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公开(公告)号：US11724407B2
公开(公告)日：2023-08-15
申请号：US17151453
申请日：2021-01-18
发明人： Robert Price , Kent Evans
CPC分类号： B25J19/023 , G06T7/80 , G06T2207/30208 , G06T2207/30244
摘要： One embodiment provides a robotic system comprising: a robot, the robot further comprising a moveable robotic arm that moves within the robot's reference space; a depth-sensing camera, the camera having a reference frame that is in substantial view of the robot's reference space; a controller, the controller further comprising a processor and a computer readable memory that comprises instructions such that, when read by the controller, the controller inputs image data from the camera and sends signals to the moveable robotic arm, the instructions further comprising the steps of: calibrating the camera to the robot by instructing the robot to engage in a number of robot poses; extracting the location of the robot poses to obtain the robot poses in the camera reference frame; and creating a transformation that transforms robot points afterwards to camera points.
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2.发明申请Computer-Implemented System And Method For Detecting Anomalies Using Sample-Based Rule Identification 审中-公开使用基于样本的规则识别来检测异常的计算机实现的系统和方法公开(公告)号：US20160042287A1
公开(公告)日：2016-02-11
申请号：US14455933
申请日：2014-08-10
CPC分类号： G06N5/025 , G06F21/00 , G06F21/552 , G06N99/005 , G06Q50/00 , H04L63/1425
摘要： A computer-implemented system and method for detecting anomalies using sample-based rule identification is provided. Data for data is maintained analytics in a database. A set of anomaly rules is defined. A rare pattern in the data is statistically identified. The identified rare pattern is labeled as at least one of anomaly and non-anomaly based on verification by a domain expert. The set of anomaly rules is adjusted based on the labeled anomaly. Other anomalies in the data are detected and classified by applying the adjusted set of anomaly rules to the data.
摘要翻译： 提供了一种使用基于样本的规则识别来检测异常的计算机实现的系统和方法。 数据数据在数据库中保持分析。 定义一组异常规则。 数据中罕见的模式被统计识别。 基于领域专家的验证，识别的罕见模式被标记为至少一种异常和非异常。 基于标记的异常来调整一组异常规则。 通过将调整后的一组异常规则应用于数据来检测和分类数据中的其他异常。
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公开(公告)号：US20220227012A1
公开(公告)日：2022-07-21
申请号：US17151453
申请日：2021-01-18
发明人： Robert Price , Kent Evans
摘要： One embodiment provides a robotic system comprising: a robot, the robot further comprising a moveable robotic arm that moves within the robot's reference space; a depth-sensing camera, the camera having a reference frame that is in substantial view of the robot's reference space; a controller, the controller further comprising a processor and a computer readable memory that comprises instructions such that, when read by the controller, the controller inputs image data from the camera and sends signals to the moveable robotic arm, the instructions further comprising the steps of: calibrating the camera to the robot by instructing the robot to engage in a number of robot poses; extracting the location of the robot poses to obtain the robot poses in the camera reference frame; and creating a transformation that transforms robot points afterwards to camera points.
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公开(公告)号：US20220188623A1
公开(公告)日：2022-06-16
申请号：US17118165
申请日：2020-12-10
发明人： Robert Price
IPC分类号： G06N3/08
摘要： A policy based on a compound reward function is learned through a reinforcement learning algorithm at a learning network. The policy is used to choose an action of a plurality of possible actions. A state-action value network is established for each of the two or more reward terms. The state-action value networks are separated from the learning network. A human-understandable output is produced to explain why the action was taken based on each of the state action value networks.
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公开(公告)号：US10140576B2
公开(公告)日：2018-11-27
申请号：US14455933
申请日：2014-08-10
摘要： A computer-implemented system and method for detecting anomalies using sample-based rule identification is provided. Data for data is maintained analytics in a database. A set of anomaly rules is defined. A rare pattern in the data is statistically identified. The identified rare pattern is labeled as at least one of anomaly and non-anomaly based on verification by a domain expert. The set of anomaly rules is adjusted based on the labeled anomaly. Other anomalies in the data are detected and classified by applying the adjusted set of anomaly rules to the data.
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