Automated Invoice Entry via OCR Data Mapping
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Solution Overview
Problem
Conventional electronic invoicing is time-consuming and inefficient, particularly for bulk invoicing, and requires manual data entry, leading to increased costs and errors for both buyers and sellers.
Innovation Solution
An automated system and method for generating electronic invoices by receiving invoice field values, mapping them to create mapped data, and applying these data fields to an invoice image or batch of invoices, streamlining the invoicing process and enabling bulk invoicing with reduced manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual entry of invoice information is used, then accuracy can be maintained through human review, but time consumption and operational costs increase significantly
Solution Approach 1:
The system enables self-service through automated data extraction from invoice images using OCR technology. The computer automatically captures, extracts, and populates invoice data without human intervention, allowing the system to serve itself in the data entry process while maintaining accuracy through validation rules.
Solution Approach 2:
The patent replaces the mechanical manual data entry process with an automated computer-based system that uses optical character recognition and data extraction algorithms. This substitution eliminates the need for human operators to manually type invoice information while maintaining data accuracy through systematic validation.
2Measurement precision
If bulk invoicing is processed individually, then data accuracy can be verified for each invoice, but productivity and efficiency decrease
Solution Approach 1:
The system merges multiple individual invoice processing operations into a single bulk processing workflow. By combining data extraction, validation, and population steps across multiple invoices into one automated sequence, the system achieves both high throughput and maintained accuracy through consistent application of validation rules.
Solution Approach 2:
The system performs preliminary data extraction and validation for all invoices in the batch before final processing. This preliminary action allows the system to prepare and verify data across multiple invoices simultaneously, enabling efficient bulk processing while maintaining accuracy checks for each individual invoice.
3Productivity
If automated data extraction is implemented, then processing speed increases, but system complexity and initial setup requirements increase
Solution Approach 1:
The system introduces an intermediary layer of standardized data fields and mapping protocols between the invoice images and the final data structure. This intermediary framework simplifies the automation process by providing a consistent interface for data extraction and population, reducing the complexity of direct system configuration while maintaining high processing speeds.
4Adaptability or versatility
If manual invoice processing is used, then flexibility in handling various invoice formats is maintained, but operational costs and time consumption increase
Solution Approach 1:
The system implements universal data extraction capabilities that can handle multiple invoice formats through a single automated process. By designing the system to recognize and extract data from various invoice layouts using pattern matching and flexible field mapping, it achieves both format adaptability and time efficiency without requiring separate manual processing for each format type.
Data Source
AI summary
The present invention is directed to methods and systems for the transfer of bulk paper invoices into electronic invoices for electronic submission from a seller to a buyer. The systems and methods described herein use matching logic to transfer details of the paper invoices into electronic invoices, thereby streamlining the process of bulk invoicing.