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公开(公告)号：US20140212854A1
公开(公告)日：2014-07-31
申请号：US13755783
申请日：2013-01-31
Applicant: SRI INTERNATIONAL
Inventor： Ajay Divakaran , Behjat Siddiquie , Saad Khan , Jeffrey Lubin , Harpreet S. Sawhney
IPC: G09B25/00
CPC classification number: G09B19/00
Abstract: A multi-modal interaction modeling system can model a number of different aspects of a human interaction across one or more temporal interaction sequences. Some versions of the system can generate assessments of the nature or quality of the interaction or portions thereof, which can be used to, among other things, provide assistance to one or more of the participants in the interaction.
Abstract translation: 多模式交互建模系统可以对一个或多个时间相互作用序列之间的人类交互的许多不同方面进行建模。 该系统的一些版本可以产生相互作用或其部分的性质或质量的评估，其可以用于向交互中的一个或多个参与者提供帮助。
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公开(公告)号：US10679063B2
公开(公告)日：2020-06-09
申请号：US14846318
申请日：2015-09-04
Applicant: SRI International
Inventor： Hui Cheng , Ajay Divakaran , Elizabeth Shriberg , Harpreet Singh Sawhney , Jingen Liu , Ishani Chakraborty , Omar Javed , David Chisolm , Behjat Siddiquie , Steven S. Weiner
IPC: G06K9/00 , H04N21/44 , H04N21/8549 , G11B27/10 , G11B27/031 , G06F16/43 , G06F16/78 , H04N21/4402 , H04N21/233 , H04N21/234 , H04N21/439
Abstract: A computing system for recognizing salient events depicted in a video utilizes learning algorithms to detect audio and visual features of the video. The computing system identifies one or more salient events depicted in the video based on the audio and visual features.
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公开(公告)号：US20160071024A1
公开(公告)日：2016-03-10
申请号：US14631124
申请日：2015-02-25
Applicant: SRI International
Inventor： Mohamed R. Amer , Behjat Siddiquie , Ajay Divakaran , Colleen Richey , Saad Khan , Harpreet S. Sawhney
CPC classification number: G06N99/005 , G06K9/6296 , G06N7/005
Abstract: Technologies for analyzing temporal components of multimodal data to detect short-term multimodal events, determine relationships between short-term multimodal events, and recognize long-term multimodal events, using a deep learning architecture, are disclosed.
Abstract translation: 公开了用于分析多模态数据的时间分量以检测短期多模态事件的技术，确定短期多模态事件之间的关系，并使用深度学习架构来识别长期多模态事件。
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公开(公告)号：US09875445B2
公开(公告)日：2018-01-23
申请号：US14631124
申请日：2015-02-25
Applicant: SRI International
Inventor： Mohamed R. Amer , Behjat Siddiquie , Ajay Divakaran , Colleen Richey , Saad Khan , Hapreet S. Sawhney , Timothy J. Shields
CPC classification number: G06N99/005 , G06K9/6296 , G06N7/005
Abstract: Technologies for analyzing temporal components of multimodal data to detect short-term multimodal events, determine relationships between short-term multimodal events, and recognize long-term multimodal events, using a deep learning architecture, are disclosed.
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Patent Agency Ranking
5.发明申请EXPLOITING MULTI-MODAL AFFECT AND SEMANTICS TO ASSESS THE PERSUASIVENESS OF A VIDEO 审中-公开开展多模式影响和语法来评估视频的独立性公开(公告)号：US20160328384A1
公开(公告)日：2016-11-10
申请号：US14874348
申请日：2015-10-02
Applicant: SRI International
Inventor： Ajay Divakaran , Behjat Siddiquie , David Chisholm , Elizabeth Shriberg
CPC classification number: G06F17/2785 , G06F17/241 , G06F17/271 , G06F17/2765 , G06K9/00718 , G06K9/00744 , G06K9/3266
Abstract: Technologies to detect persuasive multimedia content by using affective and semantic concepts extracted from the audio-visual content as well as the sentiment of associated comments are disclosed. The multimedia content is analyzed and compared with a persuasiveness model.
Abstract translation: 公开了通过使用从视听内容中提取的情感和语义概念以及相关意见的情绪来检测说服性多媒体内容的技术。 多媒体内容进行了分析，并与一个说服力模型进行了比较。
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6.发明申请RECOGNIZING SALIENT VIDEO EVENTS THROUGH LEARNING-BASED MULTIMODAL ANALYSIS OF VISUAL FEATURES AND AUDIO-BASED ANALYTICS 审中-公开通过基于学习的视觉特征和基于音频的分析的多模式分析来识别视听活动公开(公告)号：US20160004911A1
公开(公告)日：2016-01-07
申请号：US14846318
申请日：2015-09-04
Applicant: SRI International
Inventor： Hui Cheng , Ajay Divakaran , Elizabeth Shriberg , Harpreet Singh Sawhney , Jingen Liu , Ishani Chakraborty , Omar Javed , David Chisolm , Behjat Siddiquie , Steven S. Weiner
IPC: G06K9/00 , H04N21/44 , H04N21/8549 , H04N21/439 , H04N21/233 , H04N21/234
Abstract: A computing system for recognizing salient events depicted in a video utilizes learning algorithms to detect audio and visual features of the video. The computing system identifies one or more salient events depicted in the video based on the audio and visual features.
Abstract translation: 用于识别视频中描绘的突出事件的计算系统利用学习算法来检测视频的音频和视觉特征。 计算系统基于音频和视觉特征识别视频中描绘的一个或多个突出事件。
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公开(公告)号：US09734730B2
公开(公告)日：2017-08-15
申请号：US13755775
申请日：2013-01-31
Applicant: SRI International
Inventor： Ajay Divakaran , Behjat Siddiquie , Saad Khan , Jeffrey Lubin , Harpreet S. Sawhney
IPC: G09B19/00
CPC classification number: G09B19/00
Abstract: A multi-modal interaction modeling system can model a number of different aspects of a human interaction across one or more temporal interaction sequences. Some versions of the system can generate assessments of the nature or quality of the interaction or portions thereof, which can be used to, among other things, provide assistance to one or more of the participants in the interaction.
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公开(公告)号：US20140212853A1
公开(公告)日：2014-07-31
申请号：US13755775
申请日：2013-01-31
Applicant: SRI International
Inventor： Ajay Divakaran , Behjat Siddiquie , Saad Khan , Jeffrey Lubin , Harpreet S. Sawhney
IPC: G09B19/00
CPC classification number: G09B19/00
Abstract: A multi-modal interaction modeling system can model a number of different aspects of a human interaction across one or more temporal interaction sequences. Some versions of the system can generate assessments of the nature or quality of the interaction or portions thereof, which can be used to, among other things, provide assistance to one or more of the participants in the interaction.
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公开(公告)号：US10303768B2
公开(公告)日：2019-05-28
申请号：US14874348
申请日：2015-10-02
Applicant: SRI International
Inventor： Ajay Divakaran , Behjat Siddiquie , David Chisholm , Elizabeth Shriberg
Abstract: Technologies to detect persuasive multimedia content by using affective and semantic concepts extracted from the audio-visual content as well as the sentiment of associated comments are disclosed. The multimedia content is analyzed and compared with a persuasiveness model.
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