Patents by Inventor Mark D. Simon

Mark D. Simon 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: 20250060343
    Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.
    Type: Application
    Filed: June 27, 2024
    Publication date: February 20, 2025
    Inventors: Timothy KASSIS, Jefferson PRUYNE, Mark D. SIMON, Mimoun CADOSCH DELMAR AKERMAN, Jennifer CAMPBELL, Ana HENRIQUES DA COSTA, Laura KOLINSKY, John M. GEREMIA
  • Publication number: 20250043283
    Abstract: Provided herein are processes for preparing an oligomer (e.g., a morpholino oligomer). The synthetic processes described herein may be advantageous to scaling up oligomersynthesis while maintaining overall yield and purity of a synthesized oligomer.
    Type: Application
    Filed: June 20, 2024
    Publication date: February 6, 2025
    Inventors: Kyle A. Totaro, Mark D. Simon, Ming Zhou, Hong Zong, Gunnar J. Hanson, Bradley L. Pentelute
  • Patent number: 12146869
    Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.
    Type: Grant
    Filed: July 14, 2023
    Date of Patent: November 19, 2024
    Assignee: MATTERWORKS INC
    Inventors: Timothy Kassis, Jefferson Pruyne, Mark D. Simon, Mimoun Cadosch Delmar Akerman, Jennifer Campbell, Ana Henriques Da Costa, Laura Kolinsky, John M. Geremia
  • Patent number: 12100484
    Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.
    Type: Grant
    Filed: September 7, 2022
    Date of Patent: September 24, 2024
    Assignee: Matterworks Inc
    Inventors: Timothy Kassis, Jefferson Pruyne, Mark D. Simon, Mimoun Cadosch Delmar Akerman, Jennifer Campbell, Ana Henriques da Costa, Laura Kolinsky, John M. Geremia
  • Publication number: 20240167986
    Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.
    Type: Application
    Filed: July 14, 2023
    Publication date: May 23, 2024
    Inventors: Timothy KASSIS, Jefferson PRUYNE, Mark D. SIMON, Mimoun CADOSCH DELMAR AKERMAN, Jennifer CAMPBELL, Ana HENRIQUES DA COSTA, Laura KOLINSKY, John M. GEREMIA
  • Patent number: 11754536
    Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.
    Type: Grant
    Filed: September 7, 2022
    Date of Patent: September 12, 2023
    Assignee: MATTERWORKS INC
    Inventors: Timothy Kassis, Jefferson Pruyne, Mark D. Simon, Mimoun Cadosch Delmar Akerman, Jennifer Campbell, Ana Henriques Da Costa, Laura Kolinsky, John M. Geremia
  • Publication number: 20230170049
    Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.
    Type: Application
    Filed: September 7, 2022
    Publication date: June 1, 2023
    Inventors: Timothy KASSIS, Jefferson PRUYNE, Mark D. SIMON, Mimoun CADOSCH DELMAR AKERMAN, Jennifer CAMPBELL, Ana HENRIQUES DA COSTA, Laura KOLINSKY, John M. GEREMIA
  • Publication number: 20230133615
    Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.
    Type: Application
    Filed: September 7, 2022
    Publication date: May 4, 2023
    Inventors: Timothy KASSIS, Jefferson PRUYNE, Mark D. SIMON, Mimoun CADOSCH DELMAR AKERMAN, Jennifer CAMPBELL, Ana HENRIQUES DA COSTA, Laura KOLINSKY, John M. GEREMIA
  • Publication number: 20230137741
    Abstract: Disclosed are methods, libraries, and samples for quantifying a target analyte in a laboratory sample including the target analyte. The methods typically include the step of estimating the amount of the target analyte in the laboratory sample from mass spectrometric data including signal intensities for the target analyte and one or more internal standards, where the mass spectrometric data are an output of a mass spectrometric analysis of a target sample produced from the laboratory sample and a predetermined amount of the one or more internal standards. The present disclosure also provides a method for analyte quantification. The method comprises adding one or more calibrators to a sample comprising one or more analytes; applying mass spectrometry (MS) to the sample; and using a trained machine learning model to determine an absolute concentration of the one or more analytes.
    Type: Application
    Filed: September 7, 2022
    Publication date: May 4, 2023
    Inventors: Timothy KASSIS, Jefferson PRUYNE, Mark D. SIMON, Mimoun CADOSCH DELMAR AKERMAN, Jennifer CAMPBELL, Ana HENRIQUES DA COSTA, Laura KOLINSKY, John M. GEREMIA
  • Publication number: 20200362339
    Abstract: Provided herein are processes for preparing an oligomer (e.g., a morpholino oligomer). The synthetic processes described herein may be advantageous to scaling up oligomer synthesis while maintaining overall yield and purity of a synthesized oligomer.
    Type: Application
    Filed: September 25, 2018
    Publication date: November 19, 2020
    Inventors: Kyle A. Totaro, Mark D. Simon, Ming Zhou, Hong Zong, Gunnar J. Hanson, Bradley L. Pentelute