Patents by Inventor Frank Losasso Petterson
Frank Losasso Petterson 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: 20240099593Abstract: Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's low-fidelity health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator (“other-factors”) to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.Type: ApplicationFiled: December 1, 2023Publication date: March 28, 2024Inventors: Alexander Vainius Valys, Frank Losasso Petterson, Conner Daniel Cross Galloway, David E. Albert, Ravi Gopalakrishnan, Lev Korzinov, Fei Wang, Euan Thomson, Nupur Srivastava, Omar Dawood, Iman Abuzeid
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Patent number: 11915825Abstract: Disclosed systems include an electrocardiogram sensor and a processing device operatively coupled to the electrocardiogram sensor. The processing device receives electrocardiogram data from the electrocardiogram sensor and applies a machine learning model to the received electrocardiogram data. The machine learning model has been trained based on previous electrocardiogram data of a plurality of subjects. The electrocardiogram data of the plurality of subjects have one or more associated analyte measurements. The processing device may determine an indication of a level of the analyte based on the electrocardiogram data.Type: GrantFiled: February 12, 2018Date of Patent: February 27, 2024Assignee: AliveCor, Inc.Inventors: Conner Daniel Cross Galloway, Alexander Vainius Valys, Frank Losasso Petterson, Daniel Treiman
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Patent number: 11877830Abstract: Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's low-fidelity health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator (“other-factors”) to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.Type: GrantFiled: September 24, 2019Date of Patent: January 23, 2024Assignee: ALIVECOR, INC.Inventors: Alexander Vainius Valys, Frank Losasso Petterson, Conner Daniel Cross Galloway, David E. Albert, Ravi Gopalakrishnan, Lev Korzinov, Fei Wang, Euan Thomson, Nupur Srivastava, Omar Dawood, Iman Abuzeid
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Publication number: 20230131876Abstract: A set of training electrocardiograms (ECGs) for each of a plurality of subjects is processed using a machine learning model to generate an output for each training ECG of each of the plurality of subjects. Training ECGs for each subject are labeled with an identity of the subject. A machine learning model is trained by comparing the output generated for each training ECG to a corresponding label of the training ECG to generate an identity model to identify ECGs of a first subject of the plurality of subjects. A first ECG is received from an ECG sensor and input to the identity model, which generates an output indicating whether the first ECG corresponds to the first subject. In response to the output indicating that the first ECG does not correspond to the first subject, a condition that the first subject has or may develop is determined based on the output.Type: ApplicationFiled: December 20, 2022Publication date: April 27, 2023Inventors: Conner Daniel Galloway, Alexander Vainius Valys, David E. Albert, Frank Losasso Petterson
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Patent number: 11562222Abstract: Disclosed systems include an electrocardiogram sensor configured to sense electrocardiograms of a subject and a processing device operatively coupled to the electrocardiogram sensor. The processing device receives an electrocardiogram from the electrocardiogram sensor. The electrocardiogram is input into a machine learning model, the machine learning model to generate an output based on the received electrocardiogram. The processing device determines based on the electrocardiogram, that the output does not match an expected range of outputs for the target subject and generates an alert indicating a possible change in a status of the subject in response to the output not matching the expected range of outputs for the target subject.Type: GrantFiled: March 7, 2018Date of Patent: January 24, 2023Assignee: AliveCor, Inc.Inventors: Conner Daniel Galloway, Alexander Vainius Valys, David E. Albert, Frank Losasso Petterson
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Publication number: 20210345972Abstract: Disclosed are systems for non-invasively determining a measurement of an analyte. The systems include an electrocardiogram sensor and a processing device operatively coupled to the electrocardiogram sensor. The processing device can execute instructions to receive electrocardiogram data from the electrocardiogram sensor and apply a machine learning model, wherein the machine learning model has been trained based on previous electrocardiogram data associated with a subject and source of an analyte measurement associated with the subject. The system may also determine an indication of a level of the analyte based on the electrocardiogram data.Type: ApplicationFiled: July 21, 2021Publication date: November 11, 2021Inventors: Conner Daniel Cross Galloway, Alexander Vainius Valys, David E. Albert, Frank Losasso Petterson
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Patent number: 11103194Abstract: Disclosed are systems for non-invasively determining a measurement of an analyte. The systems include an electrocardiogram sensor and a processing device operatively coupled to the electrocardiogram sensor. The processing device can execute instructions to receive electrocardiogram data from the electrocardiogram sensor and apply a machine learning model, wherein the machine learning model has been trained based on previous electrocardiogram data associated with a subject and source of an analyte measurement associated with the subject. The system may also determine an indication of a level of the analyte based on the electrocardiogram data.Type: GrantFiled: December 14, 2017Date of Patent: August 31, 2021Assignee: AliveCor, Inc.Inventors: Conner Daniel Cross Galloway, Alexander Vainius Valys, David E. Albert, Frank Losasso Petterson
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Publication number: 20200281485Abstract: Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's low-fidelity health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator (“other-factors”) to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.Type: ApplicationFiled: September 24, 2019Publication date: September 10, 2020Inventors: Alexander Vainius Valys, Frank Losasso Petterson, Conner Daniel Cross Galloway, David E. Albert, Ravi Gopalakrishnan, Lev Korzinov, Fei Wang, Euan Thomson, Nupur Srivastava, Omar Dawood, Iman Abuzeid
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Publication number: 20200107733Abstract: Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's low-fidelity health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator (“other-factors”) to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.Type: ApplicationFiled: September 24, 2019Publication date: April 9, 2020Inventors: Alexander Vainius Valys, Frank Losasso Petterson, Conner Daniel Cross Galloway, David E. Albert, Ravi Gopalakrishnan, Lev Korzinov, Fei Wang, Euan Thomson, Nupur Srivastava, Omar Dawood, Iman Abuzeid
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Patent number: 10561321Abstract: Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator (“other-factors”) to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.Type: GrantFiled: October 5, 2018Date of Patent: February 18, 2020Assignee: AliveCor, Inc.Inventors: Alexander Vainius Valys, Frank Losasso Petterson, Conner Daniel Cross Galloway, David E. Albert, Ravi Gopalakrishnan, Lev Korzinov, Fei Wang, Euan Thomson, Nupur Srivastava, Omar Dawood, Iman Abuzeid
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Publication number: 20190104951Abstract: Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator (“other-factors”) to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.Type: ApplicationFiled: October 5, 2018Publication date: April 11, 2019Inventors: Alexander Vainius Valys, Frank Losasso Petterson, Conner Daniel Cross Galloway, David E. Albert, Ravi Gopalakrishnan, Lev Korzinov, Fei Wang, Euan Thomson, Nupur Srivastava, Omar Dawood, Iman Abuzeid
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Publication number: 20190076031Abstract: Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator (“other-factors”) to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.Type: ApplicationFiled: November 9, 2018Publication date: March 14, 2019Inventors: Alexander Vainius Valys, Frank Losasso Petterson, Conner Daniel Cross Galloway, David E. Albert, Ravi Gopalakrishnan, Lev Korzinov, Fei Wang, Euan Thomson, Nupur Srivastava, Omar Dawood, Iman Abuzeid
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Publication number: 20190038148Abstract: Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator (“other-factors”) to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.Type: ApplicationFiled: October 5, 2018Publication date: February 7, 2019Inventors: Alexander Vainius Valys, Frank Losasso Petterson, Conner Daniel Cross Galloway, David E. Albert, Ravi Gopalakrishnan, Lev Korzinov, Fei Wang, Euan Thomson, Nupur Srivastava, Omar Dawood, Iman Abuzeid
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Publication number: 20180260706Abstract: Disclosed systems include an electrocardiogram sensor configured to sense electrocardiograms of a subject and a processing device operatively coupled to the electrocardiogram sensor. The processing device receives an electrocardiogram from the electrocardiogram sensor. The electrocardiogram is input into a machine learning model, the machine learning model to generate an output based on the received electrocardiogram. The processing device determines based on the electrocardiogram, that the output does not match an expected range of outputs for the target subject and generates an alert indicating a possible change in a status of the subject in response to the output not matching the expected range of outputs for the target subject.Type: ApplicationFiled: March 7, 2018Publication date: September 13, 2018Inventors: Conner Daniel Galloway, Alexander Vainius Valys, David E. Albert, Frank Losasso Petterson
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Publication number: 20180233227Abstract: Disclosed systems include an electrocardiogram sensor and a processing device operatively coupled to the electrocardiogram sensor. The processing device receives electrocardiogram data from the electrocardiogram sensor and applies a machine learning model to the received electrocardiogram data. The machine learning model has been trained based on previous electrocardiogram data of a plurality of subjects. The electrocardiogram data of the plurality of subjects have one or more associated analyte measurements. The processing device may determine an indication of a level of the analyte based on the electrocardiogram data.Type: ApplicationFiled: February 12, 2018Publication date: August 16, 2018Inventors: Conner Daniel Cross Galloway, Alexander Vainius Valys, Frank Losasso Petterson, Daniel Treiman
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Publication number: 20180160983Abstract: Disclosed are systems for non-invasively determining a measurement of an analyte. The systems include an electrocardiogram sensor and a processing device operatively coupled to the electrocardiogram sensor. The processing device can execute instructions to receive electrocardiogram data from the electrocardiogram sensor and apply a machine learning model, wherein the machine learning model has been trained based on previous electrocardiogram data associated with a subject and source of an analyte measurement associated with the subject. The system may also determine an indication of a level of the analyte based on the electrocardiogram data.Type: ApplicationFiled: December 14, 2017Publication date: June 14, 2018Inventors: Conner Daniel Cross Galloway, Alexander Vainius Valys, David E. Albert, Frank Losasso Petterson
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Patent number: 8970592Abstract: A system includes a computing device that includes a memory configured to store instructions. The computing device also includes a processor configured to execute the instructions to perform a method that includes obtaining first data corresponding to a first simulation of matter in a space domain. The method also includes performing, using the first data, a second simulation that produces second data representative of particles in the space domain. The method also includes rasterizing the second data representative of the particles as defined by cells of a grid, wherein each cell has a common depth-to-size ratio, and, rendering an image of the particles from the rasterized second data.Type: GrantFiled: April 19, 2011Date of Patent: March 3, 2015Assignee: Lucasfilm Entertainment Company LLCInventor: Frank Losasso Petterson
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Patent number: 8725476Abstract: A computer-implemented method for applying details in a simulation includes obtaining first data corresponding to a first simulation of matter in a space domain. The method includes performing, using the first data, a second simulation of the matter producing second data representing details for the first simulation, the second data distributed in the space domain using a grid where each cell has a common depth-to-size ratio from a camera perspective. The method includes rendering an image of the matter, wherein the second data is obtained from the grid and used in the rendering.Type: GrantFiled: May 4, 2010Date of Patent: May 13, 2014Assignee: Lucasfilm Entertainment Company Ltd.Inventor: Frank Losasso Petterson
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Publication number: 20050253843Abstract: Plural levels of detail of a terrain are stored in memory in regular grids. In one such example, a terrain is cached in a set of nested regular grids obtained from the plural levels as a function of distance from a viewpoint. In one such example, the plural levels of detail of terrain comprise terrain elevation and texture images. If the viewpoint moves relative to the terrain, the nested regular grids are incrementally refilled relative to the viewpoints movement in the terrain. In one such example, a transition region is introduced to help blend between grid levels. The regular grids are stored as vertex buffers in video memory in one example. In one such example, a vertex data includes an elevation values from another grid level for efficient grid level boundary blending.Type: ApplicationFiled: May 14, 2004Publication date: November 17, 2005Applicant: Microsoft CorporationInventors: Frank Losasso Petterson, Hugues Hoppe
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Patent number: D920990Type: GrantFiled: November 21, 2017Date of Patent: June 1, 2021Assignee: AliveCor, Inc.Inventors: Melissa McClean, Alexander Vainius Valys, Conner Daniel Cross Galloway, Vivek P. Gundotra, Frank Losasso Petterson