BATTERY RESISTANCE MEASURING METHOD, BATTERY POWER MANAGING METHOD AND ELECTRONIC DEVICE USING THE METHOD

    公开(公告)号:US20250147108A1

    公开(公告)日:2025-05-08

    申请号:US18504120

    申请日:2023-11-07

    Applicant: MEDIATEK INC.

    Abstract: A battery resistance measuring method, applied to a battery with a battery resistance, comprising: (a) acquiring charge variation of the battery for a measuring time interval; (b) acquiring a voltage difference between a first battery voltage and a second battery voltage for the measuring time interval, wherein the first battery voltage is a battery voltage with loading and the second battery voltage is a battery voltage without loading; and (c) computing a battery resistance according to the charge variation and the voltage difference, and updating the battery resistance to a battery resistance table of the battery. The above-mentioned steps (a), (b) and (c) may be performed by the electronic device, which can be a mobile electronic device such as a mobile phone or a plate computer.

    ELECTRONIC DEVICE AND METHOD FOR HANDLING ARTIFICIAL INTELLIGENCE MODEL SWITCHING

    公开(公告)号:US20250036977A1

    公开(公告)日:2025-01-30

    申请号:US18751335

    申请日:2024-06-23

    Applicant: MEDIATEK INC.

    Abstract: An electronic device is configured to execute instructions: compiling a first AI model and second AI model(s) to a first compiled file and second compiled file(s), respectively, wherein the first compiled file comprises a first data set and a first command set, and the second compiled file(s) comprises second data set(s) and second command set(s); generating light version file(s) for the AI model(s), wherein the light version file(s) comprises the second command set(s) and data patch(es); storing the first compiled file and the light version file(s) to a storage device; loading the first compiled file from the storage device to a memory; loading the light version file(s) from the storage device to the memory; generating the second data set(s) according to the first data set and the data patch(es); and executing the second AI model(s) according to the generated second data set(s) and the second command set(s) in the memory.

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