Machine Learning Apparatus for Automated Architecture Design Problem Categorization
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Solution Overview
Problem
The architecture design process, particularly in the mid-late phases, is inefficient due to reliance on exhaustive searches and manual drafting tasks, with over 60% of project time and cost spent during design development and construction documentation, necessitating a solution to streamline design guidance and component selection.
Innovation Solution
A machine learning-based apparatus and method that utilizes a database of components, processor, and memory to receive a representative part model, categorize design problems, and generate design solutions, recommending appropriate components from a database of products from multiple manufacturers, thereby automating the design process and reducing manual labor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning modules are introduced to automate design problem categorization and solution generation, then productivity and efficiency are improved, but device complexity increases
Solution Approach 1:
The system divides the complex design automation task into distinct functional modules: a first machine learning module for categorizing design problems and a second machine learning module for generating design solutions. This segmentation allows each module to specialize in specific tasks, improving overall productivity while managing system complexity through modular architecture.
Solution Approach 2:
The patent introduces machine learning models as intermediary components between the input design data and the final design solutions. These ML modules act as intelligent mediators that automatically process design problems, reducing the need for manual intervention and thereby improving productivity despite the added system complexity.
2Loss of time
If automated design guidance and component selection are implemented, then loss of time is reduced, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning modules on extensive design data and component databases before actual design tasks. This preliminary preparation enables the system to quickly categorize design problems and generate solutions during execution, significantly reducing project time while the complexity is managed through offline training processes.
Solution Approach 2:
The automated design guidance system performs self-service by using the first ML module to automatically categorize design problems without manual input, and the second ML module to autonomously generate design solutions. This self-service capability reduces time loss by eliminating manual drafting tasks, while the complexity is contained within the automated decision-making processes.
3Adaptability or versatility
If comprehensive database of components from multiple manufacturers is integrated, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal component database that integrates products from multiple manufacturers into a single accessible repository. The second machine learning module universally queries this diverse database to generate design solutions, improving adaptability and versatility in component selection. The data management complexity is handled through standardized database schemas and ML-driven query processing.
Data Source
AI summary
An apparatus and method for determining and solving design problems is illustrated herein. Apparatus includes a processor and a database of components by manufacturer. The processor is configured to receive a representative part model which may include 2D prints and 3D models of a building design. The processor is configured to identify and categorize the representative part model to a design problem and generate design solutions to solve the design problem. The processor is also configured to encode layers of required information for a first machine-learning module. The processor determines components from the database of components, as a function of the design solution.