Patents by Inventor Carl G. SIMON

Carl G. 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).

  • USING MACHINE LEARNING AND/OR NEURAL NETWORKS TO VALIDATE STEM CELLS AND THEIR DERIVATIVES (2-D CELLS AND 3-D TISSUES) FOR USE IN CELL THERAPY AND TISSUE ENGINEERED PRODUCTS
    Publication number: 20250095390
    Abstract: A method is provided for non-invasively predicting characteristics of one or more cells and cell derivatives. The method includes training a machine learning model using at least one of a plurality of training cell images representing a plurality of cells and data identifying characteristics for the plurality of cells. The method further includes receiving at least one test cell image representing at least one test cell being evaluated, the at least one test cell image being acquired noninvasively and based on absorbance as an absolute measure of light, and providing the at least one test cell image to the trained machine learning model. Using machine learning based on the trained machine learning model, characteristics of the at least one test cell are predicted. The method further includes generating, by the trained machine learning model, release criteria for clinical preparations of cells based on the predicted characteristics of the at least one test cell.
    Type: Application
    Filed: May 13, 2024
    Publication date: March 20, 2025
    Applicant: The United States of America,as represented by the Secretary,Department of Health and Human Services
    Inventors: Kapil Bharti, Nathan A. Hotaling, Nicholas J. Schaub, Carl G. Simon
  • Using machine learning and/or neural networks to validate stem cells and their derivatives (2-D cells and 3-D tissues) for use in cell therapy and tissue engineered products
    Patent number: 12020494
    Abstract: A method is provided for non-invasively predicting characteristics of one or more cells and cell derivatives. The method includes training a machine learning model using at least one of a plurality of training cell images representing a plurality of cells and data identifying characteristics for the plurality of cells. The method further includes receiving at least one test cell image representing at least one test cell being evaluated, the at least one test cell image being acquired noninvasively and based on absorbance as an absolute measure of light, and providing the at least one test cell image to the trained machine learning model. Using machine learning based on the trained machine learning model, characteristics of the at least one test cell are predicted. The method further includes generating, by the trained machine learning model, release criteria for clinical preparations of cells based on the predicted characteristics of the at least one test cell.
    Type: Grant
    Filed: November 9, 2022
    Date of Patent: June 25, 2024
    Assignee: The United States of America, as represented by the Secretary, Department of Health and Human Services
    Inventors: Kapil Bharti, Nathan A. Hotaling, Nicholas J. Schaub, Carl G. Simon
  • USING MACHINE LEARNING AND/OR NEURAL NETWORKS TO VALIDATE STEM CELLS AND THEIR DERIVATIVES (2-D CELLS AND 3-D TISSUES) FOR USE IN CELL THERAPY AND TISSUE ENGINEERED PRODUCTS
    Publication number: 20230154215
    Abstract: A method is provided for non-invasively predicting characteristics of one or more cells and cell derivatives. The method includes training a machine learning model using at least one of a plurality of training cell images representing a plurality of cells and data identifying characteristics for the plurality of cells. The method further includes receiving at least one test cell image representing at least one test cell being evaluated, the at least one test cell image being acquired noninvasively and based on absorbance as an absolute measure of light, and providing the at least one test cell image to the trained machine learning model. Using machine learning based on the trained machine learning model, characteristics of the at least one test cell are predicted. The method further includes generating, by the trained machine learning model, release criteria for clinical preparations of cells based on the predicted characteristics of the at least one test cell.
    Type: Application
    Filed: November 9, 2022
    Publication date: May 18, 2023
    Applicant: The United States of America,as represented by the Secretary,Department of Health and Human Services
    Inventors: Kapil Bharti, Nathan A. Hotaling, Nicholas J. Schaub, Carl G. Simon
  • Using machine learning and/or neural networks to validate stem cells and their derivatives (2-D cells and 3-D tissues) for use in cell therapy and tissue engineered products
    Patent number: 11531844
    Abstract: A method is provided for non-invasively predicting characteristics of one or more cells and cell derivatives. The method includes training a machine learning model using at least one of a plurality of training cell images representing a plurality of cells and data identifying characteristics for the plurality of cells. The method further includes receiving at least one test cell image representing at least one test cell being evaluated, the at least one test cell image being acquired non-invasively and based on absorbance as an absolute measure of light, and providing the at least one test cell image to the trained machine learning model. Using machine learning based on the trained machine learning model, characteristics of the at least one test cell are predicted. The method further includes generating, by the trained machine learning model, release criteria for clinical preparations of cells based on the predicted characteristics of the at least one test cell.
    Type: Grant
    Filed: March 15, 2019
    Date of Patent: December 20, 2022
    Assignee: The United States of America, as represented by the Secretary, Department of Health & Human Services
    Inventors: Kapil Bharti, Nathan A. Hotaling, Nicholas J. Schaub, Carl G. Simon
  • USING MACHINE LEARNING AND/OR NEURAL NETWORKS TO VALIDATE STEM CELLS AND THEIR DERIVATIVES (2-D CELLS AND 3-D TISSUES) FOR USE IN CELL THERAPY AND TISSUE ENGINEERED PRODUCTS
    Publication number: 20210117729
    Abstract: A method is provided for non-invasively predicting characteristics of one or more cells and cell derivatives. The method includes training a machine learning model using at least one of a plurality of training cell images representing a plurality of cells and data identifying characteristics for the plurality of cells. The method further includes receiving at least one test cell image representing at least one test cell being evaluated, the at least one test cell image being acquired non-invasively and based on absorbance as an absolute measure of light, and providing the at least one test cell image to the trained machine learning model. Using machine learning based on the trained machine learning model, characteristics of the at least one test cell are predicted. The method further includes generating, by the trained machine learning model, release criteria for clinical preparations of cells based on the predicted characteristics of the at least one test cell.
    Type: Application
    Filed: March 15, 2019
    Publication date: April 22, 2021
    Applicant: The United States of America, as represented by the Secretary, Department of Health & Human Services
    Inventors: Kapil BHARTI, Nathan A. HOTALING, Nicholas J. SCHAUB, Carl G. SIMON
  • BIOLOGICAL SAMPLING PLATFORM AND PROCESSES FOR MAKING AND USING SAME
    Publication number: 20170021149
    Abstract: A biological sampling platform includes a substrate and a first through hole disposed in the substrate to receive a first sample and to provide the first sample to a biological system in response to the biological sampling platform being disposed in the biological system, the substrate being cleavable to provide a discrete layer having a thickness effective for analysis of the discreet layer by transmission microscopy. A process for collecting a biological sample includes disposing a first through hole in a substrate; disposing a first sample in the first through hole to form a biological sampling platform; disposing the biological sampling platform in a biological system; providing the first sample to the biological system in response to the biological sampling platform being disposed in the biological system; and receiving a first biological sample from the biological system in the first through hole to collect the first biological sample.
    Type: Application
    Filed: April 14, 2015
    Publication date: January 26, 2017
    Inventors: Carl G. Simon, JR., Subhadip Bodhak, Luia A. Fernandez de Castro Diaz, Pamela Gehron Robey
  • BIOLOGICAL SAMPLING PLATFORM AND PROCESSES FOR MAKING AND USING SAME
    Publication number: 20160303357
    Abstract: A biological sampling platform includes a substrate and a first through hole disposed in the substrate to receive a first sample and to provide the first sample to a biological system in response to the biological sampling platform being disposed in the biological system, the substrate being cleavable to provide a discrete layer having a thickness effective for analysis of the discreet layer by transmission microscopy. A process for collecting a biological sample includes disposing a first through hole in a substrate; disposing a first sample in the first through hole to form a biological sampling platform; disposing the biological sampling platform in a biological system; providing the first sample to the biological system in response to the biological sampling platform being disposed in the biological system; and receiving a first biological sample from the biological system in the first through hole to collect the first biological sample.
    Type: Application
    Filed: April 14, 2015
    Publication date: October 20, 2016
    Inventors: Carl G. Simon, JR., Subhadip Bodhak, Luia A. Fernandez de Castro Diaz, Pamela Gehron Robey