公开(公告)号：US20220351086A1

公开(公告)日：2022-11-03

申请号：US17869740

申请日：2022-07-20

Applicant: Apple Inc.

Inventor： Charles MAALOUF ,  Shawn R. SCULLY ,  Christopher B. FLEIZACH ,  Tu K. NGUYEN ,  Lilian H. LIANG ,  Warren J. SETO ,  Julian QUINTANA ,  Michael J. BEYHS ,  Hojjat SEYED MOUSAVI ,  Behrooz SHAHSAVARI

IPC: G06N20/00 ,  G06F3/01 ,  G06F3/04883 ,  G06K9/62

Abstract: A device implementing a system for machine-learning based gesture recognition includes at least one processor configured to, receive, from a first sensor of the device, first sensor output of a first type, and receive, from a second sensor of the device, second sensor output of a second type that differs from the first type. The at least one processor is further configured to provide the first sensor output and the second sensor output as inputs to a machine learning model, the machine learning model having been trained to output a predicted gesture based on sensor output of the first type and sensor output of the second type. The at least one processor is further configured to determine the predicted gesture based on an output from the machine learning model, and to perform, in response to determining the predicted gesture, a predetermined action on the device.

公开(公告)号：US20230195237A1

公开(公告)日：2023-06-22

申请号：US18109808

申请日：2023-02-14

Applicant: Apple Inc.

Inventor： Tu K. NGUYEN ,  James N. CARTWRIGHT ,  Elizabeth C. CRANFILL ,  Christopher B. FLEIZACH ,  Joshua R. FORD ,  Jeremiah R. JOHNSON ,  Charles MAALOUF ,  Heriberto NIETO ,  Jennifer D. PATTON ,  Hojjat SEYED MOUSAVI ,  Shawn R. SCULLY ,  Ibrahim G. YUSUF

IPC: G06F3/01 ,  G06F3/04812 ,  G06F3/0485 ,  G06F3/0482 ,  G06F3/04817 ,  G06F3/04842

CPC classification number: G06F3/017 ,  G06F3/04812 ,  G06F3/0485 ,  G06F3/0482 ,  G06F3/04817 ,  G06F3/04842

Abstract: The present disclosure generally relates to navigating user interfaces using hand gestures.

公开(公告)号：US20220374085A1

公开(公告)日：2022-11-24

申请号：US17747613

申请日：2022-05-18

Applicant: Apple Inc.

Inventor： Tu K. NGUYEN ,  James N. CARTWRIGHT ,  Elizabeth C. CRANFILL ,  Christopher B. FLEIZACH ,  Joshua R. FORD ,  Jeremiah R. JOHNSON ,  Charles MAALOUF ,  Heriberto NIETO ,  Jennifer D. PATTON ,  Hojjat SEYED MOUSAVI ,  Shawn R. SCULLY ,  Ibrahim G. YUSUF

IPC: G06F3/01 ,  G06F3/04812 ,  G06F3/04842 ,  G06F3/0482 ,  G06F3/04817 ,  G06F3/0485

Abstract: The present disclosure generally relates to navigating user interfaces using hand gestures.

公开(公告)号：US20220100341A1

公开(公告)日：2022-03-31

申请号：US17448866

申请日：2021-09-24

Applicant: Apple Inc.

Inventor： Hojjat SEYED MOUSAVI ,  Behrooz SHAHSAVARI ,  Bongsoo SUH ,  Utkarsh GAUR ,  Nima FERDOSI ,  Baboo V. GOWREESUNKER

IPC: G06F3/041 ,  G06F3/0354 ,  G06N20/20

Abstract: In some examples, an electronic device can use machine learning techniques, such as convolutional neural networks, to estimate the distance between a stylus tip and a touch sensitive surface (e.g., stylus z-height). A subset of stylus data sensed at electrodes closest to the location of the stylus at the touch sensitive surface including data having multiple phases and frequencies can be provided to the machine learning algorithm. The estimated stylus z-height can be compared to one or more thresholds to determine whether or not the stylus is in contact with the touch sensitive surface. In some examples, the electronic device can use machine learning techniques to estimate the (x, y) position and/or tilt and/or azimuth angles of the stylus tip at the touch sensitive surface based on a subset of stylus data.

公开(公告)号：US20220019311A1

公开(公告)日：2022-01-20

申请号：US17123015

申请日：2020-12-15

Applicant: Apple Inc.

Inventor： Hojjat SEYED MOUSAVI ,  Nima FERDOSI ,  Baboo V. GOWREESUNKER ,  Behrooz SHAHSAVARI

IPC: G06F3/041 ,  G06N3/08 ,  G06K9/62

Abstract: In some examples, touch data can include noise. The noise can be generated by a component of an electronic device that includes a touch screen. For example, one or more signals transmitted to the display circuitry of an electronic device can become capacitively coupled to the touch circuitry of the device and cause noise in the touch data. Machine learning techniques, such as gated recurrent units and/or convolutional neural networks can estimate and reduce or remove noise from touch data when provided data or information about the displayed image as input. In some examples, the algorithm includes one or more of a gated recurrent unit stage and a convolutional neural network stage. In some examples, a gated recurrent unit stage and a convolutional neural network stage can be arranged in series, such as by providing the output of the gated recurrent unit as input to the convolutional neural network.

公开(公告)号：US20210142214A1

公开(公告)日：2021-05-13

申请号：US16937481

申请日：2020-07-23

Applicant: Apple Inc.

Inventor： Charles MAALOUF ,  Shawn R. SCULLY ,  Christopher B. FLEIZACH ,  Tu K. NGUYEN ,  Lilian H. LIANG ,  Warren J. SETO ,  Julian QUINTANA ,  Michael J. BEYHS ,  Hojjat SEYED MOUSAVI ,  Behrooz SHAHSAVARI

IPC: G06N20/00 ,  G06F3/01 ,  G06F3/0488 ,  G06K9/62

Abstract: A device implementing a system for machine-learning based gesture recognition includes at least one processor configured to, receive, from a first sensor of the device, first sensor output of a first type, and receive, from a second sensor of the device, second sensor output of a second type that differs from the first type. The at least one processor is further configured to provide the first sensor output and the second sensor output as inputs to a machine learning model, the machine learning model having been trained to output a predicted gesture based on sensor output of the first type and sensor output of the second type. The at least one processor is further configured to determine the predicted gesture based on an output from the machine learning model, and to perform, in response to determining the predicted gesture, a predetermined action on the device.

公开(公告)号：US20250037033A1

公开(公告)日：2025-01-30

申请号：US18915243

申请日：2024-10-14

Applicant: Apple Inc.

Inventor： Charles MAALOUF ,  Shawn R. SCULLY ,  Christopher B. FLEIZACH ,  Tu K. NGUYEN ,  Lilian H. LIANG ,  Warren J. SETO ,  Julian QUINTANA ,  Michael J. BEYHS ,  Hojjat SEYED MOUSAVI ,  Behrooz SHAHSAVARI

IPC: G06N20/00 ,  G06F3/01 ,  G06F3/04883 ,  G06F18/214 ,  G06N3/08

Abstract: A device implementing a system for machine-learning based gesture recognition includes at least one processor configured to, receive, sensor data for a first window of time and additional sensor data for a second window of time overlapping the first window of time. The sensor data and the additional sensor data are provided as inputs to a machine learning model, the machine learning model having been trained to output a predicted gesture, predicted gesture start time, and predicted gesture end time based on the sensor data. A predicted gesture is determined based on an output from the machine learning model, and to perform, in response to determining the predicted gesture, a predetermined action on the device.

公开(公告)号：US20240345683A1

公开(公告)日：2024-10-17

申请号：US18629255

申请日：2024-04-08

Applicant: Apple Inc.

Inventor： Lichen WANG ,  Behrooz SHAHSAVARI ,  Hojjat SEYED MOUSAVI ,  Nima FERDOSI ,  Baboo V. GOWREESUNKER

IPC: G06F3/041 ,  G06F3/044 ,  G06N3/044 ,  G06N3/08

CPC classification number: G06F3/04182 ,  G06F3/044 ,  G06N3/044 ,  G06N3/08 ,  G06F2203/04104

Abstract: In some examples, touch data can include noise. Machine learning techniques, such as gated recurrent units and convolutional neural networks can be used to mitigate noise present in touch data. In some examples, a gated recurrent unit stage and a convolutional neural network stage can be arranged in series, such as by providing the output of the gated recurrent unit as input to the convolutional neural network. The gated recurrent unit can remove noise caused by a first component of the electronic device and the convolutional neural network can remove noise caused by a second component of the electronic device, for example. Thus, together, the gated recurrent unit and the convolutional neural network can remove or substantially reduce the noise in the touch data.

公开(公告)号：US20230325719A1

公开(公告)日：2023-10-12

申请号：US18202857

申请日：2023-05-26

Applicant: Apple Inc.

Inventor： Charles MAALOUF ,  Shawn R. SCULLY ,  Christopher B. FLEIZACH ,  Tu K. NGUYEN ,  Lilian H. LIANG ,  Warren J. SETO ,  Julian QUINTANA ,  Michael J. BEYHS ,  Hojjat SEYED MOUSAVI ,  Behrooz SHAHSAVARI

IPC: G06N20/00 ,  G06F3/01 ,  G06F3/04883 ,  G06F18/214

CPC classification number: G06N20/00 ,  G06F3/015 ,  G06F3/017 ,  G06F3/04883 ,  G06F18/2155 ,  G06N3/08

Abstract: A device implementing a system for machine-learning based gesture recognition includes at least one processor configured to, receive, from a first sensor of the device, first sensor output of a first type, and receive, from a second sensor of the device, second sensor output of a second type that differs from the first type. The at least one processor is further configured to provide the first sensor output and the second sensor output as inputs to a machine learning model, the machine learning model having been trained to output a predicted gesture based on sensor output of the first type and sensor output of the second type. The at least one processor is further configured to determine the predicted gesture based on an output from the machine learning model, and to perform, in response to determining the predicted gesture, a predetermined action on the device.