Patents by Inventor Scott McCrae

Scott McCrae 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).

  • UTILIZING MACHINE-LEARNING MODELS TO GENERATE IDENTIFIER EMBEDDINGS AND DETERMINE DIGITAL CONNECTIONS BETWEEN DIGITAL CONTENT ITEMS
    Publication number: 20240320288
    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilize machine learning models to generate identifier embeddings from digital content identifiers and then leverage these identifier embeddings to determine digital connections between digital content items. In particular, the disclosed systems can utilize an embedding machine-learning model that comprises a character-level embedding machine-learning model and a word-level embedding machine-learning model. For example, the disclosed systems can combine a character embedding from the character-level embedding machine-learning model and a token embedding from the word-level embedding machine-learning model. The disclosed systems can determine digital connections between the plurality of digital content items by processing these identifier embeddings for a plurality of digital content items utilizing a content management model.
    Type: Application
    Filed: May 31, 2024
    Publication date: September 26, 2024
    Inventors: Jongmin Baek, Jiarui Ding, Ermo Wei, Scott McCrae
  • Utilizing machine-learning models to generate identifier embeddings and determine digital connections between digital content items
    Patent number: 12008065
    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilize machine learning models to generate identifier embeddings from digital content identifiers and then leverage these identifier embeddings to determine digital connections between digital content items. In particular, the disclosed systems can utilize an embedding machine-learning model that comprises a character-level embedding machine-learning model and a word-level embedding machine-learning model. For example, the disclosed systems can combine a character embedding from the character-level embedding machine-learning model and a token embedding from the word-level embedding machine-learning model. The disclosed systems can determine digital connections between the plurality of digital content items by processing these identifier embeddings for a plurality of digital content items utilizing a content management model.
    Type: Grant
    Filed: January 12, 2023
    Date of Patent: June 11, 2024
    Assignee: Dropbox, Inc.
    Inventors: Jongmin Baek, Jiarui Ding, Ermo Wei, Scott McCrae
  • UTILIZING MACHINE-LEARNING MODELS TO GENERATE IDENTIFIER EMBEDDINGS AND DETERMINE DIGITAL CONNECTIONS BETWEEN DIGITAL CONTENT ITEMS
    Publication number: 20230169139
    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilize machine learning models to generate identifier embeddings from digital content identifiers and then leverage these identifier embeddings to determine digital connections between digital content items. In particular, the disclosed systems can utilize an embedding machine-learning model that comprises a character-level embedding machine-learning model and a word-level embedding machine-learning model. For example, the disclosed systems can combine a character embedding from the character-level embedding machine-learning model and a token embedding from the word-level embedding machine-learning model. The disclosed systems can determine digital connections between the plurality of digital content items by processing these identifier embeddings for a plurality of digital content items utilizing a content management model.
    Type: Application
    Filed: January 12, 2023
    Publication date: June 1, 2023
    Inventors: Jongmin Baek, Jiarui Ding, Ermo Wei, Scott McCrae
  • Utilizing machine-learning models to generate identifier embeddings and determine digital connections between digital content items
    Patent number: 11568018
    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilize machine learning models to generate identifier embeddings from digital content identifiers and then leverage these identifier embeddings to determine digital connections between digital content items. In particular, the disclosed systems can utilize an embedding machine-learning model that comprises a character-level embedding machine-learning model and a word-level embedding machine-learning model. For example, the disclosed systems can combine a character embedding from the character-level embedding machine-learning model and a token embedding from the word-level embedding machine-learning model. The disclosed systems can determine digital connections between the plurality of digital content items by processing these identifier embeddings for a plurality of digital content items utilizing a content management model.
    Type: Grant
    Filed: December 22, 2020
    Date of Patent: January 31, 2023
    Assignee: Dropbox, Inc.
    Inventors: Jongmin Baek, Jiarui Ding, Ermo Wei, Scott McCrae
  • UTILIZING MACHINE-LEARNING MODELS TO GENERATE IDENTIFIER EMBEDDINGS AND DETERMINE DIGITAL CONNECTIONS BETWEEN DIGITAL CONTENT ITEMS
    Publication number: 20220197961
    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilize machine learning models to generate identifier embeddings from digital content identifiers and then leverage these identifier embeddings to determine digital connections between digital content items. In particular, the disclosed systems can utilize an embedding machine-learning model that comprises a character-level embedding machine-learning model and a word-level embedding machine-learning model. For example, the disclosed systems can combine a character embedding from the character-level embedding machine-learning model and a token embedding from the word-level embedding machine-learning model. The disclosed systems can determine digital connections between the plurality of digital content items by processing these identifier embeddings for a plurality of digital content items utilizing a content management model.
    Type: Application
    Filed: December 22, 2020
    Publication date: June 23, 2022
    Inventors: Jongmin Baek, Jiarui Ding, Ermo Wei, Scott McCrae