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公开(公告)号：US11062142B2
公开(公告)日：2021-07-13
申请号：US16020566
申请日：2018-06-27
发明人： Janardan Misra , Sanjay Podder , Divya Rawat , Bhaskar Ghosh , Neville Dubash
IPC分类号： G06F7/00 , G06K9/00 , G06N3/00 , G06F16/36 , G06F16/9032 , G06K9/72 , G06N5/02 , G06F40/30 , G06F40/56 , G06F40/216 , B25J9/16 , G06N5/04 , G06N20/00
摘要： In some examples, natural language unification based robotic agent control may include ascertaining, by a robotic agent, an image of an object or an environment, and ascertaining a plurality of natural language insights for the image. A semantic relatedness may be determined between each insight of the plurality of insights, and a semantic relatedness graph may be generated for the plurality of insights. For each insight of the plurality of insights, at least one central concept may be identified. Based on the semantic relatedness graph and the identified at least one central concept, the plurality of insights may be clustered to generate at least one insights cluster. For insights included in the least one insights cluster, a unified insight may be generated. Further, an operation associated with the robotic agent, the object, or the environment may be controlled by the robotic agent and based on the unified insight.
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2.发明申请公开(公告)号：US20200147795A1
公开(公告)日：2020-05-14
申请号：US16186006
申请日：2018-11-09
发明人： Janardan Misra , Vikrant Kaulgud , Divya Rawat , Kapil Singi , Sanjay Podder
IPC分类号： B25J9/16 , G05B19/4155
摘要： In some examples, temporal variation identification of regulatory compliance based robotic agent control may include ascertaining a temporal sequence of compliance specification text, where the temporal sequence may include time points and versions of the compliance specification text at the time points. For each time point of the temporal sequence of the compliance specification text, a compliance specification graph may be generated. Based on an analysis of each of the generated compliance specification graphs, changes in the temporal sequence of the compliance specification text may be determined. Further, an operation associated with a robotic agent may be controlled by the robotic agent and based on the determined changes in the temporal sequence of the compliance specification text.
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公开(公告)号：US10169330B2
公开(公告)日：2019-01-01
申请号：US15370810
申请日：2016-12-06
发明人： Janardan Misra , Divya Rawat , Milind Savagaonkar , Sanjay Podder
摘要： A device may receive a set of first samples of textual content. A device may identify a set of clusters of first samples of the set of first samples. A device may identify a pattern of occurrence based on the set of clusters. The pattern of occurrence to identify two or more clusters, of the set of clusters, based on an order in which first samples associated with the two or more clusters were generated or received. A device may receive one or more second samples of textual content. A device may determine that the one or more second samples are semantically similar to one or more corresponding clusters associated with the pattern of occurrence. A device may identify a predicted sample based on the pattern of occurrence and the one or more corresponding clusters. A device may perform an action based on identifying the predicted sample.
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公开(公告)号：US11233693B2
公开(公告)日：2022-01-25
申请号：US16926129
申请日：2020-07-10
发明人： Janardan Misra , Divya Rawat , Shubhashis Sengupta
摘要： In some examples, learning based incident or defect resolution, and test generation may include ascertaining historical log data that includes incident or defect log data associated with operation of a process, and generating, based on the historical log data, step action graphs. Based on grouping of the step action graphs with respect to different incident and defect tickets, an incident and defect action graph may be generated to further generate a machine learning model. Based on an analysis of the machine learning model with respect to a new incident or defect, an output that includes a sequence of actions may be generated to reproduce, for the new incident, steps that result in the new incident, reproduce, for the new defect, an error that results in the new defect, identify a root cause of the new incident or defect, and/or resolve the new incident or defect.
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5.发明授权公开(公告)号：US10768893B2
公开(公告)日：2020-09-08
申请号：US15818456
申请日：2017-11-20
发明人： Janardan Misra , Divya Rawat , Neville Dubash , Sanjay Podder
摘要： A device may obtain test case information for a set of test cases. The test case information may include test case description information, test case environment information, and/or test case defect information. The device may determine a set of field-level similarity scores by using a set of similarity analysis techniques to analyze a set of test case field groups associated with the test case information. The device may determine a set of overall similarity scores for a set of test case groups by using a machine learning technique to analyze the set of field-level similarity scores. The device may update a data structure that stores the test case information to establish one or more associations between the test case information and the set of overall similarity scores. The device may process a request from a user device using information included in the updated data structure.
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公开(公告)号：US20190005329A1
公开(公告)日：2019-01-03
申请号：US16020611
申请日：2018-06-27
发明人： Janardan MISRA , Sanjay Podder , Divya Rawat , Bhaskar Ghosh , Neville Dubash
摘要： In some examples, natural language eminence based robotic agent control may include ascertaining, by a robotic agent, an image of an object or an environment, and ascertaining a plurality of natural language insights for the image. For each insight of the plurality of insights, an eminence score may be generated, and each insight of the plurality of insights may be ranked according to the eminence scores. An operation associated with the robotic agent, the object, or the environment may be controlled by the robotic agent and based on a highest ranked insight.
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公开(公告)号：US11361243B2
公开(公告)日：2022-06-14
申请号：US15982565
申请日：2018-05-17
发明人： Janardan Misra , Divya Rawat , Shubhashis Sengupta
摘要： A device may identify, for a first analytics application, a first set of characteristics and obtain, for a second analytics application, a second set of characteristics. The device may determine a measure of similarity between the first analytics application and the second analytics application based on the first set of characteristics and the second set of characteristics. The device may also determine a relevance score for a feature of the first analytics application, the relevance score being based on a relevance score associated with a feature of the second analytics application. In addition, the device may determine a relevance score for a machine learning technique associated with the first analytics application, the relevance score being based on a relevance score associated with a machine learning technique associated with the second analytics application. Based on the first relevance score or the second relevance score, the device may perform an action.
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公开(公告)号：US11213948B2
公开(公告)日：2022-01-04
申请号：US16186006
申请日：2018-11-09
发明人： Janardan Misra , Vikrant Kaulgud , Divya Rawat , Kapil Singi , Sanjay Podder
IPC分类号： B25J9/16 , G05B19/41 , G05B19/4155 , G06Q30/00
摘要： In some examples, temporal variation identification of regulatory compliance based robotic agent control may include ascertaining a temporal sequence of compliance specification text, where the temporal sequence may include time points and versions of the compliance specification text at the time points. For each time point of the temporal sequence of the compliance specification text, a compliance specification graph may be generated. Based on an analysis of each of the generated compliance specification graphs, changes in the temporal sequence of the compliance specification text may be determined. Further, an operation associated with a robotic agent may be controlled by the robotic agent and based on the determined changes in the temporal sequence of the compliance specification text.
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公开(公告)号：US10824870B2
公开(公告)日：2020-11-03
申请号：US16020611
申请日：2018-06-27
发明人： Janardan Misra , Sanjay Podder , Divya Rawat , Bhaskar Ghosh , Neville Dubash
IPC分类号： G06K9/00 , G06N3/00 , G06F16/36 , G06F16/9032 , G06K9/72 , G06N5/02 , G06F40/30 , G06F40/56 , G06F40/216 , B25J9/16 , G06N5/04 , G06N20/00
摘要： In some examples, natural language eminence based robotic agent control may include ascertaining, by a robotic agent, an image of an object or an environment, and ascertaining a plurality of natural language insights for the image. For each insight of the plurality of insights, an eminence score may be generated, and each insight of the plurality of insights may be ranked according to the eminence scores. An operation associated with the robotic agent, the object, or the environment may be controlled by the robotic agent and based on a highest ranked insight.
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公开(公告)号：US10771314B2
公开(公告)日：2020-09-08
申请号：US16130713
申请日：2018-09-13
发明人： Janardan Misra , Divya Rawat , Shubhashis Sengupta
摘要： In some examples, learning based incident or defect resolution, and test generation may include ascertaining historical log data that includes incident or defect log data associated with operation of a process, and generating, based on the historical log data, step action graphs. Based on grouping of the step action graphs with respect to different incident and defect tickets, an incident and defect action graph may be generated to further generate a machine learning model. Based on an analysis of the machine learning model with respect to a new incident or defect, an output that includes a sequence of actions may be generated to reproduce, for the new incident, steps that result in the new incident, reproduce, for the new defect, an error that results in the new defect, identify a root cause of the new incident or defect, and/or resolve the new incident or defect.
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