Document Classification via Embedded Date Analysis
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Solution Overview
Problem
Current systems face challenges in automatically classifying digital documents in multi-document transactions based on embedded dates, as many documents lack textual labels or metadata, making it difficult for auditors to quickly identify and sort relevant documents by logical type.
Innovation Solution
A computer system is trained to categorize documents into buckets based on relative age and context of embedded dates, using a date-based classification model that identifies logical types by scanning for dates and context elements within the documents, enabling efficient classification and sorting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If documents are stored as scanned images without textual labels or metadata, then storage simplicity is maintained, but document classification accuracy deteriorates
Solution Approach 1:
The system performs preliminary date extraction and temporal relationship analysis during the document ingestion phase, before classification is needed. By pre-processing the scanned images to extract embedded dates and establish temporal sequences, the system prepares classification-ready data structures that enable accurate logical type identification without requiring complex analysis during retrieval operations.
2Measurement precision
If manual review of documents is performed, then classification accuracy can be maintained, but productivity deteriorates
Solution Approach 1:
The system implements self-service classification by automatically extracting dates from scanned document images, determining relative ages between documents, and assigning logical types without human intervention. The classification model autonomously processes documents through temporal relationship analysis, enabling the system to serve its own classification needs while maintaining high accuracy and eliminating manual review bottlenecks.
3Measurement precision
If comprehensive date analysis is performed on all documents, then classification accuracy improves, but processing time increases
Solution Approach 1:
The system extracts only the critical date elements from documents, focusing analysis on temporal information rather than processing entire document contents. By isolating and analyzing specifically the embedded dates and their relative relationships, the system achieves accurate classification while minimizing processing overhead, avoiding the need to analyze all document text and features.
Data Source
AI summary
A generator categorizes documents in one or more transactions into buckets, each identified by a separate category for an expected time window based on a separate relative age of each of the documents evaluated from one or more dates identified in the documents. The generator trains a document classifier with a model of the separate relative age of each of the documents as a temporal characteristic correlated with the respective category of a respective bucket of the buckets. The document classifier executes on a input documents to classify each of the input documents as a particular logical type identified by a particular category from among multiple logical types.