Patents by Inventor Christopher Larson

Christopher Larson 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).

  • Publication number: 20210301811
    Abstract: An autoclaving microplate washing system for cells and non-adhering three-dimensional (3D) cell cultures includes one or more peristaltic pumps for controlling the dispensing of washing fluid and the evacuation of fluid from microwells to gently wash the cells.
    Type: Application
    Filed: March 29, 2021
    Publication date: September 30, 2021
    Applicant: BIOTEK INSTRUMENTS, INC
    Inventors: Christopher MANY, Brad Larson, Byron Smith, Brian Struhammer
  • Patent number: 11131360
    Abstract: Methods, systems, and devices are described for isolating a crystal oscillator assembly from shock and/or vibration inputs. A system may include one or more vibration isolators coupled between the crystal oscillator assembly and the base structure, and each of the vibration isolators may include a spring material layer and a damping material layer. The spring material layer may provide a spring force between the crystal oscillator assembly and the base structure. The damping material layer may be adhered to at least one side of the spring material layer, and may provide a damping force between the crystal oscillator assembly and the base structure. Some vibration isolators may further include a constraint layer adhered to the damping material layer, such that the damping material layer is coupled between the constraint layer and the spring material layer.
    Type: Grant
    Filed: December 1, 2016
    Date of Patent: September 28, 2021
    Assignee: Viasat, Inc.
    Inventors: Steven Shrinkle, Kenneth Larson, Christopher Haas
  • Publication number: 20210232925
    Abstract: Aspects described herein may allow for the application of stochastic gradient boosting techniques to the training of deep neural networks by disallowing gradient back propagation from examples that are correctly classified by the neural network model while still keeping correctly classified examples in the gradient averaging. Removing the gradient contribution from correctly classified examples may regularize the deep neural network and prevent the model from overfitting. Further aspects described herein may provide for scheduled boosting during the training of the deep neural network model conditioned on a mini-batch accuracy and/or a number of training iterations. The model training process may start un-boosted, using maximum likelihood objectives or another first loss function.
    Type: Application
    Filed: April 16, 2021
    Publication date: July 29, 2021
    Inventors: Oluwatobi Olabiyi, Erik T. Mueller, Christopher Larson
  • Publication number: 20210170671
    Abstract: A use of a filament in 3D printing is disclosed. The filament includes a thermoplastic polymer and detonation nanodiamonds. The filament exhibits increased tensile strength and thermal conductivity and higher glass transition temperature compared to filaments not including detonation nanodiamonds. 3D items produced with the filament exhibits increased tensile strength and thermal conductivity.
    Type: Application
    Filed: December 21, 2018
    Publication date: June 10, 2021
    Applicants: Carbodeon Ltd Oy, Tiamet Technologies, B.V.
    Inventors: Vesa MYLLYMĂ„KI, Reid Christopher LARSON
  • Publication number: 20210139560
    Abstract: Provided is a fusion protein, e.g., a cytokine receptor fusion protein, e.g., a TGF? trap, with a novel linker sequence to permit the fusion protein to functionally optimally, e.g., to permit a cytokine receptor portion of a cytokine receptor fusion protein to bind optimally to its target cytokine. The fusion proteins, or expression vectors encoding for the fusion proteins, e.g., oncolytic adenoviral expression vectors, can be used to treat cell proliferative diseases and disorders, including certain forms of cancer and inflammatory disorders.
    Type: Application
    Filed: January 15, 2021
    Publication date: May 13, 2021
    Applicant: EpicentRx, Inc.
    Inventors: Christopher LARSON, Tony R. REID, Bryan T. ORONSKY
  • Patent number: 10990878
    Abstract: Aspects described herein may allow for the application of stochastic gradient boosting techniques to the training of deep neural networks by disallowing gradient back propagation from examples that are correctly classified by the neural network model while still keeping correctly classified examples in the gradient averaging. Removing the gradient contribution from correctly classified examples may regularize the deep neural network and prevent the model from overfitting. Further aspects described herein may provide for scheduled boosting during the training of the deep neural network model conditioned on a mini-batch accuracy and/or a number of training iterations. The model training process may start un-boosted, using maximum likelihood objectives or another first loss function.
    Type: Grant
    Filed: March 5, 2019
    Date of Patent: April 27, 2021
    Assignee: Capital One Services, LLC
    Inventors: Oluwatobi Olabiyi, Erik T. Mueller, Christopher Larson
  • Patent number: 10906957
    Abstract: Provided is a fusion protein, e.g., a cytokine receptor fusion protein, e.g., a TGF? trap, with a novel linker sequence to permit the fusion protein to functionally optimally, e.g., to permit a cytokine receptor portion of a cytokine receptor fusion protein to bind optimally to its target cytokine. The fusion proteins, or expression vectors encoding for the fusion proteins, e.g., oncolytic adenoviral expression vectors, can be used to treat cell proliferative diseases and disorders, including certain forms of cancer and inflammatory disorders.
    Type: Grant
    Filed: September 27, 2017
    Date of Patent: February 2, 2021
    Assignee: EpicentRx, Inc.
    Inventors: Christopher Larson, Tony R. Reid, Bryan T. Oronsky
  • Publication number: 20210015878
    Abstract: The invention provides for a recombinant adenoviral vector comprising a recombinant oncolytic adenovirus which has: (1) a modified transcription regulatory sequence wherein the adenoviral vector is transcriptionally active in cancer cells and/or hyperproliferative cells and transcriptionally attenuated in normal cells, and (2) a transgene encoding one or more cancer antigens that are specific to a subject. The invention further provides a method of producing said recombinant adenoviral vector. The recombinant oncolytic adenoviral vector described above is used in methods of stimulating a heightened immune response against a cancer antigen in a subject or a method of treating cancer in a subject.
    Type: Application
    Filed: March 28, 2019
    Publication date: January 21, 2021
    Applicant: EpicentRx, Inc.
    Inventors: Christopher LARSON, Bryan T. ORONSKY, Tony R. REID
  • Publication number: 20200320982
    Abstract: Aspects described herein may relate to the determination of data that is indicative of a greater range of speech properties than input text data. The determined data may be used as input to one or more speech processing tasks, such as model training, model validation, model testing, or classification. For example, after a model is trained based on the determined data, the model's performance may exhibit more resilience to a wider range of speech properties. The determined data may include one or more modified versions of the input text data. The one or more modified versions may be associated with the one or more speakers or accents and/or may be associated with one or more levels of semantic similarity in relation to the input text data. The one or more modified versions may be determined based on one or more machine learning algorithms.
    Type: Application
    Filed: February 20, 2020
    Publication date: October 8, 2020
    Inventors: Christopher Larson, Tarek Aziz Lahlou, Diana Mingels, Zachary Kulis, Erik T. Mueller
  • Publication number: 20200265321
    Abstract: Aspects discussed herein may relate to methods and techniques for embedding constrained and unconstrained optimization programs as layers in a neural network architecture. Systems are provided that implement a method of solving a particular optimization problem by a neural network architecture. Prior systems required use of external software to pre-solve optimization programs so that previously determined parameters could be used as fixed input in the neural network architecture. Aspects described herein may transform the structure of common optimization problems/programs into forms suitable for use in a neural network. This transformation may be invertible, allowing the system to learn the solution to the optimization program using gradient descent techniques via backpropagation of errors through the neural network architecture. Thus these optimization layers may be solved via operation of the neural network itself.
    Type: Application
    Filed: February 14, 2020
    Publication date: August 20, 2020
    Inventors: Tarek Aziz Lahlou, Christopher Larson, Oluwatobi Olabiyi
  • Publication number: 20200265296
    Abstract: Aspects described herein may allow for the application of stochastic gradient boosting techniques to the training of deep neural networks by disallowing gradient back propagation from examples that are correctly classified by the neural network model while still keeping correctly classified examples in the gradient averaging. Removing the gradient contribution from correctly classified examples may regularize the deep neural network and prevent the model from overfitting. Further aspects described herein may provide for scheduled boosting during the training of the deep neural network model conditioned on a mini-batch accuracy and/or a number of training iterations. The model training process may start un-boosted, using maximum likelihood objectives or another first loss function.
    Type: Application
    Filed: March 5, 2019
    Publication date: August 20, 2020
    Inventors: Oluwatobi Olabiyi, Erik T. Mueller, Christopher Larson
  • Publication number: 20200223901
    Abstract: Provided is a fusion protein, e.g., a cytokine receptor fusion protein, e.g., an IL-10 trap, with a novel linker sequence to permit the fusion protein to functionally optimally, e.g., to permit a cytokine receptor portion of a cytokine receptor fusion protein to bind optimally to its target cytokine. The fusion protein, or an expression vector encoding for the fusion proteins, can be used to treat cell proliferative diseases and disorders, including certain forms of cancer and inflammatory disorders.
    Type: Application
    Filed: September 27, 2018
    Publication date: July 16, 2020
    Inventors: Christopher Larson, Tony R. Reid, Bryan T. Oronsky
  • Publication number: 20200155625
    Abstract: The invention relates to a recombinant adenovirus that expresses endostatin, angiostatin, or a combination of endostatin and angiostatin. The invention also relates to method of treating cancer in a subject in need thereof, the method comprising administering to the subject an effective amount of a combination of (i) a recombinant adenovirus and (ii) an anti-angiogenic agent to treat the cancer in the subject.
    Type: Application
    Filed: May 24, 2018
    Publication date: May 21, 2020
    Inventors: Christopher Larson, Tony R. Reid, Bryan T. Oronsky
  • Patent number: 10607598
    Abstract: Aspects described herein may relate to the determination of data that is indicative of a greater range of speech properties than input text data. The determined data may be used as input to one or more speech processing tasks, such as model training, model validation, model testing, or classification. For example, after a model is trained based on the determined data, the model's performance may exhibit more resilience to a wider range of speech properties. The determined data may include one or more modified versions of the input text data. The one or more modified versions may be associated with the one or more speakers or accents and/or may be associated with one or more levels of semantic similarity in relation to the input text data. The one or more modified versions may be determined based on one or more machine learning algorithms.
    Type: Grant
    Filed: July 25, 2019
    Date of Patent: March 31, 2020
    Assignee: Capital One Services, LLC
    Inventors: Christopher Larson, Tarek Aziz Lahlou, Diana Mingels, Zachary Kulis, Erik T. Mueller
  • Publication number: 20200078415
    Abstract: The invention relates to a recombinant adenovirus comprising two or more therapeutic transgenes, e.g., two components of a heterodimeric cytokine, separated by a cleavable linker.
    Type: Application
    Filed: April 12, 2018
    Publication date: March 12, 2020
    Inventors: Tony R. Reid, Christopher Larson, Bryan T. Oronsky
  • Publication number: 20200032223
    Abstract: The invention relates to a method for producing a recombinant virus, e.g., a recombinant oncolytic adenovirus, using an A549 host cell.
    Type: Application
    Filed: April 10, 2018
    Publication date: January 30, 2020
    Inventors: Tony R. Reid, Bryan T. Oronsky, Christopher Larson
  • Patent number: 10510003
    Abstract: Aspects described herein may allow for the application of stochastic gradient boosting techniques to the training of deep neural networks by disallowing gradient back propagation from examples that are correctly classified by the neural network model while still keeping correctly classified examples in the gradient averaging. Removing the gradient contribution from correctly classified examples may regularize the deep neural network and prevent the model from overfitting. Further aspects described herein may provide for scheduled boosting during the training of the deep neural network model conditioned on a mini-batch accuracy and/or a number of training iterations. The model training process may start un-boosted, using maximum likelihood objectives or another first loss function.
    Type: Grant
    Filed: March 5, 2019
    Date of Patent: December 17, 2019
    Assignee: Capital One Services, LLC
    Inventors: Oluwatobi Olabiyi, Erik T. Mueller, Christopher Larson
  • Patent number: 10510002
    Abstract: Aspects described herein may allow for the application of stochastic gradient boosting techniques to the training of deep neural networks by disallowing gradient back propagation from examples that are correctly classified by the neural network model while still keeping correctly classified examples in the gradient averaging. Removing the gradient contribution from correctly classified examples may regularize the deep neural network and prevent the model from overfitting. Further aspects described herein may provide for scheduled boosting during the training of the deep neural network model conditioned on a mini-batch accuracy and/or a number of training iterations. The model training process may start un-boosted, using maximum likelihood objectives or another first loss function.
    Type: Grant
    Filed: February 14, 2019
    Date of Patent: December 17, 2019
    Assignee: Capital One Services, LLC
    Inventors: Oluwatobi Olabiyi, Erik T. Mueller, Christopher Larson
  • Publication number: 20190352669
    Abstract: The invention provides, e.g., a recombinant virus comprising (i) a modified TATA box-based promoter, and/or (ii) a modified CAAT box-based promoter operably linked to a gene, wherein the modified TATA box-based promoter and/or modified CAAT box-based promoter lacks a functional TATA box and/or CAAT box and permit selective expression of the gene in a hyperproliferative cell. The recombinant viruses can be used to treat cell proliferative diseases and disorders, including certain forms of cancer.
    Type: Application
    Filed: January 30, 2018
    Publication date: November 21, 2019
    Inventors: Tony R. Reid, Bryan T. Oronsky, Farah Hedjran, Christopher Larson
  • Publication number: 20190352616
    Abstract: The invention provides a recombinant adenovirus comprising two (or more) therapeutic transgenes, e.g., CD80 and CD137L. The transgenes are preferably inserted into an E1b-19K insertion site and/or an E3 insertion site.
    Type: Application
    Filed: January 30, 2018
    Publication date: November 21, 2019
    Inventors: Tony R. Reid, Bryan T. Oronsky, Christopher Larson