Samsung Electronics Co., Ltd.

Junhyuk Lee

Hyunbin Park

Seungjin Yang

Jin Hwan Choi

Jin Woo Choi

Kyunghwan Lee

Kyungmin Park

Jin Sung Choi

Jiyoung Kang

Jungho Park

Soohyung Kim

Mirae Jeong

Sejeong Kwon

Hyonsok Lee

Kwangman Ok

Jeonghwan Min

Jeonghyeon Lee

Sukjin Jung

Yul Kim

Wonsuk Yang

Yoonnyung Kim

Sunghwan Moon

An electronic device is provided. The electronic device includes a memory, and a processor including a resource management unit and a neural processing unit. The processor may be configured to obtain an execution request for a specific function operating based on a specific neural network model, identify an available bandwidth of the memory through the resource management unit, and quantize the specific neural network model based on the available bandwidth of the memory through the neural processing unit.

Electronic device for processing neural network model and method of operating the same

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 identifying force touch and method for operating the same

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 learning model determination method for learning of electronic device

An electronic apparatus is provided. The electronic apparatus includes a communication interface, a memory storing log data with respect to external devices connected to the electronic apparatus, and a processor configured to identify a plurality of external devices having a history of being connected to the same internet protocol (IP) based on the log data, acquire, based on the log data, a first feature vector with respect to a relationship between the plurality of external devices and a second feature vector with respect to each of the plurality of external devices, acquire a graph of the relationship between the plurality of external devices based on the first feature vector and the second feature vector, and define at least one group configured by the plurality of external devices based on the graph.

Electronic apparatus and method for controlling thereof

An example electronic device may include a fingerprint sensor, a touch sensor, a memory, and a processor. The processor may be configured to determine whether a touch input is generated, determine whether the generated touch input continues for a given time or more, generate first data by accumulating the touch input generated based on the touch input continuing for the given time or more, determine whether an inputted fingerprint corresponds to a registered fingerprint of a registered user, analyze the first data using a first AI model based on the inputted fingerprint corresponding to the registered fingerprint, analyze the first data using a second AI model based on the inputted fingerprint not corresponding to the registered fingerprint, identify a form of the touch input based on the first data, and perform a function and/or execute a user interface corresponding to the identified form of the touch input.

Electronic device and method of recognizing a force touch, by electronic device

An electronic apparatus and controlling method thereof is provided. The electronic apparatus includes a processor configured to, based on receipt of a command to execute an application, execute the application based on the boosting level information, based on a difference between a loading time according to the application being executed and a reference loading time being equal to or greater than a threshold value, identify another boosting level information of the electronic apparatus based on the reference loading time, and update the stored boosting level information to the identified other boosting level information.

Electronic apparatus and controlling method thereof

A method of updating a server knowledge graph, is performed by a server and includes obtaining a server knowledge graph of the server, and obtaining a plurality of device knowledge graphs by receiving a device knowledge graph from each of a plurality of devices. The method further includes generating a knowledge graph for server knowledge graph extension, based on the obtained plurality of device knowledge graphs, and updating the obtained server knowledge graph, using the generated knowledge graph for server knowledge graph extension.

System and method for updating knowledge graph

Disclosed herein is a method of operating an electronic device having a camera. The electronic device includes a camera, a display unit, a sensor unit configured to detect an external event value, an image processor configured to process data received through the camera into a preview image, and a main processor. The main processor is configured to switch from a preview mode to a ready mode when the external event value is detected by the sensor unit, wherein the preview mode comprises activation of the camera, the image processor to display the preview image on the display unit, and the ready mode comprises deactivation of the camera and the image processor and display of a ready mode image on the display unit.

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Suwon-si, South Korea

Junhyuk Lee