Patents by Inventor Aniruddha Thakur
Aniruddha Thakur has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11620571Abstract: A network system may include a plurality of trainer devices and a computing system disposed within a remote network management platform. The computing system may be configured to: receive, from a client device of a managed network, information indicating (i) training data that is to be used as basis for generating a machine learning (ML) model and (ii) a target variable to be predicted using the ML model; transmit an ML training request for reception by one of the plurality of trainer devices; provide the training data to a particular trainer device executing a particular ML trainer process that is serving the ML training request; receive, from the particular trainer device, the ML model that is generated based on the provided training data and according to the particular ML trainer process; predict the target variable using the ML model; and transmit, to the client device, information indicating the target variable.Type: GrantFiled: July 9, 2019Date of Patent: April 4, 2023Assignee: ServiceNow, Inc.Inventors: Nikhil Bendre, Fernando Ros, Kannan Govindarajan, Baskar Jayaraman, Aniruddha Thakur, Sriram Palapudi, Firat Karakusoglu
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Patent number: 11080588Abstract: An example embodiment may involve a machine learning model representing relationships between a dependent variable and a plurality of n independent variables. The dependent variable may be a function of the n independent variables, where the n independent variables are measurable characteristics of computing devices, and where the dependent variable is a predicted behavior of the computing devices. The embodiment may also involve obtaining a target value of the dependent variable, and separating the n independent variables into n?1 independent variables with fixed values and a particular independent variable with an unfixed value. The embodiment may also involve performing a partial inversion of the function to produce a value of the particular independent variable such that, when the function is applied to the value of the particular independent variable and the n?1 independent variables with fixed values, the dependent variable is within a pre-defined range of the target value.Type: GrantFiled: October 17, 2017Date of Patent: August 3, 2021Assignee: ServiceNow, Inc.Inventors: Baskar Jayaraman, Aniruddha Thakur, Kannan Govindarajan
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Patent number: 10949807Abstract: Systems and methods for using a mathematical model based on historical information to automatically schedule and monitor work flows are disclosed. Prediction methods that use some variables to predict unknown or future values of other variables may assist in reducing manual intervention when addressing incident reports or other task-based work items. For example, work items that are expected to conform to a supervised model built from historical customer information. Given a collection of records in a training set, each record contains a set of attributes with one of the attributes being the class. If a model can be found for the class attribute as a function of the values of the other attributes, then previously unseen records may be assigned a class as accurately as possible based on the model. A test data set is used to determine model accuracy prior to allowing general use of the model.Type: GrantFiled: August 10, 2017Date of Patent: March 16, 2021Assignee: ServiceNow, Inc.Inventors: Baskar Jayaraman, Debashish Chatterjee, Kannan Govindarajan, Aniruddha Thakur
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Patent number: 10817788Abstract: A machine learning classifier may classify observations into one or more of i categories, and may be configured to: receive test data that includes j observations, each associated with a respective ground truth category, and produce output that provides, for each particular observation of the j observations, a set of i probabilities, one probability for each of the i categories. For each particular confidence threshold in k confidence thresholds, a computing device may: reclassify, into a null category, any of the j observations for which all of the set of i probabilities are less than the particular confidence threshold, and determine a respective precision value and a respective coverage value for a particular category of the i categories. A specific confidence threshold in the k confidence thresholds may be selected, and further observations may be reclassified into the null category in accordance with the specific confidence threshold.Type: GrantFiled: January 13, 2020Date of Patent: October 27, 2020Assignee: ServiceNow, Inc.Inventors: Baskar Jayaraman, Aniruddha Thakur, Kannan Govindarajan
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Publication number: 20200226477Abstract: A machine learning classifier may classify observations into one or more of i categories, and may be configured to: receive test data that includes j observations, each associated with a respective ground truth category, and produce output that provides, for each particular observation of the j observations, a set of i probabilities, one probability for each of the i categories. For each particular confidence threshold in k confidence thresholds, a computing device may: reclassify, into a null category, any of the j observations for which all of the set of i probabilities are less than the particular confidence threshold, and determine a respective precision value and a respective coverage value for a particular category of the i categories. A specific confidence threshold in the k confidence thresholds may be selected, and further observations may be reclassified into the null category in accordance with the specific confidence threshold.Type: ApplicationFiled: January 13, 2020Publication date: July 16, 2020Inventors: Baskar Jayaraman, Aniruddha Thakur, Kannan Govindarajan
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Patent number: 10558921Abstract: A machine learning classifier may classify observations into one or more of i categories, and may be configured to: receive test data that includes j observations, each associated with a respective ground truth category, and produce output that provides, for each particular observation of the j observations, a set of i probabilities, one probability for each of the i categories. For each particular confidence threshold in k confidence thresholds, a computing device may: reclassify, into a null category, any of the j observations for which all of the set of i probabilities are less than the particular confidence threshold, and determine a respective precision value and a respective coverage value for a particular category of the i categories. A specific confidence threshold in the k confidence thresholds may be selected, and further observations may be reclassified into the null category in accordance with the specific confidence threshold.Type: GrantFiled: December 27, 2017Date of Patent: February 11, 2020Assignee: ServiceNow, Inc.Inventors: Baskar Jayaraman, Aniruddha Thakur, Kannan Govindarajan
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Patent number: 10558920Abstract: A machine learning classifier may classify observations into one or more of i categories, and may be configured to: receive test data that includes j observations, each associated with a respective ground truth category, and produce output that provides, for each particular observation of the j observations, a set of i probabilities, one probability for each of the i categories. For each particular confidence threshold in k confidence thresholds, a computing device may: reclassify, into a null category, any of the j observations for which all of the set of i probabilities are less than the particular confidence threshold, and determine a respective precision value and a respective coverage value for a particular category of the i categories. A specific confidence threshold in the k confidence thresholds may be selected, and further observations may be reclassified into the null category in accordance with the specific confidence threshold.Type: GrantFiled: October 2, 2017Date of Patent: February 11, 2020Assignee: ServiceNow, Inc.Inventors: Baskar Jayaraman, Aniruddha Thakur, Kannan Govindarajan
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Publication number: 20200005187Abstract: A network system may include a plurality of trainer devices and a computing system disposed within a remote network management platform. The computing system may be configured to: receive, from a client device of a managed network, information indicating (i) training data that is to be used as basis for generating a machine learning (ML) model and (ii) a target variable to be predicted using the ML model; transmit an ML training request for reception by one of the plurality of trainer devices; provide the training data to a particular trainer device executing a particular ML trainer process that is serving the ML training request; receive, from the particular trainer device, the ML model that is generated based on the provided training data and according to the particular ML trainer process; predict the target variable using the ML model; and transmit, to the client device, information indicating the target variable.Type: ApplicationFiled: July 9, 2019Publication date: January 2, 2020Inventors: Nikhil Bendre, Fernando Ros, Kannan Govindarajan, Baskar Jayaraman, Aniruddha Thakur, Sriram Palapudi, Firat Karakusoglu
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Patent number: 10445661Abstract: A network system may include a plurality of trainer devices and a computing system disposed within a remote network management platform. The computing system may be configured to: receive, from a client device of a managed network, information indicating (i) training data that is to be used as basis for generating a machine learning (ML) model and (ii) a target variable to be predicted using the ML model; transmit an ML training request for reception by one of the plurality of trainer devices; provide the training data to a particular trainer device executing a particular ML trainer process that is serving the ML training request; receive, from the particular trainer device, the ML model that is generated based on the provided training data and according to the particular ML trainer process; predict the target variable using the ML model; and transmit, to the client device, information indicating the target variable.Type: GrantFiled: September 27, 2017Date of Patent: October 15, 2019Assignee: ServiceNow, Inc.Inventors: Nikhil Bendre, Fernando Ros, Kannan Govindarajan, Baskar Jayaraman, Aniruddha Thakur, Sriram Palapudi, Firat Karakusoglu
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Patent number: 10380504Abstract: A network system may include a plurality of trainer devices and a computing system disposed within a remote network management platform. The computing system may be configured to: receive, from a client device of a managed network, information indicating (i) training data that is to be used as basis for generating a machine learning (ML) model and (ii) a target variable to be predicted using the ML model; transmit an ML training request for reception by one of the plurality of trainer devices; provide the training data to a particular trainer device executing a particular ML trainer process that is serving the ML training request; receive, from the particular trainer device, the ML model that is generated based on the provided training data and according to the particular ML trainer process; predict the target variable using the ML model; and transmit, to the client device, information indicating the target variable.Type: GrantFiled: December 20, 2017Date of Patent: August 13, 2019Assignee: ServiceNow, Inc.Inventors: Nikhil Bendre, Fernando Ros, Kannan Govindarajan, Baskar Jayaraman, Aniruddha Thakur, Sriram Palapudi, Firat Karakusoglu
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Patent number: 10339441Abstract: An example embodiment may involve a machine learning model representing relationships between a dependent variable and a plurality of n independent variables. The dependent variable may be a function of the n independent variables, where the n independent variables are measurable characteristics of computing devices, and where the dependent variable is a predicted behavior of the computing devices. The embodiment may also involve obtaining a target value of the dependent variable, and separating the n independent variables into n?1 independent variables with fixed values and a particular independent variable with an unfixed value. The embodiment may also involve performing a partial inversion of the function to produce a value of the particular independent variable such that, when the function is applied to the value of the particular independent variable and the n?1 independent variables with fixed values, the dependent variable is within a pre-defined range of the target value.Type: GrantFiled: December 21, 2017Date of Patent: July 2, 2019Assignee: SERVICENOW, INC.Inventors: Baskar Jayaraman, Aniruddha Thakur, Kannan Govindarajan
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Publication number: 20190102683Abstract: A machine learning classifier may classify observations into one or more of i categories, and may be configured to: receive test data that includes j observations, each associated with a respective ground truth category, and produce output that provides, for each particular observation of the j observations, a set of i probabilities, one probability for each of the i categories. For each particular confidence threshold in k confidence thresholds, a computing device may: reclassify, into a null category, any of the j observations for which all of the set of i probabilities are less than the particular confidence threshold, and determine a respective precision value and a respective coverage value for a particular category of the i categories. A specific confidence threshold in the k confidence thresholds may be selected, and further observations may be reclassified into the null category in accordance with the specific confidence threshold.Type: ApplicationFiled: December 27, 2017Publication date: April 4, 2019Inventors: Baskar Jayaraman, Aniruddha Thakur, Kannan Govindarajan
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Publication number: 20190102682Abstract: A machine learning classifier may classify observations into one or more of i categories, and may be configured to: receive test data that includes j observations, each associated with a respective ground truth category, and produce output that provides, for each particular observation of the j observations, a set of i probabilities, one probability for each of the i categories. For each particular confidence threshold in k confidence thresholds, a computing device may: reclassify, into a null category, any of the j observations for which all of the set of i probabilities are less than the particular confidence threshold, and determine a respective precision value and a respective coverage value for a particular category of the i categories. A specific confidence threshold in the k confidence thresholds may be selected, and further observations may be reclassified into the null category in accordance with the specific confidence threshold.Type: ApplicationFiled: October 2, 2017Publication date: April 4, 2019Inventors: Baskar Jayaraman, Aniruddha Thakur, Kannan Govindarajan
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Publication number: 20180322415Abstract: A network system may include a plurality of trainer devices and a computing system disposed within a remote network management platform. The computing system may be configured to: receive, from a client device of a managed network, information indicating (i) training data that is to be used as basis for generating a machine learning (ML) model and (ii) a target variable to be predicted using the ML model; transmit an ML training request for reception by one of the plurality of trainer devices; provide the training data to a particular trainer device executing a particular ML trainer process that is serving the ML training request; receive, from the particular trainer device, the ML model that is generated based on the provided training data and according to the particular ML trainer process; predict the target variable using the ML model; and transmit, to the client device, information indicating the target variable.Type: ApplicationFiled: September 27, 2017Publication date: November 8, 2018Inventors: Nikhil Bendre, Fernando Ros, Kannan Govindarajan, Baskar Jayaraman, Aniruddha Thakur, Sriram Palapudi, Firat Karakusoglu
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Publication number: 20180322462Abstract: Systems and methods for using a mathematical model based on historical information to automatically schedule and monitor work flows are disclosed. Prediction methods that use some variables to predict unknown or future values of other variables may assist in reducing manual intervention when addressing incident reports or other task-based work items. For example, work items that are expected to conform to a supervised model built from historical customer information. Given a collection of records in a training set, each record contains a set of attributes with one of the attributes being the class. If a model can be found for the class attribute as a function of the values of the other attributes, then previously unseen records may be assigned a class as accurately as possible based on the model. A test data set is used to determine model accuracy prior to allowing general use of the model.Type: ApplicationFiled: August 10, 2017Publication date: November 8, 2018Inventors: Baskar Jayaraman, Debashish Chatterjee, Kannan Govindarajan, Aniruddha Thakur
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Publication number: 20180322417Abstract: A network system may include a plurality of trainer devices and a computing system disposed within a remote network management platform. The computing system may be configured to: receive, from a client device of a managed network, information indicating (i) training data that is to be used as basis for generating a machine learning (ML) model and (ii) a target variable to be predicted using the ML model; transmit an ML training request for reception by one of the plurality of trainer devices; provide the training data to a particular trainer device executing a particular ML trainer process that is serving the ML training request; receive, from the particular trainer device, the ML model that is generated based on the provided training data and according to the particular ML trainer process; predict the target variable using the ML model; and transmit, to the client device, information indicating the target variable.Type: ApplicationFiled: December 20, 2017Publication date: November 8, 2018Inventors: Nikhil Bendre, Fernando Ros, Kannan Govindarajan, Baskar Jayaraman, Aniruddha Thakur, Sriram Palapudi, Firat Karakusoglu
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Publication number: 20180137411Abstract: An example embodiment may involve a machine learning model representing relationships between a dependent variable and a plurality of n independent variables. The dependent variable may be a function of the n independent variables, where the n independent variables are measurable characteristics of computing devices, and where the dependent variable is a predicted behavior of the computing devices. The embodiment may also involve obtaining a target value of the dependent variable, and separating the n independent variables into n?1 independent variables with fixed values and a particular independent variable with an unfixed value. The embodiment may also involve performing a partial inversion of the function to produce a value of the particular independent variable such that, when the function is applied to the value of the particular independent variable and the n?1 independent variables with fixed values, the dependent variable is within a pre-defined range of the target value.Type: ApplicationFiled: December 21, 2017Publication date: May 17, 2018Inventors: Baskar Jayaraman, Aniruddha Thakur, Kannan Govindarajan
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Publication number: 20180107920Abstract: An example embodiment may involve a machine learning model representing relationships between a dependent variable and a plurality of n independent variables. The dependent variable may be a function of the n independent variables, where the n independent variables are measurable characteristics of computing devices, and where the dependent variable is a predicted behavior of the computing devices. The embodiment may also involve obtaining a target value of the dependent variable, and separating the n independent variables into n?1 independent variables with fixed values and a particular independent variable with an unfixed value. The embodiment may also involve performing a partial inversion of the function to produce a value of the particular independent variable such that, when the function is applied to the value of the particular independent variable and the n?1 independent variables with fixed values, the dependent variable is within a pre-defined range of the target value.Type: ApplicationFiled: October 17, 2017Publication date: April 19, 2018Inventors: Baskar Jayaraman, Aniruddha Thakur, Kannan Govindarajan
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Patent number: 7600002Abstract: A system and methods are provided for allowing a transaction based on arbitrary atomic transaction models initiated in a web services environment to be imported into a J2EE application server environment. An extensible mechanism is provided to bridge from the web services environment into XA and execute a web services operation in the context of the bridged transaction. A resource adapter deployed in the application server receives a SOAP request and converts it into a SOAPMessage, and determines whether it comprises a transaction request. The SOAPMessage is inflowed into the application server as Work, and is routed to a SOAP processing engine (instead of packaging the request for delivery to an application component). The SOAP engine processes the request within the context of the transaction imported from the web services environment. A SOAPMessage response may be returned to the resource adapter and serialized to the web services environment.Type: GrantFiled: February 4, 2005Date of Patent: October 6, 2009Assignee: Oracle International CorporationInventors: Greg Pavlik, Aniruddha Thakur, Paul Parkinson
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Publication number: 20060179125Abstract: A system and methods are provided for allowing a transaction based on arbitrary atomic transaction models initiated in a web services environment to be imported into a J2EE application server environment. An extensible mechanism is provided to bridge from the web services environment into XA and execute a web services operation in the context of the bridged transaction. A resource adapter deployed in the application server receives a SOAP request and converts it into a SOAPMessage, and determines whether it comprises a transaction request. The SOAPMessage is inflowed into the application server as Work, and is routed to a SOAP processing engine (instead of packaging the request for delivery to an application component). The SOAP engine processes the request within the context of the transaction imported from the web services environment. A SOAPMessage response may be returned to the resource adapter and serialized to the web services environment.Type: ApplicationFiled: February 4, 2005Publication date: August 10, 2006Applicant: Oracle International CorporationInventors: Greg Pavlik, Aniruddha Thakur, Paul Parkinson