Apple Inc.

Hojjat Seyed Mousavi

Behrooz Shahsavari

Nima Ferdosi

Baboo V Gowreesunker

Christopher Brian Fleizach

Tu K Nguyen

Shawn R Scully

Charles Maalouf

Michael J Beyhs

Julian Quintana

Lilian H Liang

Warren J Seto

Lichen Wang

Elizabeth Caroline Cranfill

Andrew W Joyce

Jennifer D Patton

Pavan Gupta

Albert Lin

Julian K Shutzberg

Joshua R Ford

Heriberto Nieto

Utkarsh Gaur

James Nathan Cartwright

Ibrahim G Yusuf

David Steven Graff

Jeremiah R Johnson

Yoonju Han

Joanna Arreaza-taylor

Hannah G Coleman

Mo Li

Bongsoo Suh

Yonathan Morin

Benedict Drevniok

Nariman Farsad

The present disclosure generally relates to navigating user interfaces using a cursor.

Navigating user interfaces using a cursor

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.

System and machine learning method for separating noise and signal in multitouch sensors

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.

Machine-learning based gesture recognition using multiple sensors

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.

System and machine learning method for localization of an input device relative to a touch sensitive surface

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.

Machine learning method and system for suppressing display induced noise in touch sensors using information from display circuitry

An electronic device includes a pressure sensor and a processor. The pressure sensor is disposed within an interior volume of the electronic device and configured to generate a time-dependent sequence of measurements related to a force applied to the electronic device. The processor is configured to characterize, using at least the time-dependent sequence of measurements, a venting state of the interior volume. In some embodiments, the electronic device may also include a capacitive force sensor disposed to detect distortion of the interior volume. A second time-dependent sequence of measurements related to the force may be generated by the capacitive force sensor, and used by the processor to characterize the venting state of the interior volume.

Characterization of a venting state or other system parameter that affects the characterization of a force applied to a device

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.

Methods and apparatus for finger detection and separation on a touch sensor panel using machine learning models

A device includes a housing defining part of an interior volume and an opening to the interior volume; a cover mounted to the housing to cover the opening and further define the interior volume; a display mounted within the interior volume and viewable through the cover; and a system in package (SiP) mounted within the interior volume. The SiP includes a self-capacitance sense pad adjacent a first surface of the SiP; a set of solder structures attached to a second surface of the SiP, the second surface opposite the first surface; and an IC coupled to the self-capacitance sense pad and configured to output, at one or more solder structures in the set of solder structures, a digital value related to a measured capacitance of the self-capacitance sense pad. The SiP is mounted within the interior volume with the first surface positioned closer to the cover than the second surface.

Force or touch sensing on a mobile device using capacitive or pressure sensing

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Santa Clara, CA, United States of America

Hojjat Seyed Mousavi