Natural Language MDE Generation for Portable 3D CAD Output
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Solution Overview
Problem
Existing computer-aided design (CAD) applications for multi-dimensional entities (MDEs) are time and labor intensive, require skilled developers, and limit the transferability of MDEs across different applications and environments.
Innovation Solution
A system that generates MDEs using natural language input, employing a machine learning model with a skill chain of associated prompt templates to simplify the creation and transferability of MDEs across diverse applications and environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional CAD applications are used to create multi-dimensional entities, then the MDEs can be produced with required precision, but the process becomes time and labor intensive requiring skilled developers
Solution Approach 1:
The patent replaces the mechanical interaction of manual CAD operations with an automated machine learning system. The ML model processes natural language descriptions and directly generates MDEs in standard formats (STL, OBJ, STEP), eliminating the need for manual modeling operations and reducing creation time while maintaining precision through the model's trained accuracy.
Solution Approach 2:
The system enables users with limited technical background to create MDEs independently through natural language input. The ML model automatically handles the complex generation process without requiring users to learn specialized CAD tools or techniques, making the service accessible to anyone who can describe their needs in plain language.
2Manufacturing precision
If traditional CAD applications are used to create multi-dimensional entities, then the MDEs can be produced with required precision, but skilled developers are required to operate the applications
Solution Approach 1:
The patent replaces complex mechanical CAD interfaces with a simple natural language input mechanism. Users describe their needs in everyday language rather than navigating complex toolbars and menus, dramatically improving ease of operation while the ML model ensures precise output through its trained generative capabilities.
Solution Approach 2:
The machine learning model serves as an intermediary between the user's simple natural language description and the complex MDE generation process. This intermediary translates accessible user input into precise technical outputs, eliminating the need for users to directly interact with complex CAD systems while maintaining manufacturing precision.
3Reliability
If MDEs are created in one CAD application, then the design can be finalized in that application, but transferring the MDE to another application requires significant additional work
Solution Approach 1:
The patent creates MDEs with universal compatibility by generating them in standard interchange formats (STL, OBJ, STEP) that can be read by multiple applications. This multi-functionality allows the same MDE to be used across different CAD systems, 3D printing tools, and manufacturing software without requiring application-specific formats or extensive rework.
Solution Approach 2:
The machine learning model acts as an intermediary that inherently produces application-agnostic MDE outputs. By generating universal formats directly from natural language descriptions, the system eliminates the need for manual format conversion between applications, enabling seamless transferability while maintaining design finalization reliability.
4Device complexity
If skilled developers manually create MDEs, then the complexity of the MDEs can be fully controlled, but the labor and time costs increase significantly
Solution Approach 1:
The patent substitutes manual developer work with an automated machine learning system that generates MDEs from natural language descriptions. This replacement dramatically improves productivity by eliminating repetitive manual operations while the model's generative capabilities allow for complex MDE creation through simple textual prompts, maintaining full control over complexity through the description rather than manual manipulation.
Data Source
AI summary
Aspects of the present disclosure relate to systems and methods for creating a multi-dimensional entity (MDE) based on natural language (NL) input. A user may provide NL input into an application. One or more skills may be identified for the NL input, each of which has an associated prompt template. For example, a skill is associated with a computer-aided design and/or three-dimensional manufacturing application and/or file format, thereby enabling the generation of output associated with such applications and/or file formats. In examples, a skill chain may be generated that includes one or more skills with which to generate MDE output accordingly.