Patents by Inventor Elizabeth OTTENS

Elizabeth OTTENS 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).

  • Patent number: 11614922
    Abstract: The subject technology transforms a machine learning model into a transformed machine learning model in accordance with a particular model specification when the machine learning model does not conform to the particular model specification, the particular model specification being compatible with an integrated development environment (IDE). The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in the IDE, the IDE enabling modifying of the generated code interface and the code.
    Type: Grant
    Filed: December 21, 2020
    Date of Patent: March 28, 2023
    Assignee: Apple Inc.
    Inventors: Alexander B. Brown, Michael R. Siracusa, Gaurav Kapoor, Elizabeth Ottens, Christopher M. Hanson, Zachary A. Nation, Vrushali Mundhe, Srikrishna Sridhar
  • Publication number: 20210398020
    Abstract: A training operation for training a machine learning model may be initiated. At a predetermined checkpoint during the training operation, checkpoint information comprising a representation of the machine learning model in a partially trained state may be generated and stored in a non-volatile storage medium. The training operation for training the machine learning model may be continued after the predetermined checkpoint.
    Type: Application
    Filed: May 28, 2021
    Publication date: December 23, 2021
    Inventors: Suhail AHMAD, Alexander B. BROWN, Elizabeth A. OTTENS, Alejandro ISAZA
  • Publication number: 20210109718
    Abstract: The subject technology transforms a machine learning model into a transformed machine learning model in accordance with a particular model specification when the machine learning model does not conform to the particular model specification, the particular model specification being compatible with an integrated development environment (IDE). The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in the IDE, the IDE enabling modifying of the generated code interface and the code.
    Type: Application
    Filed: December 21, 2020
    Publication date: April 15, 2021
    Inventors: Alexander B. BROWN, Michael R. SIRACUSA, Gaurav KAPOOR, Elizabeth OTTENS, Christopher M. HANSON, Zachary A. NATION, Vrushali MUNDHE, Srikrishna SRIDHAR
  • Patent number: 10871949
    Abstract: The subject technology transforms a machine learning model into a transformed machine learning model in accordance with a particular model specification when the machine learning model does not conform to the particular model specification, the particular model specification being compatible with an integrated development environment (IDE). The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in the IDE, the IDE enabling modifying of the generated code interface and the code.
    Type: Grant
    Filed: June 3, 2019
    Date of Patent: December 22, 2020
    Assignee: Apple Inc.
    Inventors: Alexander B. Brown, Michael R. Siracusa, Gaurav Kapoor, Elizabeth Ottens, Christopher M. Hanson, Zachary A. Nation, Vrushali Mundhe, Srikrishna Sridhar
  • Patent number: 10606566
    Abstract: The subject technology provides for generating machine learning (ML) model code from a ML document file, the ML document file being in a first data format, the ML document file being converted to code in an object oriented programming language different than the first data format. The subject technology further provides for receiving additional code that calls a function provided by the ML model code. The subject technology compiles the ML model code and the additional code, the compiled ML model code including object code corresponding to the compiled ML model code and the compiled additional code including object code corresponding to the additional code. The subject technology generates a package including the compiled ML model code and the compiled additional code. Further, the subject technology sends the package to a runtime environment on a target device for execution.
    Type: Grant
    Filed: September 29, 2017
    Date of Patent: March 31, 2020
    Assignee: APPLE INC.
    Inventors: Alexander B. Brown, Michael R. Siracusa, Gaurav Kapoor, Elizabeth A. Ottens, Christopher M. Hanson, Zachary A. Nation, Vrushali H. Mundhe, Srikrishna Sridhar
  • Publication number: 20190286424
    Abstract: The subject technology transforms a machine learning model into a transformed machine learning model in accordance with a particular model specification when the machine learning model does not conform to the particular model specification, the particular model specification being compatible with an integrated development environment (IDE). The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in the IDE, the IDE enabling modifying of the generated code interface and the code.
    Type: Application
    Filed: June 3, 2019
    Publication date: September 19, 2019
    Inventors: Alexander B. BROWN, Michael R. SIRACUSA, Gaurav KAPOOR, Elizabeth OTTENS, Christopher M. HANSON, Zachary A. NATION, Vrushali MUNDHE, Srikrishna SRIDHAR
  • Patent number: 10310821
    Abstract: The subject technology provides for determining that a machine learning model in a first format includes sufficient data to conform to a particular model specification in a second format, the second format corresponding to an object oriented programming language. The subject technology transforms the machine learning model into a transformed machine learning model that is compatible with the particular model specification. The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in an integrated development environment (IDE), the IDE enabling modifying of the generated code interface and the code.
    Type: Grant
    Filed: September 29, 2017
    Date of Patent: June 4, 2019
    Assignee: APPLE INC.
    Inventors: Alexander B. Brown, Michael R. Siracusa, Gaurav Kapoor, Elizabeth Ottens, Christopher M. Hanson, Zachary A. Nation, Vrushali Mundhe, Srikrishna Sridhar
  • Publication number: 20180349103
    Abstract: The subject technology provides for determining that a machine learning model in a first format includes sufficient data to conform to a particular model specification in a second format, the second format corresponding to an object oriented programming language. The subject technology transforms the machine learning model into a transformed machine learning model that is compatible with the particular model specification. The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in an integrated development environment (IDE), the IDE enabling modifying of the generated code interface and the code.
    Type: Application
    Filed: September 29, 2017
    Publication date: December 6, 2018
    Inventors: Alexander B. BROWN, Michael R. SIRACUSA, Gaurav KAPOOR, Elizabeth OTTENS, Christopher M. HANSON, Zachary A. NATION, Vrushali MUNDHE, Srikrishna SRIDHAR
  • Publication number: 20180349109
    Abstract: The subject technology provides for generating machine learning (ML) model code from a ML document file, the ML document file being in a first data format, the ML document file being converted to code in an object oriented programming language different than the first data format. The subject technology further provides for receiving additional code that calls a function provided by the ML model code. The subject technology compiles the ML model code and the additional code, the compiled ML model code including object code corresponding to the compiled ML model code and the compiled additional code including object code corresponding to the additional code. The subject technology generates a package including the compiled ML model code and the compiled additional code. Further, the subject technology sends the package to a runtime environment on a target device for execution.
    Type: Application
    Filed: September 29, 2017
    Publication date: December 6, 2018
    Inventors: Alexander B. BROWN, Michael R. SIRACUSA, Gaurav KAPOOR, Elizabeth A. OTTENS, Christopher M. HANSON, Zachary A. NATION, Vrushali H. MUNDHE, Srikrishna SRIDHAR