Machine Learning for Multi-Parcel Land Site Selection
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Solution Overview
Problem
Current systems face challenges in efficiently identifying and prioritizing suitable land parcels for multi-parcel development, as they lack effective methods to analyze and combine parcel data, user preferences, and real-time feedback, leading to suboptimal project site selection and development.
Innovation Solution
The implementation of a system that utilizes special-purpose transistor-based circuitry and machine learning algorithms to process and analyze parcel data, user interactions, and preferences, enabling real-time prioritization and development of optimal multi-parcel sites by generating and presenting viable building models and project site recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to identify and prioritize land parcels for multi-parcel development, then the process relies on manual analysis and basic data processing, but the efficiency and accuracy of project site selection deteriorates due to inability to effectively analyze and combine parcel data, user preferences, and real-time feedback
Solution Approach 1:
The patent replaces manual mechanical analysis methods with electronic data processing systems, machine learning algorithms, and automated computational models. These systems process parcel data, user preferences, and real-time feedback through digital computation rather than human analysis, simultaneously improving both efficiency and accuracy of project site selection.
Solution Approach 2:
The system implements real-time feedback loops where user preferences, market conditions, and parcel characteristics are continuously fed into the machine learning models. The models update their predictions and prioritizations based on this feedback, enabling dynamic optimization of project site selection that improves both speed and accuracy over time.
2Measurement precision
If comprehensive parcel data analysis is performed to improve project site selection accuracy, then the quality of development outcomes improves, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary processing and pre-filtering of parcel data before full analysis. Machine learning models pre-process large datasets to identify key features and patterns, so that when comprehensive analysis is needed, the system already has organized, pre-computed information ready, reducing the time required for complete analysis while maintaining accuracy.
Solution Approach 2:
The system implements multi-level analysis where basic prioritization can be achieved with partial data processing for quick results, while comprehensive analysis is available when needed. Machine learning models can operate with different levels of data input, allowing the system to provide acceptable project site selection quality in less time when full analysis is not required.
Data Source
AI summary
Machine learning systems and methods are described in regard to automating and acting upon evaluations of hypothetical composite project sites of 2+ disparate land parcels so as to allow a developer, owner, or other stakeholder to see and act upon potential land uses that are not reflected in conventional valuations. Some variants include a feature augmentation protocol for speciating one or more detailed structures feasible for development, a pattern matching protocol for identifying viable composite project sites that might suit a developer's requirements, technologies for accommodating latent preferences, proactive virtual development of co-owned disparate parcels, a notification protocol implementing offers to numerous potential sellers whose responses might affect project viability, wise virtual development and prioritization, or other such innovative configurations.