公开(公告)号：US20230196207A1

公开(公告)日：2023-06-22

申请号：US18109042

申请日：2023-02-13

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Prasenjit CHAKRABORTY ,  Narasimha Rao THURLAPATI ,  Srinidhi N ,  Eric Ho Ching YIP ,  Jyotirmoy KARJEE ,  Jaskamal KAINTH ,  Ramesh Badu VENKAT DABBIRU

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Provided is a method for adaptively streaming an artificial intelligence (AI) model file, including determining a capability of a first electronic device and a capability of a second electronic device, network information associated with the first and second electronic devices, and AI model information associated with the AI model file; determining to adaptively stream the AI model file based on the determined capabilities and information; pre-processing the AI model file; and adaptively streaming the AI model.

公开(公告)号：US20220021913A1

公开(公告)日：2022-01-20

申请号：US17372993

申请日：2021-07-12

Applicant: Samsung Electronics Co., Ltd.

Inventor： Eric Ho Ching YIP ,  Hyunkoo YANG ,  Jaeyeon SONG

IPC: H04N21/235 ,  H04N21/234 ,  H04N21/44 ,  H04N21/81

Abstract: A method of accessing 3D media content based on perspective-based random access is provided. The method includes receiving the media content and metadata, wherein the metadata includes first information about a perspective and second information about at least one face onto which the 3D object is projected, and the at least one face is associated with the perspective, and performing the perspective-based random access for the media content based on the first information and the second information.

公开(公告)号：US20250013874A1

公开(公告)日：2025-01-09

申请号：US18891095

申请日：2024-09-20

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Jyotirmoy KARJEE ,  Praveen Naik S ,  Srinidhi NAGARAJA RAO ,  Eric Ho Ching YIP ,  Prasenjit CHAKRABORTY ,  Ramesh Babu Venkat DABBIRU

IPC: G06N3/098

Abstract: Systems and methods for optimal split federated learning (O-SFL) in a wireless network, including: receiving, by a federal device in the wireless network, local split points associated with a deep neural network (DNN) model over a time period from at least one client device of a plurality of client devices, wherein the plurality of client devices are connected to an edge device for training the DNN model using split federated learning (SFL); determining, by the federal device, an average of the local split points; determining, by the federal device, a global split point for partitioning the DNN model between the at least one client device and the edge device based on the average of the local split points; and applying, by the federal device, the determined global split point to train the DNN model.