Patent ID: 11861395
Assignee: SAMSUNG ELECTRONICS CO., LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 13:
14. An application memory management system in a computing environment comprising:
a controller; and
an artificial intelligence (AI) module, wherein said controller interacts with the AI module and is configured to:
receive a selection of a preferred application;
register the selected application to a Heavy-app Memory Management unit (HAMM);
monitor transitions of the selected application between a foreground and a background during user operation of the computing environment; and
trigger retention of the application in memory based on a transition to background during said user operation, wherein retention of the application in memory comprises:
compressing memory portions of the application;
retaining the application in a retained state based on the compressed memory portions;
sensing a requirement to restore the retained application based on at least one of a user selection or an automatically generated prediction; and
restoring the application from the retained state back to the foreground based on sensing the requirement to restore the retained application, wherein restoring the application comprises decompressing the compressed portions of the application in the memory to enable occupation of additional space in the memory by the application; and
allowing the user control over the application,

wherein the controller is configured to receive the selection of the preferred application by receiving one or more of:
a selection provided through a user interface; or
an output of a machine learning (ML) model trained at least in part on a user's application usage behavior,

wherein the controller is configured to trigger retention of the application by initiating compression based on using a timeout of the application transitioning into the background, wherein the timeout is at least one of a static timeout or a decaying timeout dynamically determined by the ML model, and
wherein the HAMM is based on input features, a context data of the user and computing system, the ML model, a multi-stochastic gradient descent regression and a gradient descent optimizer.