Invoice Processing Platform for Accurate Payor Coding
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Solution Overview
Problem
Manually processing invoices for multiple properties within complex ownership and cost allocation structures is time-consuming and difficult, especially when determining which sub-entity should pay an invoice, and existing automated systems fail to fully code invoices for accurate data indexing and cost allocations.
Innovation Solution
An automated invoice processing platform using machine learning to interpret invoices, historical data, and vendor information to determine the correct sub-entity for payment, providing full coding and integration into existing accounts payable workflows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processing is used to determine which sub-entity should pay an invoice, then accuracy in determining the correct payor can be maintained, but processing time and labor requirements increase significantly
Solution Approach 1:
The patent replaces manual mechanical processing with an automated machine learning system that uses neural networks to analyze invoice data, historical data, and vendor information to automatically determine the correct sub-entity payor, eliminating human labor while maintaining high accuracy
Solution Approach 2:
The system enables self-service automation where the machine learning model independently processes invoices, makes determinations about correct payors, and integrates with existing AP workflows without requiring continuous human intervention or manual coding
2Extent of automation
If traditional automated systems with basic OCR are used, then data entry automation is achieved, but the system cannot fully code invoices for accurate indexing and cost allocations
Solution Approach 1:
The patent replaces basic OCR with advanced machine learning models including neural networks that can interpret complex invoice documents, historical data patterns, and vendor information to automatically perform accurate coding, indexing, and cost allocation without manual intervention
Solution Approach 2:
The system changes the processing parameters from simple text recognition to multi-dimensional analysis incorporating historical data, vendor profiles, and contextual information, enabling the system to accurately determine coding, GL codes, and cost allocations that were previously impossible to automate
3Measurement precision
If manual coding of invoices is performed, then accurate indexing and cost allocation can be achieved, but it forms a bottleneck at the front-end of invoice processing
Solution Approach 1:
The patent replaces manual coding operations with an automated machine learning system that processes thousands of invoices simultaneously, eliminating the bottleneck by using AI-based classification and coding capabilities that operate continuously without human limitation
Solution Approach 2:
The system enables continuous automated processing where machine learning models continuously analyze invoices, historical data, and vendor information to perform coding and indexing operations without interruption, maintaining high throughput while ensuring accurate cost allocation
Data Source
AI summary
An invoice processing system is proved. The system includes a database and a server in electronic communication with the database. The server is operative to: interpret an invoice; interpret historical data from the database, the historical data corresponding to at least one of one or more vendors of past invoices or one or more payors of past invoices; and in response to the interpretation of the invoice, determine a vendor of the invoice based at least in part on the historical data. The server is further operative to in response to the determination of the vendor, determine a value for each of one or more judgment fields via machine learning based at least in part on the historical data and the vendor; and transmit the invoice with the vendor and the value for each of the one or more judgment fields.