Patents by Inventor Sean MacAvaney

Sean MacAvaney 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).

  • Classifying terms from source texts using implicit and explicit class-recognition-machine-learning models
    Patent number: 11630952
    Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that can classify term sequences within a source text based on textual features analyzed by both an implicit-class-recognition model and an explicit-class-recognition model. For example, by applying machine-learning models for both implicit and explicit class recognition, the disclosed systems can determine a class corresponding to a particular term sequence within a source text and identify the particular term sequence reflecting the class. The dual-model architecture can equip the disclosed systems to apply (i) the implicit-class-recognition model to recognize implicit references to a class in source texts and (ii) the explicit-class-recognition model to recognize explicit references to the same class in source texts.
    Type: Grant
    Filed: July 22, 2019
    Date of Patent: April 18, 2023
    Assignee: Adobe Inc.
    Inventors: Sean MacAvaney, Franck Dernoncourt, Walter Chang, Seokhwan Kim, Doo Soon Kim, Chen Fang
  • CLASSIFYING TERMS FROM SOURCE TEXTS USING IMPLICIT AND EXPLICIT CLASS-RECOGNITION-MACHINE-LEARNING MODELS
    Publication number: 20210027141
    Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that can classify term sequences within a source text based on textual features analyzed by both an implicit-class-recognition model and an explicit-class-recognition model. For example, by applying machine-learning models for both implicit and explicit class recognition, the disclosed systems can determine a class corresponding to a particular term sequence within a source text and identify the particular term sequence reflecting the class. The dual-model architecture can equip the disclosed systems to apply (i) the implicit-class-recognition model to recognize implicit references to a class in source texts and (ii) the explicit-class-recognition model to recognize explicit references to the same class in source texts.
    Type: Application
    Filed: July 22, 2019
    Publication date: January 28, 2021
    Inventors: Sean MacAvaney, Franck Dernoncourt, Walter Chang, Seokhwan Kim, Doo Soon Kim, Chen Fang