Patents by Inventor Alex Bronstein
Alex Bronstein 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: 11944444Abstract: A method comprising: at a training stage, training a machine learning algorithm on a training set comprising: (i) Heart Rate Variability (HRV) parameters extracted from temporal beat activity samples, wherein at least some of said samples include a representation of a Ventricular Fibrillation (VF) event, (ii) labels associated with one of: a first period of time immediately preceding a VF event in a temporal beat activity sample, a second period of time immediately preceding the first period of time in a temporal beat activity sample, and all other periods of time in a temporal beat activity sample; at an inference stage, receiving, as input, a target HRV parameters representing temporal beat activity in a subject; and applying said machine learning algorithm to said target HRV parameters, to predict an onset time of a VF event in said subject.Type: GrantFiled: September 5, 2019Date of Patent: April 2, 2024Assignee: TECHNION RESEARCH & DEVELOPMENT FOUNDATION LIMITEDInventors: Yael Yaniv, Noam Keidar, Gal Eidelsztein, Alex Bronstein
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Patent number: 11844602Abstract: A diagnostic system includes an array of electrodes, which are coupled to a body surface of a living subject at different, respective positions in proximity to a region of interest within the body. A switched impedance network applies varying loads to the electrodes. A processor is coupled to receive and measure electrical signals from the electrodes as a function of the varying loads, and to analyze the measured signals so as to compute a local electrical characteristic of one or more locations within the region of interest.Type: GrantFiled: March 4, 2019Date of Patent: December 19, 2023Assignees: The Medical Research Infrastructure and Health Services Fund of the Tel Aviv Medical Center, Ramot at Tel Aviv University Ltd.Inventors: Mordekhay Medvedovsky, Tomer Gazit, Talma Hendler, Evgeny Tsizin-Goldman, Alex Bronstein
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Publication number: 20230288392Abstract: Example food processing systems and methods are described. In one implementation, a system includes a molecular embedder that receives a molecular profile of a single-molecule ingredient from multiple single-molecule ingredients and generates a representation of the single-molecule ingredient. A preparation modeler receives representations of single-molecule ingredients and preparation instructions. The molecular embedder also generates a representation of the prepared ingredients. A predictor receives a representation of the prepared ingredients and generates predicted characteristics of the prepared ingredients.Type: ApplicationFiled: March 10, 2022Publication date: September 14, 2023Inventors: Alex Bronstein, David H. Silver, Tal Knafo, Ariel Harpaz
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Publication number: 20230290444Abstract: Example molecular embedding systems and methods are described. In one implementation, a system includes a molecular embedder configured to receive structural and chemical information associated with a single-molecule ingredient from a plurality of single-molecule ingredients. The molecular embedder also generates a representation of the single-molecule ingredient. A preparation modeler receives multiple representations of single-molecule ingredients and preparation instructions. The molecular embedder generates a representation of the prepared ingredients. A predictor receives a representation of the prepared ingredients and generates predicted characteristics of the prepared ingredients.Type: ApplicationFiled: July 7, 2022Publication date: September 14, 2023Inventors: Alex Bronstein, David H. Silver, Tal Knafo, Ariel Harpaz, Omer Dahary
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Publication number: 20230289492Abstract: Example mixture modeling systems and methods are described. In one implementation, a system includes an encoder that receives multiple base ingredients and produces multiple corresponding representations. A composite modeler receives a mixture definition comprising a list of base ingredients and their relative proportions. The composite modeler generates a representation of the mixture. A decoder is receives a representation of a mixture and generates a list of features.Type: ApplicationFiled: May 27, 2022Publication date: September 14, 2023Inventors: Alex Bronstein, David H. Silver, Tal Knafo, Ariel Harpaz
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Publication number: 20230113613Abstract: Example virtual tasting systems and methods are described. In one implementation, a virtual tasting system identifies multiple food products, multiple tasters, and multiple questions. The virtual tasting system then identifies a full set of quadruplets of the form: first food product, second food product, taster, question. A subset of quadruplets from the full set of quadruplets are then identified. The virtual tasting system then measures a value associated with each quadruplet from the subset of quadruplets. The virtual tasting system then outputs estimated values associated with every triplet in a full set of triplets of the form: food product, taster, question.Type: ApplicationFiled: September 22, 2022Publication date: April 13, 2023Inventors: Alex Bronstein, Ariel Harpaz, David H. Silver, Tal Knafo
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Publication number: 20230070217Abstract: An example apparatus includes: a camera to record an image; memory to store instructions; and a processor in circuit with the memory, the processor to execute the instructions to: determine a depth based on: (a) the image and (b) a calibration parameter of the camera; and adjust the calibration parameter based on a temperature of the camera and the depth.Type: ApplicationFiled: September 2, 2022Publication date: March 9, 2023Inventors: Aviad Zabatani, Sagy Bareket, Ohad Menashe, Erez Sperling, Alex Bronstein, Michael Bronstein, Ron Kimmel, Vitaly Surazhsky
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Publication number: 20220373689Abstract: Example range estimation apparatus disclosed herein are to estimate a signal power parameter and a noise power parameter of a light detecting and ranging (LIDAR) system based on first data to be output from a light capturing device of the LIDAR system. Disclosed example range estimation apparatus are also to estimate a propagation delay associated with second data output from the light capturing device, the second data associated with a modulated light beam projected by the LIDAR system, the propagation delay estimated based on templates corresponding to different possible propagation delays, the templates based on the signal power parameter and the noise power parameter.Type: ApplicationFiled: July 25, 2022Publication date: November 24, 2022Inventors: Michael Bronstein, Ron Kimmel, Alex Bronstein, Ohad Menashe, Erez Sperling, Aviad Zabatani, Vitaly Surazhsky
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Publication number: 20220284542Abstract: The present invention extends to methods, systems, and computer program products for semantically altering a medical image. A medical image and a transform are accessed. The transform is used to transform the medical image to a simpler image having reduced complexity relative to the medical image. A semantic alteration is made to content of the simpler image. Another (and possibly inverse) transform is accessed. The other transform is used to transform the simpler image to a more complex image having increased complexity relative to the simpler image (e.g., complexity resembling the medical image). Transforming the simpler image to a more complex image can include propagating the semantic alteration with the increased complexity into content of the more complex image. A medical decision is made in view of the semantic alteration and based on at least a portion of the more complex image content.Type: ApplicationFiled: March 8, 2021Publication date: September 8, 2022Inventors: David H. Silver, Alex Bronstein, Shahar Rosentraub, Yael Gold-Zamir, Yotam Wolf
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Patent number: 11438569Abstract: An example apparatus includes: a camera to record an image; memory to store instructions; and a processor in circuit with the memory, the processor to execute the instructions to: determine a depth based on: (a) the image and (b) a calibration parameter of the camera; and adjust the calibration parameter based on a temperature of the camera and the depth.Type: GrantFiled: August 3, 2020Date of Patent: September 6, 2022Assignee: Intel CorporationInventors: Aviad Zabatani, Sagy Bareket, Ohad Menashe, Erez Sperling, Alex Bronstein, Michael Bronstein, Ron Kimmel, Vitaly Surazhsky
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Patent number: 11422263Abstract: Example range estimation apparatus disclosed herein include a first signal processor to estimate a signal power parameter and a noise power parameter of a LIDAR system based on first data to be output from a light capturing device of the LIDAR system. Disclosed example range estimation apparatus also include a second signal processor to generate templates corresponding to different possible propagation delays associated with second data to be output from the light capturing device, the second data associated with a modulated light beam projected by the LIDAR system, the templates generated based on the signal power parameter and the noise power parameter, and the second data to have a higher sampling rate and a lower quantization resolution than the first data. In some examples, the second signal processor is also to determine, based on the templates, an estimated propagation delay associated with the second data.Type: GrantFiled: December 23, 2020Date of Patent: August 23, 2022Assignee: Intel CorporationInventors: Michael Bronstein, Ron Kimmel, Alex Bronstein, Ohad Menashe, Erez Sperling, Aviad Zabatani, Vitaly Surazhsky
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Patent number: 11375939Abstract: Apparatus, including a set of N electrodes (22), configured to be located in proximity to an epidermis (24) of a subject, and to acquire signals generated by electric sources within the subject. The apparatus also includes a set of M channels, configured to transfer the signals, where M is less than N, and a switch (40), configured to select, repetitively and randomly, M signals from the N electrodes and to direct the M signals to the M channels. The apparatus further includes a processor (28), configured to activate the switch, and to receive and analyze the M signals from the M channels so as to determine respective positions of the electric sources within the subject.Type: GrantFiled: July 12, 2017Date of Patent: July 5, 2022Assignee: RAMOT AT TEL AVIV UNIVERSITY LTD.Inventors: Alex Bronstein, Evgeny Tsizin-Goldman
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Publication number: 20220012873Abstract: The present invention extends to methods, systems, and computer program products for predicting embryo implantation probability. A neural network accesses a set of images depicting an embryo. The neural network determines a correlation between the set of images and images corresponding to other embryos considered during neural network training. The neural network derives an embryo implantation probability associated with the embryo based on known implantation outcomes associated with the other embryos and in view of clinical data associated with a potential recipient of the embryo. An embryo is selected for the potential recipient based at least in part on the derived embryo implantation probability. The neural network can also derive a confidence and/or explanation of why the neural network assigned an embryo implantation probability to an embryo. The confidence can be considered in embryo selection.Type: ApplicationFiled: July 10, 2020Publication date: January 13, 2022Inventors: David H. Silver, Yael Gold-Zamir, Alex Bronstein, Martin Feder
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Publication number: 20210330238Abstract: A method comprising: at a training stage, training a machine learning algorithm on a training set comprising: (i) Heart Rate Variability (HRV) parameters extracted from temporal beat activity samples, wherein at least some of said samples include a representation of a Ventricular Fibrillation (VF) event, (ii) labels associated with one of: a first period of time immediately preceding a VF event in a temporal beat activity sample, a second period of time immediately preceding the first period of time in a temporal beat activity sample, and all other periods of time in a temporal beat activity sample; at an inference stage, receiving, as input, a target HRV parameters representing temporal beat activity in a subject; and applying said machine learning algorithm to said target HRV parameters, to predict an onset time of a VF event in said subject.Type: ApplicationFiled: September 5, 2019Publication date: October 28, 2021Inventors: Yael YANIV, Noam KEIDAR, Gal EIDELSZTEIN, Alex BRONSTEIN
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Publication number: 20210195167Abstract: An example apparatus includes: a camera to record an image; memory to store instructions; and a processor in circuit with the memory, the processor to execute the instructions to: determine a depth based on: (a) the image and (b) a calibration parameter of the camera; and adjust the calibration parameter based on a temperature of the camera and the depth.Type: ApplicationFiled: August 3, 2020Publication date: June 24, 2021Inventors: Aviad Zabatani, Sagy Bareket, Ohad Menashe, Erez Sperling, Alex Bronstein, Michael Bronstein, Ron Kimmel, Vitaly Surazhsky
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Publication number: 20210116569Abstract: Example range estimation apparatus disclosed herein include a first signal processor to estimate a signal power parameter and a noise power parameter of a LIDAR system based on first data to be output from a light capturing device of the LIDAR system. Disclosed example range estimation apparatus also include a second signal processor to generate templates corresponding to different possible propagation delays associated with second data to be output from the light capturing device, the second data associated with a modulated light beam projected by the LIDAR system, the templates generated based on the signal power parameter and the noise power parameter, and the second data to have a higher sampling rate and a lower quantization resolution than the first data. In some examples, the second signal processor is also to determine, based on the templates, an estimated propagation delay associated with the second data.Type: ApplicationFiled: December 23, 2020Publication date: April 22, 2021Inventors: Michael Bronstein, Ron Kimmel, Alex Bronstein, Ohad Menashe, Erez Sperling, Aviad Zabatani, Vitaly Surazhsky
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Patent number: 10927969Abstract: A method and apparatus for auto range control are described. In one embodiment, the apparatus comprises a projector configured to project a sequence of light patterns on an object; a first camera configured to capture a sequence of images of the object illuminated with the projected light patterns; a controller coupled to the projector and first camera and operable to receive the sequence of images and perform range control by controlling power of the sequence of light patterns being projected on the object and exposure time of a camera based on information obtained from the sequence of images captured by the camera.Type: GrantFiled: September 11, 2019Date of Patent: February 23, 2021Assignee: Intel CorporationInventors: Aviad Zabatani, Erez Sperling, Ofir Mulla, Ron Kimmel, Alex Bronstein, Michael Bronstein, David H. Silver, Ohad Menashe, Vitaly Surazhsky
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Patent number: 10877154Abstract: Example range estimation apparatus disclosed herein include a first signal processor to process first data output from a light capturing device of a LIDAR system to estimate signal and noise power parameters of the LIDAR system. Disclosed example range estimation apparatus also include a second signal processor to generate templates corresponding to different possible propagation delays associated with second data output from the light capturing device while a modulated light beam is projected by the LIDAR system, the templates generated based on the signal and noise power parameters, and the second data having a higher sampling rate and a lower quantization resolution than the first data. In some examples, the second signal processor also cross-correlates the templates with the second data to determine an estimated propagation delay associated with the second data, the estimated propagation delay convertible to an estimated range to an object that reflected the modulated light beam.Type: GrantFiled: March 27, 2018Date of Patent: December 29, 2020Assignee: Intel CorporationInventors: Michael Bronstein, Ron Kimmel, Alex Bronstein, Ohad Menashe, Erez Sperling, Aviad Zabatani, Vitaly Surazhsky
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Patent number: 10775501Abstract: Arrangements (e.g., apparatus, system, method, article of manufacture) for reconstructing a depth image of a scene. Some embodiments include: a processor; and a non-transitory computer readable medium to store a set of instructions for execution by the processor, the set of instructions to cause the processor to perform various operations. Operations include: collecting multiple data sets for a code-modulated light pulse reflected from an object in a scene, with each data set associated with a direction in a set of directions for the reflected code-modulated light pulse; assigning a fitness value to each data set based on one or more parameters of a model; and reconstructing a depth image providing a depth at each direction based on a corresponding data set and fitness value, the depth to correspond with a round-trip delay time of the code-modulated light pulse.Type: GrantFiled: June 1, 2017Date of Patent: September 15, 2020Assignee: INTEL CORPORATIONInventors: Alex Bronstein, Michael Bronstein, David H. Silver, Ron Kimmel, Erez Sperling, Vitaly Surazhsky, Aviad Zabatani, Ohad Menashe
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Patent number: 10735713Abstract: An example apparatus includes: a camera to record an image; memory to store instructions; and a processor in circuit with the memory, the processor to execute the instructions to: determine a depth based on: (a) the image and (b) a calibration parameter of the camera; and adjust the calibration parameter based on a temperature of the camera and the depth.Type: GrantFiled: June 24, 2019Date of Patent: August 4, 2020Assignee: Intel CorporationInventors: Aviad Zabatani, Sagy Bareket, Ohad Menashe, Erez Sperling, Alex Bronstein, Michael Bronstein, Ron Kimmel, Vitaly Surazhsky