Patents by Inventor Nilesh K. Jain
Nilesh K. Jain 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: 11922220Abstract: Embodiments of systems, apparatuses and methods provide enhanced function as a service (FaaS) to users, e.g., computer developers and cloud service providers (CSPs). A computing system configured to provide such enhanced FaaS service include one or more controls architectural subsystems, software and orchestration subsystems, network and storage subsystems, and security subsystems. The computing system executes functions in response to events triggered by the users in an execution environment provided by the architectural subsystems, which represent an abstraction of execution management and shield the users from the burden of managing the execution. The software and orchestration subsystems allocate computing resources for the function execution by intelligently spinning up and down containers for function code with decreased instantiation latency and increased execution scalability while maintaining secured execution.Type: GrantFiled: April 16, 2019Date of Patent: March 5, 2024Assignee: Intel CorporationInventors: Mohammad R. Haghighat, Kshitij Doshi, Andrew J. Herdrich, Anup Mohan, Ravishankar R. Iyer, Mingqiu Sun, Krishna Bhuyan, Teck Joo Goh, Mohan J. Kumar, Michael Prinke, Michael Lemay, Leeor Peled, Jr-Shian Tsai, David M. Durham, Jeffrey D. Chamberlain, Vadim A. Sukhomlinov, Eric J. Dahlen, Sara Baghsorkhi, Harshad Sane, Areg Melik-Adamyan, Ravi Sahita, Dmitry Yurievich Babokin, Ian M. Steiner, Alexander Bachmutsky, Anil Rao, Mingwei Zhang, Nilesh K. Jain, Amin Firoozshahian, Baiju V. Patel, Wenyong Huang, Yeluri Raghuram
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Patent number: 11449803Abstract: Methods, apparatus, and system determine if a data class in a plurality of data classes is separable, such as by determining an average intra-class similarity within each data class, inter-class similarity across all data classes in the plurality of data classes, and determining separability based on the average intra-class similarity relative to the inter-class similarity. Data classes determined to be highly variable may be removed. Pair(s) of data classes not separable from one another may be combined into one class or one of the data classes may be dropped. A hardware accelerator, which may comprise artificial neurons, accelerate performance of the data analysis.Type: GrantFiled: August 12, 2020Date of Patent: September 20, 2022Assignee: INTEL CORPORATIONInventors: Darshan Iyer, Nilesh K. Jain
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Publication number: 20220166846Abstract: Technologies for managing telemetry and sensor data on an edge networking platform are disclosed. According to one embodiment disclosed herein, a device monitors telemetry data associated with multiple services provided in the edge networking platform. The device identifies, for each of the services and as a function of the associated telemetry data, one or more service telemetry patterns. The device generates a profile including the identified service telemetry patterns.Type: ApplicationFiled: July 30, 2021Publication date: May 26, 2022Inventors: Ramanathan Sethuraman, Timothy Verrall, Ned M. Smith, Thomas Willhalm, Brinda Ganesh, Francesc Guim Bernat, Karthik Kumar, Evan Custodio, Suraj Prabhakaran, Ignacio Astilleros Diez, Nilesh K. Jain, Ravi Iyer, Andrew J. Herdrich, Alexander Vul, Patrick G. Kutch, Kevin Bohan, Trevor Cooper
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Publication number: 20220108224Abstract: Technologies for platform-targeted machine learning include a computing device to generate a machine learning algorithm model indicative of a plurality of classes between which a user input is to be classified and translate the machine learning algorithm model into hardware code for execution on the target platform. Example instructions cause a processor to obtain dataset features indicative of a plurality of characteristics of an input dataset, rank, using multiple ranking algorithms, the dataset features, identify feature subsets for respective ones of the ranked dataset features, predict performance metrics based on the feature subsets, and select a final subset based on the predicted performance metrics.Type: ApplicationFiled: December 17, 2021Publication date: April 7, 2022Inventors: Luis S. Kida, Nilesh K. Jain, Darshan Iyer, Ebrahim Al Safadi
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Patent number: 11216749Abstract: Technologies for platform-targeted machine learning include a computing device to generate a machine learning algorithm model indicative of a plurality of classes between which a user input is to be classified and translate the machine learning algorithm model into hardware code for execution on the target platform. The user input is to be classified as being associated with a particular class based on an application of one or more features to the user input, and each of the one or more features has an associated implementation cost indicative of a cost to perform on a target platform on which the corresponding feature is to be applied to the user input.Type: GrantFiled: July 17, 2019Date of Patent: January 4, 2022Assignee: Intel CorporationInventors: Luis S. Kida, Nilesh K. Jain, Darshan Iyer, Ebrahim Al Safadi
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Publication number: 20210263779Abstract: Embodiments of systems, apparatuses and methods provide enhanced function as a service (FaaS) to users, e.g., computer developers and cloud service providers (CSPs). A computing system configured to provide such enhanced FaaS service include one or more controls architectural subsystems, software and orchestration subsystems, network and storage subsystems, and security subsystems. The computing system executes functions in response to events triggered by the users in an execution environment provided by the architectural subsystems, which represent an abstraction of execution management and shield the users from the burden of managing the execution. The software and orchestration subsystems allocate computing resources for the function execution by intelligently spinning up and down containers for function code with decreased instantiation latency and increased execution scalability while maintaining secured execution.Type: ApplicationFiled: April 16, 2019Publication date: August 26, 2021Applicant: Intel CorporationInventors: Mohammad R. Haghighat, Kshitij Doshi, Andrew J. Herdrich, Anup Mohan, Ravishankar R. Iyer, Mingqiu Sun, Krishna Bhuyan, Teck Joo Goh, Mohan J. Kumar, Michael Prinke, Michael Lemay, Leeor Peled, Jr-Shian Tsai, David M. Durham, Jeffrey D. Chamberlain, Vadim A. Sukhomlinov, Eric J. Dahlen, Sara Baghsorkhi, Harshad Sane, Areg Melik-Adamyan, Ravi Sahita, Dmitry Yurievich Babokin, Ian M. Steiner, Alexander Bachmutsky, Anil Rao, Mingwei Zhang, Nilesh K. Jain, Amin Firoozshahian, Baiju V. Patel, Wenyong Huang, Yeluri Raghuram
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Patent number: 11082525Abstract: Technologies for managing telemetry and sensor data on an edge networking platform are disclosed. According to one embodiment disclosed herein, a device monitors telemetry data associated with multiple services provided in the edge networking platform. The device identifies, for each of the services and as a function of the associated telemetry data, one or more service telemetry patterns. The device generates a profile including the identified service telemetry patterns.Type: GrantFiled: May 17, 2019Date of Patent: August 3, 2021Assignee: Intel CorporationInventors: Ramanathan Sethuraman, Timothy Verrall, Ned M. Smith, Thomas Willhalm, Brinda Ganesh, Francesc Guim Bernat, Karthik Kumar, Evan Custodio, Suraj Prabhakaran, Ignacio Astilleros Diez, Nilesh K. Jain, Ravi Iyer, Andrew J. Herdrich, Alexander Vul, Patrick G. Kutch, Kevin Bohan, Trevor Cooper
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Publication number: 20210201200Abstract: Methods, apparatus, and system determine if a data class in a plurality of data classes is separable, such as by determining an average intra-class similarity within each data class, inter-class similarity across all data classes in the plurality of data classes, and determining separability based on the average intra-class similarity relative to the inter-class similarity. Data classes determined to be highly variable may be removed. Pair(s) of data classes not separable from one another may be combined into one class or one of the data classes may be dropped. A hardware accelerator, which may comprise artificial neurons, accelerate performance of the data analysis.Type: ApplicationFiled: August 12, 2020Publication date: July 1, 2021Inventors: Darshan Iyer, Nilesh K. Jain
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Patent number: 10956792Abstract: Methods, apparatus, systems and articles of manufacture to analyze time series data are disclosed. An example method includes sub sampling time series data collected by a sensor to generate one or more candidate samples of interest within the time series data. Feature vectors are generated for respective ones of the one or more candidate samples of interest. Classification of the feature vectors is attempted based on a model. In response to a classification of one of the feature vectors, the classification is stored in connection with the corresponding candidate sample.Type: GrantFiled: August 9, 2017Date of Patent: March 23, 2021Assignee: Intel CorporationInventors: Darshan Iyer, Nilesh K. Jain
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Patent number: 10755198Abstract: Methods, apparatus, and system determine if a data class in a plurality of data classes is separable, such as by determining an average intra-class similarity within each data class, inter-class similarity across all data classes in the plurality of data classes, and determining separability based on the average intra-class similarity relative to the inter-class similarity. Data classes determined to be highly variable may be removed. Pair(s) of data classes not separable from one another may be combined into one class or one of the data classes may be dropped. A hardware accelerator, which may comprise artificial neurons, accelerate performance of the data analysis.Type: GrantFiled: December 29, 2016Date of Patent: August 25, 2020Assignee: Intel CorporationInventors: Darshan Iyer, Nilesh K. Jain
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Patent number: 10747327Abstract: Technologies for gesture recognition using downsampling are disclosed. A gesture recognition device may capture gesture data from a gesture measurement device, and downsample the captured data to a predefined number of data points. The gesture recognition device may then perform gesture recognition on the downsampled gesture data to recognize a gesture, and then perform an action based on the recognized gesture. The number of data points to which to downsample may be determined by downsampling to several different numbers of data points and comparing the performance of a gesture recognition algorithm performed on the downsampled gesture data for each different number of data points.Type: GrantFiled: June 28, 2016Date of Patent: August 18, 2020Assignee: Intel CorporationInventors: Darshan Iyer, Nilesh K. Jain, Zhiqiang Liang
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Publication number: 20190340539Abstract: Technologies for platform-targeted machine learning include a computing device to generate a machine learning algorithm model indicative of a plurality of classes between which a user input is to be classified and translate the machine learning algorithm model into hardware code for execution on the target platform. The user input is to be classified as being associated with a particular class based on an application of one or more features to the user input, and each of the one or more features has an associated implementation cost indicative of a cost to perform on a target platform on which the corresponding feature is to be applied to the user input.Type: ApplicationFiled: July 17, 2019Publication date: November 7, 2019Inventors: Luis S. Kida, Nilesh K. Jain, Darshan Iyer, Ebrahim Al Safadi
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Publication number: 20190281132Abstract: Technologies for managing telemetry and sensor data on an edge networking platform are disclosed. According to one embodiment disclosed herein, a device monitors telemetry data associated with multiple services provided in the edge networking platform. The device identifies, for each of the services and as a function of the associated telemetry data, one or more service telemetry patterns. The device generates a profile including the identified service telemetry patterns.Type: ApplicationFiled: May 17, 2019Publication date: September 12, 2019Inventors: Ramanathan Sethuraman, Timothy Verrall, Ned M. Smith, Thomas Willhalm, Brinda Ganesh, Francesc Guim Bernat, Karthik Kumar, Evan Custodio, Suraj Prabhakaran, Ignacio Astilleros Diez, Nilesh K. Jain, Ravi Iyer, Andrew J. Herdrich, Alexander Vul, Patrick G. Kutch, Kevin Bohan, Trevor Cooper
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Patent number: 10373069Abstract: Technologies for platform-targeted machine learning include a computing device to generate a machine learning algorithm model indicative of a plurality of classes between which a user input is to be classified and translate the machine learning algorithm model into hardware code for execution on the target platform. The user input is to be classified as being associated with a particular class based on an application of one or more features to the user input, and each of the one or more features has an associated implementation cost indicative of a cost to perform on a target platform on which the corresponding feature is to be applied to the user input.Type: GrantFiled: September 26, 2015Date of Patent: August 6, 2019Assignee: Intel CorporationInventors: Luis S. Kida, Nilesh K. Jain, Darshan Iyer, Ebrahim Al Safadi
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Patent number: 10282641Abstract: Technologies for classification using sparse coding are disclosed. A compute device may include a pattern-matching accelerator, which may be able to determine the distance between an input vector (such as an image) and several basis vectors of an overcomplete dictionary stored in the pattern-matching accelerator. The pattern matching accelerator may be able to determine each of the distances simultaneously and in a fixed amount of time (i.e., with no dependence on the number of basis vectors to which the input vector is being compared). The pattern-matching accelerator may be used to determine a set of sparse coding coefficients corresponding to a subset of the overcomplete basis vectors. The sparse coding coefficients can then be used to classify the input vector.Type: GrantFiled: July 1, 2016Date of Patent: May 7, 2019Assignee: Intel CorporationInventors: Nilesh K. Jain, Soonam Lee
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Publication number: 20190050688Abstract: Methods, apparatus, systems and articles of manufacture to analyze time series data are disclosed. An example method includes sub sampling time series data collected by a sensor to generate one or more candidate samples of interest within the time series data. Feature vectors are generated for respective ones of the one or more candidate samples of interest. Classification of the feature vectors is attempted based on a model. In response to a classification of one of the feature vectors, the classification is stored in connection with the corresponding candidate sample.Type: ApplicationFiled: August 9, 2017Publication date: February 14, 2019Inventors: Darshan Iyer, Nilesh K. Jain
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Publication number: 20180189376Abstract: Methods, apparatus, and system determine if a data class in a plurality of data classes is separable, such as by determining an average intra-class similarity within each data class, inter-class similarity across all data classes in the plurality of data classes, and determining separability based on the average intra-class similarity relative to the inter-class similarity. Data classes determined to be highly variable may be removed. Pair(s) of data classes not separable from one another may be combined into one class or one of the data classes may be dropped. A hardware accelerator, which may comprise artificial neurons, accelerate performance of the data analysis.Type: ApplicationFiled: December 29, 2016Publication date: July 5, 2018Inventors: Darshan Iyer, Nilesh K. Jain
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Publication number: 20180005086Abstract: Technologies for classification using sparse coding are disclosed. A compute device may include a pattern-matching accelerator, which may be able to determine the distance between an input vector (such as an image) and several basis vectors of an overcomplete dictionary stored in the pattern-matching accelerator. The pattern matching accelerator may be able to determine each of the distances simultaneously and in a fixed amount of time (i.e., with no dependence on the number of basis vectors to which the input vector is being compared). The pattern-matching accelerator may be used to determine a set of sparse coding coefficients corresponding to a subset of the overcomplete basis vectors. The sparse coding coefficients can then be used to classify the input vector.Type: ApplicationFiled: July 1, 2016Publication date: January 4, 2018Inventors: Nilesh K. Jain, Soonam Lee
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Publication number: 20170371417Abstract: Technologies for gesture recognition using downsampling are disclosed. A gesture recognition device may capture gesture data from a gesture measurement device, and downsample the captured data to a predefined number of data points. The gesture recognition device may then perform gesture recognition on the downsampled gesture data to recognize a gesture, and then perform an action based on the recognized gesture. The number of data points to which to downsample may be determined by downsampling to several different numbers of data points and comparing the performance of a gesture recognition algorithm performed on the downsampled gesture data for each different number of data points.Type: ApplicationFiled: June 28, 2016Publication date: December 28, 2017Inventors: Darshan Iyer, Nilesh K. Jain, Zhiqiang Liang
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Publication number: 20170091657Abstract: Technologies for platform-targeted machine learning include a computing device to generate a machine learning algorithm model indicative of a plurality of classes between which a user input is to be classified and translate the machine learning algorithm model into hardware code for execution on the target platform. The user input is to be classified as being associated with a particular class based on an application of one or more features to the user input, and each of the one or more features has an associated implementation cost indicative of a cost to perform on a target platform on which the corresponding feature is to be applied to the user input.Type: ApplicationFiled: September 26, 2015Publication date: March 30, 2017Inventors: Luis S. Kida, Nilesh K. Jain, Darshan Iyer, Ebrahim Al Safadi