Samsung electronics co., ltd. (20240338122). DEVICE AND METHOD WITH TRAINED NEURAL NETWORK TO IDENTIFY TOUCH INPUT simplified abstract
Contents
DEVICE AND METHOD WITH TRAINED NEURAL NETWORK TO IDENTIFY TOUCH INPUT
Organization Name
Inventor(s)
Jinyoung Hwang of Suwon-si (KR)
DEVICE AND METHOD WITH TRAINED NEURAL NETWORK TO IDENTIFY TOUCH INPUT - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240338122 titled 'DEVICE AND METHOD WITH TRAINED NEURAL NETWORK TO IDENTIFY TOUCH INPUT
Simplified Explanation: The patent application describes an electronic device with a touch screen that can identify different types of touch inputs from a user and perform functions based on these inputs.
- The device acquires an image corresponding to the touch input of the user.
- It uses a neural network model to identify the type of touch input, based on the acquired image and images of different pressure levels on the touch screen.
- The device then performs a function based on the identified type of touch input.
Key Features and Innovation:
- Utilizes a neural network model to identify different types of touch inputs.
- Can distinguish between touch inputs with varying pressure levels on the touch screen.
- Enables the device to perform specific functions based on the type of touch input.
Potential Applications:
- Mobile devices with touch screens.
- Interactive kiosks.
- Gaming consoles.
- Educational tools for handwriting recognition.
Problems Solved:
- Enhances user experience by accurately interpreting touch inputs.
- Allows for more precise control and interaction with electronic devices.
- Improves accessibility for users with different touch input preferences.
Benefits:
- Increased efficiency in user interaction.
- Customized user experience based on touch input preferences.
- Enhanced functionality of electronic devices.
Commercial Applications: The technology can be applied in various industries such as consumer electronics, gaming, education, and interactive displays. It can improve user engagement, accessibility, and overall user experience, leading to potential commercial success in these markets.
Questions about Touch Input Recognition: 1. How does the neural network model differentiate between different types of touch inputs? 2. What are the potential limitations of this technology in accurately identifying touch inputs?
Original Abstract Submitted
an electronic device includes a touch screen and a processor configured to: based on a touch input of a user being acquired through the touch screen, acquire an image corresponding to the acquired touch input of the user; identify a type of the acquired touch input of the user by inputting, to a neural network model for identifying the type of the touch input of the user, the acquired image, a first image corresponding to a first type touch input obtained by touching the touch screen with a pressure smaller than a preconfigured pressure, and a second image corresponding to a second type touch input obtained by touching the touch screen with a pressure greater than the preconfigured pressure; and perform a function based on the identified type of the touch input.
