Machine Learning Design Assistant for Automated Problem Solving
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Solution Overview
Problem
The architecture design process, particularly in the mid-late phases such as design development and construction documentation, is inefficient due to reliance on exhaustive searches and manual drafting tasks, consuming more than 60% of the project time and cost.
Innovation Solution
An apparatus and method using machine learning to determine and solve design problems by receiving a representative part model, determining its features, categorizing it based on design progress, identifying design issues, generating solutions, and displaying these on a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If exhaustive searches and manual drafting tasks are used in design development and construction documentation, then design accuracy and completeness can be achieved, but project time and cost increase significantly (consuming more than 60% of total project time and cost)
Solution Approach 1:
The patent replaces manual drafting tasks and exhaustive search methods with machine learning-based automated systems. The ML model analyzes design data, identifies issues, and generates solutions automatically, substituting human manual work with computational processes that achieve similar or better accuracy while dramatically reducing time consumption.
Solution Approach 2:
The system enables self-service by allowing the machine learning model to autonomously perform design analysis, problem identification, and solution generation without requiring continuous human intervention. The automated design assistant serves itself by learning from data and improving its capabilities over time, reducing dependency on manual exhaustive searches.
2Manufacturing precision
If exhaustive searches and manual drafting tasks are used in design development and construction documentation, then comprehensive design coverage can be achieved, but project cost increases significantly
Solution Approach 1:
The patent replaces costly manual drafting and exhaustive search processes with automated machine learning systems. The ML-based design assistant performs comprehensive design analysis and generates solutions automatically, maintaining design completeness while reducing the labor costs and resource consumption associated with manual methods.
Solution Approach 2:
The system changes the parameters of the design process by transitioning from manual, time-intensive methods to automated, data-driven approaches. By altering the fundamental operating parameters (from human labor to machine learning computation), the system achieves comprehensive design coverage at lower cost.
3Productivity
If automated machine learning systems are implemented for design problem solving, then project time and cost are reduced, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary machine learning system that bridges the gap between manual design processes and automated solutions. The ML-based design assistant acts as a mediator that processes design data, identifies problems, and generates solutions, simplifying the overall system architecture while maintaining high productivity through automated intelligence.
4Productivity
If automated machine learning systems are implemented for design problem solving, then labor requirements are reduced, but automation extent increases
Solution Approach 1:
The system implements self-service capabilities where the machine learning model autonomously performs design analysis, problem identification, and solution generation without requiring continuous human supervision or intervention. The automated design assistant serves itself by learning from data and improving its performance over time, achieving high productivity through advanced automation.
Solution Approach 2:
The patent incorporates feedback mechanisms where the machine learning system continuously learns from design outcomes and improves its problem-solving capabilities. This feedback loop enables the system to maintain high productivity while managing automation complexity through iterative learning and adaptation.
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.