Property Typicality Analysis Using Image-Based Attribute Comparison
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Solution Overview
Problem
Conventional methods for determining property typicality relative to a reference population are subjective, inefficient, and prone to significant error, lacking objectivity and accuracy.
Innovation Solution
A system and method utilizing computer vision and machine learning to determine property typicality by comparing attribute values extracted from property images to those of a reference population, leveraging objective metrics and reducing the number of analyzed attributes to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to determine property typicality, then the process is simple, but the results are subjective and prone to significant error
Solution Approach 1:
The patent replaces subjective human judgment (mechanical/manual assessment) with computer vision and machine learning systems. The system automatically extracts attribute values from property images and compares them against reference populations, eliminating human bias and significantly improving measurement precision in typicality determination.
Solution Approach 2:
The patent introduces an intermediary computational system that acts as a mediator between property images and typicality assessment. This system uses attribute extraction, normalization, and comparison algorithms to objectively bridge the gap between raw image data and meaningful typicality metrics, reducing errors while managing complexity through structured processing.
2Measurement precision
If all property attributes are analyzed, then comprehensive assessment is achieved, but computational resources and time are excessively consumed
Solution Approach 1:
The patent extracts only the most relevant attribute values from property images using computer vision techniques. Instead of analyzing all possible property characteristics, the system identifies and extracts key attributes that are most indicative of typicality, thereby maintaining assessment accuracy while dramatically reducing computational resource consumption and processing time.
Solution Approach 2:
The patent segments the property analysis process into distinct stages: image processing, attribute extraction, normalization, and comparison. This segmentation allows the system to focus computational resources on the most critical analysis steps while skipping unnecessary processing, thereby improving productivity without sacrificing measurement precision.
3Reliability
If manual assessment methods are used, then implementation is straightforward, but objectivity and accuracy are compromised
Solution Approach 1:
The patent replaces manual assessment mechanisms with automated computer vision and machine learning systems. This substitution ensures that typicality determination is based on consistent, objective algorithms rather than subjective human judgment, significantly improving reliability. The system complexity is managed through modular architecture and standardized processing pipelines.
4Measurement precision
If extensive attribute analysis is performed, then accurate typicality metrics are obtained, but computational resources are excessively consumed
Solution Approach 1:
The patent extracts only the essential attribute values needed for typicality assessment from property images. By using computer vision to identify and extract only the most relevant features rather than processing all possible attributes, the system maintains high measurement precision while significantly reducing computational resource consumption and energy usage.
Solution Approach 2:
The patent applies partial action by performing attribute analysis only to the extent necessary for accurate typicality determination. The system identifies the minimum sufficient set of attributes needed for reliable assessment and processes only those, avoiding excessive computational effort while maintaining measurement precision through focused, targeted analysis.
Data Source
AI summary
The method for property typicality determination can include: determining a property, determining attribute values for the property, determining a reference population for the property, determining reference population attribute values, determining a typicality metric for the property, and optionally determining an influential attribute.