Dell Products Inc.

Emc IP Holding Company LLC

Parminder Singh Sethi

Lakshmi Saroja Nalam

Shelesh Chopra

Anay Kishore

Suren Kumar

Durai S Singh

Sathish Kumar Bikumala

Malathi Ramakrishnan

Deeder M Aurongzeb

Hung The Dinh

Akanksha Goel

Madhuri Dwarakanath

Bing Liu

Kanika Kapish

Anannya Roy Chowdhury

Bijan Kumar Mohanty

Atishay Jain

Parmeshwr Prasad

Vaideeswaran Ganesan

Malathi R

Anshul Saxena

Vasanth Ds

Nithish Kn

Vasanth Sathyanarayanan

Nithish Kote

Shree Ramakrishna Rathinasamy

Shibi Panikkar

Rahul Deo Vishwakarma

Sabu K Syed

Mohammad Rafey

Kannappan Ramu

Avinash Vishwanath

Thanuja C

Deepak NagarajeGowda

Sharmad Naik

Chandroma Ghose

Prabhat Chandra Biswas

Praveen Kumar

Veena Ramarao

Sanjay Singh

Arjit Rastogi

Aarsi Kumar

Jace William Files

Daniel Lawrence Hamlin

Gokul Thiruchengode Vajravel

Ravikanth Chaganti

Chitrak Gupta

Maxim Balin

Mohamed Sohail

Deepak Gowda

Vinod Durairaj

James S Watt, Jr

Natasha Gaurav

Noga Gershon

Sean Creedon

Landon Martin Chambers

Veena R

Akshita Das

Vamsi Krishna Tadikamalla

Bhuvaneswari Tamilchelvan

Vasanth D Sathyanaraya

Philip Sheaff

Priyansh Saxena

Vinay Kumar Lokeshappa Malligere

Srinath Kappgal

Lakshmi Soroja Nalam

Karan Arora

Elanjelian V

Smita Kapil

Vivekananda Hegde

Mohith Shanthigodu

Lakshmi Narashimhan Mc

C Narasimhaiah

Avinash V Patil

Liane Ghosh

Tamilchelvan Bhuvaneswari

Lakshmi Narasimhan Mc

Selvaraja Subburaja

Abhishek Santosh

Kunal Visoulia

Methods, apparatus, and processor-readable storage media for determining input sentiment by processing text data and non-text data using artificial intelligence techniques are provided herein. An example computer-implemented method includes dividing user input data into at least a first set of text data and a set of non-text data; converting at least a first portion of the non-text data into at least a second set of text data; classifying at least a portion of the at least a first set of text data and at least a portion of the at least a second set of text data in accordance with sentiment-related categories using a first set of artificial intelligence techniques; classifying at least a second portion of the non-text data in accordance with the sentiment-related categories using a second set of artificial intelligence techniques; and performing automated actions based on the classifying of the text data and/or the non-text data.

Determining input sentiment by automatically processing text data and non-text data using artificial intelligence techniques

A method for performing an application migration operation, the method includes initiating the application migration operation to migrate an application from a source device to a target device, where the source device includes a source data migration agent and the target device includes a target data migration agent. The method also includes initiating an active scan. Further, the method includes establishing a secure data path between the source device and the target device. Moreover, the method includes making a first determination, using the active scan, that the network traffic or the source performance metrics are indicative of suspicious activity and logging information about the suspicious activity. The method also includes making a second determination that the application migration operation is complete and terminating the active scan and the secure data path based on the second determination.

Application migration vulnerability and device performance monitoring

A system for remediating a recovery process such as a migration of data includes an event database, a monitoring component, programmed to: monitor a source device to detect an initiation of a recovery process, wherein the recovery process is migration of application data from the source device to a target device, an analysis component programmed to: monitor the event database to detect a new event entry, wherein the event entry is associated with the recovery process of the source device, make a determination that the new event entry is associated with a failure event, based on the determination, provide the new event entry to a remediating component, programmed to obtain a recommendation for remediation based on the failure event and initiate the remediation based on the recommendation for remediation.

Method and system for managing failure events of a recovery process using a recovery module

Methods, apparatus, and processor-readable storage media for predicting component lifespan information by processing user install base data and environment-related data using machine learning techniques are provided herein. An example computer-implemented method includes obtaining install base data associated with at least one system component and environment-related data associated with usage of the at least one system component; performing feature analysis on at least a portion of the obtained data using a first set of machine learning techniques; clustering, based on the feature analysis, at least a portion of the install base data and at least a portion of the environment-related data into one or more groups using a second set of machine learning techniques; generating at least one lifespan information prediction attributed to the at least one system component based on the clustering; and performing at least one automated action based on the at least one lifespan information prediction.

Predicting component lifespan information by processing user install base data and environment-related data using machine learning techniques

A noise and carbon dioxide (CO2) emissions minimizing load-balancing system executing on a unified endpoint management platform information handling system comprising a network interface device to receive telemetry measurements for a first server operating within peak hours, including power analytics, geographic location, ambient temperature and humidity, CO2 emissions value, and measured noise level exceeding a first CO2 load-balancing noise threshold value for the first server, a hardware processor to reject processing requests made to the first server above a CO2 optimal request load predicted, via a neural network modeling a relationship between changes in CO2 emissions values and changes in the telemetry measurements to cause the first server to emit noise below the first CO2 load-balancing noise threshold value, and the hardware processor to redistribute rejected processing requests across candidate offload servers emitting noise below a candidate-specific CO2 load-balancing noise threshold value determined for each of the candidate offload servers.

System and method for load-balancing processing requests across client information handling systems to minimize noise and carbon dioxide emissions in a data center

A hardware life extension learning recommendation and reward system of an information handling system may comprise a network interface device to receive operational telemetries from a first and a second computing device that each include a high temperature warning, an error for a labeled hardware type, and execution of a specific application, and a processor to determine, based on these telemetries, that failure of a first component of the labeled hardware type previously occurred at the first device, failure of a second component of the labeled hardware type has occurred at the second device, and execution at the first device of a previous recommendation to limit access to resources of the first component made available to the specific application extended the first component lifetime. A new recommendation may be transmitted to the second device to limit access to resources of the second component to the specific application.

System and method for recommending and rewarding configuration adjustments for extending information handling system hardware life

Systems, methods, and computer-readable media to identify replacement parts for an electronic asset using artificial intelligence/machine learning (AI/ML). In at least one embodiment, part data may be provided to an input of a trained AI/ML parts similarity model that has been trained to determine similarities between parts, including the parts used in a plurality of electronic assets of the organization. The trained AI/ML parts similarity model uses the part data to provide information relating to parts that are compatible with the part. In certain embodiments, the information relating to compatible parts includes an identifier for compatible parts and a similarity score indicating how similar each compatible part is to the part that is to be used to service the electronic asset.

System and method for identification of replacement parts using artificial intelligence/machine learning

A method comprises receiving data from a plurality of devices and analyzing the data to identify one or more parameters for segregation of the data. In the method, the data is tagged with one or more metadata tags corresponding to the one or more parameters. The method further comprises segregating the data into a plurality of data portions based at least in part on the one or more parameters, and storing respective ones of the plurality of data portions in respective data stores of a plurality of data stores. The one or more parameters comprise at least one of one or more policies corresponding to the data and respective locations of the plurality of devices.

Data management operations for data from edge locations

A method for performing data migration for devices includes identifying, by a device migration delegator of a device, a migration delegation event associated with the device; in response to identifying the migration delegation event: selecting at least one delegation action based on the migration delegation event; requesting a duplicator to allocate another device for pairing; obtaining a paired device from the duplicator; obtaining a token from the paired device; authenticating the paired device using the token; authorizing the paired device to perform the at least one delegation action using the token; performing at least one non-delegation action of migration by the device; and initiating, by the device, the performance of the at least one delegation action by the paired device.

Method and system for pairing computing devices for offloading actions during data migration from degrading computing devices

In general, embodiments relate to a method for managing a technical support (TS) session on a technical support system. The embodiments include receiving TS correspondence from a client, wherein the TS correspondence is associated with the TS session, classifying the TS correspondence to assign it a question classification, based on the question classification, making a determination that the TS correspondence matches at least one prior received TS correspondence, wherein the at least one prior received TS correspondence is associated with the TS session, and visually identifying the TS correspondence and the at least one prior received TS correspondence on a graphical customer interface (GUI) of the technical support system.

Method and system to manage tech support interactions using dynamic notification platform

An adaptive carbon dioxide (CO2) emissions reduction system operating on a client information handling system may comprise a processor, a network interface device, and a graphical user interface (GUI) to gather analytics for an executing software application hardware power consumption analytics, and a location for the client information handling system, determine a power CO2 emissions value estimating CO2 emitted during a measured consumption of power at the location based on the hardware power consumption analytics, determine that updated power consumption exceeds a maximum power draw threshold value determined based on user input for decreasing the power CO2 emissions value and location, and display a hardware reconfiguration recommendation for reconfiguring a hardware component currently configured for high performance to a power conservation setting.

System and method for adaptively reducing carbon footprint for an information handling system

A method for performing an application migration operation includes initiating the application migration operation to migrate an application from a source device to a target device, where the source device includes a source data migration agent and the target device comprises a target data migration agent. The method also includes initiating an active scan of network traffic at a transmission control protocol layer. The method further includes establishing a secure data path between the source device and the target device. In addition, the method includes beginning migration of the application from the source device to the target device using the secure data path. Moreover, the method includes making a first determination, using the active scan, that a data packet is associated with suspicious activity and rejecting the data packet based on the first determination.

End-to-end TCP monitoring during application migration

A method comprises collecting browsing activity data of at least one user in connection with at least one electronic commerce item, and collecting feature data of the at least one electronic commerce item. In the method, the browsing activity data and the feature data are analyzed using one or more machine learning models. At least one of a title and a description for the at least one electronic commerce item is generated based on the analysis, and are displayed on an interface for viewing by the at least one user.

Method and article of manufacture for automated generation of titles and descriptions for electronic commerce products

An apparatus comprises a processing device configured to receive system state information corresponding to one or more devices, to predict a usage frequency of the system state information using one or more machine learning models, and to determine, based at least in part on the usage frequency, a compression level for storage of the system state information. The compression level is applied to the system state information to generate at least one compressed file for transmission to a database.

Predicting usage of system state information to determine compression levels

An enterprise-wide carbon dioxide (CO2) emissions minimizing load-balancing system executing on a unified endpoint management platform information handling system comprising a processor receiving operational telemetry measurements for a first client information handling system including power analytics, a load of remote processing calls made to a processor, a geographic location, and a first client CO2 emissions value determined to exceed a first CO2 load-balancing emissions threshold value limiting the first client information handling system, the processor to reject remote processing call requests made to the first client information handling system above a CO2 optimal request load determined based on CO2 emitted per remote processing call request processed at the first client information handling system, and the processor to redistribute the rejected remote processing call requests across offload information handling systems having CO2 emissions values below a per-machine CO2 load-balancing emissions threshold value determined for each of the offload information handling systems.

System and method for balancing processing load distribution for data centers based on server location to minimize greenhouse gas emissions

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Bengaluru, India

Parminder Singh Sethi