View source for Apple inc. (20240345683). SYSTEM AND MACHINE LEARNING METHOD FOR SEPARATING NOISE AND SIGNAL IN MULTITOUCH SENSORS simplified abstract

=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 20240345683 titled 'SYSTEM AND MACHINE LEARNING METHOD FOR SEPARATING NOISE AND SIGNAL IN MULTITOUCH SENSORS

**Simplified Explanation:**
Machine learning techniques, such as gated recurrent units and convolutional neural networks, are used to reduce noise in touch data by removing noise caused by different components of an electronic device.

**Key Features and Innovation:**
- Use of gated recurrent units and convolutional neural networks to mitigate noise in touch data.
- Arrangement of stages in series to remove noise caused by different components of the electronic device.
- Gated recurrent unit stage removes noise from one component, while the convolutional neural network stage removes noise from another component.

**Potential Applications:**
- Improving the accuracy and reliability of touch data in electronic devices.
- Enhancing user experience in touch-sensitive devices.
- Enabling more precise touch input recognition in various applications.

**Problems Solved:**
- Noise reduction in touch data caused by different components of electronic devices.
- Improving the quality of touch input recognition.
- Enhancing the overall performance of touch-sensitive devices.

**Benefits:**
- Increased accuracy and reliability of touch data.
- Enhanced user experience with touch-sensitive devices.
- Improved performance and efficiency of touch input recognition systems.

**Commercial Applications:**
Title: "Enhanced Touch Data Processing Technology for Electronic Devices"
Potential commercial uses include:
- Smartphone and tablet touchscreens.
- Interactive kiosks and displays.
- Automotive touch interfaces.
- Gaming consoles and controllers.

**Questions about Touch Data Processing Technology:**
1. How does the use of gated recurrent units and convolutional neural networks improve touch data processing?
2. What are the specific advantages of arranging the stages in series for noise reduction in touch data?


==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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