Emc IP Holding Company LLC

Dell Products Inc.

Amihai Savir

Avitan Gefen

Anat Parush Tzur

Assaf Natanzon

Oshry Ben-Harush

Omer Sagi

Or Herman Saffar

Roi Gamliel

Shiri Gaber

Kfir Wolfson

Itay Azaria

Noga Gershon

Or Herman-Saffar

Idan Levy

Shai Harmelin

Stephen J Todd

Ofir Ezrielev

Nadav Azaria

Oron Golan

Ran Taig

Nimrod Milo

Roie Ben Eliyahu

Jehuda Shemer

Yevgeni Gehtman

Stav Sapir

Shaul Dar

Naor Radami

Idan Richman Goshen

John Lawrence Dalton

Anat Parush-Tzur

Alana Brook Marcum Barker

Elik Levin

Farzad Khosrowpour

Alon Grubshtein

Raul Shnier

Raphael Cohen

Ohad Arnon

Aviram Fireberger

Arik Sapojnik

Otniel Van-Handel

Eslam ElNakib

Alon Shitrit

Daniel Coutinho

Murale Narayanan

Guilherme Toralles Darley

Christopher P Barry

Carlos Augusto Barra

Eder Crespo

Noam Kalush

Eder Soares

Lina Al Farouk

Lindsay Braine

Ivan Mlynek

Arthur Wensing

Dhev George Kollannur

Jorge Luis Perez

Michael Rupert James Thatcher

Marcel Bernard Körner

Christopher Schneider

Sallie A Paige

Joao Adalino Lopes De Moraes, Jr

Lidiane Silva

Shay Katz

Daniel Shapira

Gal Uzan

Jeroen Zonneveld

Michael Timmy Opachevsky

Marcel Korner

Vanya Boranova

Lisa Woo

Emer Fitzpatrick

Skye Jones

Daniel S Inbar

Balaji Dhamodharan

A system can identify a group of computer services that correspond to respective computer hardware. The system can extract features that respectively identify characteristics of the computer services, a feature of the features comprising ratio information representative of a ratio of a first price of a computer service of the group of computer services to a second price of a corresponding computer hardware of the respective computer hardware that corresponds to the computer service. The system can convert the features into a numerical representation. The system can cluster the computer services into multiple clusters based on the numerical representation of the features. The system can identify, from a cluster of the multiple clusters and based on a machine learning model, an anomalous price for at least one computer service of the group of computer services that belongs to the cluster. The system can store an indication of the anomalous price.

Anomaly detection for services pricing

Techniques are provided for acceptance status classification of product-related data structures using models with multiple training periods. One method comprises obtaining data for a given product-related data structure; evaluating first features related to the given product-related data structure using the obtained data; applying the first features related to the given product-related data structure to one or more models trained using multiple different training periods to obtain a plurality of second features, wherein each of the second features indicates a prediction related to an acceptance status of the given product-related data structure by at least one model for a respective training period; and aggregating at least the second features to obtain a classification related to an aggregate acceptance status of the given product-related data structure. A weighting of at least some of the first and second features can be learned during a training phase.

Acceptance status classification of product-related data structures using models with multiple training periods

A method is provided comprising: obtaining a counterparty contract, the counterparty contract including a contract that is being proposed by a counterparty to a user; performing a segmentation of the counterparty contract to identify a plurality of sentence clusters, each of the sentence clusters corresponding to a different provision in the counterparty contract; generating a plurality of counterparty provision vectors based on the counterparty contract, each of the counterparty provision vectors being generated based on a different one of the plurality of sentence clusters; retrieving a user provision vector, the user provision vector corresponding to a user provision; calculating a plurality of similarity scores for the user provision vector; detecting whether the plurality of similarity scores satisfies a condition that is associated with the user provision; and outputting a notification associated with the user provision when the condition is satisfied.

Method and apparatus for contract analysis

Techniques described herein relate to a method for identifying breakpoints. The method may include obtaining, by a breakpoint identification device, an entity group data set corresponding to an entity group; generating, using the entity group data set, an enhanced entity group data set comprising the entity group data set and a derived data item; performing a clustering analysis using at least a portion of the enhanced entity group data set to obtain a plurality of entity clusters; training a machine learning (ML) model using the enhanced entity group data set to obtain a trained ML model; using the trained ML model and a set of simulated entities to perform a breakpoint analysis; generating a breakpoint graph based on the breakpoint analysis; and providing recommendations to an interested entity based on a breakpoint identified using the breakpoint graph.

Methods and systems for identifying breakpoints in variable impact on model results

A search engine responding to a user query to find relevant data assets in a federation business data lake (FBDL) system by monitoring and recording all of the interactions of users interacting with data assets in the FBDL system, providing all of the user interactions to a recommendation engine, calculating relevance of information in the FBDL system to each user, and recommending one or more new data assets to a target user based on the relevance of the information. The relevance comprises the target user's past interactions with the data assets based and the cumulative interactions of other users with the data assets, such that if one or more of the other users has similar interaction behavior to the target user, then knowledge of the one or more other users can impact the relevance of the information with regard to the one or more new data assets suggested to the target user.

Recommendation system for data assets in federation business data lake environments

A method for alleviating data poisoning in an edge computing resource includes receiving a numeric value from an Internet of Things (IoT) unit and associating the numeric value with a cluster selected from a plurality of clusters in accordance with a suitable clustering algorithm such as a k-means clustering algorithm. In at least some embodiments, the numeric value comprises a poisoned numeric value including an adversarial component injected by an adversary to negatively impact a trained model of a cloud-based artificial intelligence engine. Rather than permitting the injected adversarial component to corrupt the AI engine, a cluster with which the numeric value is associated is sampled in accordance with a probability distribution of the cluster to obtain a surrogate for the poisoned numeric value. The surrogate may then be provided as an input to an inference module of the AI engine to generate a prediction.

Cluster resampling for alleviation of data poisoning on the edge

Techniques for securely monitoring an air-gapped machine. Systems, methods, and devices for generating a status message representing a state of an air-gapped machine, converting the status message to a visual code, displaying the visual code to a display monitor connected to the air-gapped machine, capturing image data of the visual code at a camera connected to a monitoring machine, and transmitting the image data to the monitoring machine, thereby causing the visual code to be accessible by a user of the monitoring machine. Techniques for verifying the integrity of the status message, and optionally, encrypting the status message.

True secure airgap

Techniques are provided for user identity verification using dynamic identification policies. One method comprises obtaining, by an identity management server, a validation request to evaluate an identity of a user, wherein the validation request is processed by the identity management server in connection with an access request of the user to access a protected resource provided by a service provider that is distinct from the identity management server. The validation request may comprise an identification policy, generated by the service provider in response to receiving the access request, that specifies authentication consensus constraints that apply to the access request. The identity management server can provide an authentication request to verifier devices in a verifier pool, using the authentication consensus constraints, to evaluate the identity of the user; and can provide an identity validation result based on the evaluation of the identity of the user by the verifier devices using the authentication consensus constraints.

User identity verification using dynamic identification policies

Techniques are provided for mapping storage volumes to storage processing nodes in a storage system. One method comprises determining a number of input/output operations associated with each of multiple storage volumes, wherein the input/output operations associated with a given storage volume are processed by a corresponding storage processing node based on a mapping of the storage volumes to the storage processing nodes; obtaining constraints that limit the input/output operations processed by at least a subset of the storage processing nodes; identifying at least one possible mapping of the storage volumes to the storage processing nodes that satisfy the constraints; and selecting an alternative mapping of the storage volumes to the storage processing nodes by applying a designated function to the at least one possible mapping. The input/output operations may comprise read operations and write operations, and the read and write operations may be balanced separately.

Mapping storage volumes to storage processing nodes using input/output operation constraints and designated function

Techniques are provided for customer service ticket processing using cluster-based data driven guidebooks. One method comprises obtaining a customer service ticket; extracting features related to the customer service ticket, wherein the features comprise a representation of a problem associated with the customer service ticket; assigning the customer service ticket to a given cluster of multiple customer service ticket clusters based on the features; obtaining a customer service ticket processing guidebook associated with the given cluster that identifies independent actions to perform to address the problem; and processing the customer service ticket based on the customer service ticket processing guidebook. A customer service ticket processing guidebook may be generated for each customer service ticket cluster using historical customer service tickets from the respective cluster. The customer service ticket processing guidebooks can be generated by clustering (i) possible independent actions and (ii) possible solutions identified in the historical customer service tickets of the given cluster.

Customer service ticket processing using cluster-based data driven guidebooks

Techniques described herein relate to a method for identifying offline accounts for online adoption targeting, including obtaining an account group data set; generating, using the account group data set, an enhanced account group data set comprising the account group data set and a derived data item; obtaining, using a ML model and the enhanced account group data set, a key metric set; performing a clustering analysis using the key metric set and a portion of the enhanced account group data set to obtain account clusters; assigning a weight to a cluster of the account clusters; performing a similarity computation for an offline account of the account group using the key metric set and the portion of the enhanced account group data set to obtain a weighted similarity score; adding the weighted similarity score to a ranked list of weighted similarity scores; and providing the ranked list to an interested entity.

Sales productivity enhancement using offline to online account targeting preliminary class

One example method includes accessing an appearance history of a person, the appearance history including information concerning an appearance of a person at a particular time, generating, based on the appearance history, a forecast that comprises a probability that the person will appear again at some future point in time, determining that the probability meets or exceeds a threshold, and updating a high probability group database to include a facial image of a face of the person.

Facial recognition IOT application for multi-stream video using forecasting tools

Techniques are provided for reinforcement learning-based evaluation of software product usage. One method comprises obtaining key performance indicators indicating software product usage by a user; determining, for a predefined time window: (i) a mean and/or a median of the obtained KPIs; (ii) an amount of time that the software product was active; and (iii) an amount of interactions by the user with a user interface; evaluating possible login states of the software product using at least one reinforcement learning agent, wherein the evaluating comprises (a) observing the plurality of possible login states, including a current state comprising a current login state of the software product, and (b) obtaining an expected utility score for changing from the current login state to a different login state of the software product; and determining whether to change from the current login state to a different login state of the software product based on the expected utility score.

Evaluating software license usage using reinforcement learning agents

An information handling system may receive a plurality of rules and a file for malware testing. The information handling system may apply two or more of the plurality of rules to the received file to determine a plurality of outcomes of application of the rules to the file. The information handling system may determine whether to classify the received file as malware or not malware by applying a machine learning model to the plurality of outcomes.

Malware detection using a machine learning model

Techniques are provided for estimating a delivery time for a product in a supply chain. One method comprises obtaining an order for at least one product; calculating a similarity-based feature and/or a proximity-based feature for the order; and applying the calculated similarity-based feature and/or the calculated proximity-based feature for the order to a machine learning engine that generates an estimated delivery time for the order, wherein the machine learning engine is trained using characteristics from historical orders. The similarity-based feature for the order can be calculated using a delivery time value of historical orders in a given order cluster where the order was assigned based on a predefined distance metric between the order and features of each order cluster. The proximity-based feature for the order can be calculated using a delivery time value of the historical orders that satisfy one or more predefined recency criteria.

Data driven estimation of order delivery date

Growing community of inventors

Sansana, Israel

Amihai Savir