Patents by Inventor Gunalan Perumal Vijayan
Gunalan Perumal Vijayan 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: 20240312180Abstract: Systems and methods for preventing prediction performance degradation by detecting and extracting skews in data during both training and production environments is described herein. Feature extraction may be performed on training data during the training phase, followed by pattern analysis that assesses similarities across labeled training data sets. A reference pattern may be derived from the pattern analysis and feature extraction of the training data. Feature extraction and pattern analysis may be performed on production data during the serving phase, and a target pattern may be derived from the pattern analysis and feature extraction of the production data. The reference pattern and target pattern may be fed to a discrepancy detection functionality to detect discrepancies by using a sliding window to move the target pattern across the reference pattern to make comparisons between the patterns. The comparison may provide a quantitative skew across the training and production data.Type: ApplicationFiled: March 15, 2023Publication date: September 19, 2024Inventors: SATISH KUMAR MOPUR, GUNALAN PERUMAL VIJAYAN, SHOUNAK BANDOPADHYAY, VIJAYA SHARVANI HINDNAVIS, KRISHNAPRASAD LINGADAHALLI SHASTRY
-
Patent number: 12088476Abstract: The disclosure relates to a framework for dynamic management of analytic functions such as data processors and machine learned (“ML”) models for an Internet of Things intelligent edge that addresses management of the lifecycle of the analytic functions from creation to execution, in production. The end user will be seamlessly able to check in an analytic function, version it, deploy it, evaluate model performance and deploy refined versions into the data flows at the edge or core dynamically for existing and new end points. The framework comprises a hypergraph-based model as a foundation, and may use a microservices architecture with the ML infrastructure and models deployed as containerized microservices.Type: GrantFiled: September 21, 2022Date of Patent: September 10, 2024Assignee: Hewlett Packard Enterprise Development LPInventors: Satish Kumar Mopur, Saikat Mukherjee, Gunalan Perumal Vijayan, Sridhar Balachandriah, Ashutosh Agrawal, KrishnaPrasad Lingadahalli Shastry, Gregory S. Battas
-
Publication number: 20240220514Abstract: The present invention relates to a system and a method for updating data models. Input data received from a data source and/or prediction data obtained from a data model is reduced based on baseline reference data to obtain a plurality of representative points. The plurality of representative points are clustered to generate a plurality of clusters. An outlier cluster is detected from the plurality of clusters based on a maximum distance of the plurality of clusters from a highest density cluster and/or comparison of quantity and values of the plurality of representative points with predefined rules. Data drift is identified based on changes in densities of the plurality of clusters. The data model is updated using information corresponding to the outlier cluster and the data drift.Type: ApplicationFiled: March 18, 2024Publication date: July 4, 2024Inventors: Satish Kumar Mopur, Sridhar Balachandriah, Gunalan Perumal Vijayan, Suresh Ladapuram Soundararajan, Krishna Prasad Lingadahalli Shastry
-
Publication number: 20240160939Abstract: Anomalies and drift detection in decentralized learning environments. The method includes deploying at a first node, (1) a local unsupervised autoencoder, trained at the first node, along with a local training data reference baseline for the first node, and (2) a global unsupervised autoencoder trained across a plurality of nodes, along with a corresponding global training data reference baseline. Production data at the first node is processed with local and global ML models deployed by a user. At least one of local and global anomaly data regarding anomalous production data or local and global drift data regarding drifting production data is derived based on the local and global training data reference baselines, respectively. At least one of the local anomaly data is compared with the global anomaly data or the local drift data with the global drift data for assessing impact of anomalies/drift on the ML models.Type: ApplicationFiled: November 15, 2022Publication date: May 16, 2024Inventors: SATISH KUMAR MOPUR, KRISHNAPRASAD LINGADAHALLI SHASTRY, SATHYANARAYANAN MANAMOHAN, RAVI SARVESWARA, GUNALAN PERUMAL VIJAYAN
-
Patent number: 11954129Abstract: The present invention relates to a system and a method for updating data models. Input data received from a data source and/or prediction data obtained from a data model is reduced based on baseline reference data to obtain a plurality of representative points. The plurality of representative points are clustered to generate a plurality of clusters. An outlier cluster is detected from the plurality of clusters based on a maximum distance of the plurality of clusters from a highest density cluster and/or comparison of quantity and values of the plurality of representative points with predefined rules. Data drift is identified based on changes in densities of the plurality of clusters. The data model is updated using information corresponding to the outlier cluster and the data drift.Type: GrantFiled: April 8, 2021Date of Patent: April 9, 2024Assignee: Hewlett Packard Enterprise Development LPInventors: Satish Kumar Mopur, Sridhar Balachandriah, Gunalan Perumal Vijayan, Suresh Ladapuram Soundarajan, Krishna Prasad Lingadahalli Shastry
-
Publication number: 20230316710Abstract: Systems and methods are provided for implementing a Siamese neural network using improved “sub” neural networks and loss function. For example, the system can detect a granular change in images using a Siamese Neural Network with Convolutional Autoencoders as the twin sub networks (e.g., Siamese AutoEncoder or “SAE”). In some examples, the loss function may be an adaptive loss function to the SAE network rather than a contrastive loss function, which can help enable smooth control of granularity of change detection across the images. In some examples, an image separation distance value may be calculated to determine the value of change between the image pairs. The image separation distance value may be determined using an Euclidean distance associated with a latent space of an encoder portion of the autoencoder of the neural networks.Type: ApplicationFiled: March 29, 2022Publication date: October 5, 2023Inventors: SATISH KUMAR MOPUR, Gunalan Perumal Vijayan, Shounak Bandopadhyay, Krishnaprasad Lingadahalli Shastry
-
Publication number: 20230281958Abstract: Systems and methods are provided for retraining machine learning (ML) models. Examples may automatically identify skewed, anomalous, and/or drift occurrence data in real-world input data. By automatically identifying such data, examples can reduce subjectivity in ML model retraining as well as reduce time spent determining a need to retrain a ML model. Accordingly, a determination can be made objectively by a computing system or device according to computer-implemented instructions. Additionally, examples may automatically isolate and transfer data relevant to the retraining of a ML model to a training environment for retraining the ML model using real-world input data. Examples also synthesize large samples of data for use in retraining a ML model. The synthesized data may be generated based on the isolated and transferred data and can be used in place of actual real-world input data to reduce a corresponding delay.Type: ApplicationFiled: March 1, 2022Publication date: September 7, 2023Inventors: SATISH KUMAR MOPUR, Gunalan Perumal Vijayan, Krishna Prasad Lingadahalli Shastry
-
Publication number: 20230017701Abstract: The disclosure relates to a framework for dynamic management of analytic functions such as data processors and machine learned (“ML”) models for an Internet of Things intelligent edge that addresses management of the lifecycle of the analytic functions from creation to execution, in production. The end user will be seamlessly able to check in an analytic function, version it, deploy it, evaluate model performance and deploy refined versions into the data flows at the edge or core dynamically for existing and new end points. The framework comprises a hypergraph-based model as a foundation, and may use a microservices architecture with the ML infrastructure and models deployed as containerized microservices.Type: ApplicationFiled: September 21, 2022Publication date: January 19, 2023Inventors: Satish Kumar Mopur, Saikat Mukherjee, Gunalan Perumal Vijayan, Sridhar Balachandriah, Ashutosh Agrawal, KrishnaPrasad Lingadahalli Shastry, Gregory S. Battas
-
Patent number: 11481665Abstract: A system and method for accounting for the impact of concept drift in selecting machine learning training methods to address the identified impact. Pattern recognition is performed on performance metrics of a deployed production model in an Internet-of-Things (IoT) environment to determine the impact that concept drift (data drift) has had on prediction performance. This concurrent analysis is utilized to select one or more approaches for training machine learning models, thereby accounting for the temporal dynamics of concept drift (and its subsequent impact on prediction performance) in a faster and more efficient manner.Type: GrantFiled: November 9, 2018Date of Patent: October 25, 2022Assignee: Hewlett Packard Enterprise Development LPInventors: Satish Kumar Mopur, Gregory S. Battas, Gunalan Perumal Vijayan, Krishnaprasad Lingadahalli Shastry, Saikat Mukherjee, Ashutosh Agrawal, Sridhar Balachandriah
-
Patent number: 11469969Abstract: The disclosure relates to a framework for dynamic management of analytic functions such as data processors and machine learned (“ML”) models for an Internet of Things intelligent edge that addresses management of the lifecycle of the analytic functions from creation to execution, in production. The end user will be seamlessly able to check in an analytic function, version it, deploy it, evaluate model performance and deploy refined versions into the data flows at the edge or core dynamically for existing and new end points. The framework comprises a hypergraph-based model as a foundation, and may use a microservices architecture with the ML infrastructure and models deployed as containerized microservices.Type: GrantFiled: October 4, 2018Date of Patent: October 11, 2022Assignee: Hewlett Packard Enterprise Development LPInventors: Satish Kumar Mopur, Saikat Mukherjee, Gunalan Perumal Vijayan, Sridhar Balachandriah, Ashutosh Agrawal, Krishnaprasad Lingadahalli Shastry, Gregory S. Battas
-
Patent number: 11361245Abstract: The disclosure relates to technology that implements flow control for machine learning on data such as Internet of Things (“IoT”) datasets. The system may route outputs of a data splitter function performed on the IoT datasets to a designated target model based on a user specification for routing the outputs. In this manner, the IoT datasets may be dynamically routed to target datasets without reprogramming machine-learning pipelines, which enable rapid training, testing and validation of ML models as well as an ability to concurrently train, validate, and execute ML models.Type: GrantFiled: August 9, 2018Date of Patent: June 14, 2022Assignee: Hewlett Packard Enterprise Development LPInventors: Satish Kumar Mopur, Saikat Mukherjee, Gunalan Perumal Vijayan, Sridhar Balachandriah, Ashutosh Agrawal, Krishnaprasad Lingadahalli Shastry, Gregory S. Battas
-
Publication number: 20210365478Abstract: The present invention relates to a system and a method for updating data models. Input data received from a data source and/or prediction data obtained from a data model is reduced based on baseline reference data to obtain a plurality of representative points. The plurality of representative points are clustered to generate a plurality of clusters. An outlier cluster is detected from the plurality of clusters based on a maximum distance of the plurality of clusters from a highest density cluster and/or comparison of quantity and values of the plurality of representative points with predefined rules. Data drift is identified based on changes in densities of the plurality of clusters. The data model is updated using information corresponding to the outlier cluster and the data drift.Type: ApplicationFiled: April 8, 2021Publication date: November 25, 2021Inventors: Satish Kumar MOPUR, Sridhar BALACHANDRIAH, Gunalan PERUMAL VIJAYAN, Suresh LADAPURAM SOUNDARAJAN, Krishna Prasad Lingadahalli SHASTRY
-
Patent number: 10855791Abstract: A request that indicates a component that can be quiesced is received at a first node. It is determined that quiescence of the component might impact an endpoint. A request for identification of at least one path between a second node and the endpoint is sent to the second node. It is determined, based on a response received from the second node, whether the endpoint will be accessible to the second node if the component is quiesced. In response to a determination that the endpoint will be accessible to the second node if the component is quiesced, a positive analysis outcome is indicated. In response to a determination that the endpoint will not be accessible to the second node if the component is quiesced, a negative analysis outcome is indicated.Type: GrantFiled: November 25, 2014Date of Patent: December 1, 2020Assignee: NetApp, Inc.Inventors: Gunalan Perumal Vijayan, William D. Dallas, Sridhar Balachandriah, Bhaskar Singhal
-
Publication number: 20200151619Abstract: A system and method for accounting for the impact of concept drift in selecting machine learning training methods to address the identified impact. Pattern recognition is performed on performance metrics of a deployed production model in an Internet-of-Things (IoT) environment to determine the impact that concept drift (data drift) has had on prediction performance. This concurrent analysis is utilized to select one or more approaches for training machine learning models, thereby accounting for the temporal dynamics of concept drift (and its subsequent impact on prediction performance) in a faster and more efficient manner.Type: ApplicationFiled: November 9, 2018Publication date: May 14, 2020Inventors: Satish Kumar MOPUR, Gregory S. BATTAS, Gunalan Perumal VIJAYAN, Krishnaprasad Lingadahalli SHASTRY, Saikat MUKHERJEE, Ashutosh AGRAWAL, Sridhar BALACHANDRIAH
-
Publication number: 20200112490Abstract: The disclosure relates to a framework for dynamic management of analytic functions such as data processors and machine learned (“ML”) models for an Internet of Things intelligent edge that addresses management of the lifecycle of the analytic functions from creation to execution, in production. The end user will be seamlessly able to check in an analytic function, version it, deploy it, evaluate model performance and deploy refined versions into the data flows at the edge or core dynamically for existing and new end points. The framework comprises a hypergraph-based model as a foundation, and may use a microservices architecture with the ML infrastructure and models deployed as containerized microservices.Type: ApplicationFiled: October 4, 2018Publication date: April 9, 2020Inventors: SATISH KUMAR MOPUR, SAIKAT MUKHERJEE, GUNALAN PERUMAL VIJAYAN, SRIDHAR BALACHANDRIAH, ASHUTOSH AGRAWAL, KRISHNAPRASAD LINGADAHALLI SHASTRY, GREGORY S. BATTAS
-
Publication number: 20200050578Abstract: The disclosure relates to technology that implements flow control for machine learning on data such as Internet of Things (“IoT”) datasets. The system may route outputs of a data splitter function performed on the IoT datasets to a designated target model based on a user specification for routing the outputs. In this manner, the IoT datasets may be dynamically routed to target datasets without reprogramming machine-learning pipelines, which enable rapid training, testing and validation of ML models as well as an ability to concurrently train, validate, and execute ML models.Type: ApplicationFiled: August 9, 2018Publication date: February 13, 2020Inventors: SATISH KUMAR MOPUR, SAIKAT MUKHERJEE, GUNALAN PERUMAL VIJAYAN, SRIDHAR BALACHANDRIAH, ASHUTOSH AGRAWAL, KRISHNAPRASAD LINGADAHALLI SHASTRY, GREGORY S. BATTAS
-
Publication number: 20190028407Abstract: Example implementations relate to managing compliance of workloads to quality of service (QoS) parameters. An example includes collection of time-series network performance data from server systems and fabric interconnects related to traffic generated by workloads of the server systems. Rapid trends and long term trends for the workloads are calculated, using the collected network performance data as the input. Compliance of a high priority workload to an associated QoS parameter with the high priority workload is managed based on monitoring a rapid analytic trend for the high priority workload. Compliance of all of the workloads to respective QoS parameters is managed based on monitoring of long term analytic trends for the workloads.Type: ApplicationFiled: July 20, 2017Publication date: January 24, 2019Inventors: Gunalan Perumal Vijayan, Saikat Mukherjee, Satish Kumar Mopur, Sridhar Balachandriah
-
Publication number: 20160149828Abstract: A request that indicates a component that can be quiesced is received at a first node. It is determined that quiescence of the component might impact an endpoint. A request for identification of at least one path between a second node and the endpoint is sent to the second node. It is determined, based on a response received from the second node, whether the endpoint will be accessible to the second node if the component is quiesced. In response to a determination that the endpoint will be accessible to the second node if the component is quiesced, a positive analysis outcome is indicated. In response to a determination that the endpoint will not be accessible to the second node if the component is quiesced, a negative analysis outcome is indicated.Type: ApplicationFiled: November 25, 2014Publication date: May 26, 2016Inventors: Gunalan Perumal Vijayan, William D. Dallas, Sridhar Balachandriah, Bhaskar Singhal
-
Patent number: 8054763Abstract: A method, system and computer program product for migrating at least one switch in a storage area network is disclosed. The migration is done by analysing the I/O traffic to identify patterns in the I/O traffic of the switch; forecasting future I/O workload of the switch based on one or more identified patterns in the I/O traffic, determining appropriate timing for migration based on the identified patterns and administrator inputs; processing the storage area network configuration data to identify the storage network physical and logical access paths to the or each selected switch to create a first connectivity map; generating a second connectivity map based on the first connectivity map and administrator inputs; and migrating the or each switch migration based on the second connectivity map and the appropriate timing. The migration may comprise routing the I/O traffic from the switch to be migrated to the alternate switches in the storage area network.Type: GrantFiled: May 15, 2009Date of Patent: November 8, 2011Assignee: Hewlett-Packard Development Company, L.P.Inventors: Vivek Mehrotra, Satish Kumar Mopur, Saikat Mukherjee, Satyaprakash Rao, Gunalan Perumal Vijayan, Sridhar Balachandriah
-
Publication number: 20100293316Abstract: A method, system and computer program product for migrating at least one switch in a storage area network is disclosed. The migration is done by analysing the I/O traffic to identify patterns in the I/O traffic of the switch; forecasting future I/O workload of the switch based on one or more identified patterns in the I/O traffic, determining appropriate timing for migration based on the identified patterns and administrator inputs; processing the storage area network configuration data to identify the storage network physical and logical access paths to the or each selected switch to create a first connectivity map; generating a second connectivity map based on the first connectivity map and administrator inputs; and migrating the or each switch migration based on the second connectivity map and the appropriate timing. The migration may comprise routing the I/O traffic from the switch to be migrated to the alternate switches in the storage area network.Type: ApplicationFiled: May 15, 2009Publication date: November 18, 2010Inventors: Vivek MEHROTRA, Satish Kumar Mopur, Saikat Mukherjee, Satyaprakash Rao, Gunalan Perumal Vijayan, Sridhar Balachandriah