Patents by Inventor Rahul Katdare
Rahul Katdare 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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Patent number: 11885732Abstract: A classification training method for training classifiers adapted to identify specific mutations associated with different cancer including identifying driver mutations. First cells from mutation cell lines derived from conditions having the number of driver mutations are acquired and 3D image feature data from the number of first cells is identified. 3D cell imaging data from the number of first cells and from other malignant cells is generated, where cell imaging data includes a number of first individual cell images. A second set of 3D cell imaging data is generated from a set of normal cells where the number of driver mutations are expected to occur, where the second set of cell imaging data includes second individual cell images. Supervised learning is conducted based on cell line status as ground truth to generate a classifier.Type: GrantFiled: October 18, 2022Date of Patent: January 30, 2024Assignee: VisionGate, Inc.Inventors: Michael G. Meyer, Daniel J. Sussman, Rahul Katdare, Laimonas Kelbauskas, Alan C. Nelson, Randall Mastrangelo
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Publication number: 20230289407Abstract: A method for a system and method for morphometric detection of malignancy associated change (MAC) is disclosed including the acts of obtaining a sample; imaging cells to produce 3D cell images for each cell; measuring a plurality of different structural biosignatures for each cell from its 3D cell image to produce feature data; analyzing the feature data by first using cancer case status as ground truth to supervise development of a classifier to test the degree to which the features discriminate between cells from normal or cancer patients; using the analyzed feature data to develop classifiers including, a first classifier to discriminate normal squamous cells from normal and cancer patients, a second classifier to discriminate normal macrophages from normal and cancer patients, and a third classifier to discriminate normal bronchial columnar cells from normal and cancer patients.Type: ApplicationFiled: December 12, 2022Publication date: September 14, 2023Inventors: Michael G. Meyer, Laimonas Kelbauskas, Rahul Katdare, Daniel J. Sussman, Timothy Bell, Alan C. Nelson
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Publication number: 20230050322Abstract: A classification training method for training classifiers adapted to identify specific mutations associated with different cancer including identifying driver mutations. First cells from mutation cell lines derived from conditions having the number of driver mutations are acquired and 3D image feature data from the number of first cells is identified. 3D cell imaging data from the number of first cells and from other malignant cells is generated, where cell imaging data includes a number of first individual cell images. A second set of 3D cell imaging data is generated from a set of normal cells where the number of driver mutations are expected to occur, where the second set of cell imaging data includes second individual cell images. Supervised learning is conducted based on cell line status as ground truth to generate a classifier.Type: ApplicationFiled: October 18, 2022Publication date: February 16, 2023Inventors: Michael G. Meyer, Daniel J. Sussman, Rahul Katdare, Laimonis Kelbauskas, Alan C. Nelson, Randall Mastrangelo
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Patent number: 11551043Abstract: A method for a system and method for morphometric detection of malignancy associated change (MAC) is disclosed including the acts of obtaining a sample; imaging cells to produce 3D cell images for each cell; measuring a plurality of different structural biosignatures for each cell from its 3D cell image to produce feature data; analyzing the feature data by first using cancer case status as ground truth to supervise development of a classifier to test the degree to which the features discriminate between cells from normal or cancer patients; using the analyzed feature data to develop classifiers including, a first classifier to discriminate normal squamous cells from normal and cancer patients, a second classifier to discriminate normal macrophages from normal and cancer patients, and a third classifier to discriminate normal bronchial columnar cells from normal and cancer patients.Type: GrantFiled: February 28, 2019Date of Patent: January 10, 2023Assignee: VISIONGATE, INC.Inventors: Michael G. Meyer, Laimonas Kelbauskas, Rahul Katdare, Daniel J. Sussman, Timothy Bell, Alan C. Nelson
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Patent number: 11545237Abstract: A classification training method for training classifiers adapted to identify specific mutations associated with different cancer including identifying driver mutations. First cells from mutation cell lines derived from conditions having the number of driver mutations are acquired and 3D image feature data from the number of first cells is identified. 3D cell imaging data from the number of first cells and from other malignant cells is generated, where cell imaging data includes a number of first individual cell images. A second set of 3D cell imaging data is generated from a set of normal cells where the number of driver mutations are expected to occur, where the second set of cell imaging data includes second individual cell images. Supervised learning is conducted based on cell line status as ground truth to generate a classifier.Type: GrantFiled: September 26, 2018Date of Patent: January 3, 2023Assignee: VISIONGATE, INC.Inventors: Michael G. Meyer, Daniel J. Sussman, Rahul Katdare, Laimonas Kelbauskas, Alan C. Nelson, Randall Mastrangelo
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Publication number: 20210210169Abstract: A classification training method for training classifiers adapted to identify specific mutations associated with different cancer including identifying driver mutations. First cells from mutation cell lines derived from conditions having the number of driver mutations are acquired and 3D image feature data from the number of first cells is identified. 3D cell imaging data from the number of first cells and from other malignant cells is generated, where cell imaging data includes a number of first individual cell images. A second set of 3D cell imaging data is generated from a set of normal cells where the number of driver mutations are expected to occur, where the second set of cell imaging data includes second individual cell images. Supervised learning is conducted based on cell line status as ground truth to generate a classifier.Type: ApplicationFiled: September 26, 2018Publication date: July 8, 2021Applicant: VISIONGATE, INC.Inventors: Michael G. MEYER, Daniel J. SUSSMAN, Rahul KATDARE, Laimonis KELBAUSKAS, Alan C. NELSON, Randall MASTRANGELO
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Publication number: 20210049425Abstract: A method for a system and method for morphometric detection of malignancy associated change (MAC) is disclosed including the acts of obtaining a sample; imaging cells to produce 3D cell images for each cell; measuring a plurality of different structural biosignatures for each cell from its 3D cell image to produce feature data; analyzing the feature data by first using cancer case status as ground truth to supervise development of a classifier to test the degree to which the features discriminate between cells from normal or cancer patients; using the analyzed feature data to develop classifiers including, a first classifier to discriminate normal squamous cells from normal and cancer patients, a second classifier to discriminate normal macrophages from normal and cancer patients, and a third classifier to discriminate normal bronchial columnar cells from normal and cancer patients.Type: ApplicationFiled: February 28, 2019Publication date: February 18, 2021Applicant: VISIONGATE, INC.Inventors: Michael G. Meyer, Laimonis Kelbauskas, Rahul Katdare, Daniel J. Sussman, Timothy Bell, Alan C. Nelson
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Patent number: 9594072Abstract: A cytological analysis test for 3D cell classification from a specimen. The method includes isolating and preserving a cell from the specimen and enriching the cell before embedding the enriched cell into an optical medium. The embedded cell is injected into a capillary tube where pressure is applied until the cell appears in a field of view of a pseudo-projection viewing subsystem to acquire a pseudo-projection image. The capillary tube rotates about a tube axis to provide a set of pseudo-projection images for each embedded cell which are reconstructed to produce a set of 3D cell reconstructions. Reference cells are classified and enumerated and a second cell classifier detects target cells. An adequacy classifier compares the number of reference cells against a threshold value of enumerated reference cells to determine specimen adequacy.Type: GrantFiled: June 30, 2015Date of Patent: March 14, 2017Assignee: VISIONGATE, INC.Inventors: Michael G. Meyer, Rahul Katdare, Chris Presley, Timothy Bell
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Publication number: 20170003267Abstract: A cytological analysis test for 3D cell classification from a specimen. The method includes isolating and preserving a cell from the specimen and enriching the cell before embedding the enriched cell into an optical medium. The embedded cell is injected into a capillary tube where pressure is applied until the cell appears in a field of view of a pseudo-projection viewing subsystem to acquire a pseudo-projection image. The capillary tube rotates about a tube axis to provide a set of pseudo-projection images for each embedded cell which are reconstructed to produce a set of 3D cell reconstructions. Reference cells are classified and enumerated and a second cell classifier detects target cells. An adequacy classifier compares the number of reference cells against a threshold value of enumerated reference cells to determine specimen adequacy.Type: ApplicationFiled: June 30, 2015Publication date: January 5, 2017Applicant: VISIONGATE, INC.Inventors: Michael G. Meyer, Rahul Katdare, Chris Presley, Timothy Bell
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Patent number: 8155420Abstract: A system and method for detecting poor quality images in an optical tomography system includes an acquisition apparatus for acquiring a set of pseudo-projection images of an object having a center of mass, where each of the set of pseudo-projection images is acquired at a different angle of view. A reconstruction apparatus is coupled to receive the pseudo-projection images, for reconstruction of the pseudo-projection images into 3D reconstruction images. A quality apparatus is coupled to receive the 3D reconstruction images and operates to detect of selected features that characterize poor quality reconstructions.Type: GrantFiled: May 21, 2009Date of Patent: April 10, 2012Assignee: Visiongate, IncInventors: Michael G. Meyer, Rahul Katdare, David Ethan Steinhauer, J. Richard Rahn
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Publication number: 20100296713Abstract: A system and method for detecting poor quality images in an optical tomography system includes an acquisition apparatus for acquiring a set of pseudo-projection images of an object having a center of mass, where each of the set of pseudo-projection images is acquired at a different angle of view. A reconstruction apparatus is coupled to receive the pseudo-projection images, for reconstruction of the pseudo-projection images into 3D reconstruction images. A quality apparatus is coupled to receive the 3D reconstruction images and operates to detect of selected features that characterize poor quality reconstructions.Type: ApplicationFiled: May 21, 2009Publication date: November 25, 2010Applicant: VISIONGATE, INC.Inventors: Michael G. Meyer, Rahul Katdare, David E. Steinhauer, J. Richard Rahn