Contextual Recommendations for 3D Design Spaces
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Solution Overview
Problem
Conventional CAD applications provide limited feedback during the design process, offering only high-level or generalized recommendations that do not allow designers to request specific recommendations for particular design components or to refine generated designs.
Innovation Solution
A computer-implemented method that allows users to select a prompt volume within a design space, identifying design objects within the volume, generating candidate actions associated with these objects, and displaying relevant recommendations in a recommendation window.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional CAD applications provide generalized recommendations based on predetermined actions, then the system complexity is reduced and ease of operation is improved, but the recommendation quality and relevance to specific design components deteriorates
Solution Approach 1:
The system segments the design space into multiple selectable volumes, allowing recommendations to be generated for specific portions of the design rather than providing only global generalized recommendations. This segmentation enables targeted analysis of particular design components while maintaining ease of operation through intuitive volume selection interfaces.
Solution Approach 2:
The system implements feedback mechanisms where the AI model analyzes user selections and design state, then provides contextual recommendations that are fed back to the user. This feedback loop allows the system to adapt recommendations based on specific design objects selected within prompt volumes, improving recommendation quality while maintaining operational simplicity.
2Adaptability or versatility
If the AI model generates numerous design alternatives and presents them in a design space, then the design exploration capability is improved, but the time required for users to manually evaluate and select the best design alternative increases
Solution Approach 1:
The system provides automated feedback by analyzing design objects within selected prompt volumes and generating contextual recommendations. This feedback mechanism reduces the manual evaluation burden on users by highlighting promising design alternatives and providing actionable insights, thereby reducing the time required to explore and select optimal designs while maintaining comprehensive design exploration capabilities.
Solution Approach 2:
The AI model performs self-service analysis by automatically evaluating design objects within prompt volumes and generating recommendations without requiring extensive manual user intervention. This self-service capability allows the system to process numerous design alternatives efficiently, reducing the time users spend on manual evaluation while preserving adaptability in design exploration.
3Stability of the object's composition
If conventional CAD applications analyze the overall design when generating recommendations, then the global design context is maintained, but the ability to provide specific recommendations for particular components deteriorates
Solution Approach 1:
The system segments the overall design into selectable prompt volumes, enabling users to focus analysis on specific components while maintaining awareness of the global design context. The AI model can generate recommendations at both component level (within selected volumes) and global level, providing measurement precision for specific components without losing the stability of overall design context.
Solution Approach 2:
The system applies local quality by providing tailored recommendations for specific design objects within selected prompt volumes, rather than applying uniform generalized recommendations across the entire design. This allows component-level precision in recommendations while the system continues to maintain and analyze the overall design context, resolving the contradiction between global context stability and local recommendation specificity.
Data Source
AI summary
In various embodiments, a computer-implemented method for generating recommendations for a generative design, comprises receiving a selection of a prompt volume within a design space, wherein the design space is generated by a design exploration application, and the prompt volume defines a sphere of influence within the prompt volume, identifying one or more design objects within the prompt volume, generating a plurality of candidate actions associated with the one or more design objects, and displaying, within a recommendation window included in the design space, at least one candidate action from the plurality of candidate actions.