Patents by Inventor Firat Karakusoglu
Firat Karakusoglu 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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Publication number: 20220012431Abstract: Systems and methods are provided to compare a target sample of text to a set of textual records, each textual record including a sample of text and an indication of one or more segments of text within the sample of text. Semantic similarity values between the target sample of text and each of the textual records are determined. Determining a particular semantic similarity value between the target sample of text and a particular textual record of the corpus includes: (i) determining individual semantic similarity values between the target sample of text and each of the segments of text indicated by the particular textual record, and (ii) generating the particular semantic similarity value between the target sample of text and the particular textual record based on the individual semantic similarity values. A textual record is then selected based on the semantic similarities.Type: ApplicationFiled: September 23, 2021Publication date: January 13, 2022Inventors: Omer Anil Turkkan, Firat Karakusoglu, Sriram Palapudi
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Patent number: 11151325Abstract: Systems and methods are provided to compare a target sample of text to a set of textual records, each textual record including a sample of text and an indication of one or more segments of text within the sample of text. Semantic similarity values between the target sample of text and each of the textual records are determined. Determining a particular semantic similarity value between the target sample of text and a particular textual record of the corpus includes: (i) determining individual semantic similarity values between the target sample of text and each of the segments of text indicated by the particular textual record, and (ii) generating the particular semantic similarity value between the target sample of text and the particular textual record based on the individual semantic similarity values. A textual record is then selected based on the semantic similarities.Type: GrantFiled: March 22, 2019Date of Patent: October 19, 2021Assignee: ServiceNow, Inc.Inventors: Omer Anil Turkkan, Firat Karakusoglu, Sriram Palapudi
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Publication number: 20200302018Abstract: Systems and methods are provided to compare a target sample of text to a set of textual records, each textual record including a sample of text and an indication of one or more segments of text within the sample of text. Semantic similarity values between the target sample of text and each of the textual records are determined. Determining a particular semantic similarity value between the target sample of text and a particular textual record of the corpus includes: (i) determining individual semantic similarity values between the target sample of text and each of the segments of text indicated by the particular textual record, and (ii) generating the particular semantic similarity value between the target sample of text and the particular textual record based on the individual semantic similarity values. A textual record is then selected based on the semantic similarities.Type: ApplicationFiled: March 22, 2019Publication date: September 24, 2020Inventors: Omer Anil Turkkan, Firat Karakusoglu, Sriram Palapudi
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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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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: 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