Property State Tracking With Object Version Analysis
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Solution Overview
Problem
Conventional imagery-based methods struggle to reliably distinguish between changes in property data due to property modifications or measurement errors, leading to inaccurate data aggregation and inability to disambiguate visual changes in property states.
Innovation Solution
A method and system for property state tracking that uses neural networks trained with self-supervised learning to generate consistent object representations despite appearance changes, associating different measurements with a universal object identifier and determining relationships between representations to track property versions over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If conventional imagery-based methods are used to track property changes, then measurement coverage can be extended, but reliability of property data deteriorates due to inability to distinguish between property modifications and measurement errors
Solution Approach 1:
The patent segments property tracking into distinct object versions, where each version represents a specific state of the property. By dividing the continuous property data into discrete versioned segments, the system can track transformations between versions and distinguish intentional property modifications from measurement errors, thereby maintaining reliability while extending measurement coverage.
Solution Approach 2:
The patent establishes object versions in advance as properties change, creating a preliminary structured framework before analysis. This preliminary versioning allows the system to pre-identify potential property modifications versus measurement errors, enabling reliable data aggregation across extended measurement coverage without requiring real-time disambiguation.
2Quantity of substance
If multiple measurements from different sources and time periods are aggregated, then data completeness improves, but measurement precision deteriorates due to appearance changes and data disparities
Solution Approach 1:
The patent performs preliminary object version identification and association before aggregation. By pre-linking measurements to specific object versions based on spatial and temporal criteria, the system can aggregate diverse measurements while maintaining precision through version-aware matching, preventing false matches between different property states.
Solution Approach 2:
The patent implements feedback mechanisms where measurement results inform subsequent version identification and association. The system uses detected changes to refine version boundaries and association criteria, creating a feedback loop that improves measurement precision while aggregating increasingly complete data sets from multiple sources.
3Difficulty of detecting and measuring
If comprehensive property monitoring is performed across all regions, then detection coverage increases, but computational effort increases due to extensive comparisons required
Solution Approach 1:
The patent segments the monitoring space into discrete geographic regions and associates each with relevant object versions. This spatial segmentation allows the system to perform detection and comparisons only within relevant regional contexts, reducing computational effort while maintaining comprehensive detection coverage across all monitored areas.
Solution Approach 2:
The patent applies local quality optimization by tailoring version association criteria and comparison parameters to specific geographic regions and property types. This localized approach reduces unnecessary computational comparisons while ensuring appropriate detection sensitivity for each region, balancing coverage with computational efficiency.
Data Source
AI summary
In variants, the method can include: determining a timeseries of measurements of a geographic region; determining a set of object representations from the timeseries of measurements; and determining a timeseries of object versions based on relationships between the object representations.