Date Parsing System for Financial Documents
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Solution Overview
Problem
Existing date parsing technologies fail to accurately determine date information from financial and accounting documents due to ambiguities in date formats, such as omitted years, ambiguous month and day ordering, and mixed formats within a single document, often resulting in incomplete or incorrect date parsing.
Innovation Solution
A method and system that determine candidate date strings from documents using machine learning models, preprocess the strings to remove ordinals and day of week indicators, and perform ambiguity resolution by assessing the validity of date formats, allowing for the identification of inferred dates without prior knowledge of the date format, and utilizing additional document data to refine date determinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing date parsing technologies are used to extract date information from financial documents, then the process is simple and fast, but the accuracy is low due to ambiguities in date formats
Solution Approach 1:
The patent segments the date parsing process into multiple distinct stages: candidate date string extraction, ambiguity detection, context gathering from surrounding text, and disambiguation. This segmentation allows each stage to be optimized independently, improving overall accuracy without overwhelming system complexity.
Solution Approach 2:
The patent introduces an intermediary disambiguation layer that sits between simple date extraction and final date interpretation. This intermediary analyzes contextual clues, document metadata, and multiple candidate dates to resolve ambiguities, thereby improving accuracy without requiring complete redesign of the parsing system.
2Measurement precision
If ambiguity resolution mechanisms are implemented to handle multiple valid dates, then date determination accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-identifying and flagging ambiguous date patterns during the extraction phase. By detecting potential ambiguities early and preparing candidate resolutions beforehand, the system avoids time-consuming analysis during final processing, thus reducing overall processing time while maintaining high accuracy.
Solution Approach 2:
The system uses self-service mechanisms where ambiguous date strings automatically trigger context-gathering queries from the document itself. The system extracts relevant contextual information (such as surrounding text, document type, and metadata) to resolve ambiguities without requiring external intervention or manual review, thereby maintaining speed while improving accuracy.
3Measurement precision
If context analysis is performed to resolve date ambiguities, then date information accuracy improves, but computational resources consumed increase
Solution Approach 1:
The patent applies local quality by performing context analysis selectively only on ambiguous date strings rather than all dates in the document. By identifying which dates require disambiguation and applying computational resources only to those specific cases, the system improves accuracy for problematic dates while minimizing overall computational resource consumption.
Solution Approach 2:
The system performs partial action by extracting only the specific contextual elements needed to resolve each ambiguity rather than analyzing the entire document context. This selective approach gathers sufficient information for disambiguation without the excessive computational cost of comprehensive document analysis, thus improving accuracy while controlling resource usage.
Data Source
AI summary
A method comprises: determining a first candidate date string from a document; determining that the first candidate date string corresponds with two or more valid dates; determining a document date value of the document; determining a relevant date range based on the document date value; determining that at least one of the two or more valid dates falls within the relevant date range; and responsive to determining that at least one of the two or more valid dates falls within the relevant date range, determining the at least one of the two or more valid dates as an inferred date for the first candidate date string.