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公开(公告)号：US12088476B2
公开(公告)日：2024-09-10
申请号：US17933934
申请日：2022-09-21
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Satish Kumar Mopur , Saikat Mukherjee , Gunalan Perumal Vijayan , Sridhar Balachandriah , Ashutosh Agrawal , KrishnaPrasad Lingadahalli Shastry , Gregory S. Battas
IPC: H04L41/16 , G06N20/00 , H04L41/0816 , H04L41/22 , H04L43/045 , H04L43/091 , H04L67/125
CPC classification number: H04L41/16 , G06N20/00 , H04L41/0816 , H04L41/22 , H04L43/045 , H04L43/091 , H04L67/125
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.
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公开(公告)号：US20230017701A1
公开(公告)日：2023-01-19
申请号：US17933934
申请日：2022-09-21
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Satish Kumar Mopur , Saikat Mukherjee , Gunalan Perumal Vijayan , Sridhar Balachandriah , Ashutosh Agrawal , KrishnaPrasad Lingadahalli Shastry , Gregory S. Battas
IPC: H04L41/16 , H04L43/045 , H04L43/091 , H04L67/125 , H04L41/22 , G06N20/00 , H04L41/0816
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.
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公开(公告)号：US11469969B2
公开(公告)日：2022-10-11
申请号：US16152394
申请日：2018-10-04
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Satish Kumar Mopur , Saikat Mukherjee , Gunalan Perumal Vijayan , Sridhar Balachandriah , Ashutosh Agrawal , Krishnaprasad Lingadahalli Shastry , Gregory S. Battas
IPC: G06F15/173 , H04L41/16 , H04L67/125 , H04L43/045 , H04L41/0816 , H04L41/22 , G06N20/00 , H04L43/091
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.
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公开(公告)号：US11361245B2
公开(公告)日：2022-06-14
申请号：US16100076
申请日：2018-08-09
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Satish Kumar Mopur , Saikat Mukherjee , Gunalan Perumal Vijayan , Sridhar Balachandriah , Ashutosh Agrawal , Krishnaprasad Lingadahalli Shastry , Gregory S. Battas
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.
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公开(公告)号：US20190028407A1
公开(公告)日：2019-01-24
申请号：US15654846
申请日：2017-07-20
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
IPC: H04L12/911 , H04L12/24 , H04L12/26 , H04L29/08
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.
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Patent Agency Ranking
公开(公告)号：US20240220514A1
公开(公告)日：2024-07-04
申请号：US18607923
申请日：2024-03-18
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Inventor： Satish Kumar Mopur , Sridhar Balachandriah , Gunalan Perumal Vijayan , Suresh Ladapuram Soundararajan , Krishna Prasad Lingadahalli Shastry
IPC: G06F16/28 , G06F18/214 , G06F18/2321 , G06F18/23213 , G06F18/2413 , G06F18/2433
CPC classification number: G06F16/285 , G06F18/214 , G06F18/2321 , G06F18/23213 , G06F18/24137 , G06F18/2433
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.
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公开(公告)号：US11954129B2
公开(公告)日：2024-04-09
申请号：US17225805
申请日：2021-04-08
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Satish Kumar Mopur , Sridhar Balachandriah , Gunalan Perumal Vijayan , Suresh Ladapuram Soundarajan , Krishna Prasad Lingadahalli Shastry
IPC: G06F16/28 , G06F18/214 , G06F18/2321 , G06F18/23213 , G06F18/2413 , G06F18/2433
CPC classification number: G06F16/285 , G06F18/214 , G06F18/2321 , G06F18/23213 , G06F18/24137 , G06F18/2433
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.
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8.发明公开公开(公告)号：US20230316710A1
公开(公告)日：2023-10-05
申请号：US17707612
申请日：2022-03-29
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Inventor： SATISH KUMAR MOPUR , Gunalan Perumal Vijayan , Shounak Bandopadhyay , Krishnaprasad Lingadahalli Shastry
IPC: G06V10/762 , G06V10/776 , G06V10/82 , G06N3/04
CPC classification number: G06V10/763 , G06V10/776 , G06V10/82 , G06N3/0454
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.
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公开(公告)号：US20230281958A1
公开(公告)日：2023-09-07
申请号：US17683816
申请日：2022-03-01
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
IPC: G06V10/762 , G06V10/82 , G06N7/00 , G06N3/08
CPC classification number: G06V10/762 , G06V10/82 , G06N7/005 , G06N3/08
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.
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公开(公告)号：US11481665B2
公开(公告)日：2022-10-25
申请号：US16186422
申请日：2018-11-09
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Satish Kumar Mopur , Gregory S. Battas , Gunalan Perumal Vijayan , Krishnaprasad Lingadahalli Shastry , Saikat Mukherjee , Ashutosh Agrawal , Sridhar Balachandriah
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.
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