Patents by Inventor Jeevaa VELAYUTHAM
Jeevaa VELAYUTHAM 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).
-
Patent number: 11978206Abstract: The present disclosure relates generally to an autonomous cell imaging and modeling platform, and more specifically to machine-learning techniques for using microscopy imaging data to continuously study live biological cells. The autonomous cell imaging and modeling platform can be applied to evaluate various cellular processes, such as cellular differentiation, optimization of cell culture (e.g., in-plate cytometry), disease modeling, histopathology imaging, and genetic and chemical screening, using a dynamic universal imaging system. In some embodiments, the platform comprises a set of label-free computational imaging techniques, self-supervised learning models, and robotic devices configured in an autonomous imaging system to study positional and morphological characteristics in particular cellular substructures of a cell culture in an efficient and non-destructive manner over time.Type: GrantFiled: December 1, 2023Date of Patent: May 7, 2024Assignee: Insitro, Inc.Inventors: Hervé Marie-Nelly, Jeevaa Velayutham, Zachary Phillips, Shengjiang Tu
-
Publication number: 20240119593Abstract: The present disclosure relates generally to an autonomous cell imaging and modeling platform, and more specifically to machine-learning techniques for using microscopy imaging data to continuously study live biological cells. The autonomous cell imaging and modeling platform can be applied to evaluate various cellular processes, such as cellular differentiation, optimization of cell culture (e.g., in-plate cytometry), disease modeling, histopathology imaging, and genetic and chemical screening, using a dynamic universal imaging system. In some embodiments, the platform comprises a set of label-free computational imaging techniques, self-supervised learning models, and robotic devices configured in an autonomous imaging system to study positional and morphological characteristics in particular cellular substructures of a cell culture in an efficient and non-destructive manner over time.Type: ApplicationFiled: December 1, 2023Publication date: April 11, 2024Applicant: Insitro, Inc.Inventors: Hervé MARIE-NELLY, Jeevaa VELAYUTHAM, Zachary PHILLIPS, Shengjiang TU
-
Publication number: 20240104734Abstract: The present disclosure relates generally to an autonomous cell imaging and modeling platform, and more specifically to machine-learning techniques for using microscopy imaging data to continuously study live biological cells. The autonomous cell imaging and modeling platform can be applied to evaluate various cellular processes, such as cellular differentiation, optimization of cell culture (e.g., in-plate cytometry), disease modeling, histopathology imaging, and genetic and chemical screening, using a dynamic universal imaging system. In some embodiments, the platform comprises a set of label-free computational imaging techniques, self-supervised learning models, and robotic devices configured in an autonomous imaging system to study positional and morphological characteristics in particular cellular substructures of a cell culture in an efficient and non-destructive manner over time.Type: ApplicationFiled: December 1, 2023Publication date: March 28, 2024Applicant: Insitro, Inc.Inventors: Hervé MARIE-NELLY, Jeevaa VELAYUTHAM, Zachary PHILLIPS, Shengjiang TU
-
Patent number: 11875506Abstract: The present disclosure relates generally to an autonomous cell imaging and modeling platform, and more specifically to machine-learning techniques for using microscopy imaging data to continuously study live biological cells. The autonomous cell imaging and modeling platform can be applied to evaluate various cellular processes, such as cellular differentiation, optimization of cell culture (e.g., in-plate cytometry), disease modeling, histopathology imaging, and genetic and chemical screening, using a dynamic universal imaging system. In some embodiments, the platform comprises a set of label-free computational imaging techniques, self-supervised learning models, and robotic devices configured in an autonomous imaging system to study positional and morphological characteristics in particular cellular substructures of a cell culture in an efficient and non-destructive manner over time.Type: GrantFiled: February 17, 2023Date of Patent: January 16, 2024Assignee: Insitro, Inc.Inventors: Hervé Marie-Nelly, Jeevaa Velayutham, Zachary Phillips, Shengjiang Tu
-
Publication number: 20220358331Abstract: Described are systems and methods for training a machine-learning model to generate image of biological samples, and systems and methods for generating enhanced images of biological samples. The method for training a machine-learning model to generate images of biological samples may include obtaining a plurality of training images comprising a training image of a first type, and a training image of a second type. The method may also include generating, based on the training image of the first type, a plurality of wavelet coefficients using the machine-learning model; generating, based on the plurality of wavelet coefficients, a synthetic image of the second type; comparing the synthetic image of the second type with the training image of the second type; and updating the machine-learning model based on the comparison.Type: ApplicationFiled: July 18, 2022Publication date: November 10, 2022Applicant: Insitro, Inc.Inventors: Herve MARIE-NELLY, Jeevaa VELAYUTHAM
-
Patent number: 11423256Abstract: Described are systems and methods for training a machine-learning model to generate image of biological samples, and systems and methods for generating enhanced images of biological samples. The method for training a machine-learning model to generate images of biological samples may include obtaining a plurality of training images comprising a training image of a first type, and a training image of a second type. The method may also include generating, based on the training image of the first type, a plurality of wavelet coefficients using the machine-learning model; generating, based on the plurality of wavelet coefficients, a synthetic image of the second type; comparing the synthetic image of the second type with the training image of the second type; and updating the machine-learning model based on the comparison.Type: GrantFiled: September 20, 2021Date of Patent: August 23, 2022Assignee: Insitro, Inc.Inventors: Herve Marie-Nelly, Jeevaa Velayutham
-
Publication number: 20220076067Abstract: Described are systems and methods for training a machine-learning model to generate image of biological samples, and systems and methods for generating enhanced images of biological samples. The method for training a machine-learning model to generate images of biological samples may include obtaining a plurality of training images comprising a training image of a first type, and a training image of a second type. The method may also include generating, based on the training image of the first type, a plurality of wavelet coefficients using the machine-learning model; generating, based on the plurality of wavelet coefficients, a synthetic image of the second type; comparing the synthetic image of the second type with the training image of the second type; and updating the machine-learning model based on the comparison.Type: ApplicationFiled: September 20, 2021Publication date: March 10, 2022Applicant: Insitro, Inc.Inventors: Herve MARIE-NELLY, Jeevaa VELAYUTHAM