Journal Entry Parsing via Subset Matching
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Solution Overview
Problem
Existing methods for parsing large journal entries in financial reporting are computationally unworkable due to exponential growth in possible combinations, making it difficult to identify sub-journal entries within larger entries without prior knowledge or direct links between debit and credit records.
Innovation Solution
The system identifies and isolates sub-journal entries by selecting subsets of debit or credit records, determining matching subsets, and removing them from consideration, using hash tables to efficiently reduce the combinatorial space and increase parsing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If brute force computation of every possible combination is used to identify sub-journal entries, then complete parsing accuracy is achieved, but computational complexity becomes unworkable as combinations grow exponentially
Solution Approach 1:
The patent segments the journal entry parsing problem by separating debit records and credit records into distinct sets, then systematically matching subsets between them. Instead of evaluating all possible combinations of line items, the system divides the combinatorial space into manageable subsets (debit subsets and credit subsets) that can be independently analyzed and matched, dramatically reducing computational complexity while maintaining complete parsing accuracy.
2Measurement precision
If brute force computation of every possible combination is used to identify sub-journal entries, then complete parsing accuracy is achieved, but computation time becomes prohibitive for large journal entries
Solution Approach 1:
The patent applies preliminary action by pre-organizing debit and credit records into structured subsets before performing matching operations. The system prepares debit subsets and credit subsets in advance, establishing a framework that enables efficient systematic matching. This preliminary structuring eliminates the need for exhaustive real-time computation of all combinations, significantly reducing computation time while ensuring complete accuracy is achieved.
3Reliability
If no direct link between debit and credit records is available, then auditing independence and objectivity are maintained, but parsing becomes nearly impossible for large batch journal entries
Solution Approach 1:
The patent enables the parsing system to be self-sufficient by developing algorithms that automatically identify sub-journal entries through systematic subset matching without requiring external guidance or direct links between records. The system independently analyzes the relationships between debit and credit subsets, deriving the parsing structure autonomously. This self-service capability maintains auditing objectivity while achieving practical parsing efficiency for large batch journal entries.
Data Source
AI summary
A system for parsing journal entries is configured to receive input data comprising a plurality of journal entries from one or more data sources and consecutively parse each respective journal entry. The journal entries can be parsed according to an algorithm that selects subsets of either credit or debit records from the journal entry, identifies matching subsets of records that foot to zero when combined with the selected records, and removes subsets containing records in the matching subsets from respective credit and debit data structures.