Emc IP Holding Company LLC

Dell Products Inc.

Noga Gershon

Amihai Savir

Or Herman Saffar

Parminder Singh Sethi

Ofir Ezrielev

Omer Sagi

John Lawrence Dalton

Dhev George Kollannur

Jorge Luis Perez

Michael Rupert James Thatcher

Marcel Bernard Körner

Ivan Mlynek

Marcel Korner

Arthur Wensing

Vanya Boranova

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

Techniques are provided for sentiment-based adaptations of user representations in virtual environments. One method comprises obtaining information characterizing a first user and a second user interacting with a session of a virtual environment; applying the information to an analytics engine to obtain a sentiment status indicating a sentiment of the second user in the virtual environment, wherein the analytics engine processes the obtained sentiment status of the second user to select an adaptation of a facial expression and/or a body language of a representation of the first user from learned sentiment-based facial expression examples and/or learned sentiment-based body language examples, using the obtained sentiment status of the second user; and automatically initiating a rendering of the virtual environment using the selected adaptation of the facial expression and/or the body language of the representation of the first user in the virtual environment.

Sentiment-based adaptations of user representations in virtual environments

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

Artificial intelligence (AI)-based techniques are provided that predict a quality score for a product-related data structure associated with one or more products. One method comprises obtaining data for a given product-related data structure; evaluating a plurality of first features related to a customer account associated with the given product-related data structure using the obtained data; evaluating a plurality of second features related to the given product-related data structure using the obtained data; processing at least some of the first features and the second features using at least one model that provides a predicted quality score for the given product-related data structure; and applying one or more thresholds to the predicted quality score to determine an acceptance status related to the given product-related data structure. A weighting of the first features and the second features can be learned during a training phase.

Evaluation of product-related data structures using machine-learning techniques

A method includes extracting data pertaining to a plurality of user actions in connection with one or more changes to one or more of a plurality of applications, and training one or more machine learning models with the extracted data. The one more machine learning models are used to predict whether a user should receive a given notification in connection with a given change to a given application of the plurality of applications. In response to predicting that the user should receive the given notification, content of the given notification is determined. The method further includes generating the given notification for the user, and transmitting the given notification to the user.

Using machine learning to customize notifications for users

Techniques are provided for data record anomaly reconciliation using machine learning models. One method comprises obtaining a data record comprising multiple line items; assigning the line items to a given cluster of similar line items to determine a line item neighborhood score for each line item based on a comparison of a given line item to other available line items in the assigned cluster; applying features of the data record to a machine learning model to determine a data record score for the data record based on a combination of the line item neighborhood scores for the data record; identifying anomalies in the data record based on the data record score and/or the line item neighborhood scores for the data record; and adjusting parameters of the line items to address the anomalies identified in the data record to produce a reconciled data record, based on the line item neighborhood scores and/or predefined adjustment rules.

Data record anomaly reconciliation using machine learning models

A method, system and computer-usable medium are disclosed for voice-based alerting of an individual wearing an obstructive listening device. Certain embodiments include detecting speech in an ambient environment in which the person wearing the obstructive listening device is present; determining whether the detected speech includes a name of the person wearing the obstructive listening device; if the detected speech includes the name of the person wearing the obstructive listening device determining whether the name of the person was spoken using a calling speech characteristic; and if the name of the person was spoken using a calling speech characteristic, automatically alerting the person wearing the obstructive listening device that another person in the ambient environment is calling for attention of the person wearing the obstructive listening device.

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Dimona, Israel

Noga Gershon