AI Record Generation System for Manufacturing Billing Accuracy
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Solution Overview
Problem
Current systems for generating invoices in the manufacturing industry are inefficient, inaccurate, and not scalable, particularly when dealing with complex technical product issues, leading to revenue loss due to manual interpretation and billing errors.
Innovation Solution
A Record Generation System (RGS) utilizing Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning (ML) techniques to automatically interpret problem statements, identify billable services, and accurately estimate billing amounts by creating a knowledge graph and applying cognitive learning operations to generate invoices efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual interpretation of problem statements is used, then billing accuracy can be maintained through human judgment, but processing time and labor costs increase significantly
Solution Approach 1:
The patent replaces the mechanical system of manual human interpretation with an automated NLP-based system. The system uses natural language processing to parse problem statements, extract relevant entities, and determine billing amounts automatically, eliminating the need for manual mechanical interpretation while maintaining accuracy through structured processing rules and machine learning models.
Solution Approach 2:
The system enables self-service by allowing the billing system to automatically process and interpret problem statements without human intervention. The NLP system autonomously extracts entities, matches them with service catalogs, applies pricing rules, and generates billing amounts, making the system self-sufficient in handling the complete billing workflow.
2Reliability
If trained professionals manually process problem requests, then complex billing scenarios can be handled with expert judgment, but scalability is limited and errors increase with volume
Solution Approach 1:
The patent creates a universal billing system that can handle multiple types of problem statements, service categories, and pricing scenarios through a single NLP-based platform. The system is designed to process diverse input formats and apply different billing rules universally, replacing the need for multiple specialized human processors while maintaining reliability through consistent rule application.
Solution Approach 2:
The system incorporates feedback mechanisms where processing results are continuously validated against billing rules and historical data. The NLP model learns from processing outcomes and adjusts its entity extraction and classification accuracy, while the billing engine validates calculations against predefined rules, creating a feedback loop that maintains reliability at scale.
3Productivity
If pre-determined prices are used for service requests, then processing speed increases, but billing accuracy decreases due to inability to handle complex scenarios
Solution Approach 1:
The system performs preliminary actions by pre-processing problem statements to extract entities and classify service types before applying pricing. The NLP system prepares structured data representations in advance, identifying key components and matching them with service catalogs, which enables both fast processing and accurate billing by preparing the groundwork before final calculation.
Solution Approach 2:
The billing system transitions from static pre-determined prices to dynamic pricing that adapts based on extracted problem characteristics. The system dynamically selects pricing rules based on service type, product category, and problem complexity identified through NLP, allowing processing speed to remain high while billing accuracy improves through context-aware price selection.
4Reliability
If dedicated support personnel are deployed for billing, then billing accuracy can be maintained, but operational costs and resource allocation complexity increase
Solution Approach 1:
The patent extracts the billing interpretation function from human support personnel and isolates it into a dedicated NLP-based processing system. This separation removes the complexity of human resource management, training, and allocation from the billing process, while the extracted function is encapsulated in a standardized software module that can be deployed and scaled without additional operational complexity.
Data Source
AI summary
Examples of a record generation system are provided. The system may receive a record generation requirement from a user. The system may obtain record data, a plurality of user documents, and identify a record corpus from the record data. The system may sort the record data into a plurality of data domains. The system may determine at least one record mapping context including a record value from the plurality of user documents. The system may determine a selection rule from the plurality of data domains for each of the record mapping context. The system may create a record index corresponding to the plurality of user documents. The system may create a record generation model corresponding to the record generation requirement based on the record index. The system may generate a record generation result corresponding to the record generation requirement comprising the relevant record generation model.