AI Tax Estimation System Using Risk Tolerance and Classification Queries
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Solution Overview
Problem
Existing technologies fail to provide accurate and efficient per-jurisdiction tax compliance information for cross-border sales, making it difficult for businesses to determine and remit taxes and duties timely and correctly due to the complexity of over 10,000 tax jurisdictions and nearly 10 million taxability rules.
Innovation Solution
A method that allows sellers to estimate taxes associated with transactions by selecting a risk tolerance value, generating classification code queries, retrieving potential taxation amounts, and determining an estimated tax due amount without requiring precise input parameters like HS codes, thereby improving the speed and efficiency of tax and duty estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional tax estimation methods are used requiring precise HS codes and classification parameters, then measurement precision of tax amounts is improved, but device complexity and ease of operation deteriorate due to the complexity of over 10,000 tax jurisdictions and nearly 10 million taxability rules
Solution Approach 1:
The patent introduces an intermediary AI/ML-based classification system that automatically maps product descriptions to tax jurisdictions and determines taxability. This intermediary layer handles the complexity of 10,000+ jurisdictions and 10 million taxability rules, allowing users to input simple product descriptions while the system performs the complex classification and calculation in the background, thus maintaining precision without increasing user-facing complexity
Solution Approach 2:
The patent extracts and separates the complex tax classification logic from the user input process. By using AI/ML models to automatically perform HS code assignment, jurisdiction identification, and taxability determination based on simple product descriptions, the system removes the burden of complex parameter collection from the user while maintaining accurate tax calculation through the extracted classification logic
2Measurement precision
If comprehensive classification code queries are performed to ensure accurate tax determination, then measurement precision is improved, but productivity and speed of tax estimation deteriorate
Solution Approach 1:
The patent performs preliminary classification by using AI/ML models to pre-determine the most likely tax jurisdiction and taxability status before executing detailed classification code queries. The system pre-processes product descriptions to identify key tax-relevant features, allowing it to focus subsequent queries on the most probable tax categories, thus maintaining accuracy while reducing the overall query time and computational burden
Solution Approach 2:
The patent implements a two-stage query approach where it first performs partial classification using AI/ML predictions to identify the most likely tax jurisdiction and category, then executes limited additional verification queries only when needed. This partial action strategy avoids exhaustive querying for all possible tax categories while maintaining sufficient precision for accurate tax determination, thereby improving processing speed
3Measurement precision
If detailed product information and HS codes are required for tax calculation, then measurement precision is improved, but ease of operation deteriorates as businesses struggle to provide accurate input parameters
Solution Approach 1:
The patent enables the system to self-determine HS codes, tax jurisdictions, and taxability status by automatically analyzing product descriptions provided by users. The AI/ML classification system performs self-service classification without requiring users to manually input or verify complex parameters like HS codes, automatically assigning the correct classification based on product characteristics, thus maintaining precision while dramatically improving ease of operation
Solution Approach 2:
The patent creates a universal input interface that accepts simple product descriptions and automatically adapts to different tax jurisdictions, product categories, and classification requirements. The system's multi-functional AI classifier can handle various product types and automatically adjusts its classification approach based on the input, eliminating the need for users to understand or provide jurisdiction-specific parameters while maintaining accurate tax calculation across all scenarios
Data Source
AI summary
A service engine of a processor-based system determines an estimated amount of taxes due in association with a proposed transaction based on a risk tolerance value specified by a party to the transaction, such as a seller. Multiple classification code queries are generated for classifying an item that is the subject of the proposed transaction, from which a plurality of classification code candidates are determined. Each such classification code candidate is considered in determination of multiple corresponding possible tax-due amounts, and the taxes due for the proposed transaction are determined by the service engine based on a statistical calculation corresponding to the specified risk tolerance value. The service engine provides the estimated tax due amount to one or more parties to the proposed transaction.