AI Property Data Linking System Resolving Inconsistencies
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Solution Overview
Problem
Existing systems face challenges in linking and matching real estate property data across different data stores due to inconsistencies, changes in property ownership or structure, and varying terminology, leading to increased processing times and navigational complexities for users.
Innovation Solution
A data linking system utilizing artificial intelligence and machine learning to identify and link data records across multiple data stores, assigning unique identifiers and updating records based on analysis of property attribute values, while handling changes and inconsistencies, and providing user interfaces to visualize property data over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is stored in multiple different data stores with different identifiers and formats, then data coverage and completeness are improved, but data linking difficulty and processing time increase
Solution Approach 1:
The patent introduces a machine learning model as an intermediary that automatically matches records across different data stores. The model takes identifiers and property attributes from multiple data stores as input and outputs match probabilities, enabling automated data linking without manual intervention. This resolves the contradiction by providing a scalable solution that handles increasing data volume and complexity while maintaining consistent processing times.
2Measurement precision
If manual navigation to different data stores is required to access property data, then data accuracy is improved, but user navigation time and complexity increase
Solution Approach 1:
The patent merges data from multiple different data stores into a unified view presented to users. Instead of requiring users to navigate to separate data stores, the system combines property data, tax data, listing data, and other information from various sources into a single integrated interface. This resolves the contradiction by maintaining data accuracy through systematic matching while dramatically improving ease of operation through unified access.
3Adaptability or versatility
If different identifier formats are used across data stores, then data source independence is improved, but record matching accuracy decreases
Solution Approach 1:
The patent transforms different identifier formats into a common comparison framework by extracting and analyzing multiple property attributes (address, owner name, property characteristics) rather than relying on single identifiers. The machine learning model processes these varied parameters and outputs match probabilities, enabling accurate matching across data stores that use different identifier formats and structures while maintaining data source independence.
Data Source
AI summary
A data linking system is described herein that links data records corresponding to a particular real estate property even if there are inconsistencies in the data records, the physical presence of the real estate property has changed over time, and/or the data records use different terminology. In some cases the data records are matched using a trained machine learning model. The data linking system can optionally generate a visualization of the data record linkage via interactive user interfaces. By linking data records despite the issues described above, the data linking system reduces the number of navigational steps a user performs to obtain data associated with a property and/or reduces data processing times. The disclosed system may be used to generate and maintain a comprehensive database of substantially all properties within a jurisdiction, in which a unique identifier is assigned to each property.