Automated Invoice Processing via Template Matching
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Solution Overview
Problem
Current invoice management processes for businesses are largely manual or partially automated, prone to human error, and lack scalability, failing to detect and automatically process incoming invoices effectively.
Innovation Solution
A document processing system that detects, retrieves, tags, and processes invoices by converting them into binary files, searching for matches in a database, extracting structural information, and applying data to corresponding fields, thereby automating the invoicing process and integrating with accounting software.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual invoice management processes are used, then ease of operation is maintained for small volumes, but productivity and reliability deteriorate as business grows
Solution Approach 1:
The patent replaces manual mechanical invoice processing with an automated optical character recognition (OCR) system. The system captures invoice images, extracts data using template matching and pattern recognition algorithms, and automatically populates accounting software fields, eliminating the need for manual data entry and significantly increasing processing capacity.
Solution Approach 2:
The invoice processing system performs self-service by automatically detecting invoice templates, extracting relevant data fields, and categorizing invoices without human intervention. The system learns from processed invoices and improves its template matching capabilities autonomously, enabling scalable processing as business grows.
2Reliability
If partially automated processes are used, then some automation benefits are achieved, but reliability deteriorates due to human error in detection and tagging
Solution Approach 1:
The patent replaces human judgment and manual invoice tagging with automated image recognition and template matching algorithms. The system consistently identifies invoice types, extracts data fields, and categorizes documents based on learned patterns, eliminating human error and improving processing reliability.
Solution Approach 2:
The system incorporates feedback mechanisms where processed invoices are used to refine and update template patterns. The system learns from successful matches and incorrect identifications, continuously improving its accuracy in detecting and categorizing invoice types, thereby enhancing reliability over time.
3Productivity
If manual invoice entry is required, then adaptability to different invoice formats is maintained, but productivity and time efficiency deteriorate
Solution Approach 1:
The patent implements a dynamic template matching system that adapts to different invoice formats. The system uses multiple predefined templates for various invoice types and dynamically selects the appropriate template based on image recognition. It can also learn new template patterns from previously processed invoices, maintaining flexibility while achieving high processing speeds.
Solution Approach 2:
The invoice processing system is designed with universal capabilities to handle multiple invoice formats and types through a single platform. The template-based approach allows the system to process various invoice structures by matching against stored templates, eliminating the need for separate manual processing methods for different formats.
4Productivity
If businesses grow, then market opportunity increases, but the scalability of current processes deteriorates
Solution Approach 1:
The patent replaces time-consuming manual invoice processing with automated optical character recognition and data extraction systems. The automated system processes invoices in seconds by capturing images, recognizing text through OCR, and populating fields automatically, maintaining consistent processing time regardless of business size and enabling scalable growth.
Data Source
AI summary
Disclosed is a document processing system that can be used to automatically detect and process an invoice for a business entity. The system receives a document and determines the structural information from the document. The structural information includes a template object and its location on the document. The system further retrieves structural information of documents stored in the database and compares the structural information of retrieved documents to the received document based on matching criteria such as a template object name and its location. If a match is found within a predefined threshold range, the data is extracted from the received document and the structural information is extracted from the matching document and the received document is processed by applying the extracted data to the corresponding extracted structural information. The processed document is transmitted for a user review or for processing by an accounting or bookkeeping software application.