Document to Order Conversion System for Vendor Data Matching
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Solution Overview
Problem
The manual processing of customer order documents received by vendors is inefficient due to varying formats that do not correspond with the vendor system's format, requiring time-consuming manual entry and processing.
Innovation Solution
A system comprising a customer interface, converting module, processing module, data population module, and purchase order generation module that converts customer documents into text, matches data fields, populates order fields, and generates purchase orders, capable of recognizing and processing documents in various formats, including HTML, PDF, and image formats, with optional manual input for unrecognized fields and training for specific customer formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual processing is used to handle customer documents in various formats, then the vendor system can process orders, but the processing time and labor requirements increase significantly
Solution Approach 1:
The system enables self-service by automatically converting customer documents into text and extracting data fields without human intervention. The converting module processes various document formats (PDF, images, HTML) and the processing module identifies and matches data fields to the vendor system's order format autonomously, eliminating the need for manual data entry while maintaining adaptability to different document types.
Solution Approach 2:
The patent replaces the mechanical manual processing system with an automated computational system. The converting module uses optical character recognition and text extraction techniques to convert documents, while the processing module employs pattern recognition and data matching algorithms to populate order fields, substituting human manual operations with automated digital processes that handle multiple formats efficiently.
2Productivity
If manual data entry is required for each order document, then data accuracy can be maintained, but productivity and output per unit time decrease
Solution Approach 1:
The system creates accurate digital copies of customer document data by converting physical or digital documents into text format through the converting module. This copying process preserves the original information while transforming it into a structured format that can be automatically processed, matched, and entered into the vendor system without manual transcription errors.
Solution Approach 2:
The system incorporates feedback mechanisms where the processing module validates extracted data fields against the vendor system's required format. The matching process compares customer data fields with expected order fields, and any discrepancies or unmatched fields can be flagged for review, ensuring data accuracy while maintaining high automated processing throughput.
3Stability of the object's composition
If the vendor system uses a fixed format for order documents, then data structure consistency is maintained, but the system cannot accommodate customer documents in varying formats
Solution Approach 1:
The converting module serves as an intermediary between customer documents in various formats and the vendor system's fixed order format. It transforms diverse input formats (PDF, images, HTML) into a standardized text representation, while the processing module acts as another intermediary that maps extracted data fields to the vendor system's required structure, bridging the format gap without compromising data consistency.
Solution Approach 2:
The system applies parameter changes by dynamically adjusting the data extraction and mapping process based on the input document format. The processing module identifies data fields in the customer document and transforms them to match the vendor system's order format parameters, maintaining structural consistency in the output while accommodating format variability in the input through flexible parameter mapping.
Data Source
AI summary
Provided are methods and systems for processing customer orders. An example method can commence with receiving a customer document and converting the customer document into a text document. The method can further include analyzing the text document to determine at least one customer data field in the text document. The method can continue with matching the at least one customer data field to at least one order data field. The method can further include populating, based on the matching, the at least one order data field with at least one text value corresponding to the at least one customer data field. The method can continue with generating a purchase order document based on the at least one text value. The purchase order document can include at least one product corresponding to the at least one order data field.