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公开(公告)号：US20220319151A1
公开(公告)日：2022-10-06
申请号：US17509615
申请日：2021-10-25
Inventor： Jong Sung KIM , Seong Il YANG , Min Sung YOON , Si Hwan JANG
IPC: G06V10/764 , G06V20/40 , G06V10/28
Abstract: Provided is an impact motion recognition system for screen-based multi-sport coaching. The impact motion recognition system for screen-based multi-sport coaching includes: a high-speed stereo camera device installed on an upper end of a target object including a hitting body and an moving body and configured to acquire a stereo image sequence in which the hitting body and the moving body are photographed; an object detection unit configured to detect an object in the acquired stereo image sequence; a moving body recognition unit configured to identify whether an impact motion has occurred in the detected object to recognize the moving body; a hitting body detection unit configured to, based on the moving body being recognized through the moving body recognition unit, detect the object excluding the moving body as the hitting body; a target object tracking unit configured to continuously detect a center point of the moving body and a feature point of the hitting body in a subsequent stereo image sequence using characteristics of the moving body and the hitting body that are detected, and continuously track the center point of the moving body and the feature point of the hitting body until the center point of the moving body and the feature point of the hitting body are not detected; and a motion analysis unit configured to analyze three-dimensional (3D) motion information of the target object using the center point of the moving body and the feature point of the hitting body, which are detected through the target object tracking unit, and high-speed stereo camera correction data.
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公开(公告)号：US20210200923A1
公开(公告)日：2021-07-01
申请号：US17139216
申请日：2020-12-31
Inventor： Sihwan JANG , Chan Sub KIM , Seong Il YANG
Abstract: Device for simulation environment for training Al agent includes scene object providing module to provide scene and object in virtual content converted from original content; a reward function providing module to provide reward function used by agent to perform reinforcement learning in the virtual content; an environment information providing module to provide virtual environment information including information on environment where the agent performs the reinforcement learning in the virtual content; a status information providing module to provide virtual status information indicating status of the agent in the virtual content; an action space providing module to provide virtual action space indicating action of the agent; and a virtual learning module to create simulation environment based on at least one of the scene, the object, the reward function, the virtual environment information, the virtual status information, and the virtual action space, and perform virtual learning for the agent in the simulation environment.
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公开(公告)号：US20240029307A1
公开(公告)日：2024-01-25
申请号：US18070924
申请日：2022-11-29
Inventor： Jong Sung KIM , Seong Il YANG , Minsung YOON
CPC classification number: G06T7/85 , G06V40/10 , G06T2207/10024
Abstract: Provided is a method of synthesizing 3D joint data based on a multi-view RGB-D camera. The method of synthesizing 3D joint data includes: converting joint data collected from a depth camera of each of a plurality of RGB-D cameras from a depth camera coordinate system of each of the RGB-D cameras to a color camera coordinate system of each of the RGB-D cameras; calculating a confidence level of the joint data converted to the color camera coordinate system using a 3D joint recognition algorithm based on the joint data converted to the color camera coordinate system; applying a rotation matrix and a translation vector to the joint data and converting the joint data to a predetermined reference coordinate system; and obtaining a weighted-average of the joint data converted to the reference coordinate system using a weight calculated based on the confidence level to synthesize the joint data.
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公开(公告)号：US20200179808A1
公开(公告)日：2020-06-11
申请号：US16707781
申请日：2019-12-09
Inventor： Sang Kwang LEE , Seong Il YANG
Abstract: An agent creating method of creating an agent in a game environment is provided. The agent creating method includes creating, by an agent creating apparatus, a base agent on the basis of a common action characteristic pattern of players to transfer the base agent to the game server, receiving, by the agent creating apparatus, match result data obtained by performing a match between the base agent and a character of an individual player, and performing, by the agent creating apparatus, machine learning by using the match result data and creating an evolved agent customized for matchmaking of the individual player on the basis of a result of the machine learning.
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5.发明申请公开(公告)号：US20190209928A1
公开(公告)日：2019-07-11
申请号：US16151561
申请日：2018-10-04
Inventor： Si Hwan JANG , Seong Il YANG , Hyung Keun JEE
Abstract: Disclosed is an apparatus for analyzing a game update effect according to a change in a gamer action sequence, the apparatus including: a gamer action information collector configured to collect action information of a gamer from a game operating server that provides a gamer terminal with a game and stores the action information of the gamer therein; a gamer action information sequence identifier configured to detect an action sequence of the gamer from the collected action information of the gamer; and an update result analyzer configured to analyze an consequence on behavior of the gamer with respect to an update of a game service by comparing action sequences of the gamer detected through the gamer action information sequence identifier on the basis of a time point of the update of the game service.
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Patent Agency Ranking
公开(公告)号：US20210197087A1
公开(公告)日：2021-07-01
申请号：US17137741
申请日：2020-12-30
Inventor： Sang Kwang LEE , Seong Il YANG , Seung-Jin HONG
IPC: A63F13/47
Abstract: An apparatus and a method for predicting a result of a computer game at a specific time point using obtaining play attributes of the computer game in a time slot including the specific time point; and predicting the result of the computer game at the specific time point by inputting the play attributes to a game result prediction model corresponding to the time slot are provided.
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7.发明申请SPOKEN DIALOG SYSTEM BASED ON DUAL DIALOG MANAGEMENT USING HIERARCHICAL DIALOG TASK LIBRARY 有权基于双向对话管理的语音对话系统使用分层对话任务库公开(公告)号：US20140136212A1
公开(公告)日：2014-05-15
申请号：US13849853
申请日：2013-03-25
Inventor： Oh Woog KWON , Yoon Hyung ROH , Seong Il YANG , Ki Young LEE , Sang Keun JUNG , Sung Kwon CHOI , Eun Jin PARK , Jinxia HUANG , Young Kil KIM , Chang Hyun KIM , Seung Hoon NA , Young Ae SEO , Yun JIN , Jong Hun SHIN , Sang Kyu PARK
IPC: G10L15/22
CPC classification number: G10L15/22 , G06F3/167 , G10L2015/225 , H04M3/4938
Abstract: The present invention relates to a spoken dialog system and method based on dual dialog management using a hierarchical dialog task library that may increase reutilization of dialog knowledge by constructing and packaging the dialog knowledge based on a task unit having a hierarchical structure, and may construct and process the dialog knowledge using a dialog plan scheme about relationship therebetween by classifying the dialog knowledge based on a task unit to make design of a dialog service convenient, which is different from an existing spoken dialog system in which it is difficult to reuse dialog knowledge since a large amount of construction costs and time is required.
Abstract translation: 本发明涉及一种基于双对话管理的口语对话系统和方法，其中使用层次对话任务库，其可以通过基于具有层次结构的任务单元构建和打包对话知识来增加对话知识的再利用，并且可以构建和 使用关于它们之间的关系的对话计划方案处理对话知识，通过对基于任务单元的对话知识进行分类，使对话服务的设计方便，这与现有的口语对话系统不同，其中难以重用对话知识，因为 需要大量的施工成本和时间。
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公开(公告)号：US20240292057A1
公开(公告)日：2024-08-29
申请号：US18539047
申请日：2023-12-13
Inventor： Seong Il YANG , Jong Sung KIM , Min Sung YOON
IPC: H04N21/442 , G06F40/30 , H04N21/4788
CPC classification number: H04N21/44213 , G06F40/30 , H04N21/4788
Abstract: The present disclosure provides a method and device for analyzing user satisfaction of screen sports contents. According to one embodiment, the present disclosure provides a method of analyzing user satisfaction of a screen sports content, including generating first satisfaction detection information based on real-time chat data of a user using a text feature recognition model, generating second satisfaction detection information based on real-time chat data of the user using a language feature recognition model, and predicting real-time satisfaction of the user based on the first satisfaction detection information and the second satisfaction detection information using a satisfaction prediction model.
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公开(公告)号：US20230097454A1
公开(公告)日：2023-03-30
申请号：US17873430
申请日：2022-07-26
Inventor： Jong Sung KIM , Seong Il YANG , Min Sung YOON , Si Hwan JANG
IPC: G06T7/73
Abstract: According to an embodiment of the present disclosure, a method for learning an exercise posture of a user is disclosed. The method includes: checking joint feature point information which is constructed based on a joint of the user; learning a ready posture learning model by learning the joint feature point information corresponding to a ready posture of the user; and learning an exercise posture learning model by learning the joint feature point information corresponding to an exercise posture of the user.
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公开(公告)号：US20220197784A1
公开(公告)日：2022-06-23
申请号：US17554579
申请日：2021-12-17
Inventor： Si Hwan JANG , Chan Sub KIM , Seong Il YANG
Abstract: A method of guaranteeing game quality by using an artificial intelligence (AI) agent is provided. The method includes extracting an item list (hereinafter referred to as an inspection item list) for inspecting quality of a target game, extracting and storing log data corresponding to a test performance result for each item of the inspection item list, performing imitation learning of an AI agent model on the basis of the stored log data, performing an automatic test for inspecting quality of the target game by using the AI agent model on which the imitation learning is completed, and automatically recording a bug and an error detected by the AI agent model.
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