Patents by Inventor Nikhil Krishnan
Nikhil Krishnan 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).
-
Publication number: 20240111809Abstract: A method, performed by one or more processors, including: receiving one or more event records; generating, using the one or more event records, an event descriptor object descriptive of one or more events occurring in a networked system, wherein the event descriptor object comprises a plurality of event properties; receiving one or more entity records; generating, using the one or more entity records, an entity descriptor object descriptive of one or more entities relevant to the security of the networked system, wherein the entity descriptor object comprises a plurality of entity properties; incorporating, into an object graph, the event descriptor object and the entity descriptor object; and associating, in the object graph, the event descriptor object with the entity descriptor object using at least one of the plurality of event properties and at least one of the plurality of entity properties.Type: ApplicationFiled: November 30, 2023Publication date: April 4, 2024Inventors: Andrew Eggleton, Alexandra Serenhov, Ankit Shankar, Brandon Helms, Brian Keohane, Darren Zhao, Elliot Colquhoun, Gautam Punukollu, Morten Kromann, Nikhil Seetharaman, Ranec Highet, Raj Krishnan, Xiao Tang, Sriram Krishnan, Simon Vahr, Tareq Alkhatib, Thomas Mathew
-
Publication number: 20240045659Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating to a plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.Type: ApplicationFiled: October 23, 2023Publication date: February 8, 2024Inventors: Thomas M. Siebel, Edward Y. Abbo, Houman Behzadi, Avid Boustani, Nikhil Krishnan, Kuenley Chiu, Henrik Ohlsson, Louis Poirier, Jeremy Kolter
-
Patent number: 11886843Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating toa plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.Type: GrantFiled: August 1, 2022Date of Patent: January 30, 2024Assignee: C3.ai, Inc.Inventors: Thomas M. Siebel, Edward Y. Abbo, Houman Behzadi, Avid Boustani, Nikhil Krishnan, Kuenley Chiu, Henrik Ohlsson, Louis Poirier, Jeremy Kolter
-
Publication number: 20230351323Abstract: The present disclosure provides systems and methods that may advantageously apply machine learning to accurately manage and predict inventory variables with future uncertainty. In an aspect, the present disclosure provides a system that can receive an inventory dataset comprising a plurality of inventory variables that indicate at least historical (i) inventory levels, (ii) inventory holding costs, (iii) supplier orders, and/or (iv) lead times over time. The plurality of inventory variables can be characterized by having one or more future uncertainty levels. The system can process the inventory dataset using a trained machine learning model to generate a prediction of the plurality inventory variables. The system can provide the processed in inventory dataset to an optimization algorithm. The optimization algorithm can be used to predict a target inventory level for optimizing an inventory holding cost. The optimization algorithm can comprise one or more constraint conditions.Type: ApplicationFiled: April 4, 2023Publication date: November 2, 2023Inventors: Henrik Ohlsson, Gowtham Bellala, Sina Khoshfetrat Pakazad, Dibyajyoti Banerjee, Nikhil Krishnan
-
Publication number: 20230291755Abstract: A method includes obtaining data associated with operation of a monitored system. The method also includes using one or more first machine learning models to identify anomalies in the monitored system based on the obtained data, where each anomaly identifies an anomalous behavior. The method further includes using one or more second machine learning models to classify each of at least some of the identified anomalies into one of multiple classifications. Different ones of the classifications are associated with different types of cyberthreats to the monitored system, and the identified anomalies are classified based on risk scores determined using the one or more second machine learning models. In addition, the method includes identifying, for each of at least some of the anomalies, one or more actions to be performed in order to counteract the cyberthreat associated with the anomaly.Type: ApplicationFiled: March 10, 2022Publication date: September 14, 2023Inventors: Thomas M. Siebel, Aaron W. Brown, Varun Badrinath Krishna, Nikhil Krishnan, Ansh J. Hirani
-
Patent number: 11620612Abstract: The present disclosure provides systems and methods that may advantageously apply machine learning to accurately manage and predict inventory variables with future uncertainty. In an aspect, the present disclosure provides a system that can receive an inventory dataset comprising a plurality of inventory variables that indicate at least historical (i) inventory levels, (ii) inventory holding costs, (iii) supplier orders, and/or (iv) lead times over time. The plurality of inventory variables can be characterized by having one or more future uncertainty levels. The system can process the inventory dataset using a trained machine learning model to generate a prediction of the plurality inventory variables. The system can provide the processed inventory dataset to an optimization algorithm. The optimization algorithm can be used to predict a target inventory level for optimizing an inventory holding cost. The optimization algorithm can comprise one or more constraint conditions.Type: GrantFiled: July 9, 2019Date of Patent: April 4, 2023Assignee: C3.AI, Inc.Inventors: Henrik Ohlsson, Gowtham Bellala, Sina Khoshfetrat Pakazad, Dibyajyoti Banerjee, Nikhil Krishnan
-
Publication number: 20230027296Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating toa plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.Type: ApplicationFiled: August 1, 2022Publication date: January 26, 2023Inventors: Thomas M. Siebel, Edward Y. Abbo, Houman Behzadi, Avid Boustani, Nikhil Krishnan, Kuenley Chiu, Henrik Ohlsson, Louis Poirier, Jeremy Kolter
-
Publication number: 20220407885Abstract: The disclosed technology can acquire a first set of data from a first group of data sources including a plurality of network components within an energy delivery network. A first metric indicating a likelihood that a particular network component, from the plurality of network components, is affected by cyber vulnerabilities can be generated based on the first set of data. A second set of data can be acquired from a second group of data sources including a collection of services associated with the energy delivery network. A second metric indicating a calculated impact on at least a portion of the energy delivery network when the cyber vulnerabilities affect the particular network component can be generated based on the second set of data. A third metric indicating an overall level of cybersecurity risk associated with the particular network component can be generated based on the first metric and the second metric.Type: ApplicationFiled: July 5, 2022Publication date: December 22, 2022Inventors: Kuenley Chiu, Jeremy Kolter, Nikhil Krishnan, Henrik Ohlsson
-
Publication number: 20220405775Abstract: A method includes curating CRM data by employing a type system of a model-driven architecture and selecting an AI CRM application from a group of applications. Each CRM application may generate one or more use case insights with one or more objectives. The method also includes obtaining one or more data models including an industry-specific data model from the curated CRM data and orchestrating a plurality of machine learning models for the selected CRM application with the obtained data model(s) to determine one or more machine learning models effective for at least one objective of the selected CRM application. The method further includes applying the determined machine learning model(s) and the obtained data model(s) to predict probabilities that optimize the at least one objective and using the predicted probabilities to apply at least one of the one or more use case insights that optimizes the at least one objective.Type: ApplicationFiled: June 21, 2022Publication date: December 22, 2022Inventors: Thomas M. Siebel, Houman Behzadi, Nikhil Krishnan, Varun Badrinath Krishna, Anna L. Ershova, Mark Woollen, Ruiwen An, Gabriele Boncoraglio, Aaron James Christensen, Kush Khosla, Hoda Razavi, Ryan Compton
-
Publication number: 20220398700Abstract: A method for enhancing media includes: receiving, by an electronic device, a media stream; performing, by the electronic device, an alignment of a plurality of frames of the media stream; correcting, by the electronic device, a brightness of the plurality of frames; selecting, by the electronic device, one of a first neural network, a second neural network, or a third neural network, by analyzing parameters of the plurality of frames having the corrected brightness, wherein the parameters include at least one of shot boundary detection and artificial light flickering; and generating, by the electronic device, an output media stream by processing the plurality of frames of the media stream using the selected one of the first neural network, the second neural network, or the third neural network.Type: ApplicationFiled: August 17, 2022Publication date: December 15, 2022Applicant: SAMSUNG ELECTRONICS CO. LTD.Inventors: Green Rosh K S, Nikhil Krishnan, Yash Harbhajanka, Badhisatwa Mandal
-
Publication number: 20220335631Abstract: Some devices, systems, and methods obtain training images; select a first reference image and a second reference image from the training images; generate a first set of aligned training images, wherein generating the first set of aligned training images includes aligning the training images to the first reference image; generate a first anomaly-detection model based on the first set of aligned training images; generate a second set of aligned training images, wherein generating the second set of aligned training images includes aligning the training images to the second reference image; and generate a second anomaly-detection model based on the second set of aligned training images.Type: ApplicationFiled: April 13, 2022Publication date: October 20, 2022Inventors: Bradley Scott Denney, Nikhil Krishnan
-
Patent number: 11449315Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating to a plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.Type: GrantFiled: April 5, 2019Date of Patent: September 20, 2022Assignee: C3.AI, INC.Inventors: Thomas M. Siebel, Edward Y. Abbo, Houman Behzadi, Avid Boustani, Nikhil Krishnan, Kuenley Chiu, Henrik Ohlsson, Louis Poirier, Zico Kolter
-
Patent number: 11411977Abstract: The disclosed technology can acquire a first set of data from a first group of data sources including a plurality of network components within an energy delivery network. A first metric indicating a likelihood that a particular network component, from the plurality of network components, is affected by cyber vulnerabilities can be generated based on the first set of data. A second set of data can be acquired from a second group of data sources including a collection of services associated with the energy delivery network. A second metric indicating a calculated impact on at least a portion of the energy delivery network when the cyber vulnerabilities affect the particular network component can be generated based on the second set of data. A third metric indicating an overall level of cybersecurity risk associated with the particular network component can be generated based on the first metric and the second metric.Type: GrantFiled: February 8, 2018Date of Patent: August 9, 2022Assignee: C3.AI, INC.Inventors: Kuenley Chiu, Jeremy Kolter, Nikhil Krishnan, Henrik Ohlsson
-
Publication number: 20220215295Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to train a Bayesian network model based on a given set of data. Information associated with a user can be received. The information can include aggregated energy consumption data at one or more low frequency time intervals. At least a portion of the information can be inputted into the Bayesian network model. A plurality of energy consumption values for a plurality of energy consumption sources associated with the user can be inferred based on inputting the at least the portion of the information into the Bayesian network model.Type: ApplicationFiled: March 22, 2022Publication date: July 7, 2022Inventors: Zico Kolter, Nikhil Krishnan, Mehdi Maasoumy, Henrik Ohlsson
-
Patent number: 11301771Abstract: Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to train a Bayesian network model based on a given set of data. Information associated with a user can be received. The information can include aggregated energy consumption data at one or more low frequency time intervals. At least a portion of the information can be inputted into the Bayesian network model. A plurality of energy consumption values for a plurality of energy consumption sources associated with the user can be inferred based on inputting the at least the portion of the information into the Bayesian network model.Type: GrantFiled: November 21, 2014Date of Patent: April 12, 2022Assignee: C3.AI, INC.Inventors: Zico Kolter, Nikhil Krishnan, Mehdi Maasoumy, Henrik Ohlsson
-
Publication number: 20220076385Abstract: A method for processing image data, may include: receiving at least one image; segregating the at least one image into at least one region, based on a requested noise reduction level; and denoising the at least one image by varying at least one control feature of the segregated at least one region by a neural network to achieve the requested noise reduction level.Type: ApplicationFiled: November 15, 2021Publication date: March 10, 2022Applicant: SAMSUNG ELECTRONICS CO., LTD.Inventors: Green Rosh K S, Bindigan Hariprasanna Pawan Prasad, Nikhil Krishnan, Sachin Deepak Lomte, Anmol Biswas
-
Publication number: 20210390498Abstract: The present disclosure provides systems and methods that may advantageously apply machine learning to accurately manage and predict inventory variables with future uncertainty. In an aspect, the present disclosure provides a system that can receive an inventory dataset comprising a plurality of inventory variables that indicate at least historical (i) inventory levels, (ii) inventory holding costs, (iii) supplier orders, or (iv) lead times over time. The plurality of inventory variables can be characterized by having one or more future uncertainty levels. The system can process the inventory dataset using a trained machine learning model to generate a prediction of the plurality inventory variables. The system can provide the processed inventory dataset to an optimization algorithm. The optimization algorithm can predict one or more inventory management parameters that result in a particular probability of achieving a target service level while minimizing a cost.Type: ApplicationFiled: April 29, 2021Publication date: December 16, 2021Inventors: Henrik Ohlsson, Gowtham Bellala, Sina Khoshfetratpakazad, Dibyajyoti Banerjee, Nikhil Krishnan
-
Patent number: 11189023Abstract: Devices, systems, and methods obtain a reference image; obtain a test image; globally align the test image to the reference image; select subfields in the test image; align the subfields in the test image with respective areas in the reference image; warp the test image based on the aligning of the subfields; select anchor points in the reference image; select anchor-edge points in the reference image; realign the subfields in the warped test image with respective areas in the reference image based on the anchor points in the reference image and on the anchor-edge points in the reference image; and warp the warped test image based on the realigning of the subfields.Type: GrantFiled: November 19, 2020Date of Patent: November 30, 2021Assignee: Canon Virginia, Inc.Inventors: Xiwu Cao, Nikhil Krishnan, Bradley Scott Denney, Hung Khei Huang
-
Patent number: 11132791Abstract: Devices, systems, and methods obtain a reference image; obtain a test image; globally align the test image to the reference image; select subfields in the test image; align the subfields in the test image with respective areas in the reference image; warp the test image based on the aligning of the subfields; select anchor points in the reference image; select anchor-edge points in the reference image; realign the subfields in the warped test image with respective areas in the reference image based on the anchor points in the reference image and on the anchor-edge points in the reference image; and warp the warped test image based on the realigning of the subfields.Type: GrantFiled: November 19, 2020Date of Patent: September 28, 2021Assignee: Canon Virginia, Inc.Inventors: Xiwu Cao, Nikhil Krishnan, Bradley Scott Denney, Hung Khei Huang
-
Patent number: 10997462Abstract: Devices, systems, and methods obtain training images; generate image-alignment data based on the training images; cluster the training images based at least in part on the image-alignment data, thereby generating clusters of training images; and select one or more representative images from the training images based on the clusters of training images.Type: GrantFiled: March 27, 2019Date of Patent: May 4, 2021Assignee: Canon Virginia, Inc.Inventors: Nikhil Krishnan, Bradley Scott Denney