Abstract: This disclosure provides systems, methods, and devices for automatic product classification using deep learning and generative artificial intelligence (AI) models for a harmonized system (HS) product classification. A method includes generating embeddings based on an input dataset. The method includes applying a deep learning model to the embeddings to produce a prediction set including classifications corresponding to the embeddings. The method includes converting the classifications to a first set of similarity metrics. The method includes determining a second set of similarity metrics based on the embeddings using a semantic similarity model. The method includes generating a third set of similarity metrics based on an output of the semantic similarity model. The method includes outputting a ranked set of predictions corresponding to the input dataset based on the first set of similarity metrics, the second set of similarity metrics, and the third set of similarity metrics.

Abstract: Aspects of the present disclosure provide systems, methods, apparatus, and computer-readable storage media that support local and cloud-based tax determination operations. In some examples, a transaction system may execute a local tax engine to enable localized tax calculation services in a lightweight footprint while still achieving one or more benefits of a cloud-based system (e.g., technical support and automatic updates of software and tax content). As a result, some aspects of the present disclosure may enable a “full power” tax engine that is local to a transactional system, such as an edge device, while also enabling the tax engine to automatically sync back to a cloud-based system, such as to receive one or more of a configuration update, a software release, a software update, or updated tax content. Further, the transaction system may transmit results of tax calculations to the cloud-based system, such as for reporting, compliance, or both.