Emc IP Holding Company LLC

Anat Parush Tzur

Amihai Savir

Assaf Natanzon

Oshry Ben-Harush

Nir Goldschmidt

Idan Levy

Arik Sapojnik

Otniel Van-Handel

Avitan Gefen

Or Herman Saffar

Roi Gamliel

Nimrod Milo

Shai Harmelin

Farzad Khosrowpour

Sairam Veeraswamy

Ajay Potnis

Ran Taig

Raphael Cohen

Idan Richman Goshen

Eslam ElNakib

Alon Shitrit

Eder Crespo

Noam Kalush

Eder Soares

Lina Al Farouk

Lisa Woo

Emer Fitzpatrick

Skye Jones

Balaji Dhamodharan

Christopher Schneider

Daniel Coutinho

Joao Adalino Lopes De Moraes, Jr

Guilherme Toralles Darley

Lidiane Silva

Carlos Augusto Barra

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

A method, comprising: receiving a first search query that is associated with a video file; retrieving one or more search results in response to the first search query, each of the search results corresponding to a different section in the video file; and displaying the search results on a display device, wherein displaying any of the search results includes displaying a link that points to the section of the video file, which corresponds to the search result.

Method and apparatus for presenting search results

Techniques are provided for customer service ticket prioritization using multiple time-based machine learning models. One method comprises obtaining a customer service ticket; collecting, in response to the obtaining, features related to the customer service ticket comprising dynamic information regarding activities performed to address the customer service ticket and textual information describing the customer service ticket; applying the collected features to one of multiple machine learning models to obtain a distress score indicating a likelihood that the customer service ticket will become a distressed customer service ticket, wherein the multiple machine learning models each correspond to different time periods and are trained on historical data and the collected features are applied to the one machine learning model based on an age of the customer service ticket; and processing multiple customer support tickets based on the distress score. The machine learning models are trained using cumulative corresponding historical training data associated with a respective one of hourly, daily, weekly and monthly time periods.

Customer service ticket evaluation using multiple time-based machine learning models customer

Techniques are provided for recommending hardware configuration changes using a user satisfaction rating. One method comprises obtaining usage data indicating user activity for users on computing devices; generating a user profile for each user; clustering the users into user clusters based on the user profiles; determining, for a given user cluster, a satisfaction score for each user in the given user cluster based on the obtained usage data for each user on the computing device; providing suggested hardware upgrades for the computing device of a given user in the given user cluster, wherein the given user is selected based on a lower corresponding satisfaction score relative to the satisfaction scores of other users in the given cluster, and wherein the one or more suggested hardware upgrades are based on hardware configurations of other users in the given cluster having a higher corresponding satisfaction score.

Data-driven hardware configuration recommendation system based on user satisfaction rating

A customer blockchain data store is provided. An exemplary method comprises obtaining a blockchain associated with a given customer of an enterprise having multiple customer communication channels, wherein the blockchain comprises transaction data for the given customer with the customer communication channels; obtaining new transaction data for the given customer for a given one of the customer communication channels; providing the new transaction data for the given customer to additional customer communication channels; receiving a validation of the new transaction data from the additional customer communication channels based on one or more predefined validation criteria; and storing the validated new transaction data for the given customer in the blockchain associated with the given customer. The validation optionally comprises the additional customer communication channels verifying information about the given customer in the new transaction data and/or aligning a customer identifier across the plurality of customer communication channels.

Enterprise storage of customer transaction data using a blockchain

One example method includes generating a policy recommendation that can be implemented in data protection systems. The generation of the policy is based on information garnered from multiple distinct users. Initially, the users are clustered based on their user profiles. The virtual machines operated by these users are then clustered based on virtual machine profiles. This allows, for each resulting cluster, policy recommendations to be made based on a policy distribution of the corresponding cluster.

Recommendation system for replication policies

Techniques are disclosed for determining the run-time performance of an application executing on a computing system with low impact on the performance of the computing system. For example, a time series telemetry data stream is obtained for each of a plurality of key performance indicators during run-time execution of the application on a computing system having a given system configuration. One or more statistical features are extracted from each time series telemetry data stream. Model parameters of a machine learning performance score model are populated with values of the extracted statistical features. A run-time performance score of the application is then determined using the model parameters of the machine learning performance score model populated with the values of the extracted statistical features.

Run-time determination of application performance with low overhead impact on system performance

Techniques are provided for correcting sensor data in a multi-sensor environment. An exemplary method comprises obtaining sensor data from a first sensor; applying an anomaly detection technique to detect an anomaly in the sensor data from the first sensor based on additional sensor data from one or more of the first sensor and at least one additional sensor in proximity to the first sensor; and correcting the anomalous sensor data from the first sensor using additional sensor data from one or more of the first sensor and the at least one additional sensor. In some embodiments, additional sensor data from a plurality of neighboring sensors is used to predict the sensor data from the first sensor. The anomalous sensor data is optionally corrected substantially close in time to the detection of the anomaly in the sensor data.

Correction of sensor data in a multi-sensor internet of things environment

Described is a system and method that provides a data protection risk assessment for the overall functioning of a backup and recovery system. Accordingly, the system may provide a single overall risk assessment score that provide an operator with an 'at-a-glance' overview of the entire system. Moreover, the system may account for changes that occur over time based on leveraging statistical methods to automatically generate assessment scores for various components (e.g. application, server, network, load, etc.). In order to determine a risk assessment score, the system may utilize a predictive model based on historical data. Accordingly, residual values for newly observed data may be determined using the predictive model and the system may identify potentially anomalous or high risk indicators.

Generating a data protection risk assessment score for a backup and recovery storage system

An alert configuration system facilitates accurate and reliable configuration of alerts for data protection policy in a data protection system, including eliminating or reducing manual configuration of data protection policy. The system identifies risks through trend analysis and behavioral statistics as applied to historical data, and automatically configures alerts for the identified risks so that alerts are generated upon detection of the identified risks. After detecting differences between tracked values for a data protection system and predicted values obtained through trend analysis and behavioral statistics as applied to the historical data, the alert configuration system automatically adjusts the configuration of alerts for data protection policy in accordance with the predicted values. The tracked and predicted values include attributes of a data protection system embodied in key performance indicators associated with the identified risks based on the trend analysis and behavioral statistics as applied to historical data.

Alert configuration for data protection

Methods and systems for managing the utilization of cloud computing resources are described. The system monitors cloud computing resource utilization for a first set of active jobs to determine real-time utilization data. The system compares the real-time utilization data with historic utilization data to generate a utilization pattern and determines future cloud computing resource utilization for at least one future time period based on the utilization pattern and a second set of scheduled jobs. The system further generates a pricing matrix for utilizing the cloud computing resources during a future time period based on the determined future cloud computing resource utilization. The pricing matrix includes prices associated with utilization of the cloud computing resources for each of the at least one future time period. The system transmits the pricing matrix to one or more devices requesting utilization of the cloud computing resources.

Managing utilization of cloud computing resources

Flexible scheduling for backup jobs includes backup policies that allow for flexibility in scheduling execution time, and an automated data driven backup job execution scheduler. The scheduler balances load on the customer's and data protection system's resources using a dynamic pricing calculator that takes into account the real-time and predicted near-future status of the protection environment. The backup job scheduler includes a user interface that enables users to specify user-customized backup policies that enable flexibility in scheduling execution times and transparency in pricing.

Flexible scheduler for backups

Techniques are provided for sensor data compression in a multi-sensor Internet of Things environment. An exemplary method comprises obtaining sensor data from a plurality of sensors satisfying one or more of predefined sensor proximity criteria and predefined similar sensor type criteria; applying an image-based compression technique to the sensor data to generate compressed sensor data; and providing the compressed sensor data to a data center. The image-based compression technique comprises a discrete cosine transform technique, a video compression technique, and/or an auto-encoder deep learning technique that utilizes one or more over-fitted bidirectional recurrent convolutional neural networks. The sensor data is optionally normalized prior to being applied to the image-based compression technique.

Sensor data compression in a multi-sensor internet of things environment

A report cost estimation module is used with cloud storage systems that store and process client data. Reports on the cloud stored client data take a significant amount of CPU, memory, storage and networking resources. A data analysis model dynamically estimates pricing for client data reports according to resource consumption by identifying a cluster group for the report and using the designated regression model for the cluster group. The data analysis can store the estimated and actual report costs and improve the estimated report costs using machine learning algorithms and crowed sourcing techniques. The report price is accurately provided to the client before running the report and thus allowing the customer to carefully manage a client report budget.

System and method for price estimation of reports before execution in analytics

Embodiments are described for detecting a data organization issue in a storage system by analyzing static and dynamic layout characteristics for data stored in the storage system, workload characteristics for applications utilizing the data, virtualization effects of the storage system on the organization of the data, input/output characteristics of processes storing the data in the storage system, and cache usage characteristics of the data. For fragmentation issues, the system uses the analysis to provide defragmentation advisories to reduce fragmentation by at least one of: re-layout or re-tiering of the data in the data storage system, or cache memory reconfiguration.

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Beer Sheva, Israel

Anat Parush Tzur