Patents by Inventor Jonathan GRYAK
Jonathan GRYAK 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: 20230394340Abstract: A method for denoising magnetic resonance images and data is disclosed herein. An example method includes receiving a series of MRF images from a scanning device; identifying one or more subsets of voxels for the series of MRF images; generating one or more sets of eigenvectors, each set of the one or more sets of eigenvectors corresponding to one of the one or more subsets of voxels, and each eigenvector of the one or more sets of eigenvectors having a corresponding eigenvalue; applying a noise distribution model to each of the eigenvalues; identifying a subset of the eigenvalues as corresponding to noise based on the noise distribution model; and reconstructing the series of MRF images without the subset of eigenvalues identified as corresponding to noise.Type: ApplicationFiled: May 30, 2023Publication date: December 7, 2023Inventors: Kayvan Najarian, Justin Zhang, Keith D. Aaronson, Jessica R. Golbus, Jonathan Gryak, Harm Derksen, Heming Yao
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Publication number: 20230350973Abstract: Methods and systems for identifying and classifying multilinear data sets into a plurality of classes using invariant theory are disclosed herein. An example method includes receiving an input data set; computing a change of coordinates for each mode of the plurality of modes for the input data set using an invariant theory optimization algorithm by (i) constructing a chosen group and (ii) determining a group element in the chosen group; transforming the input data set into a relocated data set by applying each change of coordinates for each respective mode of the plurality of modes for the input data set by multiplying the subset of the input data set for each mode by the at least one matrix corresponding to each respective mode; and classifying, based on distances between coordinates in the relocated data set, the input data set into the plurality of classes.Type: ApplicationFiled: April 26, 2023Publication date: November 2, 2023Inventors: Kayvan Najarian, Olivia Pifer Alge, Jonathan Gryak, Harm Derksen, Cristian Minoccheri
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Patent number: 11615527Abstract: A system for automatically analyzing a video recording of a colonoscopy includes a processor and memory storing instructions, which when executed by the processor, cause the processor to receive the video recording of the colonoscopy performed on the colon and detect informative frames in the video recording. A frame is informative if the clarity of the frame is above a threshold or if the frame includes clinically relevant information about the colon. The instructions cause the processor to generate scores indicating severity levels of a disease for a plurality of the informative frames, estimate locations of the plurality of the informative frames in the colon, and generate an output indicating a distribution of the scores over one or more segments of the colon by combining the scores generated for the plurality of the informative frames and the estimated locations of the plurality of the informative frames in the colon.Type: GrantFiled: May 15, 2020Date of Patent: March 28, 2023Assignee: THE REGENTS OF THE UNIVERSITY OF MICHIGANInventors: Kayvan Najarian, Heming Yao, Sayedmohammadreza Soroushmehr, Jonathan Gryak, Ryan W. Stidham
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Patent number: 11531851Abstract: Computational algorithms integrate and analyze data to consider multiple interdependent, heterogeneous sources and forms of patient data, and using a classification model, provide new learning paradigms, including privileged learning and learning with uncertain clinical data, to determine patient status for conditions such as acute respiratory distress syndrome (ARDS) or non-ARDS.Type: GrantFiled: February 5, 2020Date of Patent: December 20, 2022Assignee: THE REGENTS OF THE UNIVERSITY OF MICHIGANInventors: Kayvan Najarian, Jonathan Gryak, Elyas Sabeti, Joshua Drews
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Publication number: 20210338171Abstract: A method of generating an assessment of medical condition for a patient includes obtaining a patient data tensor indicative of a plurality of tests conducted on the patient, obtaining a set of tensor factors, each tensor factor of the set of tensor factors being indicative of a decomposition of training tensor data for the plurality of tests, the decomposition amplifying low rank structure of the training tensor data, determining a patient tensor factor for the patient based on the obtained patient data tensor and the obtained set of tensor factors, applying the determined patient tensor factor to a classifier such that the determined further tensor factor establishes a feature vector for the patient, the classifier being configured to process the feature vector to generate the assessment, and providing output data indicative of the assessment.Type: ApplicationFiled: February 4, 2021Publication date: November 4, 2021Inventors: Hendrikus Derksen, Neriman Tokcan, Kayvan Najarian, Jonathan Gryak
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Patent number: 11154254Abstract: Systems and methods for predicting and/or detecting cardiac events based on real-time biomedical signals are discussed herein. In various embodiments, a machine learning algorithm may be utilized to predict and/or detect one or more medical conditions based on obtained biomedical signals. For example, the systems and methods described herein may utilize ECG signals to predict and detect cardiac events. In various embodiments, patterns identified within a signal may be assigned letters (i.e., encoded as distributions of letters). Based on the known morphology of a signal, states within the signal may be identified based on the distribution of letters in the signal. When applied in the in-vehicle environment, drivers or passengers within the vehicle may be alerted when an individual within the vehicle is, or is about to, experience a cardiac event.Type: GrantFiled: July 22, 2020Date of Patent: October 26, 2021Assignees: TOYOTA MOTOR ENGINEERING & MANUFACTURING NORTH AMERICA, INC., THE REGENTS OF THE UNIVERSITY OF MICHIGANInventors: Kayvan Najarian, Hendrikus Derksen, Zhi Li, Jonathan Gryak, Pujitha Gunaratne
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Patent number: 10936913Abstract: An automated pruning technique is proposed for reducing the size of a convolutional neural network. A large-sized network is trained and then connections between layers are explored to remove redundant parameters. Specifically, a scaling neural subnetwork is connected to the neural network and designed to infer importance of the filters in the neural network during training of the neural network. Output from the scaling neural subnetwork can then be used to remove filters from the neural network, thereby reducing the size of the convolutional neural network.Type: GrantFiled: March 19, 2019Date of Patent: March 2, 2021Assignees: THE REGENTS OF THE UNIVERSITY OF MICHIGAN, DENSO International America, Inc.Inventors: Heming Yao, Kayvan Najarian, Jonathan Gryak, Wei Zhang
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Publication number: 20210022631Abstract: A method of determining a diameter of a sheath of an optic nerve includes obtaining, by a processor, scan data representative of the optic nerve sheath, analyzing, by the processor, the scan data to find a position of a globe-optic nerve interface point, segmenting, by the processor, the scan data, processing, by the processor, the segmented scan data at an offset from the position of the globe-optic nerve interface point to determine boundary positions of the optic nerve sheath, and calculating, by the processor, the diameter of the optic nerve sheath based on the determined boundary positions.Type: ApplicationFiled: August 28, 2019Publication date: January 28, 2021Inventors: Sayedmohammadreza Soroushmehr, Kayvan Najarian, Venkatakrishna Rajajee, Kevin Ward, Jonathan Gryak, Craig A. Williamson, Mohamad H. Tiba
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Publication number: 20200364859Abstract: A system for automatically analyzing a video recording of a colonoscopy includes a processor and memory storing instructions, which when executed by the processor, cause the processor to receive the video recording of the colonoscopy performed on the colon and detect informative frames in the video recording. A frame is informative if the clarity of the frame is above a threshold or if the frame includes clinically relevant information about the colon. The instructions cause the processor to generate scores indicating severity levels of a disease for a plurality of the informative frames, estimate locations of the plurality of the informative frames in the colon, and generate an output indicating a distribution of the scores over one or more segments of the colon by combining the scores generated for the plurality of the informative frames and the estimated locations of the plurality of the informative frames in the colon.Type: ApplicationFiled: May 15, 2020Publication date: November 19, 2020Inventors: Kayvan NAJARIAN, Heming YAO, Sayedmohammadreza SOROUSHMEHR, Jonathan GRYAK, Ryan W. STIDHAM
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Publication number: 20200345313Abstract: Systems and methods for predicting and/or detecting cardiac events based on real-time biomedical signals are discussed herein. In various embodiments, a machine learning algorithm may be utilized to predict and/or detect one or more medical conditions based on obtained biomedical signals. For example, the systems and methods described herein may utilize ECG signals to predict and detect cardiac events. In various embodiments, patterns identified within a signal may be assigned letters (i.e., encoded as distributions of letters). Based on the known morphology of a signal, states within the signal may be identified based on the distribution of letters in the signal. When applied in the in-vehicle environment, drivers or passengers within the vehicle may be alerted when an individual within the vehicle is, or is about to, experience a cardiac event.Type: ApplicationFiled: July 22, 2020Publication date: November 5, 2020Inventors: KAYVAN NAJARIAN, HENDRIKUS DERKSEN, ZHI LI, JONATHAN GRYAK, PUJITHA GUNARATNE
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Patent number: 10786208Abstract: Systems and methods for predicting and/or detecting cardiac events based on real-time biomedical signals are discussed herein. In various embodiments, a machine learning algorithm may be utilized to predict and/or detect one or more medical conditions based on obtained biomedical signals. For example, the systems and methods described herein may utilize ECG signals to predict and detect cardiac events. In various embodiments, patterns identified within a signal may be assigned letters (i.e., encoded as distributions of letters). Based on the known morphology of a signal, states within the signal may be identified based on the distribution of letters in the signal. When applied in the in-vehicle environment, drivers or passengers within the vehicle may be alerted when an individual within the vehicle is, or is about to, experience a cardiac event.Type: GrantFiled: September 20, 2019Date of Patent: September 29, 2020Assignees: TOYOTA MOTOR ENGINEERING & MANUFACTURING NORTH AMERICA, INC., THE REGENTS OF THE UNIVERSITY OF MICHIGANInventors: Kayvan Najarian, Hendrikus Derksen, Zhi Li, Jonathan Gryak, Pujitha Gunaratne
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Publication number: 20200250496Abstract: Computational algorithms integrate and analyze data to consider multiple interdependent, heterogeneous sources and forms of patient data, and using a classification model, provide new learning paradigms, including privileged learning and learning with uncertain clinical data, to determine patient status for conditions such as acute respiratory distress syndrome (ARDS) or non-ARDS.Type: ApplicationFiled: February 5, 2020Publication date: August 6, 2020Inventors: Kayvan Najarian, Jonathan Gryak, Elyas Sabeti, Joshua Drews
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Publication number: 20200022658Abstract: Systems and methods for predicting and/or detecting cardiac events based on real-time biomedical signals are discussed herein. In various embodiments, a machine learning algorithm may be utilized to predict and/or detect one or more medical conditions based on obtained biomedical signals. For example, the systems and methods described herein may utilize ECG signals to predict and detect cardiac events. In various embodiments, patterns identified within a signal may be assigned letters (i.e., encoded as distributions of letters). Based on the known morphology of a signal, states within the signal may be identified based on the distribution of letters in the signal. When applied in the in-vehicle environment, drivers or passengers within the vehicle may be alerted when an individual within the vehicle is, or is about to, experience a cardiac event.Type: ApplicationFiled: September 20, 2019Publication date: January 23, 2020Inventors: Kayvan Najarian, Hendrikus Derksen, Zhi Li, Jonathan Gryak, Pujitha Gunaratne
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Patent number: 10463314Abstract: Systems and methods for predicting and/or detecting cardiac events based on real-time biomedical signals are discussed herein. In various embodiments, a machine learning algorithm may be utilized to predict and/or detect one or more medical conditions based on obtained biomedical signals. For example, the systems and methods described herein may utilize ECG signals to predict and detect cardiac events. In various embodiments, patterns identified within a signal may be assigned letters (i.e., encoded as distributions of letters). Based on the known morphology of a signal, states within the signal may be identified based on the distribution of letters in the signal. When applied in the in-vehicle environment, drivers or passengers within the vehicle may be alerted when an individual within the vehicle is, or is about to, experience a cardiac event.Type: GrantFiled: July 19, 2018Date of Patent: November 5, 2019Assignees: TOYOTA MOTOR ENGINEERING & MANUFACTURING NORTH AMERICA, INC., THE REGENTS OF THE UNIVERSITY OF MICHIGANInventors: Kayvan Najarian, Hendrikus Derksen, Zhi Li, Jonathan Gryak, Pujitha Gunaratne
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Publication number: 20190294929Abstract: An automated pruning technique is proposed for reducing the size of a convolutional neural network. A large-sized network is trained and then connections between layers are explored to remove redundant parameters. Specifically, a scaling neural subnetwork is connected to the neural network and designed to infer importance of the filters in the neural network during training of the neural network. Output from the scaling neural subnetwork can then be used to remove filters from the neural network, thereby reducing the size of the convolutional neural network.Type: ApplicationFiled: March 19, 2019Publication date: September 26, 2019Inventors: Heming YAO, Kayvan NAJARIAN, Jonathan GRYAK, Wei ZHANG