Patents by Inventor JOSEPH YITAN CHENG
JOSEPH YITAN CHENG 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).
-
Publication number: 20240134449Abstract: A head-mounted device having a plurality of electrodes configured to detect optical events such as the movement of one or more eyes or coarse eye gestures is disclosed. In some examples, the one or more electrodes can be coupled to dielectric elastomer materials whose shape can be changed to vary contact between a user of the head-mounted device and the one or more electrodes to ensure sufficient contact and electrode signal quality. In some examples, the one or more electrodes can be coupled to pressure sensors and control circuitry to monitor and adjust the applied pressure. In some examples, the optical events can be used as triggers for operating the device, including transitioning between operational power modes. In some examples, the triggers can invoke higher resolution sensing capabilities of the head-mounted device. In some examples, the electrodes can be used as an on-head detector to wake-up and/or unlock the device.Type: ApplicationFiled: January 3, 2024Publication date: April 25, 2024Inventors: Erdrin AZEMI, Ali MOIN, Christoph H. KRAH, Joseph Yitan CHENG, Kaan Emre DOGRUSOZ, Mohammad YEKE YAZDANDOOST
-
Patent number: 11874958Abstract: A head-mounted device having a plurality of electrodes configured to detect optical events such as the movement of one or more eyes or coarse eye gestures is disclosed. In some examples, the one or more electrodes can be coupled to dielectric elastomer materials whose shape can be changed to vary contact between a user of the head-mounted device and the one or more electrodes to ensure sufficient contact and electrode signal quality. In some examples, the one or more electrodes can be coupled to pressure sensors and control circuitry to monitor and adjust the applied pressure. In some examples, the optical events can be used as triggers for operating the device, including transitioning between operational power modes. In some examples, the triggers can invoke higher resolution sensing capabilities of the head-mounted device. In some examples, the electrodes can be used as an on-head detector to wake-up and/or unlock the device.Type: GrantFiled: September 24, 2021Date of Patent: January 16, 2024Assignee: Apple Inc.Inventors: Erdrin Azemi, Ali Moin, Christoph H. Krah, Joseph Yitan Cheng, Kaan Emre Dogrusoz, Mohammad Yeke Yazdandoost
-
Publication number: 20240012480Abstract: Techniques are disclosed for defining a training data set to include biosignals and categorical labels representative of a context. For example, a categorical label may indicate whether a user was performing a difficult or easy mental task while the biosignal was being recorded. A set of first layers in a neural network can be trained using a portion of the training data set associated with a first set of users and at least one second layer can be trained using a portion of the training data set associated with a particular other user. The neural network can then be used to process other biosignals from the particular other user to generate predicted categorical context labels.Type: ApplicationFiled: July 20, 2023Publication date: January 11, 2024Applicant: Apple Inc.Inventors: Erdrin Azemi, Joseph Yitan Cheng, Hanlin Goh
-
Patent number: 11747902Abstract: Techniques are disclosed for defining a training data set to include biosignals and categorical labels representative of a context. For example, a categorical label may indicate whether a user was performing a difficult or easy mental task while the biosignal was being recorded. A set of first layers in a neural network can be trained using a portion of the training data set associated with a first set of users and at least one second layer can be trained using a portion of the training data set associated with a particular other user. The neural network can then be used to process other biosignals from the particular other user to generate predicted categorical context labels.Type: GrantFiled: February 12, 2021Date of Patent: September 5, 2023Assignee: Apple Inc.Inventors: Erdrin Azemi, Joseph Yitan Cheng, Hanlin Goh
-
Publication number: 20230102507Abstract: A head-mounted device having a plurality of electrodes configured to detect optical events such as the movement of one or more eyes or coarse eye gestures is disclosed. In some examples, the one or more electrodes can be coupled to dielectric elastomer materials whose shape can be changed to vary contact between a user of the head-mounted device and the one or more electrodes to ensure sufficient contact and electrode signal quality. In some examples, the one or more electrodes can be coupled to pressure sensors and control circuitry to monitor and adjust the applied pressure. In some examples, the optical events can be used as triggers for operating the device, including transitioning between operational power modes. In some examples, the triggers can invoke higher resolution sensing capabilities of the head-mounted device. In some examples, the electrodes can be used as an on-head detector to wake-up and/or unlock the device.Type: ApplicationFiled: September 24, 2021Publication date: March 30, 2023Inventors: Erdrin AZEMI, Ali MOIN, Christoph H. KRAH, Joseph Yitan CHENG, Kaan Emre DOGRUSOZ, Mohammad YEKE YAZDANDOOST
-
Publication number: 20220383189Abstract: Methods and systems are provided for predicting cognitive load. A computing device receives sensor measurements from sensors. The sensor measurements correspond to characteristics of a user during the performance of a task. For each sensor, the computing device derives, from the sensor measurements of the sensor, a set of features predictive of the cognitive load of the user; generates, from those features, a self-attention vector that characterizes each feature of the set of features relative to another feature; and defines a feature vector from the features and the self-attention vector. The computing device generates an input feature vector from the feature vector of at least one sensor. The computing device then uses a machine-learning model to generate an indication of the cognitive load of the user during the performance of a task from the feature vector.Type: ApplicationFiled: December 17, 2021Publication date: December 1, 2022Applicant: Apple Inc.Inventors: Joseph Yitan Cheng, Amruta Pai, Erdrin Azemi, Matthias R. Hohmann
-
Patent number: 11125846Abstract: A method is disclosed for phase contrast magnetic resonance imaging (MRI) comprising: acquiring phase contrast 3D spatiotemporal MRI image data; inputing the 3D spatiotemporal MRI image data to a three-dimensional spatiotemporal convolutional neural network to produce a phase unwrapping estimate; generating from the phase unwrapping estimate an integer number of wraps per pixel; and combining the integer number of wraps per pixel with the phase contrast 3D spatiotemporal MRI image data to produce final output.Type: GrantFiled: March 20, 2020Date of Patent: September 21, 2021Assignee: The Board of Trustees of the Leland Stanford Junior UniversityInventors: Christopher Michael Sandino, Shreyas S. Vasanawala, Joseph Yitan Cheng, Jiacheng Jason He
-
Publication number: 20210286429Abstract: Techniques are disclosed for defining a training data set to include biosignals and categorical labels representative of a context. For example, a categorical label may indicate whether a user was performing a difficult or easy mental task while the biosignal was being recorded. A set of first layers in a neural network can be trained using a portion of the training data set associated with a first set of users and at least one second layer can be trained using a portion of the training data set associated with a particular other user. The neural network can then be used to process other biosignals from the particular other user to generate predicted categorical context labels.Type: ApplicationFiled: February 12, 2021Publication date: September 16, 2021Applicant: Apple Inc.Inventors: Erdrin Azemi, Joseph Yitan Cheng, Hanlin Goh
-
Patent number: 11085988Abstract: A method for magnetic resonance imaging (MRI) includes steps of acquiring by an MRI scanner undersampled magnetic-field-gradient-encoded k-space data; performing a self-calibration of a magnetic-field-gradient-encoding point-spread function using a first neural network to estimate systematic waveform errors from the k-space data, and computing the magnetic-field-gradient-encoding point-spread function from the systematic waveform errors; reconstructing an image using a second neural network from the magnetic-field-gradient-encoding point-spread function and the k-space data.Type: GrantFiled: March 19, 2020Date of Patent: August 10, 2021Assignee: The Board of Trustees of the Leland Stanford Junior UniversityInventors: Feiyu Chen, Christopher Michael Sandino, Joseph Yitan Cheng, John M. Pauly, Shreyas S. Vasanawala
-
Patent number: 11062490Abstract: A magnetic resonance imaging scan performs an MRI acquisition using an undersampling pattern to produce undersampled k-space data; adds the undersampled k-space data to aggregate undersampled k-space data for the scan; reconstructs an image from the aggregate undersampled k-space data; updates the undersampling pattern from the reconstructed image and aggregate undersampled k-space data using a deep reinforcement learning technique defined by an environment, reward, and agent, where the environment comprises an MRI reconstruction technique, where the reward comprises an image quality metric, and where the agent comprises a deep convolutional neural network and fully connected layers; and repeats these steps to produce a final reconstructed MRI image for the scan.Type: GrantFiled: October 16, 2019Date of Patent: July 13, 2021Assignee: The Board of Trustees of the Leland Stanford Junior UniversityInventors: David Y. Zeng, Shreyas S. Vasanawala, Joseph Yitan Cheng
-
Publication number: 20200300955Abstract: A method is disclosed for phase contrast magnetic resonance imaging (MRI) comprising: acquiring phase contrast 3D spatiotemporal MRI image data; inputing the 3D spatiotemporal MRI image data to a three-dimensional spatiotemporal convolutional neural network to produce a phase unwrapping estimate; generating from the phase unwrapping estimate an integer number of wraps per pixel; and combining the integer number of wraps per pixel with the phase contrast 3D spatiotemporal MRI image data to produce final output.Type: ApplicationFiled: March 20, 2020Publication date: September 24, 2020Inventors: Christopher Michael Sandino, Shreyas S. Vasanawala, Joseph Yitan Cheng, Jiacheng Jason He
-
Publication number: 20200300957Abstract: A method for magnetic resonance imaging (MRI) includes steps of acquiring by an MRI scanner undersampled magnetic-field-gradient-encoded k-space data; performing a self-calibration of a magnetic-field-gradient-encoding point-spread function using a first neural network to estimate systematic waveform errors from the k-space data, and computing the magnetic-field-gradient-encoding point-spread function from the systematic waveform errors; reconstructing an image using a second neural network from the magnetic-field-gradient-encoding point-spread function and the k-space data.Type: ApplicationFiled: March 19, 2020Publication date: September 24, 2020Inventors: Feiyu Chen, Christopher Michael Sandino, Joseph Yitan Cheng, John M. Pauly, Shreyas S. Vasanawala
-
Publication number: 20200249300Abstract: Various methods and systems are provided for reconstructing magnetic resonance images from accelerated magnetic resonance imaging (MM) data. In one embodiment, a method for reconstructing a magnetic resonance (MR) image includes: estimating multiple sets of coil sensitivity maps from undersampled k-space data, the undersampled k-space data acquired by a multi-coil radio frequency (RF) receiver array; reconstructing multiple initial images using the undersampled k-space data and the estimated multiple sets of coil sensitivity maps; iteratively reconstructing, with a trained deep neural network, multiple images by using the initial images and the multiple sets of coil sensitivity maps to generate multiple final images, each of the multiple images corresponding to a different set of the multiple sets of sensitivity maps; and combining the multiple final images output from the trained deep neural network to generate the MR image.Type: ApplicationFiled: February 5, 2019Publication date: August 6, 2020Inventors: Christopher Michael Sandino, Peng Lai, Shreyas Vasanawala, Joseph Yitan Cheng
-
Patent number: 10712416Abstract: Various methods and systems are provided for reconstructing magnetic resonance images from accelerated magnetic resonance imaging (MRI) data. In one embodiment, a method for reconstructing a magnetic resonance (MR) image includes: estimating multiple sets of coil sensitivity maps from undersampled k-space data, the undersampled k-space data acquired by a multi-coil radio frequency (RF) receiver array; reconstructing multiple initial images using the undersampled k-space data and the estimated multiple sets of coil sensitivity maps; iteratively reconstructing, with a trained deep neural network, multiple images by using the initial images and the multiple sets of coil sensitivity maps to generate multiple final images, each of the multiple images corresponding to a different set of the multiple sets of sensitivity maps; and combining the multiple final images output from the trained deep neural network to generate the MR image.Type: GrantFiled: February 5, 2019Date of Patent: July 14, 2020Assignees: GE PRECISION HEALTHCARE, LLC, THE BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIVERSITYInventors: Christopher Michael Sandino, Peng Lai, Shreyas Vasanawala, Joseph Yitan Cheng
-
Patent number: 10692250Abstract: A method for magnetic resonance imaging acquires multi-channel subsampled k-space data using multiple receiver coils; performs singular-value-decomposition on the multi-channel subsampled k-space data to produce compressed multi-channel k-space data which normalizes the multi-channel subsampled k-space data; applies a first center block of the compressed multi-channel k-space data as input to a first convolutional neural network to produce a first estimated k-space center block that includes estimates of k-space data missing from the first center block; generates an n-th estimated k-space block by repeatedly applying an (n?1)-th estimated k-space center block combined with an n-th center block of the compressed multi-channel k-space data as input to an n-th convolutional neural network to produce an n-th estimated k-space center block that includes estimates of k-space data missing from the n-th center block; reconstructs image-space data from the n-th estimated k-space block.Type: GrantFiled: January 29, 2019Date of Patent: June 23, 2020Assignee: The Board of Trustees of the Leland Stanford Junior UniversityInventors: Joseph Yitan Cheng, Morteza Mardani Korani, John M. Pauly, Shreyas S. Vasanawala
-
Publication number: 20200134887Abstract: A magnetic resonance imaging scan performs an MRI acquisition using an undersampling pattern to produce undersampled k-space data; adds the undersampled k-space data to aggregate undersampled k-space data for the scan; reconstructs an image from the aggregate undersampled k-space data; updates the undersampling pattern from the reconstructed image and aggregate undersampled k-space data using a deep reinforcement learning technique defined by an environment, reward, and agent, where the environment comprises an MRI reconstruction technique, where the reward comprises an image quality metric, and where the agent comprises a deep convolutional neural network and fully connected layers; and repeats these steps to produce a final reconstructed MRI image for the scan.Type: ApplicationFiled: October 16, 2019Publication date: April 30, 2020Inventors: David Y. Zeng, Shreyas S. Vasanawala, Joseph Yitan Cheng
-
Publication number: 20190236817Abstract: A method for magnetic resonance imaging acquires multi-channel subsampled k-space data using multiple receiver coils; performs singular-value-decomposition on the multi-channel subsampled k-space data to produce compressed multi-channel k-space data which normalizes the multi-channel subsampled k-space data; applies a first center block of the compressed multi-channel k-space data as input to a first convolutional neural network to produce a first estimated k-space center block that includes estimates of k-space data missing from the first center block; generates an n-th estimated k-space block by repeatedly applying an (n?1)-th estimated k-space center block combined with an n-th center block of the compressed multi-channel k-space data as input to an n-th convolutional neural network to produce an n-th estimated k-space center block that includes estimates of k-space data missing from the n-th center block; reconstructs image-space data from the n-th estimated k-space block.Type: ApplicationFiled: January 29, 2019Publication date: August 1, 2019Inventors: Joseph Yitan Cheng, Morteza Mardani Korani, John M. Pauly, Shreyas S. Vasanawala
-
Patent number: 10185016Abstract: A method for phase-contrast imaging a fluid within a volume of an imaged subject is provided. The method includes acquiring a plurality of slabs, each slab imaging the fluid flowing within a portion of the volume; and volume merging the plurality of slabs to form an image of the volume. Each slab of the plurality is aligned with respect to the volume such that each slab of the plurality is continuously supplied with a plurality of magnetically unsaturated portions of the fluid during acquisition.Type: GrantFiled: April 22, 2016Date of Patent: January 22, 2019Assignees: General Electric Company, The Board of Trustees of the Leland Stanford Junior UniversityInventors: Peng Lai, Joseph Yitan Cheng
-
Publication number: 20170307713Abstract: A method for phase-contrast imaging a fluid within a volume of an imaged subject is provided. The method includes acquiring a plurality of slabs, each slab imaging the fluid flowing within a portion of the volume; and volume merging the plurality of slabs to form an image of the volume. Each slab of the plurality is aligned with respect to the volume such that each slab of the plurality is continuously supplied with a plurality of magnetically unsaturated portions of the fluid during acquisition.Type: ApplicationFiled: April 22, 2016Publication date: October 26, 2017Applicants: GENERAL ELECTRIC COMPANY, THE BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIVERSITYInventors: PENG LAI, JOSEPH YITAN CHENG