View source for 18629255. SYSTEM AND MACHINE LEARNING METHOD FOR SEPARATING NOISE AND SIGNAL IN MULTITOUCH SENSORS simplified abstract (Apple Inc.)

=SYSTEM AND MACHINE LEARNING METHOD FOR SEPARATING NOISE AND SIGNAL IN MULTITOUCH SENSORS=


==Organization Name==

[[:Category:Apple Inc.|Apple Inc.]]


[[Category:Apple Inc.]]

==Inventor(s)==

[[:Category:Lichen Wang of San Jose CA (US)|Lichen Wang of San Jose CA (US)]][[Category:Lichen Wang of San Jose CA (US)]]

[[:Category:Behrooz Shahsavari of Los Altos Hills CA (US)|Behrooz Shahsavari of Los Altos Hills CA (US)]][[Category:Behrooz Shahsavari of Los Altos Hills CA (US)]]

[[:Category:Hojjat Seyed Mousavi of San Jose CA (US)|Hojjat Seyed Mousavi of San Jose CA (US)]][[Category:Hojjat Seyed Mousavi of San Jose CA (US)]]

[[:Category:Nima Ferdosi of San Jose CA (US)|Nima Ferdosi of San Jose CA (US)]][[Category:Nima Ferdosi of San Jose CA (US)]]

[[:Category:Baboo V. Gowreesunker of Mountain View CA (US)|Baboo V. Gowreesunker of Mountain View CA (US)]][[Category:Baboo V. Gowreesunker of Mountain View CA (US)]]

==SYSTEM AND MACHINE LEARNING METHOD FOR SEPARATING NOISE AND SIGNAL IN MULTITOUCH SENSORS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18629255 titled 'SYSTEM AND MACHINE LEARNING METHOD FOR SEPARATING NOISE AND SIGNAL IN MULTITOUCH SENSORS

'''Simplified Explanation:'''
The patent application discusses using machine learning techniques to mitigate noise in touch data, specifically by employing gated recurrent units and convolutional neural networks in series to remove noise caused by different components of an electronic device.

'''Key Features and Innovation:'''
* Utilizes gated recurrent units and convolutional neural networks to reduce noise in touch data.
* Arranges the gated recurrent unit and convolutional neural network stages in series to address noise from different components of the electronic device.

'''Potential Applications:'''
This technology can be applied in various touch-sensitive electronic devices to improve the accuracy and reliability of touch data, such as smartphones, tablets, and touchscreens in vehicles.

'''Problems Solved:'''
This technology addresses the issue of noise in touch data, which can affect the performance and user experience of touch-sensitive devices.

'''Benefits:'''
* Improved accuracy and reliability of touch data.
* Enhanced user experience with touch-sensitive devices.
* Increased performance and efficiency of touch-based interactions.

'''Commercial Applications:'''
Potential commercial applications include integrating this technology into the development of smartphones, tablets, automotive touchscreens, and other touch-sensitive devices to enhance user experience and performance.

'''Prior Art:'''
Readers can explore prior research on machine learning techniques for noise reduction in touch data, as well as studies on the application of gated recurrent units and convolutional neural networks in signal processing.

'''Frequently Updated Research:'''
Stay updated on advancements in machine learning algorithms for noise reduction in touch data, as well as research on improving the performance of touch-sensitive devices through neural networks.

'''Questions about Touch Data Noise Reduction:'''
1. How does the arrangement of gated recurrent units and convolutional neural networks help in reducing noise in touch data?
2. What are the potential limitations of using machine learning techniques for noise reduction in touch-sensitive devices?

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==Original Abstract Submitted==

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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