Automated Title Description Selection Using Textual Centrality
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Solution Overview
Problem
The title insurance underwriting process is labor-intensive, leading to delays and errors due to the need for extensive human effort in searching multiple databases for title evidence and applying business rules, which increases transaction time and cost in real estate transactions.
Innovation Solution
A method that retrieves entity descriptions from various data sources, performs numerical comparisons to discard irrelevant descriptions, and applies a textual centrality measure to automatically select the most agreeing description, which is then used to populate title documents with a calculated confidence score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual searching and comparison of title evidence from multiple databases is performed, then accuracy of title examination can be maintained through human judgment, but transaction time increases significantly and labor costs rise
Solution Approach 1:
The patent replaces the mechanical human examination process with an automated computer-based system that retrieves title evidence from multiple databases, extracts entities and descriptions, performs numerical comparisons, and applies textual centrality measures to select the best descriptions. This substitution eliminates manual labor while maintaining examination accuracy through systematic automated processing.
Solution Approach 2:
The patent transforms the title examination process by changing key parameters: it extracts numerical identifiers (APN, tract number, lot number) from descriptions and uses these as comparison parameters to filter and rank descriptions. By converting unstructured text into structured numerical data for comparison, the system achieves rapid automated processing while maintaining accuracy.
2Reliability
If extensive human effort is applied to search multiple databases and apply business rules, then comprehensive title evidence evaluation is achieved, but errors increase due to human fatigue and transaction time increases
Solution Approach 1:
The patent replaces human examiners with an automated system that consistently applies retrieval and comparison algorithms across all title evidence. This eliminates human fatigue and variability, ensuring uniform application of business rules and reducing errors while maintaining comprehensive evaluation through systematic database searching and evidence comparison.
Solution Approach 2:
The patent implements a feedback mechanism where the system calculates a confidence score based on the textual centrality measure and numerical comparison results. This feedback allows the system to identify cases with low confidence scores that may require additional review, ensuring comprehensive evaluation while maintaining high accuracy through automated consistency.
3Reliability
If multiple databases are searched for complete title evidence, then thoroughness of title examination is improved, but device complexity and processing time increase
Solution Approach 1:
The patent creates a universal automated system that handles multiple database sources (county databases, public record databases, court databases, proprietary databases) through a single integrated process. The system performs entity extraction, numerical comparison, and textual centrality analysis across all sources uniformly, simplifying the complexity by providing a multi-functional solution that manages diverse data sources through consistent algorithms.
4Manufacturing precision
If manual creation of title reports and generation of title insurance products is performed, then customization and accuracy can be maintained, but productivity decreases significantly
Solution Approach 1:
The patent replaces manual title report creation and product generation with automated processing that retrieves evidence, analyzes descriptions using numerical and textual methods, and generates title insurance products systematically. This substitution dramatically increases throughput while maintaining accuracy through consistent application of extraction and comparison algorithms across all cases.
Solution Approach 2:
The patent performs preliminary entity extraction and description analysis before final product generation. By pre-processing the title evidence to extract numerical identifiers and calculate textual centrality measures in advance, the system prepares structured data that enables rapid and accurate product generation, increasing overall productivity without sacrificing precision.
Data Source
AI summary
Techniques are described for collecting descriptions of an entity from different data sources and using a numeric comparison and textual centrality measure to automatically select a best description. In one implementation, a method includes: retrieving a real property description dataset, the real property description dataset including descriptions from multiple data sources that describe the real property; extracting, from each of the descriptions, numbers that identify the property; performing a numerical comparison of the numbers extracted from each of the descriptions to determine if any descriptions needs to be discarded from further consideration; applying a text cleaning process to normalize the descriptions; and performing a textual centrality measure of remaining descriptions to determine a level agreement of each of the remaining descriptions with each of the other remaining descriptions; and using at least the textual centrality measure to select a description. The selected description may be used to populate a document.