Patents by Inventor Alexandra Johnson

Alexandra Johnson 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: 10828847
    Abstract: A fiber placement system including a fiber placement station at a first location, the fiber placement station including a tool and a fiber placement assembly configured to construct a reinforcement layup on the tool, the first fiber placement assembly including a compaction roller rotatable about an axis of rotation, the compaction roller at least partially defining a nip, a thermoplastic composite ply extending through the nip and a heating unit positioned to heat the thermoplastic composite ply proximate the nip, and a consolidation station at a consolidation location, the consolidation location being different from the first location, the consolidation station including a consolidation tool and a consolidation system configured to consolidate a reinforcement layup assembly that includes the reinforcement layup.
    Type: Grant
    Filed: June 14, 2018
    Date of Patent: November 10, 2020
    Assignee: The Boeing Company
    Inventors: Marc R. Matsen, Mark A. Negley, Jeffery L. Marcoe, Stephen G. Moore, Brice A. Johnson, Alexandra K. Dillon, Megan M. Petersen
  • Publication number: 20200299633
    Abstract: A composition includes at least one carrier containing a polysaccharide, at least one antioxidant and at least one amino acid selected from cysteine, lysine, alanine and arginine. Such a composition can be used for protection of microorganisms during drying, storage and/or reconstitution. A culture powder, a process of making the culture powder, and products containing the culture powder are also disclosed.
    Type: Application
    Filed: June 10, 2020
    Publication date: September 24, 2020
    Inventors: Edwin Ananta, Biljana Bogicevic, Carsten Cramer, Alexandra Dubois, Judith Gaulocher, Katja Johnson, Jeroen Andre Muller, Matthias Perren, Guenolee Eliane Marie Prioult, Sabine Sres, Wilbert Sybesma
  • Publication number: 20200291354
    Abstract: The present invention relates to compositions and methods for using a minibody. Minibodies described herein comprise a secretion signal, a variable heavy chain fragment, a variable light chain fragment, a constant chain fragment, and a hinge domain between the variable light chain fragment and the constant chain fragment. One aspect includes a nucleic acid encoding a minibody. Other aspects include compositions comprising a minibody and a modified T cell comprising a nucleic acid encoding a minibody. Also included are methods and pharmaceutical compositions comprising the modified T cells for adoptive therapy and treating a condition, such as cancer.
    Type: Application
    Filed: March 23, 2017
    Publication date: September 17, 2020
    Inventors: Laura Alexandra Johnson, Danielle Cook
  • Patent number: 10711240
    Abstract: The present invention relates to a composition comprising at least one carrier comprising a polysaccharide, at least one antioxidant and at least one amino acid selected from cysteine, lysine, alanine and arginine. It also relates to the use of such a composition for the protection of microorganisms during drying, storage and/or reconstitution, to a culture powder, to a process of making the culture powder and to products comprising the culture powder.
    Type: Grant
    Filed: June 30, 2016
    Date of Patent: July 14, 2020
    Assignee: Societe des Produits Neslte S.A.
    Inventors: Edwin Ananta, Biljana Bogicevic, Carsten Cramer, Alexandra Dubois, Judith Gaulocher, Katja Johnson, Jeroen Andre Muller, Matthias Perren, Guenolee Eliane Marie Prioult, Sabine Sres, Wilbert Sybesma
  • Publication number: 20200202254
    Abstract: A system and method includes receiving a tuning work request for tuning an external machine learning model; implementing a plurality of distinct queue worker machines that perform various tuning operations based on the tuning work data of the tuning work request; implementing a plurality of distinct tuning sources that generate values for each of the one or more hyperparameters of the tuning work request; selecting, by one or more queue worker machines of the plurality of distinct queue worker machines, one or more tuning sources of the plurality of distinct tuning sources for tuning the one or more hyperparameters; and using the selected one or more tuning sources to generate one or more suggestions for the one or more hyperparameters, the one or more suggestions comprising values for the one or more hyperparameters of the tuning work request.
    Type: Application
    Filed: February 20, 2020
    Publication date: June 25, 2020
    Inventors: Patrick Hayes, Michael McCourt, Alexandra Johnson, George Ke, Scott Clark
  • Publication number: 20200151029
    Abstract: Systems and methods for implementing an application programming interface (API) that controls operations of a machine learning tuning service for tuning a machine learning model for improved accuracy and computational performance includes an API that is in control communication the tuning service that: executes a first API call function that includes an optimization work request that sets tuning parameters for tuning hyperparameters of a machine learning model; and initializes an operation of distinct tuning worker instances of the service that each execute distinct tuning tasks for tuning the hyperparameters; executes a second API call function that identifies raw values for the hyperparameters; and generates suggestions comprising proposed hyperparameter values selected from the plurality of raw values for each of the hyperparameters; and executes a third API call function that returns performance metrics relating to a real-world performance of the subscriber machine learning model executed with the propose
    Type: Application
    Filed: January 14, 2020
    Publication date: May 14, 2020
    Inventors: Alexandra Johnson, Patrick Hayes, Scott Clark
  • Patent number: 10607159
    Abstract: A system and method includes receiving a tuning work request for tuning an external machine learning model; implementing a plurality of distinct queue worker machines that perform various tuning operations based on the tuning work data of the tuning work request; implementing a plurality of distinct tuning sources that generate values for each of the one or more hyperparameters of the tuning work request; selecting, by one or more queue worker machines of the plurality of distinct queue worker machines, one or more tuning sources of the plurality of distinct tuning sources for tuning the one or more hyperparameters; and using the selected one or more tuning sources to generate one or more suggestions for the one or more hyperparameters, the one or more suggestions comprising values for the one or more hyperparameters of the tuning work request.
    Type: Grant
    Filed: January 9, 2019
    Date of Patent: March 31, 2020
    Assignee: SigOpt, Inc.
    Inventors: Patrick Hayes, Michael McCourt, Alexandra Johnson, George Ke, Scott Clark
  • Patent number: 10565025
    Abstract: Systems and methods for implementing an application programming interface (API) that controls operations of a machine learning tuning service for tuning a machine learning model for improved accuracy and computational performance includes an API that is in control communication the tuning service that: executes a first API call function that includes an optimization work request that sets tuning parameters for tuning hyperparameters of a machine learning model; and initializes an operation of distinct tuning worker instances of the service that each execute distinct tuning tasks for tuning the hyperparameters; executes a second API call function that identifies raw values for the hyperparameters; and generates suggestions comprising proposed hyperparameter values selected from the plurality of raw values for each of the hyperparameters; and executes a third API call function that returns performance metrics relating to a real-world performance of the subscriber machine learning model executed with the propose
    Type: Grant
    Filed: September 4, 2019
    Date of Patent: February 18, 2020
    Assignee: SigOpt, Inc.
    Inventors: Alexandra Johnson, Patrick Hayes, Scott Clark
  • Publication number: 20190391859
    Abstract: Systems and methods for implementing an application programming interface (API) that controls operations of a machine learning tuning service for tuning a machine learning model for improved accuracy and computational performance includes an API that is in control communication the tuning service that: executes a first API call function that includes an optimization work request that sets tuning parameters for tuning hyperparameters of a machine learning model; and initializes an operation of distinct tuning worker instances of the service that each execute distinct tuning tasks for tuning the hyperparameters; executes a second API call function that identifies raw values for the hyperparameters; and generates suggestions comprising proposed hyperparameter values selected from the plurality of raw values for each of the hyperparameters; and executes a third API call function that returns performance metrics relating to a real-world performance of the subscriber machine learning model executed with the propose
    Type: Application
    Filed: September 4, 2019
    Publication date: December 26, 2019
    Inventors: Alexandra Johnson, Patrick Hayes, Scott Clark
  • Publication number: 20190330356
    Abstract: The invention provides compositions and methods for treating diseases associated with expression of EGFRvIII. The invention also relates to chimeric antigen receptor (CAR) specific to EGFRvIII, vectors encoding the same, and recombinant T cells comprising the anti-EGFRvIII CAR. The invention also includes methods of administering a genetically modified T cell expressing a CAR that comprises an anti-EGFRvIII binding domain.
    Type: Application
    Filed: April 12, 2019
    Publication date: October 31, 2019
    Inventors: Jennifer Brogdon, Laura Alexandra Johnson, Carl H. June, Andreas Loew, Marcela Maus, John Scholler, Hideho Okada
  • Patent number: 10445150
    Abstract: Systems and methods for implementing an application programming interface (API) that controls operations of a machine learning tuning service for tuning a machine learning model for improved accuracy and computational performance includes an API that is in control communication the tuning service that: executes a first API call function that includes an optimization work request that sets tuning parameters for tuning hyperparameters of a machine learning model; and initializes an operation of distinct tuning worker instances of the service that each execute distinct tuning tasks for tuning the hyperparameters; executes a second API call function that identifies raw values for the hyperparameters; and generates suggestions comprising proposed hyperparameter values selected from the plurality of raw values for each of the hyperparameters; and executes a third API call function that returns performance metrics relating to a real-world performance of the subscriber machine learning model executed with the propose
    Type: Grant
    Filed: June 24, 2019
    Date of Patent: October 15, 2019
    Assignee: SigOpt, Inc.
    Inventors: Alexandra Johnson, Patrick Hayes, Scott Clark
  • Publication number: 20190310898
    Abstract: Systems and methods for implementing an application programming interface (API) that controls operations of a machine learning tuning service for tuning a machine learning model for improved accuracy and computational performance includes an API that is in control communication the tuning service that: executes a first API call function that includes an optimization work request that sets tuning parameters for tuning hyperparameters of a machine learning model; and initializes an operation of distinct tuning worker instances of the service that each execute distinct tuning tasks for tuning the hyperparameters; executes a second API call function that identifies raw values for the hyperparameters; and generates suggestions comprising proposed hyperparameter values selected from the plurality of raw values for each of the hyperparameters; and executes a third API call function that returns performance metrics relating to a real-world performance of the subscriber machine learning model executed with the propose
    Type: Application
    Filed: June 24, 2019
    Publication date: October 10, 2019
    Inventors: Alexandra Johnson, Patrick Hayes, Scott Clark
  • Patent number: 10379913
    Abstract: Systems and methods for implementing an application programming interface (API) that controls operations of a machine learning tuning service for tuning a machine learning model for improved accuracy and computational performance includes an API that is in control communication the tuning service that: executes a first API call function that includes an optimization work request that sets tuning parameters for tuning hyperparameters of a machine learning model; and initializes an operation of distinct tuning worker instances of the service that each execute distinct tuning tasks for tuning the hyperparameters; executes a second API call function that identifies raw values for the hyperparameters; and generates suggestions comprising proposed hyperparameter values selected from the plurality of raw values for each of the hyperparameters; and executes a third API call function that returns performance metrics relating to a real-world performance of the subscriber machine learning model executed with the propose
    Type: Grant
    Filed: March 20, 2019
    Date of Patent: August 13, 2019
    Assignee: SigOpt, Inc.
    Inventors: Alexandra Johnson, Patrick Hayes, Scott Clark
  • Publication number: 20190213056
    Abstract: Systems and methods for implementing an application programming interface (API) that controls operations of a machine learning tuning service for tuning a machine learning model for improved accuracy and computational performance includes an API that is in control communication the tuning service that: executes a first API call function that includes an optimization work request that sets tuning parameters for tuning hyperparameters of a machine learning model; and initializes an operation of distinct tuning worker instances of the service that each execute distinct tuning tasks for tuning the hyperparameters; executes a second API call function that identifies raw values for the hyperparameters; and generates suggestions comprising proposed hyperparameter values selected from the plurality of raw values for each of the hyperparameters; and executes a third API call function that returns performance metrics relating to a real-world performance of the subscriber machine learning model executed with the propose
    Type: Application
    Filed: March 20, 2019
    Publication date: July 11, 2019
    Inventors: Alexandra Johnson, Patrick Hayes, Scott Clark
  • Patent number: 10308717
    Abstract: The invention provides compositions and methods for treating diseases associated with expression of EGFRvIII. The invention also relates to chimeric antigen receptor (CAR) specific to EGFRvIII, vectors encoding the same, and recombinant T cells comprising the anti-EGFRvIII CAR. The invention also includes methods of administering a genetically modified T cell expressing a CAR that comprises an anti-EGFRvIII binding domain.
    Type: Grant
    Filed: June 15, 2016
    Date of Patent: June 4, 2019
    Assignees: Novartis AG, The Trustees of the University of Pennsylvania, University of Pittsburgh—of The Commonwealth System of Higher Education
    Inventors: Jennifer Brogdon, Laura Alexandra Johnson, Carl H. June, Andreas Loew, Marcela Maus, John Scholler, Hideho Okada
  • Publication number: 20190147362
    Abstract: A system and method includes receiving a tuning work request for tuning an external machine learning model; implementing a plurality of distinct queue worker machines that perform various tuning operations based on the tuning work data of the tuning work request; implementing a plurality of distinct tuning sources that generate values for each of the one or more hyperparameters of the tuning work request; selecting, by one or more queue worker machines of the plurality of distinct queue worker machines, one or more tuning sources of the plurality of distinct tuning sources for tuning the one or more hyperparameters; and using the selected one or more tuning sources to generate one or more suggestions for the one or more hyperparameters, the one or more suggestions comprising values for the one or more hyperparameters of the tuning work request.
    Type: Application
    Filed: January 9, 2019
    Publication date: May 16, 2019
    Inventors: Patrick Hayes, Michael McCourt, Alexandra Johnson, George Ke, Scott Clark
  • Patent number: 10282237
    Abstract: Systems and methods for implementing an application programming interface (API) that controls operations of a machine learning tuning service for tuning a machine learning model for improved accuracy and computational performance includes an API that is in control communication the tuning service that: executes a first API call function that includes an optimization work request that sets tuning parameters for tuning hyperparameters of a machine learning model; and initializes an operation of distinct tuning worker instances of the service that each execute distinct tuning tasks for tuning the hyperparameters; executes a second API call function that identifies raw values for the hyperparameters; and generates suggestions comprising proposed hyperparameter values selected from the plurality of raw values for each of the hyperparameters; and executes a third API call function that returns performance metrics relating to a real-world performance of the subscriber machine learning model executed with the propose
    Type: Grant
    Filed: October 29, 2018
    Date of Patent: May 7, 2019
    Assignee: SigOpt, Inc.
    Inventors: Alexandra Johnson, Patrick Hayes, Scott Clark
  • Publication number: 20190129764
    Abstract: Systems and methods for implementing an application programming interface (API) that controls operations of a machine learning tuning service for tuning a machine learning model for improved accuracy and computational performance includes an API that is in control communication the tuning service that: executes a first API call function that includes an optimization work request that sets tuning parameters for tuning hyperparameters of a machine learning model; and initializes an operation of distinct tuning worker instances of the service that each execute distinct tuning tasks for tuning the hyperparameters; executes a second API call function that identifies raw values for the hyperparameters; and generates suggestions comprising proposed hyperparameter values selected from the plurality of raw values for each of the hyperparameters; and executes a third API call function that returns performance metrics relating to a real-world performance of the subscriber machine learning model executed with the propose
    Type: Application
    Filed: October 29, 2018
    Publication date: May 2, 2019
    Inventors: Alexandra Johnson, Patrick Hayes, Scott Clark
  • Patent number: 10217061
    Abstract: A system and method includes receiving a tuning work request for tuning an external machine learning model; implementing a plurality of distinct queue worker machines that perform various tuning operations based on the tuning work data of the tuning work request; implementing a plurality of distinct tuning sources that generate values for each of the one or more hyperparameters of the tuning work request; selecting, by one or more queue worker machines of the plurality of distinct queue worker machines, one or more tuning sources of the plurality of distinct tuning sources for tuning the one or more hyperparameters; and using the selected one or more tuning sources to generate one or more suggestions for the one or more hyperparameters, the one or more suggestions comprising values for the one or more hyperparameters of the tuning work request.
    Type: Grant
    Filed: May 11, 2018
    Date of Patent: February 26, 2019
    Assignee: SigOpt, Inc.
    Inventors: Patrick Hayes, Michael McCourt, Alexandra Johnson, George Ke, Scott Clark
  • Publication number: 20180336493
    Abstract: A system and method includes receiving a tuning work request for tuning an external machine learning model; implementing a plurality of distinct queue worker machines that perform various tuning operations based on the tuning work data of the tuning work request; implementing a plurality of distinct tuning sources that generate values for each of the one or more hyperparameters of the tuning work request; selecting, by one or more queue worker machines of the plurality of distinct queue worker machines, one or more tuning sources of the plurality of distinct tuning sources for tuning the one or more hyperparameters; and using the selected one or more tuning sources to generate one or more suggestions for the one or more hyperparameters, the one or more suggestions comprising values for the one or more hyperparameters of the tuning work request.
    Type: Application
    Filed: May 11, 2018
    Publication date: November 22, 2018
    Inventors: Patrick Hayes, Michael McCourt, Alexandra Johnson, George Ke, Scott Clark