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：US20220100310A1

公开(公告)日：2022-03-31

申请号：US17161499

申请日：2021-01-28

Applicant: Apple Inc.

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

IPC: G06F3/041 ,  G06F3/0354 ,  G06N3/04 ,  G06K9/62 ,  G06F3/044

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.

公开(公告)号：US20240077965A1

公开(公告)日：2024-03-07

申请号：US18450771

申请日：2023-08-16

Applicant: Apple Inc.

Inventor： Dor SHAVIV ,  Behrooz SHAHSAVARI ,  David S. GRAFF ,  Baboo V. GOWREESUNKER ,  Nima FERDOSI ,  Yash S. AGARWAL ,  Sai ZHANG

IPC: G06F3/041

CPC classification number: G06F3/0412 ,  G06F3/04186 ,  G06F2203/04101

Abstract: Touch sensor panels/screens can include a first region having a plurality of touch electrodes and a second region without touch electrodes. In some examples, to improve touch sensing performance, a first algorithm or a second algorithm is applied to determine whether an object corresponding to the touch patch is in contact with the touch screen. Whether to apply the first algorithm or the second algorithm is optionally dependent on the location of the touch patch.

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.