Emc IP Holding Company LLC

Dell Products Inc.

Iron Mountain Incorporated

Nicole Reineke

James Robert King

Hanna Yehuda

Stephen J Todd

Robert Anthony Lincourt, Jr

Ofir Ezrielev

Avitan Gefen

Amihai Savir

Robert A Lincourt

Farida Shafik

Joel Evans Christner

Corinne Schulze

Anne-Marie McReynolds

Shary Beshara

JoAnne Hubbard

William James Allen

Robert Barrett

Alex Robbins

Aurelian Dumitru

Trevor Scott Conn

Valerie Charry

Debra Arneson

Alan Sevajian

Sumedh Wasudeo Sathaye

Amy N Seibel

Arthur F Lent

Jennifer M Minarik

Seth Jacob Rothschild

Donagh A Buckley

Eloy Francisco Macha

Kelly L Lisai

Reut Kovetz

Patrick East

Ming Qian

Justin A Kenney

Jiale Huo

David Edward Frattura

Akshaya Khare

Barbara Ballard

Brad Corey Maltz

Ahmad Refaat Abdelfadeel Ahmad ElRouby

Omar Mohammed Mohammed Abdulaal

Shary George Waheeb Beshara

Steve Todd

Calvin He

Denis Leconte

Gabriel Nguyen

Rodrigo Coelho Ferreira

Gregory Maratea

Omar Abdulaal

Ahmad Refaat Abdel Fadeel Ahmad El Rouby

Eitan Segal

Christopher Hill

Ashok Palani

Omar Abdul Aal

Farida Ahmed Ali Shafik

Charles Joseph Williams

One example method includes receiving a user request for a data set, and the user request includes information concerning user requirements for the data set, identifying data records that satisfy one or more of the user requirements, calculating a respective relative value for each of the data records, and the relative values are based in part on the user requirements, and providing access controls for each data record that enable tracing of accesses of the data record.

Method of determining data set membership and delivery

One example method includes receiving an orchestration automation request for an asset, identifying an ethics rule that applies to the asset, comparing the ethics rule to asset values contained in an AI ethics datastore, based on the comparing, identifying a list of assets that conform to the ethics rule, and when the asset appears in the list of assets that conform to the ethics rule, automatically placing the asset at an entity of a computing infrastructure.

AI ethics scores in automated orchestration decision-making

One example method includes receiving a request from a user to access data stored in a filesystem, in response to the request, retrieving metadata of the data and metadata of the user, validating the metadata of the data and the metadata of the user against a data access rule, and granting the user access to the data upon successful validation of the metadata of the data and the metadata of the user.

Classifying data and enforcing data access control using a context-based hierarchical policy

Features described herein generally relate to systems and methods for generating and managing tokens for authenticated assets. In some aspects, generating a token is performed according to one or more rules and includes generating metadata that links the token to a representation of an asset. In some other aspects, tracking token access includes monitoring a blockchain for token access events and generating a token based on a modified historian and received feedback. In other aspects, validating a token for an authenticated asset includes matching representations of the authenticated asset.

Systems and methods for generating and managing tokens for authenticated assets

Methods and systems for managing storage of data are disclosed. To manage storage of data, images may be stored across a number of storages that provide varying levels of storage performance and have correspondingly varying costs for storing data. To store the images across the storages, the images may be segmented into image segments and a likelihood of each of the image segments being used in the future may be identified. The image segments that are more likely to be used in the future may be stored in higher performance storages while the image segments that are less likely to be used in the future may be stored in lower performance storages. To identify the likelihood of each of the image segments being used in the future, the image segments may be classified based on their membership in one or more areas of interest within the images.

System and method for storage management of images

Techniques for authenticating a digital asset represented by an NFT that is stored in a blockchain are disclosed. An NFT that has been minted for an asset is identified. The NFT was minted by generating metadata for the asset and by generating an original hash of the asset. The NFT is stored in a blockchain, and the metadata is stored in a centralized repository. The original hash is stored in either the repository or the blockchain. A potential transfer of the NFT is triggered. Triggering the potential transfer includes downloading, from the centralized repository, a local version of the asset. A local hash of the local version of the asset is generated. The local hash is compared against the original hash. If the two hashes are the same, then the transfer is permitted; otherwise, the transfer is not permitted.

Method and system of NFT validation in a broker marketplace

Generating policy-based confidence scores for data is disclosed. Data captured by a data confidence fabric is annotated when the data is created, mutated, transited or otherwise handled in the data confidence fabric. The annotations are weighted by a policy to generate policy-based confidence scores. The policy-based confidence scores are used in determining whether the data is sufficiently trusted for use by an application.

Data confidence fabric policy-based scoring

One example method includes monitoring a data access pattern, registering a data access request directed to data, comparing metadata associated with the data access request to a rule, based on a result of the comparing, sending a trigger to a graph service, and using information in the trigger to generate a visual representation of the data access request, wherein the visual representation indicates an extent to which the data access request is considered to constitute a potential threat to the data.

Malicious data access as highlighted graph visualization

Generating compilation methods using sustainability. Code is compiled in a manner that accounts for sustainability values. When a compilation request is received, sustainability values are identified. The resources needed to fulfill the compilation request are identified based on the sustainability values and available resources. Once the resources that are likely to best meet the sustainability values, the compilation request is performed using those resources.

System and method of using sustainability to establish compilation methods aligned with sustainability goals

One example method includes crawling data included in a dataset, based on the crawling of the data, creating and/or obtaining metadata concerning the data, crawling the metadata and obtaining information about a data change event involving the data, based on the crawling of the metadata, creating a meta label that documents occurrence of the data change event, and associating the meta label with the metadata. An analysis of the data, metadata, and meta label, may be performed and a trigger generated based on the analysis.

Informed labeling of records for faster discovery of business critical information

One example method includes integrating user space applications with kernel space events including primitives. The events are intercepted in kernel space and processed in user space. The events can be stored in a session cache that allows a holistic view of behavior to be determined with regard to resources of the computing system. The events in the session cache can be correlated to user or process behavior by provided a time-based view of the events.

System and method for correlating filesystem events into meaningful behaviors

Methods and systems for obtaining synthesized images are disclosed. To obtain synthesized images, an imaging system may obtain areas of interest associated with an image. The areas of interest may be keyed to various processes and/or services. A downstream consumer of the services may retrieve the areas of interest to perform further analysis. In order to more easily interpret the areas of interest, the areas of interest may be displayed to the downstream consumer as part of a synthesized image. The synthesized image may include the areas of interest and a synthesized environment. The synthesized environment may be intended to emulate an environment in which the downstream consumer may expect to find the areas of interest. By doing so, the downstream consumer may be able to understand and contextualize the areas of interest more easily than if viewing the areas of interest alone.

System and method for procedural reconstruction of synthesized images

Methods and systems for identifying areas of interest in an image and management of images are disclosed. To manage identification of areas of interest in an image, subject matter expert driven processes may be used to identify the areas of interest. The identified areas of interest may be used to establish plans to guide subsequent use of the image. The identified areas of interest may also be used to establish plans to cache portions of the image to speed subsequent use of the image.

System and method for image exploration using areas of interest

The technologies described herein are generally directed toward deploying networked equipment based on a selected deployment model. According to an embodiment, a system can comprise a processor and a memory that can enable performance of operations including receiving a deployment request from a request source, for a deployment task comprising networked equipment to be deployed and a location for deployment of the networked equipment. The operations can further include querying a data source for deployment information implicating the location and the deployment task. Based on the request source, the deployment task, and the deployment information, the operations include selecting a deployment model to facilitate processing the deployment request. The operations additionally include, based on the deployment task and the deployment model, deploying the networked equipment at the location.

Deploying networked equipment based on a deployment model

One example method includes an optimized approach to real-time utilization, data transfer, data storage, and transience in a T Hz-enabled zone that may be implemented in edge networks, data management, and machine learning. Multiple policies can be enforced based on new sets of attributes such as data type, location, and device movement within a zone or between zones.

Method of secondary device real-time utilization, transience, and data movement in THZ-enabled devices

Growing community of inventors

Northborough, MA, United States of America

Nicole Reineke