Anomalous Billing Event Correlation Engine for Invoice Data
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Solution Overview
Problem
Entities face difficulties in processing accounts payable data due to disparate formats and nomenclature used by vendors, making it challenging to categorize and manage costs effectively.
Innovation Solution
A processor module extracts billing event identifiers from invoices, queries a machine memory index for corresponding descriptors, and joins provisional descriptors to records, flagging them to indicate presence, while iteratively reformulating identifiers to find matching descriptors and using objective closeness metrics to determine the closest match.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If vendors use disparate formats and nomenclature to describe goods and services, then each vendor can present their invoice in their preferred manner, but the entity cannot effectively categorize and manage costs across different vendors
Solution Approach 1:
The patent introduces a standardized intermediate representation (normalized goods and services categories) that mediates between vendor-specific disparate descriptions and the entity's cost management requirements. This intermediary layer translates various vendor nomenclatures into a common framework, enabling effective categorization and management while preserving vendor presentation flexibility.
Solution Approach 2:
The system changes the parameter of description standardization by transforming vendor-specific nomenclature into a standardized category system. This parameter transformation allows the same goods and services to be represented consistently across different vendors, resolving the contradiction between vendor flexibility and cost management effectiveness.
2Measurement precision
If the entity manually categorizes each invoice item, then categorization accuracy can be maintained, but processing time and labor requirements increase significantly
Solution Approach 1:
The patent implements self-service categorization through automated algorithms that independently classify invoice items based on their descriptions. The system uses machine learning models and rule-based engines to automatically map vendor descriptions to standardized categories, eliminating manual intervention while maintaining high accuracy and reducing processing time.
Solution Approach 2:
The manual mechanical process of categorization is replaced with automated computational systems. The patent employs natural language processing, machine learning algorithms, and decision trees to automatically categorize invoice items, substituting human manual work with automated mechanisms that are both faster and consistently accurate.
3Reliability
If the system creates a comprehensive index of billing event descriptors, then the ability to match and categorize billing events improves, but the complexity of maintaining and updating the index increases
Solution Approach 1:
The patent incorporates feedback mechanisms that automatically learn from correction patterns and user interactions to refine the index. The system monitors categorization results and uses this feedback to continuously improve matching algorithms, maintaining high reliability while reducing manual maintenance requirements through adaptive learning.
Solution Approach 2:
The system performs preliminary actions by pre-processing and pre-categorizing billing event descriptors into a comprehensive index before actual invoicing occurs. This preliminary organization enables rapid matching during invoice processing, improving reliability while the automated nature of the pre-processing reduces ongoing maintenance complexity.
Data Source
AI summary
Apparatus, methods and computer readable media for processing invoice data. The apparatus may include, and the methods and media may involve a processor module and a machine memory module. The processor module may extract from an invoice a billing event identifier that identifies a billing event. The processor module may query an index in the machine memory module for a billing event descriptor that is designated for the billing event. The processor module may identify in the machine memory index a provisional billing event descriptor that corresponds to a derivative of the billing event identifier. The processor module may join the provisional billing event descriptor to a record corresponding to the billing event identifier. The processor module may flag the record to indicate the presence of the provisional billing event descriptor.