公开(公告)号：US12277768B2

公开(公告)日：2025-04-15

申请号：US18357627

申请日：2023-07-24

Applicant: Samsung Electronics Co., Ltd.

Inventor： Jayesh Rajkumar Vachhani ,  Sourabh Vasant Gothe ,  Barath Raj Kandur Raja ,  Pranay Kashyap ,  Rakshith Srinivas ,  Rishabh Khurana

IPC: H04N21/85 ,  G06V20/40 ,  G06V40/20

Abstract: A method for generating at least one segment of a video by an electronic device is provided. The method includes identifying at least one of a context associated with the video and an interaction of a user in connection with the video, analyzing at least one parameter in at least one frame of the video with reference to at least one of the context and the interaction of the user, wherein the at least one parameter includes at least one of a subject, an environment, an action of the subject, and an object, determining the at least one frame in which a change in the at least one parameter occurs, and generating at least one segment of the video comprising the at least one frame in which the parameter changed as a temporal boundary of the at least one segment.

公开(公告)号：US20230377717A1

公开(公告)日：2023-11-23

申请号：US18228455

申请日：2023-07-31

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Barath Raj KANDUR RAJA ,  Sumit Kumar ,  Sriram Shashank ,  Harichandahana Bhogaraju Sawaraja Sai ,  Chinmay Anand ,  Jayesh Rajkumar Vachhani ,  Ankita Bhardwaj ,  Shwetank Choudhary ,  Srishti Malaviya ,  Tarun Gopalakrishnan ,  Dwaraka Bhamidipati Sreevastsa

IPC: G16H20/70 ,  G06F11/34

CPC classification number: G16H20/70 ,  G06F11/3438

Abstract: Provided is a method for predicting emotion of a user by an electronic device. The method includes receiving, by the electronic device, a user context, a device context and an environment context from the electronic device and one or more other electronic device connected to the electronic device and determining, by the electronic device, a combined representation of the user context, the device context and the environment context. The method also includes determining, by the electronic device, a plurality of user characteristics based on the combined representation of the user context, the device context and the environment context; and predicting, by the electronic device, an emotion of the user based on the combined representation of the user context, the device context, the environment context and the plurality of user characteristics.

公开(公告)号：US11776289B2

公开(公告)日：2023-10-03

申请号：US17740585

申请日：2022-05-10

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Sourabh Vasant Gothe ,  Rakshith S ,  Jayesh Rajkumar Vachhani ,  Yashwant Singh Saini ,  Barath Raj Kandur Raja ,  Himanshu Arora ,  Rishabh Khurana

IPC: G06V10/44 ,  G06V30/32

CPC classification number: G06V30/347 ,  G06V30/36

Abstract: Embodiments herein disclose a method and electronic device for predicting multi-modal drawings. The method includes: receiving, by the electronic device, at least one of a text input and strokes of a drawing and determining, by the electronic device, features associated with the text input and features associated with the strokes of the drawing. The method includes classifying, by the electronic device, the features associated with the text input and the features associated with the strokes of the drawing into one of a dominant feature and a non-dominant feature and performing, by the electronic device, early concatenation or late concatenation of the features based on the classification; classifying, by the electronic device, the strokes of the drawing based on the concatenation into a category using a deep neural network (DNN) model; and predicting, by the electronic device, primary drawings corresponding to the category.

公开(公告)号：US20240304010A1

公开(公告)日：2024-09-12

申请号：US18609887

申请日：2024-03-19

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Sourabh Vasant Gothe ,  Vibhav Agarwal ,  Jayesh Rajkumar Vachhani ,  Sourav Ghosh

IPC: G06V20/00 ,  G06T7/11 ,  G06T7/20 ,  G06V10/774 ,  G06V10/80 ,  G06V10/82 ,  G06V20/40

CPC classification number: G06V20/95 ,  G06T7/11 ,  G06T7/20 ,  G06V10/774 ,  G06V10/806 ,  G06V20/41 ,  G06V20/46 ,  G06T2207/10016 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30196 ,  G06T2207/30241 ,  G06V10/82

Abstract: A method for detecting artificial intelligence (AI) generated content in a video, includes: receiving the video comprising a plurality of frames; detecting, an object, a person, and a background in each frame; determining pixel-motion information of each pixel in each frame; determining a relationship among the object, the person, and the background and the corresponding pixel-motion information in each frame; determining one or more intrinsic properties of the object, the person, and the background in each frame based on the relationship among the object, the person, and the background and the corresponding pixel-motion information; detecting inconsistent motion of the object, the person, and the background in at least one frame of the video based on the one or more intrinsic properties of the object, the person, and the background; and indicating AI generated content in the at least one frame based on the detected inconsistent motion.

公开(公告)号：US20220301331A1

公开(公告)日：2022-09-22

申请号：US17740585

申请日：2022-05-10

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Sourabh Vasant GOTHE ,  Rakshith S ,  Jayesh Rajkumar Vachhani ,  Yashwant Singh Saini ,  Barath Raj Kandur Raja ,  Himanshu Arora ,  Rishabh Khurana

IPC: G06V30/32

Abstract: Embodiments herein disclose a method and electronic device for predicting multi-modal drawings. The method includes: receiving, by the electronic device, at least one of a text input and strokes of a drawing and determining, by the electronic device, features associated with the text input and features associated with the strokes of the drawing. The method includes classifying, by the electronic device, the features associated with the text input and the features associated with the strokes of the drawing into one of a dominant feature and a non-dominant feature and performing, by the electronic device, early concatenation or late concatenation of the features based on the classification; classifying, by the electronic device, the strokes of the drawing based on the concatenation into a category using a deep neural network (DNN) model; and predicting, by the electronic device, primary drawings corresponding to the category.