公开(公告)号：US20230239381A1

公开(公告)日：2023-07-27

申请号：US18296107

申请日：2023-04-05

Applicant: Samsung Electronics Co., Ltd.

Inventor： Dongjin SEO ,  Sungyoung LEE ,  Sunghyun YOON

IPC: H04M1/02

CPC classification number: H04M1/026

Abstract: An electronic device including a side key is provided. The electronic device includes a housing including a first surface facing a first direction, a second surface facing a second direction opposite to the first direction, and a side part surrounding the space formed between the first surface and the second surface, and a side key coupled to the side part of the housing, wherein the side key includes a contact part exposed outside the housing through a through-hole formed from the side surface of the housing, and accommodation part formed in the side part of the housing, is connected to the through-hole, and includes an opening open from the inside of the housing in the first direction, a printed circuit board accommodated in the accommodation part and a cover bracket arranged in a third direction together with the contact part and the printed circuit board, and covers the opening.

公开(公告)号：US20220147870A1

公开(公告)日：2022-05-12

申请号：US17421292

申请日：2020-01-06

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Vivek AGARWAL ,  Jatin GARG ,  Jayoon KOO ,  Dongjin SEO ,  Kwanki AHN ,  Keuntek LEE

IPC: G06N20/00 ,  G06F16/435 ,  H04N21/466

Abstract: An electronic device according to an embodiment of the disclosure includes: a communicator; a memory storing one or more instructions; at least one processor configured to execute the one or more instructions stored in the memory to collect content metadata and user metadata from a plurality of different servers that provide content, obtain a content latent factor including information about similarities between pieces of the content based on characteristics of the content metadata, by using a first learning network model, obtain a user latent factor related to user preferred content information based on characteristics of the user metadata, by using a second learning network model, obtain a user preference score for the content based on the content latent factor and the user latent factor, by using a third learning network model, and provide a recommended content list based on the user preference score.