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公开(公告)号：US20210192972A1
公开(公告)日：2021-06-24
申请号：US17129541
申请日：2020-12-21
Applicant: SRI International
Inventor： Girish Acharya , Louise Yarnall , Anirban Roy , Michael Wessel , Yi Yao , John J. Byrnes , Dayne Freitag , Zachary Weiler , Paul Kalmar
Abstract: This disclosure describes machine learning techniques for capturing human knowledge for performing a task. In one example, a video device obtains video data of a first user performing the task and one or more sensors generate sensor data during performance of the task. An audio device obtains audio data describing performance of the task. A computation engine applies a machine learning system to correlate the video data to the audio data and sensor data to identify portions of the video, sensor, and audio data that depict a same step of a plurality of steps for performing the task. The machine learning system further processes the correlated data to update a domain model defining performance of the task. A training unit applies the domain model to generate training information for performing the task. An output device outputs the training information for use in training a second user to perform the task.
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公开(公告)号：US20170076219A1
公开(公告)日：2017-03-16
申请号：US14997447
申请日：2016-01-15
Applicant: SRI International
Inventor： John J. Byrnes , Clint Frederickson , Kyle J. McIntyre , Tulay Muezzinoglu , Edmond D. Chow , William T. Deans
CPC classification number: G06F16/93 , G06F17/2785 , G06N20/00
Abstract: Systems and methods for forecasting the prominence of various attributes in a future subject matter area are disclosed. An attribute is determined based on inputs received by a computing system. A set of indicators is determined based on the attribute and features extracted from an existing document set. The prominence of the attribute in the existing document set is determined. A prominence estimate of the attribute in a future document set is determined.
Abstract translation: 公开了用于预测未来主题领域中各种属性突出的系统和方法。 基于计算系统接收的输入来确定属性。 基于从现有文档集中提取的属性和特征确定一组指标。 确定现有文件集中属性的突出性。 确定未来文档集中的属性的突出估计。
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公开(公告)号：US12118773B2
公开(公告)日：2024-10-15
申请号：US17129541
申请日：2020-12-21
Applicant: SRI International
Inventor： Girish Acharya , Louise Yarnall , Anirban Roy , Michael Wessel , Yi Yao , John J. Byrnes , Dayne Freitag , Zachary Weiler , Paul Kalmar
IPC: G06V10/82 , G06F18/22 , G06N20/00 , G06V20/20 , G06V20/40 , G06V30/19 , G06V30/262 , G06V40/10 , G06V40/20 , G09B5/06 , G09B19/00 , G10L15/18 , G10L25/57
CPC classification number: G06V10/82 , G06F18/22 , G06N20/00 , G06V20/20 , G06V20/41 , G06V30/19173 , G06V30/274 , G06V40/10 , G06V40/113 , G06V40/28 , G09B5/065 , G09B19/003 , G10L15/1815 , G10L25/57
Abstract: This disclosure describes machine learning techniques for capturing human knowledge for performing a task. In one example, a video device obtains video data of a first user performing the task and one or more sensors generate sensor data during performance of the task. An audio device obtains audio data describing performance of the task. A computation engine applies a machine learning system to correlate the video data to the audio data and sensor data to identify portions of the video, sensor, and audio data that depict a same step of a plurality of steps for performing the task. The machine learning system further processes the correlated data to update a domain model defining performance of the task. A training unit applies the domain model to generate training information for performing the task. An output device outputs the training information for use in training a second user to perform the task.
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