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).
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Publication number: 20250060343Abstract: 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: ApplicationFiled: June 27, 2024Publication date: February 20, 2025Inventors: Timothy KASSIS, Jefferson PRUYNE, Mark D. SIMON, Mimoun CADOSCH DELMAR AKERMAN, Jennifer CAMPBELL, Ana HENRIQUES DA COSTA, Laura KOLINSKY, John M. GEREMIA
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Publication number: 20250043283Abstract: 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: ApplicationFiled: June 20, 2024Publication date: February 6, 2025Inventors: Kyle A. Totaro, Mark D. Simon, Ming Zhou, Hong Zong, Gunnar J. Hanson, Bradley L. Pentelute
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Patent number: 12146869Abstract: 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: GrantFiled: July 14, 2023Date of Patent: November 19, 2024Assignee: MATTERWORKS INCInventors: Timothy Kassis, Jefferson Pruyne, Mark D. Simon, Mimoun Cadosch Delmar Akerman, Jennifer Campbell, Ana Henriques Da Costa, Laura Kolinsky, John M. Geremia
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Patent number: 12100484Abstract: 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: GrantFiled: September 7, 2022Date of Patent: September 24, 2024Assignee: Matterworks IncInventors: Timothy Kassis, Jefferson Pruyne, Mark D. Simon, Mimoun Cadosch Delmar Akerman, Jennifer Campbell, Ana Henriques da Costa, Laura Kolinsky, John M. Geremia
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Publication number: 20240167986Abstract: 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: ApplicationFiled: July 14, 2023Publication date: May 23, 2024Inventors: Timothy KASSIS, Jefferson PRUYNE, Mark D. SIMON, Mimoun CADOSCH DELMAR AKERMAN, Jennifer CAMPBELL, Ana HENRIQUES DA COSTA, Laura KOLINSKY, John M. GEREMIA
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Patent number: 11754536Abstract: 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: GrantFiled: September 7, 2022Date of Patent: September 12, 2023Assignee: MATTERWORKS INCInventors: Timothy Kassis, Jefferson Pruyne, Mark D. Simon, Mimoun Cadosch Delmar Akerman, Jennifer Campbell, Ana Henriques Da Costa, Laura Kolinsky, John M. Geremia
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Publication number: 20230170049Abstract: 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: ApplicationFiled: September 7, 2022Publication date: June 1, 2023Inventors: Timothy KASSIS, Jefferson PRUYNE, Mark D. SIMON, Mimoun CADOSCH DELMAR AKERMAN, Jennifer CAMPBELL, Ana HENRIQUES DA COSTA, Laura KOLINSKY, John M. GEREMIA
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Publication number: 20230133615Abstract: 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: ApplicationFiled: September 7, 2022Publication date: May 4, 2023Inventors: Timothy KASSIS, Jefferson PRUYNE, Mark D. SIMON, Mimoun CADOSCH DELMAR AKERMAN, Jennifer CAMPBELL, Ana HENRIQUES DA COSTA, Laura KOLINSKY, John M. GEREMIA
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Publication number: 20230137741Abstract: 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: ApplicationFiled: September 7, 2022Publication date: May 4, 2023Inventors: Timothy KASSIS, Jefferson PRUYNE, Mark D. SIMON, Mimoun CADOSCH DELMAR AKERMAN, Jennifer CAMPBELL, Ana HENRIQUES DA COSTA, Laura KOLINSKY, John M. GEREMIA
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Publication number: 20200362339Abstract: 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: ApplicationFiled: September 25, 2018Publication date: November 19, 2020Inventors: Kyle A. Totaro, Mark D. Simon, Ming Zhou, Hong Zong, Gunnar J. Hanson, Bradley L. Pentelute