Objective Property Scores From Human Preference Rankings
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods struggle to provide accurate, objective scores for subjective characteristics of properties due to human inconsistency in rating such traits, leading to inaccuracies in valuation and condition assessment.
Innovation Solution
A method and system that trains a model to determine an objective score for subjective characteristics by leveraging human consistency in relative rankings, using image and description data to predict rankings and scores for properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human rating methods are used to evaluate subjective characteristics, then flexibility in capturing human preferences is maintained, but consistency and objectivity of scores deteriorate due to human inconsistency
Solution Approach 1:
The patent creates a computational model that copies and formalizes human rating behavior. The model learns from human preference data and produces consistent, objective scores that replicate human judgment patterns without human inconsistency. This allows the system to maintain the flexibility of human preference capture while achieving reliable, consistent scoring through automated model reproduction of human evaluation logic.
2Measurement precision
If objective scoring methods are implemented, then consistency and standardization improve, but accuracy in capturing subjective human preferences deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the model continuously learns from human rating data and adjusts its scoring predictions. By comparing model outputs with actual human preferences and iteratively refining its parameters, the system achieves both objective, standardized scoring and high accuracy in capturing subjective human preferences. The feedback loop allows the model to adapt to nuanced preference patterns while maintaining consistent measurement protocols.
3Measurement precision
If comprehensive property analysis is performed using multiple data types, then valuation accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary processing and pre-computation of feature representations and model parameters during training phases. By pre-processing extensive property data and pre-computing feature interactions, the system reduces real-time processing requirements. This allows comprehensive multi-type data analysis to be performed efficiently during inference, maintaining high valuation accuracy while minimizing processing time through advance computational preparation.
Data Source
AI summary
In variants, the method for subjective property scoring can include determining an objective score for a subjective characteristic of a property using a model trained using subjective labels for a set of training properties. In examples, the model can be trained on subjective property rankings, determined using the subjective labels, for the set of training properties.