DAG-Based Invoice Generation for Complex Pricing Models
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Solution Overview
Problem
Existing billing software tools face complexity and errors when handling hierarchical and bundled pricing models, especially in industries like Software as a Service (SaaS), due to the need for manual effort and increased computations, which can lead to inaccuracies in generating invoices.
Innovation Solution
A processor-implemented method and system that encode usage-based pricing plans into directed acyclic graphs (DAGs) to automatically generate customized invoices, allowing for the creation of DAG templates, encoding pricing plans, and executing them with usage data to produce invoice line items, thereby simplifying the computation and reducing manual effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If hierarchical and bundled pricing models are implemented in existing billing software, then pricing flexibility and customization are improved, but system complexity and error rates increase
Solution Approach 1:
The pricing model is segmented into discrete, composable pricing rules that can be independently defined and combined. Each pricing rule represents a modular unit that can be assembled hierarchically to create complex pricing structures without increasing overall system complexity.
Solution Approach 2:
A pricing engine acts as an intermediary layer between the pricing rules and the billing system. This engine automatically computes and resolves the hierarchical pricing relationships, eliminating the need for manual computation and reducing error rates while maintaining pricing flexibility.
2Measurement precision
If manual customization of quotations is performed for multiple products and services, then pricing accuracy is improved, but time consumption and labor effort increase
Solution Approach 1:
Pricing rules are pre-configured and stored in the system before actual billing operations. These pre-defined rules automatically apply to relevant transactions, eliminating the need for manual customization while maintaining pricing accuracy.
Solution Approach 2:
The billing system automatically applies the appropriate pricing rules based on the transaction data, without requiring manual intervention. The system self-configures the pricing calculations by matching transactions with relevant pre-defined rules, reducing both time consumption and labor effort.
3Reliability
If complex pricing computations are performed for SaaS cloud services, then pricing completeness is improved, but error rates and computational overhead increase
Solution Approach 1:
Manual computation methods are replaced with an automated pricing engine that uses algorithmic processing to compute complex pricing scenarios. This substitution eliminates human error while maintaining pricing completeness, as the engine systematically applies all relevant pricing rules to each transaction.
Data Source
AI summary
Disclosed is a method for encoding a usage-based pricing plan corresponding to at least one product or service to generate directed acyclic graph (DAG) in a machine-readable format, to automatically generate a customized invoice based on the usage-based pricing plan when an event triggers the DAG. The method includes: (i) encoding the usage-based pricing plan for the at least one product or service into the DAG template, to generate the DAG, (ii) storing the DAG in a machine-readable format to obtain machine-readable DAG, (iii) executing the machine-readable DAG to generate at least one invoice line item output, and (iv) automatically generating a customized invoice by obtaining the at least one invoice line output.