Patents by Inventor Satish Kumar Mopur

Satish Kumar Mopur 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: 20240354218
    Abstract: Systems and methods are provided for utilization of optimal data access interface usage in machine learning pipelines. Examples of the systems and methods disclosed herein include identifying data access interfaces comprising at least a first data access interface for a persistent storage distributed across a plurality of storage nodes and at least a second data access interface for an in-memory object store, and receiving, from a compute node, a data operation request as part of a machine learning pipeline. Additionally, performance metrics are obtained for the plurality of access interfaces, and based on a type of data operation request, the data operation is executed using a data access interface selected from the plurality of data access interface based on the performance metrics and providing an object handle to the compute node.
    Type: Application
    Filed: April 18, 2023
    Publication date: October 24, 2024
    Inventors: SRIDHAR BALACHANDRIAH, SATISH KUMAR MOPUR, LANCE MACKIMME EVANS, SHERIN THYIL GEORGE, KEVAN REHM
  • Publication number: 20240312180
    Abstract: 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: Application
    Filed: March 15, 2023
    Publication date: September 19, 2024
    Inventors: SATISH KUMAR MOPUR, GUNALAN PERUMAL VIJAYAN, SHOUNAK BANDOPADHYAY, VIJAYA SHARVANI HINDNAVIS, KRISHNAPRASAD LINGADAHALLI SHASTRY
  • Patent number: 12088476
    Abstract: 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: Grant
    Filed: September 21, 2022
    Date of Patent: September 10, 2024
    Assignee: Hewlett Packard Enterprise Development LP
    Inventors: Satish Kumar Mopur, Saikat Mukherjee, Gunalan Perumal Vijayan, Sridhar Balachandriah, Ashutosh Agrawal, KrishnaPrasad Lingadahalli Shastry, Gregory S. Battas
  • Publication number: 20240220514
    Abstract: 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: Application
    Filed: March 18, 2024
    Publication date: July 4, 2024
    Inventors: Satish Kumar Mopur, Sridhar Balachandriah, Gunalan Perumal Vijayan, Suresh Ladapuram Soundararajan, Krishna Prasad Lingadahalli Shastry
  • Publication number: 20240160939
    Abstract: 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: Application
    Filed: November 15, 2022
    Publication date: May 16, 2024
    Inventors: SATISH KUMAR MOPUR, KRISHNAPRASAD LINGADAHALLI SHASTRY, SATHYANARAYANAN MANAMOHAN, RAVI SARVESWARA, GUNALAN PERUMAL VIJAYAN
  • Patent number: 11954129
    Abstract: 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: Grant
    Filed: April 8, 2021
    Date of Patent: April 9, 2024
    Assignee: Hewlett Packard Enterprise Development LP
    Inventors: Satish Kumar Mopur, Sridhar Balachandriah, Gunalan Perumal Vijayan, Suresh Ladapuram Soundarajan, Krishna Prasad Lingadahalli Shastry
  • Publication number: 20240028417
    Abstract: Systems and methods provide for a federated workflow solution to orchestrate entire machine learning (ML) workflows comprising multiple tasks, across silos. In other words, one or more sets/pluralities of tasks making up an ML workflow, can be executed across multiple resource partitions or domains. Federated workflow state can be maintained and shared through some form of distributed database/ledger, such as a blockchain. Agents that are locally deployed locally at the silos may orchestrate an ML workflow at a particular resource domains, each such agent having access, via the blockchain (acting as a globally visible/consistent state store), to the aforementioned workflow state. Such systems are capable of operating regardless of the existence of heterogeneous resources/aspects of a silo.
    Type: Application
    Filed: July 19, 2022
    Publication date: January 25, 2024
    Inventors: SATHYANARAYANAN MANAMOHAN, SATISH KUMAR MOPUR, KRISHNAPRASAD LINGADAHALLI SHASTRY, RAVI SARVESWARA
  • Publication number: 20240012852
    Abstract: Bias in Machine Learning (ML) is when an ML algorithm tends to incompletely learn relevant and important patterns from a dataset, or learns the patterns from data incorrectly. Such inaccuracy can cause the algorithm to miss important relationships between patterns and features in data, resulting in inaccurate algorithm predictions. Systems and methods for detecting potential ML bias in input image datasets are described herein. After a target image is received, a subset of images related to the target image is extracted. The target image and subset of images are analyzed under an imbalance assessment and data bias assessment to determine the presence of any potential data bias in a ML training pipeline. If any data bias is determined, one or more messages summarizing the assessments and including explanations to enable more accurate predictions in image assessments are sent to the user.
    Type: Application
    Filed: July 7, 2022
    Publication date: January 11, 2024
    Inventors: Satish Kumar Mopur, Krishnaprasad Lingadahalli Shastry
  • Publication number: 20230316710
    Abstract: 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: Application
    Filed: March 29, 2022
    Publication date: October 5, 2023
    Inventors: SATISH KUMAR MOPUR, Gunalan Perumal Vijayan, Shounak Bandopadhyay, Krishnaprasad Lingadahalli Shastry
  • Publication number: 20230281958
    Abstract: 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: Application
    Filed: March 1, 2022
    Publication date: September 7, 2023
    Inventors: SATISH KUMAR MOPUR, Gunalan Perumal Vijayan, Krishna Prasad Lingadahalli Shastry
  • Publication number: 20230017701
    Abstract: 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: Application
    Filed: September 21, 2022
    Publication date: January 19, 2023
    Inventors: Satish Kumar Mopur, Saikat Mukherjee, Gunalan Perumal Vijayan, Sridhar Balachandriah, Ashutosh Agrawal, KrishnaPrasad Lingadahalli Shastry, Gregory S. Battas
  • Patent number: 11481665
    Abstract: 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: Grant
    Filed: November 9, 2018
    Date of Patent: October 25, 2022
    Assignee: Hewlett Packard Enterprise Development LP
    Inventors: Satish Kumar Mopur, Gregory S. Battas, Gunalan Perumal Vijayan, Krishnaprasad Lingadahalli Shastry, Saikat Mukherjee, Ashutosh Agrawal, Sridhar Balachandriah
  • Patent number: 11469969
    Abstract: 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: Grant
    Filed: October 4, 2018
    Date of Patent: October 11, 2022
    Assignee: Hewlett Packard Enterprise Development LP
    Inventors: Satish Kumar Mopur, Saikat Mukherjee, Gunalan Perumal Vijayan, Sridhar Balachandriah, Ashutosh Agrawal, Krishnaprasad Lingadahalli Shastry, Gregory S. Battas
  • Publication number: 20220215289
    Abstract: A system and a method for updating a Machine Learning (ML) model are described The method involves capturing reconstruction errors associated with reconstruction of images by a pre-trained autoencoder. Data points representing the reconstruction errors are clustered using affinity propagation. A preference value used by the affinity propagation for determining similarity between the data points is dynamically set through linear regression. Outliers and data drifts are determined from clusters of the data points. Classification output of the ML model is associated with the outliers and the data drift, for refinement of the ML model over a device hosting a training environment.
    Type: Application
    Filed: April 22, 2021
    Publication date: July 7, 2022
    Inventors: Satish Kumar MOPUR, Sridhar BALACHANDRIAH, Krishnaprasad Lingadahalli SHASTRY, Maximilian CLASSEN, Milind B. DESAI
  • Patent number: 11361245
    Abstract: 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: Grant
    Filed: August 9, 2018
    Date of Patent: June 14, 2022
    Assignee: Hewlett Packard Enterprise Development LP
    Inventors: Satish Kumar Mopur, Saikat Mukherjee, Gunalan Perumal Vijayan, Sridhar Balachandriah, Ashutosh Agrawal, Krishnaprasad Lingadahalli Shastry, Gregory S. Battas
  • Publication number: 20210365478
    Abstract: 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: Application
    Filed: April 8, 2021
    Publication date: November 25, 2021
    Inventors: Satish Kumar MOPUR, Sridhar BALACHANDRIAH, Gunalan PERUMAL VIJAYAN, Suresh LADAPURAM SOUNDARAJAN, Krishna Prasad Lingadahalli SHASTRY
  • Publication number: 20200151619
    Abstract: 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: Application
    Filed: November 9, 2018
    Publication date: May 14, 2020
    Inventors: Satish Kumar MOPUR, Gregory S. BATTAS, Gunalan Perumal VIJAYAN, Krishnaprasad Lingadahalli SHASTRY, Saikat MUKHERJEE, Ashutosh AGRAWAL, Sridhar BALACHANDRIAH
  • Publication number: 20200112490
    Abstract: 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: Application
    Filed: October 4, 2018
    Publication date: April 9, 2020
    Inventors: SATISH KUMAR MOPUR, SAIKAT MUKHERJEE, GUNALAN PERUMAL VIJAYAN, SRIDHAR BALACHANDRIAH, ASHUTOSH AGRAWAL, KRISHNAPRASAD LINGADAHALLI SHASTRY, GREGORY S. BATTAS
  • Publication number: 20200050578
    Abstract: 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: Application
    Filed: August 9, 2018
    Publication date: February 13, 2020
    Inventors: SATISH KUMAR MOPUR, SAIKAT MUKHERJEE, GUNALAN PERUMAL VIJAYAN, SRIDHAR BALACHANDRIAH, ASHUTOSH AGRAWAL, KRISHNAPRASAD LINGADAHALLI SHASTRY, GREGORY S. BATTAS
  • Publication number: 20190028407
    Abstract: 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: Application
    Filed: July 20, 2017
    Publication date: January 24, 2019
    Inventors: Gunalan Perumal Vijayan, Saikat Mukherjee, Satish Kumar Mopur, Sridhar Balachandriah