Samsung Electronics Co., Ltd.

Anuradha Kanukotla

Sripurna Mutalik

Jaeho Kim

Naresh Kumar Narasimma Moorthy

Kwanjin Jung

Wonseo Choi

Sumeen Agrawal

Pavan Nittur

Manith Shetty

Kumar Abhisek

Hyunjoon Kim

Mayank Gupta

Renju Chirakarotu Nair

Manjunath Channappagoudar

Kyungseok Lee

Nidhal Kottamoola Ibrahimkutty

Ramalingam Ramamoorthy

Atikant Singh

Syama Sudheesh

Dwait Bhatt

Prateek Bansal

Senthil Kumar Iyyappan

Narendra Mutyala

Disclosed is a method for boosting (e.g., improving) graphics performance of an electronic device. The method for boosting graphics performance of an electronic device comprises: determining a context of the electronic device for boosting the graphics performance, determining a current state of the electronic device, determining at least one graphics configuration of hardware components of the electronic device and at least one application of the electronic device based on the context of the electronic device and the current state of the electronic device and generating a virtual display for displaying the at least one application based on the at least one graphics configuration of the hardware components and the at least one application.

Method and electronic device for boosting graphics performance

A method for managing a boost time required for an application launch in an electronic device is provided. The method includes detecting, by the electronic device, a user input to launch the application. Further, the method includes measuring, by the electronic device, real-time system health parameters of the electronic device. Further, the method includes predicting, by the electronic device, an application launch time by inputting the real-time system health parameters to an AI-based application prediction model. Further, the method includes boosting, by the electronic device, at least one hardware of the electronic device based on the predicted application launch time.

Method and electronic device for managing a boost time required for an application launch

Embodiments of the disclosure provide a method for accelerating an asset execution in an electronic device. The method includes: detecting, by the electronic device, a user interface (UI) transition associated with a first application; predicting, by the electronic device, a sequence of at least one asset to be offloaded for a subsequent UI transition based on an asset parameter using a ML model, wherein the subsequent UI transition is associated with the first application or a second application; offloading, by the electronic device, the at least one asset in the sequence; and storing, by the electronic device, the at least one offloaded asset to a memory of the electronic device.

Method and electronic device for accelerating asset execution

Accordingly the embodiments herein provide a method for managing scheduling of services during a boot-up process in an electronic device including a multi-core processor. The method includes determining a plurality of services initiated during the boot-up process of the electronic device. Further, the method includes registering system parameters associated with the electronic device for each one of the determined services. Further, the method includes determining whether the service is critical or non-critical for the boot-up process. Further, the method includes tagging a label data to each one of the determined services, wherein the label data represents whether the service is critical or non-critical. Further, the method includes clustering each of the services into one of an accelerating cluster and a decelerating cluster based on the registered system parameters associated with the electronic device and the tagged label data.

Method and apparatus for managing scheduling of services during boot-up

A method for machine learning based prediction of at least one subsequent UI layout is provided. The method may include detecting, by the electronic device, a first transition event. Further, the method may include identifying, by the electronic device, a UI layout associated with a first application of the electronic device. Further, the method may include predicting, by the electronic device, the at least one subsequent UI layout to be displayed based on at least one transition parameter, wherein the at least one subsequent UI layout is associated with at least one of the first application or at least one second application. Further, the method may include loading, by the electronic device, the at least one subsequent UI layout in a memory of the electronic device.

Method and electronic device for machine learning based prediction of subsequent user interface layouts

Provided are a method and apparatus for controlling an unresponsive state of an electronic device. The method includes: obtaining a plurality of device parameters related to at least one of a hardware module and a software module of the electronic device, and a user usage pattern parameter based on receiving at least one user input to the electronic device; predicting the unresponsive state of the electronic device based on the collected plurality of device parameters and the collected user usage pattern parameter; and avoiding the predicted unresponsive state of the electronic device by applying a policy selected from among a plurality of policies associated with the plurality of the device parameters to a device parameter related to the predicted unresponsive state of the electronic device.

Methods and systems for managing an electronic device

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Bengaluru, India

Anuradha Kanukotla