Automated Document Intake for Trade Data Extraction
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Solution Overview
Problem
Current systems face challenges in efficiently processing and identifying key information from diverse physical trade documents, particularly struggling with unstructured documents and determining document boundaries, which leads to inaccuracies in processing and decision-making in logistics operations.
Innovation Solution
A system comprising a document intake machine with a scanner and data extractor that converts physical trade documents into electronic files, and a trade executing machine that identifies entity information, assigns electronic customer numbers, and indexes documents, enabling the identification of key information and differentiation between document types without manual separators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processing methods are used to identify key information from physical trade documents, then processing accuracy may be maintained, but processing speed and efficiency deteriorate significantly
Solution Approach 1:
The patent replaces manual mechanical processing with an automated system comprising a document scanner, data extractor, and trade executing machine. The scanner optically captures document images, the data extractor uses pattern recognition to identify key information locations, and the system automatically extracts and processes trade document data, eliminating manual labor while maintaining accuracy through learned patterns.
Solution Approach 2:
The system creates electronic copies of physical trade documents through scanning, then processes these digital replicas to identify key information. The electronic files serve as copies that can be analyzed without handling original physical documents, enabling automated processing while preserving document integrity.
2Productivity
If automated processing systems are implemented to increase processing speed, then productivity improves, but the ability to accurately identify key information in heterogeneous document formats deteriorates
Solution Approach 1:
The trade executing machine is designed with universal capabilities to process multiple types of trade documents (invoices, purchase orders, shipping documents, etc.) from various entities. The system uses a unified processing approach that adapts to different document formats through pattern recognition and learning, enabling it to handle heterogeneous documents through a single multi-functional platform.
Solution Approach 2:
The system performs preliminary scanning and pattern recognition to identify document boundaries and key information locations before full processing. By pre-identifying document structures and information patterns in advance, the system prepares for efficient accurate extraction across different document types without requiring format-specific processing logic.
3Reliability
If document boundaries are determined using manual separators, then document differentiation is accurate, but device complexity and operational effort increase
Solution Approach 1:
The system performs self-service by automatically determining document boundaries without requiring external separators or manual intervention. The data extractor analyzes the scanned document images to autonomously identify where one document ends and another begins, using pattern recognition and learned characteristics of document structures to make these determinations independently.
Solution Approach 2:
The system extracts and removes the need for physical separators by implementing virtual boundary detection through software analysis. Instead of relying on manual separators to delineate documents, the system extracts boundary information directly from the document images themselves through pattern recognition, eliminating the separator component entirely.
4Reliability
If extensive manual review is performed to prevent incorrect shipments and payments, then accuracy improves, but processing time and operational costs increase
Solution Approach 1:
The system implements feedback mechanisms where the data extractor continuously learns from processed documents to improve future identification accuracy. By analyzing extracted information and comparing against expected patterns, the system refines its pattern recognition capabilities, providing feedback that enhances reliability over time while maintaining automated processing speeds without requiring manual review of each document.
Data Source
AI summary
A system for enhancing communications based on physical trade documents includes a document intake machine comprising a document scanner and a data extractor. The document intake machine receives a physical trade document from an entity. The document scanner may scan the physical trade document to create an electronic file of the physical trade document. The data extractor determines entity identification information from the electronic file and entity performance information from the electronic file. A trade executing machine receives the entity identification information and entity performance information from the document intake machine and assigns an electronic customer number to the electronic file, wherein the electronic customer number is associated with the entity identification information. The trade executing machine may index the electronic file in a memory communicatively coupled to the trade executing machine, wherein the electronic file is indexed according to the electronic customer number and the entity performance information.