Machine Learning Compliance Review for Architectural Plans
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Solution Overview
Problem
Architectural design plans face long lead times and unnecessary reviews due to multiple government agency approvals, which can be inefficient and time-consuming.
Innovation Solution
A machine learning-based apparatus and method that uses a processor and memory to determine design plan compliance by receiving structure parameters, obtaining construction and geographical constraints, and identifying a compliance threshold, thereby suggesting potential design changes and optimizing the review process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple government agencies review architectural design plans, then compliance accuracy is improved, but review time and process complexity increase significantly
Solution Approach 1:
The patent segments the compliance review process into distinct modules handled by different machine learning models. Each model specializes in evaluating specific building code requirements (e.g., structural safety, fire codes, zoning regulations), allowing parallel processing of different compliance aspects simultaneously, thus reducing overall review time while maintaining comprehensive accuracy
Solution Approach 2:
The patent introduces an intermediary machine learning system that acts as a mediator between design plans and final approval. This intermediary automatically pre-evaluates designs against building codes, filtering out obvious non-compliances and preparing pre-analysis reports, which reduces the workload and time required for subsequent human or agency reviews
2Reliability
If multiple government agencies review architectural design plans, then compliance thoroughness is improved, but device complexity and coordination requirements increase
Solution Approach 1:
The patent merges multiple compliance evaluation functions into a single integrated machine learning platform. The system consolidates various building code requirements, geographical constraints, and construction regulations into one unified system that processes designs comprehensively, eliminating the need for multiple separate agency review processes and reducing coordination complexity
Solution Approach 2:
The patent creates a universal machine learning platform that performs multiple compliance evaluation functions simultaneously. The system is designed to handle diverse building code requirements, different geographical constraints, and various construction regulations through a single multi-functional system, replacing the need for multiple specialized agency reviews
3Productivity
If automated machine learning review is implemented, then review speed and efficiency are improved, but measurement precision and compliance accuracy may deteriorate
Solution Approach 1:
The patent implements preliminary training actions where machine learning models are extensively trained on historical compliance data, approved designs, and building code requirements before actual review deployment. This preliminary preparation ensures that the automated system develops high precision in compliance evaluation, reducing errors and improving accuracy while maintaining efficient automated processing
Solution Approach 2:
The patent incorporates feedback mechanisms where the machine learning system continuously learns from review outcomes, compliance verification results, and corrections made to automated decisions. This feedback loop refines model accuracy over time, ensuring that automated reviews maintain or improve compliance precision while preserving processing efficiency
Data Source
AI summary
An apparatus and method for determining architectural plan compliance is illustrated herein. Determining design plan compliance using machine learning includes at least a processor and a memory communicatively connected to the processor, the memory containing instructions configuring the at least a processor to receive, by the processor, a design plan, wherein the design plan comprises structure parameters; obtain, by the processor, construction constraint and geographical constraint, identify, by the processor, a compliance threshold as a function of the construction constraints and geographical constraints, determine a divergence element related to compliance of the user design as a function of the compliance threshold and the design plan.