Splunk Inc.

Ledgerdomain Inc.

Leonid Alekseyev

Benjamin James Taylor

Victor Bovee Dods

Alok Anant Bhide

William Jack

Adam Jamison Oliner

Jacob Barton Leverich

Manish Sainani

Sonal Maheshwari

Brian C Reyes

Poorva Malviya

Alan Vincent Hardin

Tristan Antonio Fletcher

Brian John Bingham

Clint Sharp

Brent Stephen Boe

Benjamin Gregory Nichols

Alex Read Colgan

Multi-layer ensembles of neural subnetworks are disclosed. Implementations can classify inputs indicating various anomalous sensed conditions into probabilistic anomalies using an anomaly subnetwork. Determined probabilistic anomalies are classified into remedial application triggers invoked to recommend or take actions to remediate, and/or report the anomaly. Implementations can select a report type to submit, or a report recipient, based upon the situation state, e.g., FDA: Field Alert Report (FAR), Biological Product Deviation Report (BPDR), Medwatch, voluntary reporting by healthcare professionals, consumers, and patients (Forms 3500, 3500A, 3500B, Reportable Food Registry, Vaccine Adverse Event Reporting System (VAERS), Investigative Drug/Gene Research Study Adverse Event Reports, Potential Tobacco Product Violations Reporting (Form 3779), USDA:Center for Veterinary Biologics Reports, Animal and Plant Health Inspection Service: Adverse Event Reporting, FSIS Electronic Consumer Complaints, DEA Tips, Animal Drug Safety Reporting, Consumer Product Safety Commission Reports, State/local reports: Health Department, Board of Pharmacy.

Secure messaging in a machine learning blockchain network

Disclosed is a method for authenticating requestors and granting access to a permissioned blockchain network shared among enterprise entities. A decentralized registry of credentialled users, in which credentialled users guard their own access information by keeping a private key of a public-private keypair enables systems to avoid keeping information of a large number of users in large, vulnerable containers. A further method removes authenticated users seeking to be forgotten from the registry of users and deletes any personally identifiable information of the withdrawing users.

Decentralized identity authentication framework for distributed data

Disclosed is a neural network enabled interface server and blockchain interface establishing a blockchain network implementing event detection, tracking and management for rule based compliance, with significant implications for anomaly detection, resolution and safety and compliance reporting.

Neural network classifiers for block chain data structures

The technology disclosed allows for leveraging decentralized credentials to achieve bidirectional authentication between two actors leveraging a messaging platform/system, such as email or another text-based system, verifiable credentials (VCs), and secure web endpoints. It empowers one party ('Sender') to send a message enclosed with a Verifiable Presentation which allows another party (“Recipient”) to authenticate the message's provenance and the identity of the sender. Moreover, the message contains a link to a secure web endpoint, where the recipient can submit a response signed by their own Verifiable Presentation, allowing the Sender to authenticate the identity of the Recipient. In this way, both participants are able to authenticate each other's identities with an additional factor of authentication, with neither participant being required to share a single service.

Verifiable credentialling and message content provenance authentication

Establishing a trained machine learning classifier in a blockchain network

Techniques are disclosed for anomaly detection based on a predicted value. A search query can be executed over a period of time to produce values for a key performance indicator (KPI), the search query defining the KPI and deriving a value indicative of the performance of a service at a point in time or during a period of time, the value derived from machine data pertaining to one or more entities that provide the service. A graphical user interface (GUI) enabling a user to indicate a sensitivity setting can be displayed. A user input indicating the sensitivity setting can be received via the GUI. Zero or more of the values as anomalies can be identified in consideration of the sensitivity setting indicated by the user input.

Detecting anomalies in key performance indicator values

Disclosed is a method for cross-authenticating non-credentialed devices and trusted blockchain enabled applications using multiple communications modalities and gathering information upon request for a blockchain network.

Secure messaging in a blockchain network

Disclosed is a method for authenticating requestors and granting access to a permissioned blockchain network shared among enterprise entities. A decentralized registry of credentialed users, in which credentialed users guard their own access information by keeping a private key of a public-private keypair enables systems to avoid keeping information of a large number of users in large, vulnerable containers. A further method removes authenticated users seeking to be forgotten from the registry of users and deletes any personally identifiable information of the withdrawing users.

The disclosed embodiments include a method for identifying a performance metric to diagnose a cause of a performance issues of virtual machine. The method includes obtaining data of a virtual machine, an indication that a storage volume contains data of the virtual machine, data about the storage volume, and an identification of the storage volume. The data of the virtual machine is correlated with the data about the storage volume based on the indication that the storage volume contains data of the virtual machine and the identification of the storage volume. A performance metric is identified based at least in part on an outcome of the correlating. The performance metric indicates that the storage volume is a cause of a performance issue of the virtual machine. A state related to the storage volume is changed to mitigate the cause of the performance issue of the virtual machine.

Growing community of inventors

San Francisco, CA, United States of America

Leonid Alekseyev