公开(公告)号：US11430171B2

公开(公告)日：2022-08-30

申请号：US16231059

申请日：2018-12-21

Applicant: SRI International

Inventor： Mohamed R. Amer ,  Timothy J. Meo ,  Xiao Lin

IPC: G06F16/738 ,  G06T13/80 ,  G06N3/04 ,  G06N3/08 ,  G06N20/00 ,  G06K9/62 ,  G06F16/34 ,  G06F16/901 ,  G06T13/40 ,  G06T7/246 ,  G06F40/205 ,  G06V20/40 ,  G06V40/20

Abstract: This disclosure describes techniques that include generating, based on a description of a scene, a movie or animation that represents at least one possible version of a story corresponding to the description of the scene. This disclosure also describes techniques for training a machine learning model to generate predefined data structures from textual information, visual information, and/or other information about a story, an event, a scene, or a sequence of events or scenes within a story. This disclosure also describes techniques for using GANs to generate, from input, an animation of motion (e.g., an animation or a video clip). This disclosure also describes techniques for implementing an explainable artificial intelligence system that may provide end users with information (e.g., through a user interface) that enables an understanding of at least some of the decisions made by the AI system.

公开(公告)号：US20190304156A1

公开(公告)日：2019-10-03

申请号：US16230987

申请日：2018-12-21

Applicant: SRI International

Inventor： Mohamed R. Amer ,  Alex C. Tozzo ,  Dejan Jovanovic ,  Timothy J. Meo

IPC: G06T13/40 ,  G06K9/00 ,  G06N3/04 ,  G06F16/901 ,  G06F16/34 ,  G06N3/08

Abstract: This disclosure describes techniques that include generating, based on a description of a scene, a movie or animation that represents at least one possible version of a story corresponding to the description of the scene. This disclosure also describes techniques for training a machine learning model to generate predefined data structures from textual information, visual information, and/or other information about a story, an event, a scene, or a sequence of events or scenes within a story. This disclosure also describes techniques for using GANs to generate, from input, an animation of motion (e.g., an animation or a video clip). This disclosure also describes techniques for implementing an explainable artificial intelligence system that may provide end users with information (e.g., through a user interface) that enables an understanding of at least some of the decisions made by the AI system.

公开(公告)号：US20190303404A1

公开(公告)日：2019-10-03

申请号：US16231059

申请日：2018-12-21

Applicant: SRI International

Inventor： Mohamed R. Amer ,  Timothy J. Meo ,  Xiao Lin

IPC: G06F16/738 ,  G06K9/62 ,  G06N20/00

Abstract: This disclosure describes techniques that include generating, based on a description of a scene, a movie or animation that represents at least one possible version of a story corresponding to the description of the scene. This disclosure also describes techniques for training a machine learning model to generate predefined data structures from textual information, visual information, and/or other information about a story, an event, a scene, or a sequence of events or scenes within a story. This disclosure also describes techniques for using GANs to generate, from input, an animation of motion (e.g., an animation or a video clip). This disclosure also describes techniques for implementing an explainable artificial intelligence system that may provide end users with information (e.g., through a user interface) that enables an understanding of at least some of the decisions made by the AI system.

公开(公告)号：US20170364792A1

公开(公告)日：2017-12-21

申请号：US15625578

申请日：2017-06-16

Applicant: SRI International

Inventor： Sek M. Chai ,  David C. Zhang ,  Mohamed R. Amer ,  Timothy J. Shields ,  Aswin Nadamuni Raghavan ,  Bhaskar Ramamurthy

IPC: G06N3/04 ,  G06N3/08 ,  G06N3/063

CPC classification number: G06N3/0454 ,  G06F9/46 ,  G06F9/50 ,  G06N3/0445 ,  G06N3/063 ,  G06N3/08

Abstract: Operations of computing devices are managed using one or more deep neural networks (DNNs), which may receive, as DNN inputs, data from sensors, instructions executed by processors, and/or outputs of other DNNs. One or more DNNs, which may be generative, can be applied to the DNN inputs to generate DNN outputs based on relationships between DNN inputs. The DNNs may include DNN parameters learned using one or more computing workloads. The DNN outputs may be, for example, control signals for managing operations of computing devices, predictions for use in generating control signals, warnings indicating an acceptable state is predicted, and/or inputs to one or more neural networks. The signals enhance performance, efficiency, and/or security of one or more of the computing devices. DNNs can be dynamically trained to personalize operations by updating DNN weights or other parameters.

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

IPC: G06N99/00 ,  G06N7/00 ,  G06K9/62

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.

公开(公告)号：US11328206B2

公开(公告)日：2022-05-10

申请号：US15625578

申请日：2017-06-16

Applicant: SRI International

Inventor： Sek M. Chai ,  David C. Zhang ,  Mohamed R. Amer ,  Timothy J. Shields ,  Aswin Nadamuni Raghavan ,  Bhaskar Ramamurthy

IPC: G06N3/04 ,  G06N3/063 ,  G06N3/08 ,  G06F9/46 ,  G06F9/50

Abstract: Operations of computing devices are managed using one or more deep neural networks (DNNs), which may receive, as DNN inputs, data from sensors, instructions executed by processors, and/or outputs of other DNNs. One or more DNNs, which may be generative, can be applied to the DNN inputs to generate DNN outputs based on relationships between DNN inputs. The DNNs may include DNN parameters learned using one or more computing workloads. The DNN outputs may be, for example, control signals for managing operations of computing devices, predictions for use in generating control signals, warnings indicating an acceptable state is predicted, and/or inputs to one or more neural networks. The signals enhance performance, efficiency, and/or security of one or more of the computing devices. DNNs can be dynamically trained to personalize operations by updating DNN weights or other parameters.

公开(公告)号：US10789755B2

公开(公告)日：2020-09-29

申请号：US16230945

申请日：2018-12-21

Applicant: SRI International

Inventor： Mohamed R. Amer ,  Timothy J. Meo ,  Aswin Nadamuni Raghavan ,  Alex C. Tozzo ,  Amir Tamrakar ,  David A. Salter ,  Kyung-Yoon Kim

IPC: G06T13/80 ,  G06N3/04 ,  G06N3/08 ,  G06K9/00 ,  G06F16/738 ,  G06N20/00 ,  G06K9/62 ,  G06F16/34 ,  G06F16/901 ,  G06T13/40 ,  G06T7/246 ,  G06F40/205

Abstract: This disclosure describes techniques that include generating, based on a description of a scene, a movie or animation that represents at least one possible version of a story corresponding to the description of the scene. This disclosure also describes techniques for training a machine learning model to generate predefined data structures from textual information, visual information, and/or other information about a story, an event, a scene, or a sequence of events or scenes within a story. This disclosure also describes techniques for using GANs to generate, from input, an animation of motion (e.g., an animation or a video clip). This disclosure also describes techniques for implementing an explainable artificial intelligence system that may provide end users with information (e.g., through a user interface) that enables an understanding of at least some of the decisions made by the AI system.

公开(公告)号：US20190304157A1

公开(公告)日：2019-10-03

申请号：US16230945

申请日：2018-12-21

Applicant: SRI International

Inventor： Mohamed R. Amer ,  Timothy J. Meo ,  Aswin Nadamuni Raghavan ,  Alex C. Tozzo ,  Amir Tamrakar ,  David A. Salter ,  Kyung-Yoon Kim

IPC: G06T13/80 ,  G06F17/27 ,  G06K9/00 ,  G06N3/08 ,  G06N3/04

Abstract: This disclosure describes techniques that include generating, based on a description of a scene, a movie or animation that represents at least one possible version of a story corresponding to the description of the scene. This disclosure also describes techniques for training a machine learning model to generate predefined data structures from textual information, visual information, and/or other information about a story, an event, a scene, or a sequence of events or scenes within a story. This disclosure also describes techniques for using GANs to generate, from input, an animation of motion (e.g., an animation or a video clip). This disclosure also describes techniques for implementing an explainable artificial intelligence system that may provide end users with information (e.g., through a user interface) that enables an understanding of at least some of the decisions made by the AI system.

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

IPC: G06N99/00 ,  G06K9/62

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。

公开(公告)号：US12073305B2

公开(公告)日：2024-08-27

申请号：US16085859

申请日：2017-03-17

Applicant: SRI International

Inventor： Mohamed R. Amer ,  Timothy J. Shields ,  Amir Tamrakar ,  Max Ehrlich ,  Timur Almaev

IPC: G06N3/045 ,  G06F18/2132 ,  G06F18/24 ,  G06N5/04 ,  G06N20/00

CPC classification number: G06N3/045 ,  G06F18/2132 ,  G06F18/24 ,  G06N5/04 ,  G06N20/00

Abstract: Technologies for analyzing multi-task multimodal data to detect multi-task multimodal events using a deep multi-task representation learning, are disclosed. A combined model with both generative and discriminative aspects is used to share information during both generative and discriminative processes. The technologies can be used to classify data and also to generate data from classification events. The data can then be used to morph data into a desired classification event.