公开(公告)号：US11887204B2

公开(公告)日：2024-01-30

申请号：US17876100

申请日：2022-07-28

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Sathyanarayanan Manamohan ,  Vishesh Garg ,  KrishnaPrasad Lingadahalli Shastry

IPC: G06N20/20 ,  G06Q30/018 ,  G06N20/00 ,  G06Q10/10 ,  G06Q20/38 ,  G06Q20/40 ,  G06Q30/0207 ,  G06Q50/26 ,  H04L9/32 ,  H04L9/40 ,  G06Q20/06 ,  G06F16/23 ,  G06F16/22 ,  G06Q40/04

CPC classification number: G06Q50/265 ,  G06F16/2246 ,  G06F16/2255 ,  G06F16/2315 ,  G06F16/2365 ,  G06N20/00 ,  G06N20/20 ,  G06Q10/10 ,  G06Q20/0655 ,  G06Q20/388 ,  G06Q20/401 ,  G06Q30/0185 ,  G06Q30/0215 ,  G06Q40/04 ,  H04L9/3236 ,  H04L63/0435 ,  G06Q2220/00 ,  G06Q2220/10

Abstract: Systems and methods are provided for leveraging blockchain technology in a swarm learning context, where nodes of a blockchain network that contribute data to training a machine learning model using their own local data can be rewarded. In order to conduct such data monetization in a fair and accurate manner, the systems and methods rely on various phases in which Merkle trees are used and corresponding Merkle roots are registered in a blockchain ledger. Moreover, any claims for a reward are challenged by peer nodes before the reward is distributed.

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.

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.

公开(公告)号：US12273394B2

公开(公告)日：2025-04-08

申请号：US17691744

申请日：2022-03-10

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Sathyanarayanan Manamohan ,  KrishnaPrasad Lingadahalli Shastry ,  Avinash Chandra Pandey ,  Ravi Sarveswara

IPC: H04L9/40 ,  H04L9/08 ,  H04L67/10

Abstract: The disclosure relates to decentralized management of edge nodes operating outside an enterprise network using blockchain technology. A management node may operate within a firewall of the enterprise to manage the edge nodes operating outside the firewall using blockchain technology. The management node may coordinate management by writing change requests to a decentralized ledger. The edge nodes may read the change requests from its local copy of the distributed ledger and implement the change requests. Upon implementation, an edge node may broadcast its status to the blockchain network. The management node may mine the transactions from the edge nodes into the distributed ledger, thereby creating a secure and scalable way to coordinate management and record the current and historical system state. The system also provides the edge nodes with a cryptographically secured, machine-to-machine maintained, single version of truth, enabling them to take globally valid decision based on local data.

公开(公告)号：US20240135257A1

公开(公告)日：2024-04-25

申请号：US18528477

申请日：2023-12-04

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Sathyanarayanan Manamohan ,  KrishnaPrasad Lingadahalli Shastry ,  Vishesh Garg

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Decentralized machine learning to build models is performed at nodes where local training datasets are generated. A blockchain platform may be used to coordinate decentralized machine learning (ML) over a series of iterations. For each iteration, a distributed ledger may be used to coordinate the nodes communicating via a blockchain network. A node can include self-healing features to recover from a fault condition within the blockchain network in manner that does not negatively impact the overall learning ability of the decentralized ML system. During self-healing, the node can determine that a local ML state is not consistent with the global ML state and trigger a corrective action to recover the local ML state. Thereafter, the node can generate a blockchain transaction indicating that it is in-sync with the most recent iteration of training, and informing other nodes to reintegrate the node into ML.