Abstract: An artificial intelligence (AI) based method to enhance launching of an application at a user equipment (UE) is provided. The method includes monitoring launch of the application on the UE. The method includes determining an event to be executed upon the launch of the application. The method includes categorizing the event into one of a UI updating event and a non-UI updating event. Further, generating an execution flow based on the categorization, wherein the execution flow is indicative of prioritizing the UI updating event for execution before the non-UI updating event such that prioritization prevents a mismatch of the UI components during the launch. The method includes storing the execution flow and executing the stored execution flow during a subsequent launch of the application, such that launching of the at application is enhanced.

Abstract: 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.

Abstract: A method for managing application launching in an electronic device is provided. The method includes identifying a plurality of user interaction parameters and a plurality of hardware parameters of the electronic device, identifying, based on the plurality of user interaction parameters and the plurality of hardware parameters, an optimal time to pre-launch an application in a background, identifying at least one next probable application to be launched, pre-launching the at least one next probable application in the background at the optimal time, detecting at least one event to launch the at least one second application in the foreground, and launching the at least one second application in the foreground based on a user input.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.