Transaction Table Identification Using DOM Graph Analysis
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Solution Overview
Problem
Current techniques for extracting financial transaction data from transactions tables hosted by different financial institutions lack a uniform approach, requiring software applications to support unique scripts for each institution, leading to inefficient utilization of computing resources.
Innovation Solution
A graph-based technique that represents a document as a document object model (DOM), identifies candidate tables, converts them to textual tables, and uses a machine learning model to classify columns, determining if the table is a transactions table, allowing data extraction uniformly across institutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple institution-specific scripts are implemented to extract transaction data from different financial institutions, then compatibility with various institutions is improved, but device complexity and maintenance burden increase
Solution Approach 1:
The patent implements a universal extraction mechanism that uses document object models (DOMs) and graph-based analysis to identify and extract transaction tables from different financial institution websites. Instead of maintaining separate scripts for each institution, the system uses institution-agnostic DOM representation and graph algorithms that work across multiple institutions, making one script serve multiple functions.
Solution Approach 2:
The patent creates standardized DOM representations (copies) of transaction tables from different institutions, normalizing their structures into a common format. This allows the extraction system to work with standardized copies rather than dealing with the diverse original formats directly, reducing complexity while maintaining compatibility.
2Adaptability or versatility
If multiple institution-specific scripts are maintained, then support for different institutions is improved, but loss of time and computational resources increase
Solution Approach 1:
The system employs a universal extraction script that leverages DOM representation and graph-based table identification to handle multiple institutions simultaneously. This single multi-functional script eliminates the need to execute multiple separate scripts, reducing computational overhead and time loss while maintaining broad institutional support.
3Adaptability or versatility
If traditional extraction methods are used without uniform approach, then flexibility for different institutions is maintained, but productivity decreases
Solution Approach 1:
The patent transforms the extraction approach by changing the representation parameters of transaction tables into standardized DOM structures with consistent graph-based characteristics. This parameter transformation enables a uniform extraction methodology that maintains flexibility for different institutions while significantly improving extraction efficiency and productivity through standardized processing.
Data Source
AI summary
A method for automatically identifying a transactions table in a document includes obtaining a document object model of the document. The document object model includes a plurality of nodes and a plurality of edges, with each of the nodes corresponding to a respective element of the document. The method includes generating a plurality of hash values, with each of the plurality of hash values corresponding to a respective node of the plurality of nodes. The method includes determining the document includes a candidate table based on the plurality of hash values. The method includes generating a textual table based on the candidate table. The method includes analyzing one or more columns of the textual table to determine whether the textual table satisfies one or more criteria. The method includes determining the textual table is the transactions table based on determining the textual table satisfies the one or more criteria.