Apple Inc.

Sai Zhang

Ari Y Benbasat

Eric S Winokur

Andreas Johannes Koeberl

Branislav Jovanovic

Nima Ferdosi

Behrooz Shahsavari

Sabino Joseph Pietrangelo, Ii

Andrew William Shorten

Liqun Yang

Sean Erik O'Connor

Baboo V Gowreesunker

David Steven Graff

Matthew Dominic Hollands

Dor Shaviv

Yash S Agarwal

Emmanuell Jose Merced-Grafals

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.

Systems and methods for touch sensing on devices including regions with and without touch electrodes

Techniques for rejecting apparent (but false) touches caused by objects such as water droplets located in areas with parasitic capacitive paths to ground are disclosed. To minimize these false touches, one or more guard conductors can be located in proximity to the housing and driven with a stimulation signal to shield objects from being capacitively coupled to ground through the housing. In some examples images of touch can be obtained from a non-bootstrapped or bootstrapped scan and also an extended bootstrapped scan wherein the guard conductor is driven with a stimulation signal that has the same characteristics as the stimulation signal being applied to the sensed touch nodes. In some examples, the results of the extended bootstrapped scan can be subtracted from the non-bootstrapped or bootstrapped scan to identify and reject apparent touches resulting from capacitive coupling to ground.

Touch sensing with water rejection

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.

Methods and apparatus for cross-coupling correction on a touch sensor panel

In some examples, an electronic device can classify patches of touch data obtained by a touch screen as corresponding to intentional user inputs or not corresponding to intentional user inputs. The device can include a touch screen on a first side of housing of the device and a drive electrode on a second side of the housing of the device in some examples. In some examples, the drive electrode on the second side of the housing of the device can apply a signal to the body of the user and the device can classify patches of touch data including characteristics of this signal as corresponding to intentional touches provided by the user. The device can perform subsequent operations in response to intentional touches provided by the user and forgo performing operations in response to patches of touch data that do not correspond to intentional touches provided by the user.

Systems and methods for improved touch screen selectivity and sensitivity

Touch input processing for touch-sensitive devices can be used to filter unintended contact detected on a touch-sensitive surface. Moist or wet fabrics on the edge of a touch-sensitive surface can be erroneously be detected as touch input and degrade touch performance. In some examples, input paths can be classified as touch paths or non-touch paths (corresponding to wet fabrics). Non-touch paths can be filtered out to avoid unintended input to a touch-sensitive device. Classifying paths can improve touch performance in environments where a wet fabric may come in contact with the edge of the touch-sensitive surface. In some examples, paths can be classified as touch paths or non-touch paths based on characteristics of edge touch nodes. In some examples, paths can be classified as touch paths or non-touch paths based on a determined state.

Touch rejection for wet fabric

A touch sensitivity of a touch-sensitive surface can be adjusted based on a state of a device including the touch-sensitive surface. The state of the device can be a first state or a second state. In the first state, for example, the touch sensing system of the device can be programmed to recognize and process a wide range of touch signals including relatively weak touch signals, which may correspond to water, liquid or other unintentional touches. In the second state, for example, the touch detection threshold can be adjusted to better reject water or unintended touches. In some examples, a ratio of measurements captured using the different types of scans of a selected touch node can be used to determine the state of the device.

State-based touch threshold

Growing community of inventors

Santa Clara, CA, United States of America

Sai Zhang