Dell Products Inc.

Emc IP Holding Company LLC

Sathish Kumar Bikumala

Jace William Files

Parminder Singh Sethi

Deepak NagarajeGowda

Shibi Panikkar

Karthik Ranganathan

Amit Sawhney

Tejas Naren Tennur Narayanan

Bijan Kumar Mohanty

Deepak Gowda

Gautam Kaura

Ares Sakamoto

Neha Wadhwani

Lakshmi Saroja Nalam

Mohit Arora

Shubham Gupta

Lawrence Allen Stone

Fernando L Guerrero

James S Watt, Jr

Hariprasad Janardana Iyer

Ashish Kumar Palo

Shalu Singh

Jose Alejandro Boillat

Marcio Fragoso Stumpf Lena

Hung The Dinh

Charles D Robison

Danilo Ong Tan

Sabu K Syed

Rohan Sharma

Jatin Kamlesh Thakkar

Sean Creedon

Anshul Saxena

Vasile Radoaca

Arturo Ramos, Jr

Vijaya Panguluru Sekhar

Siddharth Agrawal

Karunanithi Shanmugam

Gregory Michael Ramsey

Ponnayan Sekar

Selvadeepan Gunasekaran

Soumya Maity

Vani Budhati

Carlos Lourenco Machado

Harshit Mehta

Harshita Dubey

Avanthika Sankararaman

Apoorva Sharma

Guruprasad Rao

Marcio Stumpf Lena

Sridevi Chinnasamy

David Thomas Kirkpatrick

Sibi Philip

Kiran Kumar Gadamshetty

Stephen Ray Young

Deepa Sreekumar

Mallory Anne Kolodzey

Anish Arora

Prateek Mathur

Puneet Srivastava

Nataraj Rachakonda

Donald Charles Guthan, Jr

Chandra Sekhar Uppuluri

Vasudev Ka

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 system, method, and computer-readable medium are disclosed for authentication users to information handling systems. A user's user biometric, heartbeat pattern, and heart rate information are acquired and registered to an information handling system. To access the information handling system, a user's biometric, heartbeat pattern, and heart rate information are acquired and compared to the registered information. Reacquiring and recalibrating is performed for the user biometric, heartbeat pattern, and heart rate information during a session on the information handling system.

Method and system for authenticating users with a combination of biometrics, heartbeat pattern, and heart rate

In some examples, a computing device may determine that an issue (e.g., crash, restart etc.) occurred, gather context data (e.g., logs, device profile, etc.) associated with the issue, and generate a contact address to technical support based on the context data. The computing device may upload the context data to a location accessible to a server. After a user of the computing device initiates a communication to technical support using the contact address, the server may automatically route the call, based on the contact address, to a particular technician that has experience addressing the issue. The server may retrieve the context data and use machine learning to determine recommendations to address the issue. The machine learning may prioritize the recommendations and provide the context data and the prioritized recommendations to enable the particular technician to quickly resolve the issue.

Dynamically creating a contact address to customer support based on information associated with a computing device

In some examples, a device includes a charging circuit with a thermoelectric generator and a kinetic energy generator. An embedded controller (EC) monitors a level of a battery in the device. If the level falls below a threshold, the EC may determine, using an accelerometer, whether the device is in motion. If the device is in motion, the EC may use the kinetic energy generator to charge the battery. If the device is not in motion, the EC may determine, using a temperature sensor, whether there is a temperature difference between two portions of the device. If there is a temperature difference, then the EC may use the thermoelectric generator to charge the battery. If the EC determines that the device is not in motion and there is no temperature difference between the two portions, then the EC may instruct the user to charge the device.

Using thermal energy, kinetic energy, or both to charge a device

A method, system, and computer-usable medium are disclosed for acquiring and using audio detected during operation of a hard disk drive to determine the status of the hard disk drive. One general aspect of the disclosure is directed to a computer-implemented method that includes receiving analog audio detected by an audio transducer in proximity to moving mechanical components of a hard disk drive, converting the received analog audio to digital audio, and assigning a health classification status to the hard disk drive based on a classification of the digital audio.

System and method for acquiring and using audio detected during operation of a hard disk drive to determine drive health

A system of one or more computers can be configured to facilitate the design of a service. The disclosed system may operate to add a process block to a service design structure for the service. The process block is provided to a trained AI/ML process prediction model. The trained AI/ML process prediction model suggests one or more further process blocks for addition to the service design structure based, at least in part, on the addition of the process block to the service design structure. In certain embodiments, a process block is selected from the suggested one or more further process blocks and added to the service design structure. Other embodiments of this aspect of the disclosure include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

Automated service design using AI/ML to suggest process blocks for inclusion in service design structure

A method comprises extracting data for one or more assets corresponding to a user, and analyzing the data using one or more machine learning models. The analyzing comprises predicting whether the one or more assets will require at least one of replacement and service. In the method, one or more entitlement recommendations for the user are generated based on the analysis, and the one or more entitlement recommendations are transmitted to the user.

Automated and customized entitlement recommendation

In some examples, a computing device may receive, from an email server, an email rule comprising conditions and actions and determine a subset of archived emails stored on the computing device that satisfy the conditions. The computing device may perform the actions to the subset of archived emails, such as placing a legal hold on the subset of archived emails, creating and sending copies of the subset of archived emails to the email server, or deleting, from the computing device, the subset of archived emails. After determining that a user is attempting to perform a prohibited action on (e.g., delete, edit, forward) an email of the subset of archived emails, the computing device may perform a protective action, such as logging the user out of an email account, preventing the user from logging in to the email account, automatically sending an email to an administrator or the like.

Automated email legal hold

The described technology is generally directed towards processing various customer input data to extract frequently recurring customer experience themes, including positive and negative sentiment regarding customer experiences. Natural language processing, image processing, speech recognition and/or computer vision techniques can be used on customer-related data to determine themes, tests and scenarios, as well as discover insights that can be used to improve customer experiences. The technology can be used to recreate a customer engagement, journey and overall experience by designing test scenarios around failure themes.

Generating test scenarios by detecting failure patterns and themes in customer experiences

Methods, apparatus, and processor-readable storage media for connecting configuration services using blockchain technology are provided herein. An example computer-implemented method includes obtaining at least one user request comprising configuration-related data pertaining to at least one item of hardware and at least one user network; encrypting at least a portion of the configuration-related data using one or more cryptographic hash functions; storing copies of the encrypted configuration-related data across multiple nodes within at least one distributed blockchain technology-based cloud storage system; and performing one or more automated actions, in connection with the at least one user request, using at least a portion of the stored data within the at least one distributed blockchain technology-based cloud storage system.

Connecting configuration services using blockchain technology

A method includes retrieving information regarding a candidate from a plurality of sources, and analyzing the information regarding the candidate using one or more machine learning techniques. A plurality of questions for the candidate are generated based on the analysis. The method further includes receiving and analyzing a plurality of natural language responses to the plurality of questions from the candidate, and computing a plurality of confidence scores for the plurality of natural language responses using the one or more machine learning techniques. The plurality of questions and the plurality of confidence scores are provided to a user via a user interface.

Artificial intelligence-based resource selection

Methods, apparatus, and processor-readable storage media for implementing an intelligent network of distributed compute nodes are provided herein. An example computer-implemented method includes processing information pertaining to multiple compute nodes within a network of distributed compute nodes; mapping available compute nodes, within the network, having compute capabilities and bandwidth capabilities for executing compute tasks onto dedicated portions of the network; processing information pertaining to at least one compute task requested within the network, including determining at least bandwidth requirements for the compute task and latency requirements for the compute task; and performing, based on the mapping and the processed information pertaining to the compute task, at least one automated action pertaining to allocating at least a portion of the compute task to at least one of the available compute nodes within the network.

Implementing an intelligent network of distributed compute nodes

In some examples, a server may determine that a case, created to address an issue of a computing device, is closed and perform an analysis of steps in a process used to close the case. The analysis may determine a length of time of each step and determine that a time to close the case or complete a particular step was at least a predetermined amount faster than average. The server may use machine learning to create a survey question to determine a technique used to close the case or complete the particular step faster than average and to determine one or more incentives to provide a technician that closed the case. An answer from the technician to the survey question may include the technique used to close the case or complete the particular step faster than average. The technique may be shared with other technicians.

Training a machine learning algorithm to create survey questions

The disclosed systems and methods are directed to a computer-implemented method for use in designing a service. In at least one embodiment, a method includes designing a service with one or more generic virtual network functions (VNFs), where the generic VNFs are defined independent of vendor sourcing information. One or more trained machine learning (ML) models are used to identify VNFs available from VNF vendors that may source one or more VNFs similar to the generic VNFs. The service is implemented using VNFs provided by one or more VNF vendors, where the VNFs provided by the one or more VNF vendors have network functionality generating similar to the one or generic VNFs.

Services using AI/ML to select virtual network functions and vendors for supplying the virtual network functions

Techniques described herein relate to a method for managing nodes. The method may include sending, by a first node of nodes, a node information request to a social manager, where the node information request specifies a portion of a service to be provided to the first node; obtaining node information associated with a portion of the nodes from the social manager, where the portion of the plurality of nodes previously expressed node capability information and node configuration information associated with the portion of the service; identifying a second node of the portion of the nodes based on the node information to perform the portion of the service; and performing the service using the second node, where the second node performs the portion of the service.

Method and system for establishing trust between nodes in a network based on recommendations

Growing community of inventors

Round Rock, TX, United States of America

Sathish Kumar Bikumala