Dell Products Inc.

Emc IP Holding Company LLC

Sabu K Syed

Hung The Dinh

Kannappan Ramu

Jatin Kamlesh Thakkar

Manikandan Pammal Rathinavelu

Panguluru Vijaya Sekhar

Satish Ranjan Das

Reddeppa Kollu

Tousif Mohammed

Vijaya Panguluru Sekhar

Parminder Singh Sethi

Abhijit Mishra

Rajesh Krishnan

John Kenneth Maxi

Bijan Kumar Mohanty

Vinod Kumar

Shubham Gupta

Krishna Mohan Akkinapalli

Pallavi Jaini

Kiran Kumar Pidugu

Muhammed Mohiuddin

Craig J Van Der Bogart

Geetha Venkatesan

Pradeepta Ranjan Choudhury

Ranjani Muthyam Venkata

Matthew Christopher Frank

Vellore Mohammed Imran

Sathish Kumar Bikumala

Puneet Kumar Arora

Anay Kishore

Tarun Gupta

James S Watt, Jr

Akanksha Goel

Sean Creedon

Seshadri Srinivasan

Venkat S Ramachandran

Vinay Sathyanarayana

Nagireddy Bonthu

Sourav Datta

Nithiyanandham Tamilselvan

Nikhil Reddy Kota

Madhusudhana Reddy Chilipi

Ramu Kannappan

Chakradhar Kommana

Karan Kapoor

Lakshman Kumar Tiwari

Pravash Ranjan Panda

Gnanesh Gowda

Naga Kalyan Kambapu

Marcio Fragoso Stumpf Lena

Vibhor Sharma

Md Shadab Ali

Sandeep Govindraj

Sejal Mohata

Sanitha Muttil

Adam Mahram

Venkat Allaka

Jyothi K R

Anu Bala Thakur

Abhishek Joshi

Puttaraju Bommanna Chikkanna

Anubhab Mohanty

Prince Mathew

Naveen Silvester Muttikal Thomas

Sethukarasi Sockalingam

Akanksha Bansal

Sharath Kumar Mudigere Yathiraj

Amirthraj Ramakrishnan

Khadeer Ahmed Shaikh

Rajan Shrestha

Greg Winslow

Puttaraju Chikkanna

Madhu Chilipi

Navin Kumar

Desai Yarlagadda

Lakshmi Prasad Banala

In some examples, a computing device may implement a method that includes receiving microservice profile information at a microservice profiler, performing lexical analysis of the microservice profile information (where the lexical analysis produces tokenized information), generating microservice modification information by performing machine learning analysis of one or more inputs (where the one or more inputs comprise the tokenized information), and outputting the microservice modification information from the microservice profiler. The microservice profile information describes one or more characteristics of a microservice. The lexical analysis is performed by a lexical analysis engine of the microservice profiler, and the machine learning analysis is performed by a machine learning system of the microservice profiler.

Microservice management using machine learning

A method comprises receiving data corresponding to execution of one or more applications, accessing at least one function from a codes as a service source, and training the at least one function based, at least in part, on one or more parameters, wherein the training is performed using a first portion of the data. In the method, a deployment version of the at least one function is generated based, at least in part, on the training, and the deployment version of the at least one function is applied to a second portion of the data to perform at least one service.

Framework for codes as a service management and deployment

Methods, apparatus, and processor-readable storage media for data integration demand management using artificial intelligence are provided herein. An example computer-implemented method includes obtaining at least one data integration demand, wherein the at least one data integration demand comprises textual information provided by at least one user; determining multiple parameters of the at least one data integration demand by applying one or more machine learning natural language processing techniques to at least a portion of the textual information provided by the at least one user; generating at least one delivery date prediction for the at least one data integration demand by applying one or more artificial intelligence techniques to the multiple determined parameters of the at least one data integration demand; and performing one or more automated actions based at least in part on the at least one generated delivery date prediction.

Data integration demand management using artificial intelligence

A method includes receiving code for computer programming, analyzing the code and extracting a plurality of configuration properties from the code. In the method, one or more configuration files are generated from the extracted plurality of configuration properties, and microservice code is generated from the one or more configuration files. The microservice code is configured for deployment on one or more cloud computing platforms.

Code development for deployment on a cloud platform

Methods, apparatus, and processor-readable storage media for device component management using deep learning techniques are provided herein. An example computer-implemented method includes obtaining telemetry data from one or more enterprise devices; determining, for each of the one or more enterprise devices, values for multiple device attributes by processing the obtained telemetry data; generating, for each of the one or more enterprise devices, at least one prediction related to lifecycle information of at least one device component by processing the determined attribute values using one or more deep learning techniques; and performing one or more automated actions based at least in part on the at least one generated prediction.

Device component management using deep learning techniques

A system and method mediate transfer of encrypted data files between local applications and external computer systems. Application containers perform cryptographic operations using stored credentials to decrypt data coming from these external systems and configurably forward them to the local applications, and to encrypt data sent from the local applications to the external systems. Access to this encryption-as-a-service (EaaS) functionality is gated by a fingerprint service that classifies requests by security level, and detects anomalous requests. Security classification is performed by a supervised machine learning algorithm, while anomalous request detection is performed by unsupervised machine learning algorithm. Stored keys are monitored, and when they near expiration or are damaged, embodiments proactively undertake key renewal and key exchange with the external computer systems. Containerization enables key storage in multiple vaults, thereby making such storage vendor-agnostic.

Encryption as a service with request pattern anomaly detection

In some examples, a server may determine a specification associated with a software module that is to be integrated with a software system. The specification identifies how the software module interacts with the software system. The server may execute a machine learning module to perform an analysis of the specification. The machine learning module may suggest at least one modification to at least a first portion of the specification and may automatically modify at least a second portion of the specification. The server may convert the specification to one or more application programming interface (API) calls and provide a system interface that includes the one or more API calls to enable the software module to interact with the software system. The API calls may include calls to a data integration API, a file transfer API, a messaging API, a database API, or any combination thereof.

Use machine learning to verify and modify a specification of an integration interface for a software module

Methods, apparatus, and processor-readable storage media for identifying and remediating anomalies through cognitively assorted machine learning algorithms are provided herein. A computer-implemented method includes: identifying, using system log data, a target variable based at least in part on correlations between a set of performance indicators of a system and the target variable, and threshold values for the performance indicators relative to the target variable; generating an inference model to predict when the system will enter an adverse state and identify one or more root causes of the system entering the adverse state; using machine reinforcement learning to determine an action policy including actions that remediate the adverse state; predicting that the system will enter the adverse state by applying the inference model to further system log data; and automatically executing one or more actions of the action policy in response to the prediction.

Identifying and remediating system anomalies through machine learning algorithms

A method includes receiving a natural language query requesting data from a message oriented middleware infrastructure comprising a plurality of message oriented middleware providers, and analyzing the natural language query to determine one or more types of the data being requested. In the method, one or more stored queries corresponding to the determined one or more types of the data are identified. The one or more stored queries are in native command formats corresponding to respective ones of the plurality of message oriented middleware providers. The method also includes executing the identified one or more stored queries in the native command formats to retrieve the data from the plurality of message oriented middleware providers, and providing a response to the natural language query based on the retrieved data to a user via a user interface.

Using artificial intelligence and natural language processing for data collection in message oriented middleware frameworks

A method includes monitoring and logging a plurality of transactions between one or more clients and an application programming interface gateway, and analyzing data corresponding to the plurality of transactions using one or more machine learning techniques. The method further includes determining, based on the analyzing, one or more issues corresponding to one or more application programming interfaces associated with the application programming interface gateway and resulting from one or more of the plurality of transactions. In the method, one or more corrective actions are performed to address the one or more issues.

Application programming interface platform management using machine learning

A method includes receiving a natural language query requesting data from a message oriented middleware infrastructure comprising a plurality of message oriented middleware providers, and analyzing the natural language query to determine one or more types of the data being requested. In the method, one or more queries corresponding to the determined one or more types of the data are dynamically generated. The one or more queries are in native command formats corresponding to respective ones of the plurality of message oriented middleware providers. The method also includes executing the one or more queries in the native command formats to retrieve the data from the plurality of message oriented middleware providers, and providing a response to the natural language query based on the retrieved data to a user via a user interface.

Methods, apparatus, and processor-readable storage media for cognitive device management using artificial intelligence are provided herein. An example computer-implemented method includes determining an initial telemetry data collection frequency value for a given device by applying machine learning techniques to historic data pertaining to additional devices; collecting an initial set of telemetry data associated with the given device and one or more subsequent sets of telemetry data associated with the given device in accordance with the initial telemetry data collection frequency value; performing a comparison of the one or more subsequent sets of telemetry data to the initial set of telemetry data; updating the initial telemetry data collection frequency value by applying the machine learning techniques to information resulting from the comparison; determining automated actions related to the given device by utilizing a neural network in connection with the collected telemetry data; and automatically initiating the automated actions.

Cognitive device management using artificial intelligence

Methods, apparatus, and processor-readable storage media for device manufacturing cycle time reduction using machine learning techniques are provided herein. An example computer-implemented method includes obtaining video input related to one or more manufacturing resources in a manufacturing environment; determining availability status information for at least one of the one or more manufacturing resources by applying one or more machine learning models to the obtained video input; and outputting the determined availability status information to at least one user device associated with the manufacturing environment.

Device manufacturing cycle time reduction using machine learning techniques

Methods, apparatus, and processor-readable storage media for device temperature impact management using machine learning techniques are provided herein. An example computer-implemented method includes obtaining one or more notifications pertaining to temperature information associated with one or more devices; generating one or more predictions pertaining to at least one potential problem with at least one of the one or more devices by applying one or more machine learning models to the one or more obtained notifications; determining one or more automated actions related to the one or more predictions by utilizing at least one neural network to process data associated with temperature control for the at least one device; and automatically initiating the one or more automated actions.

Device temperature impact management using machine learning techniques

An information handling system determines context features for an electronic mail message based on a payload of the electronic mail message. A processor adds the context features to the payload of the electronic mail message to create an updated payload. Based on the updated payload, the processor sets a quarantine indication for the electronic mail message to either a first state or a second state. In response to the quarantine indication for the electronic mail message being in the first state, the processor assigns the electronic mail message to an electronic mail storm. In response to the assigning of the electronic mail message being to the electronic mail storm, the processor quarantines the electronic mail message.

System and method for detecting and removing electronic mail storms

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Austin, TX, United States of America

Sabu K Syed