Multi-Scale Generative Building Design Optimization

The patent segments the building design into discrete modular components (rooms, walls, floors, ceilings) that can be independently generated and then combined. This segmentation allows each component to be optimized separately while maintaining the ability to explore multiple combinations across different design goals, breaking the cascading dependency chain of conventional sequential design.

The patent introduces a new dimension of optimization by using generative design algorithms that can simultaneously evaluate multiple design goals (cost, habitability, carbon emissions) across all design scales at once, rather than sequentially. This multi-objective optimization in an additional dimensional space allows exploration of design trade-offs that would be impossible in traditional sequential approaches.

The patent applies partial action by using generative design algorithms that explore a representative subset of the design space rather than exhaustively evaluating all possible permutations. The system generates multiple design iterations with varying levels of detail, focusing computational resources on promising design regions while using simplified models for preliminary evaluation, thus achieving good optimization quality without requiring impossible computational resources.

The patent performs preliminary action by using lower-fidelity generative models to quickly evaluate design concepts before committing to detailed design. The system generates initial design options at a coarse level, filters them based on multiple objectives, and only then proceeds to detailed design of selected candidates, dramatically reducing the total computational effort required while maintaining optimization quality.