Patents by Inventor Elizabeth E. Connell

Elizabeth E. Connell has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Systems and methods for harmonized product classification
    Patent number: 12651016
    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.
    Type: Grant
    Filed: November 5, 2024
    Date of Patent: June 9, 2026
    Inventors: Sina Gholamian, Stavroula Skylaki, Varun Chandra, Emre Caglar, Eduardo Vitor, Steven Rogers, Anne Woelke, Jacqueline Nicole Martinez, Gianfranco Romani, Elizabeth E. Connell, Fernando Tochini Aliaga
  • SYSTEMS AND METHODS FOR HARMONIZED PRODUCT CLASSIFICATION
    Publication number: 20250147998
    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.
    Type: Application
    Filed: November 5, 2024
    Publication date: May 8, 2025
    Inventors: Sina Gholamian, Stavroula Skylaki, Varun Chandra, Emre Caglar, Eduardo Vitor, Steven Rogers, Anne Woelke, Jacqueline Nicole Martinez, Gianfranco Romani, Elizabeth E. Connell, Fernando Tochini Aliaga