ELECTRONIC DEVICE IDENTIFYING FORCE TOUCH AND METHOD FOR OPERATING THE SAME

    公开(公告)号:US20240152234A1

    公开(公告)日:2024-05-09

    申请号:US18414453

    申请日:2024-01-16

    CPC classification number: G06F3/0418 G06N3/045

    Abstract: According to various embodiments, an electronic device includes a memory storing deep learning models for determining a force touch, a touchscreen, and a processor configured to identify a touch input of a user through the touchscreen, receive touch pixel data for frames having a time difference based on the touch input, and identify whether the touch input is a force touch based on the touch pixel data. The processor is configured to identify whether the touch input is the force touch using a first determination model among the deep learning models in response to identifying that the touch input is reinputted a designated first number of times or more within a designated time, and otherwise, identify whether the touch input is the force touch using a determination model having a lower computation load than the first determination model among the deep learning models.

    ELECTRONIC DEVICE AND METHOD OF RECOGNIZING A TOUCH, BY ELECTRONIC DEVICE

    公开(公告)号:US20230084315A1

    公开(公告)日:2023-03-16

    申请号:US17965373

    申请日:2022-10-13

    Abstract: An example electronic device according to various embodiments may include a fingerprint sensor, a touch sensor, a memory storing at least one instruction, and a processor operatively connected to the fingerprint sensor, the touch sensor, and the memory. The processor may determine whether a touch input is generated in a fingerprint recognition area in which a fingerprint sensor is disposed using a touch sensor, may determine whether the generated touch input continues for a given time or more, may generate first data by accumulating the touch input generated based on the touch input continuing for the given time or more, may determine whether an inputted fingerprint corresponds to a registered fingerprint of a registered user by analyzing the touch input in the fingerprint recognition area, using the fingerprint sensor, may analyze the first data using a first AI model based on the inputted fingerprint corresponding to the fingerprint of a registered user, may analyze the first data using a second AI model based on the inputted fingerprint not corresponding to the fingerprint of a registered user, may identify a form of the touch input based on analysis of the first data, and may perform a function corresponding to the identified form of the touch input and/or executing a user interface corresponding to the identified form of the touch input.

    ELECTRONIC DEVICE AND LEARNING MODEL DETERMINATION METHOD FOR LEARNING OF ELECTRONIC DEVICE

    公开(公告)号:US20240077970A1

    公开(公告)日:2024-03-07

    申请号:US18507634

    申请日:2023-11-13

    CPC classification number: G06F3/0416 G06F3/0488

    Abstract: An electronic device is provided. The electronic device includes a touch sensor, a processor, and a memory. The processor may determine a touch input from a user as at least one of a force-touch input or a long-touch input, based on received touch data, determine whether a result of determining the touch data matches an intention of the user, store data that does not match the intention of the user as a result of determination among the touch data in the memory, and determine a type of an artificial intelligence (AI)-based pre-learning model to be used in the electronic device, based on touch input accuracy and the data that does not match the intention of the user.

    ELECTRONIC DEVICE AND OPERATION METHOD OF ELECTRONIC DEVICE FOR PERFORMING CALCULATION USING ARTIFICIAL INTELLIGENCE MODEL

    公开(公告)号:US20240202590A1

    公开(公告)日:2024-06-20

    申请号:US18415946

    申请日:2024-01-18

    CPC classification number: G06N20/00

    Abstract: According to certain embodiments, an electronic device comprises: a processor and memory storing instructions; and wherein the instructions, when executed by the processor, further cause the electronic device to: load and compile an artificial intelligence model stored in the memory; determine whether the compiled artificial intelligence model includes a first-type activation function; when the first-type activation function is included in the compiled artificial intelligence model, skip a calculation with respect to a designated value when the designated value exists in a feature map and calculate a value to be calculated subsequent to the designated value; and when the first-type activation function is not included in the compiled artificial intelligence model, perform a calculation with respect to input values of the feature map.

    ELECTRONIC DEVICE IDENTIFYING FORCE TOUCH AND METHOD FOR OPERATING THE SAME

    公开(公告)号:US20230098019A1

    公开(公告)日:2023-03-30

    申请号:US17899138

    申请日:2022-08-30

    Abstract: According to various embodiments, an electronic device includes a memory storing deep learning models for determining a force touch, a touchscreen, and a processor configured to identify a touch input of a user through the touchscreen, receive touch pixel data for frames having a time difference based on the touch input, and identify whether the touch input is a force touch based on the touch pixel data. The processor is configured to identify whether the touch input is the force touch using a first determination model among the deep learning models in response to identifying that the touch input is reinputted a designated first number of times or more within a designated time, and otherwise, identify whether the touch input is the force touch using a determination model having a lower computation load than the first determination model among the deep learning models.

    ELECTRONIC DEVICE FOR PROCESSING USER UTTERANCE AND CONTROLLING METHOD THEREOF

    公开(公告)号:US20220319499A1

    公开(公告)日:2022-10-06

    申请号:US17245751

    申请日:2021-04-30

    Abstract: An electronic device is provided. The electronic device includes a microphone, and at least one processor operatively connected to the microphone, wherein the at least one processor may include a buffer memory configured to store a first feature vector for a first voice signal obtained from the microphone as an inverse value, and an operation circuit configured to perform a norm operation for a first feature vector and a second feature vector, based on the second feature vector, based on a second voice signal streamed from the microphone and an inverse value of the first feature vector stored in the buffer memory, or calculate a similarity between the first feature vector and the second feature vector. In addition, various embodiments identified through the specification are possible.

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