Document-Embedded Structured Data Comparison and Insight Generation
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Solution Overview
Problem
Conventional document analysis methods fail to accurately compare and contextualize structured and unstructured data within documents, often ignoring structured data or treating it as unstructured, leading to incomplete and inaccurate analyses.
Innovation Solution
A computer-implemented method that extracts and normalizes structured data from hybrid documents, preserving formatting and structural parameters, and compares the normalized tables to identify differences and similarities using various similarity measures, applying analytical models to derive insights and perform automated actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional document analysis approaches are used to compare documents, then the analysis process is simple, but the analysis accuracy is poor because structured data is ignored or treated as unstructured data
Solution Approach 1:
The patent segments the document analysis process into distinct modules: structured data extraction (identifying tables, forms, key-value pairs), unstructured data extraction (text content), normalization processes, and comparison operations. This segmentation allows each component to be optimized independently, improving overall analysis accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The patent introduces a normalization module as an intermediary between data extraction and comparison. This mediator transforms extracted structured and unstructured data into standardized formats, enabling accurate comparison while abstracting the complexity of format variations from the comparison logic itself.
2Measurement precision
If structured data is extracted and normalized from hybrid documents, then data comparison accuracy improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary extraction and normalization of structured data from documents before the comparison operation. By preparing the data in advance and organizing it into standardized formats (tables, key-value pairs), the system reduces the computational burden during the actual comparison phase, thereby improving accuracy while managing processing time through advance preparation.
Solution Approach 2:
The patent applies different processing strategies to different types of structured data based on their specific characteristics. Tables are normalized with row and column alignment, while key-value pairs are standardized with type coercion. This localized optimization allows accurate comparison of each data type using the most appropriate method, improving overall accuracy without uniformly increasing processing time for all data.
3Loss of information
If multiple alignment techniques are applied to normalize extracted tables, then the contextualization of structured data improves, but the computational complexity increases
Solution Approach 1:
The patent segments the normalization process into distinct alignment techniques: row alignment (matching rows between tables based on content similarity), column alignment (matching columns based on headers and data types), and cell-level alignment (comparing individual cells). This segmentation allows the system to apply appropriate alignment strategies to different aspects of table structure, improving information completeness while managing complexity through modular processing of each alignment dimension.
Solution Approach 2:
The patent employs parameter changes in the form of similarity thresholds and matching criteria to control the alignment process. By adjusting these parameters, the system can balance between achieving high information completeness through thorough alignment and managing computational complexity by setting appropriate stopping criteria and similarity thresholds for matching rows, columns, and cells.
Data Source
AI summary
Methods, systems, and computer program products for contrasting document-embedded structured data and generating summaries thereof are provided herein. A computer-implemented method includes extracting two or more tables from two or more input documents, wherein each of the two or more input documents comprises structured data and unstructured data; normalizing the two or more extracted tables using one or more alignment techniques; determining at least one of (i) one or more differences and (ii) one or more similarities across the two or more extracted tables by performing a comparison of the two or more normalized tables; deriving one or more insights from the comparison by applying at least one analytical model to the at least one of the one or more determined differences and one or more determined similarities; and outputting at least a portion of the one or more insights to at least one user.