Tax Category Prediction Model Using Text Embeddings
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing tax modeling methods for ecommerce fail to accurately classify items into appropriate tax categories, leading to misclassifications, delayed predictions, and increased processing costs, which affect the efficiency and accuracy of tax compliance and market operations.
Innovation Solution
A tax category prediction model is developed using a natural language processing model to generate text embeddings from item listings, which are then mapped to predetermined tax categories, allowing for real-time and accurate tax category predictions, incorporating both text and image embeddings for enhanced classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional tax modeling methods are used for classifying items, then processing costs and latency increase, but classification accuracy deteriorates leading to misclassifications
Solution Approach 1:
The system performs preliminary actions by generating text embeddings from item listings before tax category classification. The embedding model pre-processes the text data into structured representations that capture semantic meaning, enabling faster and more accurate classification in subsequent steps. This preliminary transformation of raw text into embeddings resolves the contradiction by preparing data in advance for efficient processing.
Solution Approach 2:
The patent introduces text embeddings as an intermediary representation between raw item listings and tax category classifications. Instead of directly classifying raw text, the system transforms text into embedding vectors that serve as a mediator, capturing semantic relationships and enabling more accurate and efficient classification. This intermediary step resolves the technical contradiction by bridging the gap between unstructured text and structured classification.
2Measurement precision
If manual tax category classification is performed, then accuracy may improve, but processing time and operational complexity increase significantly
Solution Approach 1:
The system implements self-service by enabling automatic tax category classification through machine learning models. The embedding model and classification system work autonomously to categorize items without requiring manual intervention, thereby maintaining high accuracy while dramatically reducing processing time. This automation resolves the contradiction by making the system serve itself rather than relying on manual operations.
Solution Approach 2:
The patent replaces manual mechanical classification processes with computational mechanisms. Instead of human operators manually categorizing items, the system uses embedding models and machine learning algorithms to automatically perform classification. This substitution of mechanical human labor with computational processes resolves the contradiction by achieving both accuracy and speed through automated intelligent systems.
3Measurement precision
If comprehensive item analysis is performed to improve classification, then accuracy improves, but processing complexity and computational resources increase
Solution Approach 1:
The system applies segmentation by breaking down the classification task into distinct components: text embedding generation, embedding processing, and tax category prediction. Each component is handled by a specialized model or module, allowing comprehensive analysis to be distributed across multiple simpler, focused processes. This segmentation resolves the contradiction by dividing complex analysis into manageable segments that collectively achieve high accuracy without overwhelming complexity.
Data Source
AI summary
A tax category prediction model, which is trained using a tax category prediction dataset, provides a tax category prediction for an item having an item listing. The tax category prediction model enhances the ability and accuracy of identifying a tax category associated with an item for sale via the online marketplace (e.g., identifying the tax category before the item is offered for sale via the online marketplace). The tax category prediction dataset is generated based on one or more of a text embedding, an image embedding, another type of embedding, or a combination thereof. The text embedding may be identified by applying a natural language processing model to a text string of the item listing. The natural language processing model may comprise bidirectional encoder representations from transformers (BERT).