Solution Overview
Problem
Conventional CAD applications limit the use of AI models for generating 3D design objects to highly skilled users due to complex interaction requirements, reducing design quality for less experienced users and limiting the incorporation of AI-generated content into overall designs.
Innovation Solution
A computer-implemented method using a trained machine learning model to generate 3D design objects from user inputs, converting them into parametric-based objects with editable features and adding them to a design space, enabling accurate inference of design intent and automated incorporation into 3D designs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional CAD applications use AI models to generate 3D design objects, then design quality can be improved through automated generation, but the complexity of interaction requirements limits usage to highly skilled users only
Solution Approach 1:
The patent introduces an intermediary system that automatically converts AI-generated content into parametric-based 3D design objects. This intermediary process eliminates the need for users to manually nominate geometries or specify technical parameters, bridging the gap between AI generation capabilities and CAD system requirements while making the technology accessible to less experienced users.
Solution Approach 2:
The system enables self-service by automatically performing tasks that previously required skilled user intervention. The automated conversion process allows the system to self-adapt AI-generated content into usable design objects without requiring users to understand complex CAD operations or manually modify generated content.
2Adaptability or versatility
If users manually convert AI-generated content into 3D design objects, then the content can be incorporated into overall designs, but the manual modification process conflicts with design parameters and reduces overall design quality
Solution Approach 1:
The patent replaces the manual mechanical process of converting and modifying 3D objects with an automated computational system. This substitution eliminates human error and inconsistency in the conversion process, ensuring that design parameters are preserved while enabling seamless incorporation of AI-generated content into overall designs.
3Manufacturing precision
If more time is allocated for generating design alternatives, then design quality improves due to more options, but the manual generation process is very labor-intensive and time-consuming
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on design data before actual design generation. This preliminary training enables the models to automatically generate multiple design alternatives quickly without requiring manual intervention for each object, thus providing numerous options without the labor-intensive time cost of manual generation.
Solution Approach 2:
The system changes parameters by transitioning from manual control of design generation to automated AI-driven generation with adjustable parameters. Users can specify high-level design requirements, and the system automatically generates multiple alternatives by varying design parameters, dramatically reducing time consumption while maintaining or improving design quality.
Data Source
AI summary
In various embodiments, a computer-implemented method for generating a design object via a design exploration application comprises receiving an intent input, where the intent input includes at least a textual input, generating, based on the intent input, a design prompt, generating, via a trained machine learning (ML) model, a three-dimensional object based on the design prompt, converting the three-dimensional object to the design object, where the design object includes one or more editable features, and adding the design object to a design space.