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公开(公告)号：US12242695B1
公开(公告)日：2025-03-04
申请号：US18462234
申请日：2023-09-06
Applicant: Apple Inc.
Inventor： Thomas Deselaers , Peder Blekken , Francisco Alvaro Munoz , Ryan S. Dixon , Nima Ferdosi , Tianchang Gu , Mayank Garg
IPC: G06F3/041 , G06F3/0354 , G06F3/038
Abstract: In some examples, an electronic device including a touch screen can sense a stylus proximate to the touch screen. For example, the user can draw or write with the stylus to create a simulated drawing or simulated handwriting displayed on the touch screen. In some examples, the electronic device can sense a series of “points” corresponding to the location of the stylus over time while the drawing/writing input was provided. In some situations, noise in the stylus data can cause the electronic device not to sense one or more noisy points included in the drawing or handwriting input. In some examples, the electronic device can interpolate the noisy points based on sensed points before and/or after the time of the skipped points.
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公开(公告)号：US11954288B1
公开(公告)日：2024-04-09
申请号：US17406990
申请日：2021-08-19
Applicant: Apple Inc.
Inventor： Lichen Wang , Behrooz Shahsavari , Hojjat Seyed Mousavi , Nima Ferdosi , Baboo V. Gowreesunker
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.
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公开(公告)号：US11899881B2
公开(公告)日：2024-02-13
申请号：US17123015
申请日：2020-12-15
Applicant: Apple Inc.
Inventor： Hojjat Seyed Mousavi , Nima Ferdosi , Baboo V. Gowreesunker , Behrooz Shahsavari
CPC classification number: G06F3/0418 , G06F18/251 , G06N3/08
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.
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公开(公告)号：US11599223B1
公开(公告)日：2023-03-07
申请号：US17249791
申请日：2021-03-12
Applicant: Apple Inc.
Inventor： Baboo V. Gowreesunker , Behrooz Shahsavari , Hojjat Seyed Mousavi , Nima Ferdosi , Lichen Wang , Nariman Farsad
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.
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公开(公告)号：US11301099B1
公开(公告)日：2022-04-12
申请号：US16833394
申请日：2020-03-27
Applicant: Apple Inc.
Inventor： Behrooz Shahsavari , Hojjat Seyed Mousavi , Nima Ferdosi , Baboo V. Gowreesunker
Abstract: Finger detection and separation techniques on a multi-touch touch sensor panel can be improved using machine learning models (particularly for touch sensor panels with relatively low signal-to-noise ratio). In some examples, a machine learning model can be used to process an input patch to disambiguate whether the input patch corresponds to one contact or two contacts. In some examples, the machine learning model can be implemented using a neural network. The neural network can receive a sub-image including an input patch as an input, and can output a number of contacts. In some examples, the neural network can output one or more sub-image masks representing the one or more contacts.
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Patent Agency Ranking
6.发明授权公开(公告)号：US12197668B2
公开(公告)日：2025-01-14
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
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.
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公开(公告)号：US12001636B2
公开(公告)日：2024-06-04
申请号：US18361004
申请日：2023-07-28
Applicant: Apple Inc.
Inventor： Guangtao Zhang , Apexit Shah , Heemin Yang , Kevin D. Spratt , Mayank Garg , Nima Ferdosi , Vikram Garg , William J. Esposito , Tavys Q. Ashcroft
CPC classification number: G06F3/044 , G06F3/0416
Abstract: Computing devices and methods are used to detect and compensate for the presence of a cover layer on a touch input device. A computing device includes a processing device, a touch input device in electronic communication with the processing device, and a memory device in electronic communication with the processing device and having electronic instructions encoded thereon. The electronic instructions, when executed by the processing device, cause the processor to receive a first signal obtained from the touch input device over a first duration of time, the first signal including a first signal pattern, receive a second signal obtained from the touch input device over a second duration of time separate from the first duration of time, the second signal including a second signal pattern, determine a difference between the first signal pattern and the second signal pattern, and adjust a touch input detection setting based on the difference.
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公开(公告)号：US20230280868A1
公开(公告)日：2023-09-07
申请号：US17653439
申请日：2022-03-03
Applicant: Apple Inc.
Inventor： Guangtao Zhang , Apexit Shah , Heemin Yang , Kevin D. Spratt , Mayank Garg , Nima Ferdosi , Vikram Garg , William J. Esposito , Tavys Q. Ashcroft
CPC classification number: G06F3/044 , G06F3/0416
Abstract: Computing devices and methods are used to detect and compensate for the presence of a cover layer on a touch input device. A computing device includes a processing device, a touch input device in electronic communication with the processing device, and a memory device in electronic communication with the processing device and having electronic instructions encoded thereon. The electronic instructions, when executed by the processing device, cause the processor to receive a first signal obtained from the touch input device over a first duration of time, the first signal including a first signal pattern, receive a second signal obtained from the touch input device over a second duration of time separate from the first duration of time, the second signal including a second signal pattern, determine a difference between the first signal pattern and the second signal pattern, and adjust a touch input detection setting based on the difference.
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公开(公告)号：US11620021B1
公开(公告)日：2023-04-04
申请号：US17020749
申请日：2020-09-14
Applicant: Apple Inc.
Inventor： Sai Zhang , Behrooz Shahsavari , Ari Y. Benbasat , Nima Ferdosi
Abstract: Cross-coupling correction techniques on a touch sensor panel can be improved using machine learning models (particularly for touch sensor panels with relatively low signal-to-noise ratio). In some examples, the machine learning model can be implemented using a neural network. The neural network can receive a touch image and perform cross-coupling correction to mitigate cross-talk due to routing traces of the touch sensor panel. Mitigating cross-talk can improve touch sensing accuracy, reduce jitter, and/or reduce false positive touch detection.
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10.发明授权公开(公告)号：US11287926B1
公开(公告)日：2022-03-29
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 , G06F3/044 , G06K9/62 , G06N3/04
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.
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