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公开(公告)号：US11625609B2
公开(公告)日：2023-04-11
申请号：US16008058
申请日：2018-06-14
发明人： Boaz Carmeli , Guy Hadash , Einat Kermany , Ofer Lavi , Guy Lev , Oren Sar-Shalom
摘要： During end-to-end training of a Deep Neural Network (DNN), a differentiable estimator subnetwork is operated to estimate a functionality of an external software application. Then, during inference by the trained DNN, the differentiable estimator subnetwork is replaced with the functionality of the external software application, by enabling API communication between the DNN and the external software application.
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公开(公告)号：US20220059097A1
公开(公告)日：2022-02-24
申请号：US17000397
申请日：2020-08-24
发明人： Ofer Lavi , Alon Jacovi , David Amid , David Boaz , Inbal Ronen , Ateret Anaby Tavor , Ori Bar El
IPC分类号： G10L15/30 , G10L15/32 , G10L15/197
摘要： The computer receives a group of conversation data associated with the escalation node, identifies agent responses in the conversation data, and clusters them into agent response types. The computer identifies dialog state feature value sets for the conversations. The computer identifies feature value set associations with response types, and generates, Boolean expressions representing the feature value sets associated with each of the response types. The computer makes a recommendation to add to at least one child node for the escalation node, with the child node corresponding to one of the response types. The child node has, as an entry condition, the Boolean expression for the response type to which the child node corresponds. The child node has as an action, which according to some aspects, provides a response representative of the cluster of agent responses for the response type to which the child node corresponds.
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公开(公告)号：US10915711B2
公开(公告)日：2021-02-09
申请号：US16214082
申请日：2018-12-09
发明人： Einat Kermany , Guy Hadash , George Kour , Ofer Lavi , Boaz Carmeli
摘要： In some examples, a system for executing natural language processing techniques can include a processor to detect text comprising a word and a number. The processor can also embed, via a word embedding model, the word into a first vector of a vector space and embed the number by converting the number into a second vector of the vector space. Additionally, the processor can train a deep neural network to execute instructions based on the first embedded vector of the word and the second embedded vector of the number. Furthermore, the processor can process an instruction based on the trained deep neural network.
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公开(公告)号：US20180357555A1
公开(公告)日：2018-12-13
申请号：US15973581
申请日：2018-05-08
CPC分类号： G06N99/005 , G06F17/30958 , G06N7/005 , Y04S10/54
摘要： A method, apparatus and product for automatic detection of anomalies in graphs. The method comprising obtaining training data, the training data comprising a plurality of graphs, each defined by nodes and edges connecting between the nodes, at least some of the nodes are labeled; determining a statistical model of a graph in accordance with the training data, the statistical model takes into account at least one structured and labeled feature of the graph, wherein the structured and labeled feature of the graph is defined based on a connection between a plurality of nodes and based on at least a portion of the labels of the plurality of nodes; obtaining an examined graph; and determining a score of the examined graph indicative of a similarity between the examined graph and the training data, wherein the score is based on a value of the structured and labeled feature in the examined graph.
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公开(公告)号：US09245233B2
公开(公告)日：2016-01-26
申请号：US13947126
申请日：2013-07-22
发明人： Yaara Goldschmidt , Ofer Lavi , Matan Ninio
CPC分类号： G06N99/005 , G06F17/30958 , G06N7/005
摘要： A method, apparatus and product for automatic detection of anomalies in graphs. The method comprising obtaining training data, the training data comprising a plurality of graphs, each defined by nodes and edges connecting between the nodes, at least some of the nodes are labeled; determining a statistical model of a graph in accordance with the training data, the statistical model takes into account at least one structured and labeled feature of the graph, wherein the structured and labeled feature of the graph is defined based on a connection between a plurality of nodes and based on at least a portion of the labels of the plurality of nodes; obtaining an examined graph; and determining a score of the examined graph indicative of a similarity between the examined graph and the training data, wherein the score is based on a value of the structured and labeled feature in the examined graph.
摘要翻译： 用于自动检测图中异常的方法，设备和产品。 所述方法包括获得训练数据，训练数据包括多个图，每个图由节点和连接节点之间的边缘定义，至少一些节点被标记; 根据训练数据确定图形的统计模型，所述统计模型考虑到所述图形的至少一个结构化和标记特征，其中所述图形的所述结构化和标记特征基于多个 并且基于所述多个节点的标签的至少一部分; 获得检查图; 以及确定所检查的图表的得分，其指示所检查的图和训练数据之间的相似性，其中所述分数基于所检查的图中的结构化和标记的特征的值。
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公开(公告)号：US20160004978A1
公开(公告)日：2016-01-07
申请号：US14839981
申请日：2015-08-30
发明人： Yaara Goldschmidt , Ofer Lavi , Matan Ninio
CPC分类号： G06N7/005 , G06F16/9024 , G06F16/9027 , G06N20/00
摘要： A method, apparatus and product for automatic detection of anomalies in graphs. The method comprising obtaining training data, the training data comprising a plurality of graphs, each defined by nodes and edges connecting between the nodes, at least some of the nodes are labeled; determining a statistical model of a graph in accordance with the training data, the statistical model takes into account at least one structured and labeled feature of the graph, wherein the structured and labeled feature of the graph is defined based on a connection between a plurality of nodes and based on at least a portion of the labels of the plurality of nodes; obtaining an examined graph; and determining a score of the examined graph indicative of a similarity between the examined graph and the training data, wherein the score is based on a value of the structured and labeled feature in the examined graph.
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公开(公告)号：US20150026103A1
公开(公告)日：2015-01-22
申请号：US13947126
申请日：2013-07-22
发明人： Yaara Goldschmidt , Ofer Lavi , Matan Ninio
IPC分类号： G06N99/00
CPC分类号： G06N99/005 , G06F17/30958 , G06N7/005
摘要： A method, apparatus and product for automatic detection of anomalies in graphs. The method comprising obtaining training data, the training data comprising a plurality of graphs, each defined by nodes and edges connecting between the nodes, at least some of the nodes are labeled; determining a statistical model of a graph in accordance with the training data, the statistical model takes into account at least one structured and labeled feature of the graph, wherein the structured and labeled feature of the graph is defined based on a connection between a plurality of nodes and based on at least a portion of the labels of the plurality of nodes; obtaining an examined graph; and determining a score of the examined graph indicative of a similarity between the examined graph and the training data, wherein the score is based on a value of the structured and labeled feature in the examined graph.
摘要翻译： 用于自动检测图中异常的方法，装置和产品。 所述方法包括获得训练数据，训练数据包括多个图，每个图由节点和连接节点之间的边缘定义，至少一些节点被标记; 根据训练数据确定图形的统计模型，所述统计模型考虑到所述图形的至少一个结构化和标记特征，其中所述图形的所述结构化和标记特征基于多个 并且基于所述多个节点的标签的至少一部分; 获得检查图; 以及确定所检查的图表的得分，其指示所检查的图和训练数据之间的相似性，其中所述分数基于所检查的图中的结构化和标记的特征的值。
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公开(公告)号：US11790239B2
公开(公告)日：2023-10-17
申请号：US16236428
申请日：2018-12-29
发明人： George Kour , Guy Hadash , Yftah Ziser , Ofer Lavi , Guy Lev
CPC分类号： G06N3/088 , G05D1/0088 , G05D1/021 , G05D1/101 , G06F18/217
摘要： A specification of a property required to be upheld by a computerized machine learning system is obtained. A training data set corresponding to the property and inputs and outputs of the system is built. The system is trained on the training data set. Activity of the system is monitored before, during, and after the training. Based on the monitoring, performance of the system is evaluated to determine whether the system, once trained on the training data set, upholds the property.
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公开(公告)号：US11605386B2
公开(公告)日：2023-03-14
申请号：US17000397
申请日：2020-08-24
发明人： Ofer Lavi , Alon Jacovi , David Amid , David Boaz , Inbal Ronen , Ateret Anaby Tavor , Ori Bar El
IPC分类号： G10L15/30 , G10L15/197 , G10L15/32
摘要： The computer receives a group of conversation data associated with the escalation node, identifies agent responses in the conversation data, and clusters them into agent response types. The computer identifies dialog state feature value sets for the conversations. The computer identifies feature value set associations with response types, and generates, Boolean expressions representing the feature value sets associated with each of the response types. The computer makes a recommendation to add to at least one child node for the escalation node, with the child node corresponding to one of the response types. The child node has, as an entry condition, the Boolean expression for the response type to which the child node corresponds. The child node has as an action, which according to some aspects, provides a response representative of the cluster of agent responses for the response type to which the child node corresponds.
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公开(公告)号：US11157257B2
公开(公告)日：2021-10-26
申请号：US16735767
申请日：2020-01-07
发明人： Ophir Azulai , Ofer Lavi , Eran Raichstein
摘要： Automatic cloning of a PYTHON CONDA environment into a DOCKER image, such that at least one CONDA container that functions the same as the PYTHON CONDA environment can be started from the DOCKER image. The automatic cloning may include: First, creating a Dockerfile that comprises commands to: install a PYTHON ANACONDA environment or obtain a PYTHON ANACONDA environment image, copy the PYTHON CONDA environment into the DOCKER image, and run a CONDA command, in the ANACONDA environment, to create a cloned PYTHON CONDA environment from the copied PYTHON CONDA environment. Second, building the DOCKER image from the Dockerfile.
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