Dell Products Inc.

Emc IP Holding Company LLC

Bijan Kumar Mohanty

Hung The Dinh

Dhilip S Kumar

Parminder Singh Sethi

Shishir Kumar Parhi

David John Linsey

Sashibhusan Panda

Sambasivarao Gaddam

Navin Kumar Neithalath

Sathish Kumar Bikumala

Mohammad Rafey

Satish Ranjan Das

James S Watt, Jr

Krishna Mohan Akkinapalli

Seshadri Srinivasan

Antarlina Tripathy

Shivangi Geetanjali

Gregory Michael Ramsey

Sweta Kumari

Baishali Roy

Divya Maddi

Shivangi Maharana

Harikrishna Reyyi

Sowmya Kumar

Rômulo Teixeira De Abreu Pinho

Sabu K Syed

Ponnayan Sekar

Sujit Kumar Sahoo

Devipriya Selvaraju

Manoj Nambirajan

Mohit Kumar Agarwal

Harish Mysore Jayaram

Vinod V Nair

Vinod Kumar

Vishal Agarwal

Abhijit Mishra

Deepak NagarajeGowda

Mohamed Sohail

Panguluru Vijaya Sekhar

Kannappan Ramu

Rajesh Krishnan

Karthik Sethuramalingam

Kanika Kapish

Reddeppa Kollu

Subramanya Padubidri

Craig J Van Der Bogart

Kiran Kumar Pidugu

Sourav Datta

Jaganathan Subramanian

Nithiyanandham Tamilselvan

Franklin Jebadoss Mohandoss

Ramesh Rajendra Rao

Naveen Silvester

Lokajit Tikayatray

Bhaskar Todi

Saravanan Kannan

Vikram Ramdev Bokka

Lisandro Ramos

Krunal Kanubhai Mevada

Satyam Sheshansh

Brajesh Chandra Yadav

Sheik Saleem

Dhamodharan Duraisamy

Karen Lee Jones

Sushma Punugubati

Damon Sonnenberg

Elizabeth Anne Toth

Ashok Nitin

Hariharan Suresh Babu

Vinotth Ramalingam

Sampath Kumar Kalyana Sundaram

Mahesh Reddy Nagaiah Reddy

Immanuel Savio Donbosco

Prerit Pathak

Narendra Buwade

Bharath Alla

Damodaran Sivaraman

Sivaraman Damodaran

Venkata Kishore Kumar Reddy Chintham

Vijayasri Chikmagalur Shivakumar

Girish VenkateshaMurthy

Kulin Shaival Chokshi

Rohit Das

Madhanamohana Reddy Gandluri

Sristirupa Tripathy

Navnit Varu

Shijin Babu

Sarath Kumar Kalavala

Rajan Shrestha

Tanvi Korlam

Greg Winslow

Shashikiran Rajagopal

Sujay Prasheel Sundaram

Shamik Kacker

Girish Murthy

Mohit Kolluri

Kasnadi Sitaram Nandan

Subrato Nath

Sarat Manchiraju

Nanjundaiah Champakadhama Swamy Shreeram

Methods, apparatus, and processor-readable storage media for security-based image classification using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining one or more images; extracting text from at least a portion of the one or more images by processing the one or more images using at least a first set of one or more artificial intelligence techniques; classifying at least one of the one or more images, into one or more of multiple security-based classification categories, by processing at least a portion of the extracted text using at least a second set of one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on the classifying of the at least one of the one or more images.

Security-based image classification using artificial intelligence techniques

A method comprises collecting data corresponding to one or more code changes in response to committing of the one or more code changes to a code repository, and formatting the data into at least one data string. The at least one data string is inputted to one or machine learning models. Using the one or machine learning models, a natural language description of the one or more code changes is generated based at least in part on the at least one data string. The method further comprises causing transmission of the natural language description of the one or more code changes to a document repository.

Code modification management using machine learning

Methods, apparatus, and processor-readable storage media for automatically classifying images for storage-related determinations using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining at least one image associated with an enterprise; classifying one or more portions of the at least one image into one or more enterprise-related categories by processing at least a portion of the at least one image using one or more artificial intelligence techniques; and performing, based at least in part on the classifying of the one or more portions of the at least one image, one or more automated actions pertaining to storing the at least one image in at least one of multiple storage options.

Automatically classifying images for storage-related determinations using artificial intelligence techniques

Methods, apparatus, and processor-readable storage media for generating and processing personal information chains using machine learning techniques are provided herein. An example computer-implemented method includes processing data, from one or more data sources, pertaining to one or more events involving an individual; generating a personal information chain associated with the individual by processing at least a portion of the processed data using at least one cryptographic function and linking that at least a portion of the processed data in accordance with at least one temporal parameter; performing anomaly detection by processing at least a portion of the personal information chain associated with the individual using one or more machine learning techniques; and performing one or more automated actions based at least in part on one or more of the personal information chain and results from the anomaly detection.

Generating and processing personal information chains using machine learning techniques

A method comprises receiving configuration data comprising a plurality of parameters of at least one computing environment. One or more of the parameters correspond to integration of one or more elements of the least one computing environment with one or more other elements at least one of within and external to the least one computing environment. In the method, the parameters are analyzed to detect one or more anomalies in the configuration data, and the configuration data and the one or more detected anomalies are inputted to one or more machine learning models. The method also comprises determining, using the one or more machine learning models, one or more modifications to at least one of the plurality of parameters based on the inputted configuration data and one or more detected anomalies, and transmitting the determination comprising the one or more modifications to a user over a communications network.

Integration optimization using machine learning algorithms

A method comprises receiving work order data, wherein the work order data identifies at least one technical support issue requiring resolution. The work order data is analyzed using one or more machine learning algorithms. The method further comprises predicting, based at least in part on the analyzing, whether the at least one technical support issue will be resolved at one or more respective service locations of a plurality of service locations. Based at least in part on the predicting, a recommendation to respond to the at least one technical support issue at a given service location of the plurality of service locations is generated.

Technical support service location recommendation using machine learning

A method comprises collecting network data associated with data transmission in a computing environment. The method also comprises identifying, using one or more machine learning models, at least one intrusion type affecting the computing environment. The identification of the at least one intrusion type is based at least in part on the collected network data. In the method, one or more remedial communications addressing the at least one intrusion type are generated, and the one or more remedial communications are transmitted to a user.

Intrusion detection using machine learning

Methods, apparatus, and processor-readable storage media for automatically summarizing event-related data using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining text-based data and non-text-based data associated with at least one virtual event comprising one or more participants; generating a content-related summarization of one or more of at least a portion of the text-based data and at least a portion of the non-text-based data using at least a first set of one or more artificial intelligence techniques; generating a participant sentiment-related summarization associated with one or more of at least a portion of the text-based data and at least a portion of the non-text-based data using at least a second set of one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on one or more of the content-related summarization and the participant sentiment-related summarization.

Automatically summarizing event-related data using artificial intelligence techniques

Methods, apparatus, and processor-readable storage media for augmenting identifying metadata related to group communication session participants using artificial intelligence techniques are provided herein. An example computer-implemented method includes processing, using a first set of artificial intelligence techniques, image data associated with participants of a group communication session and/or video data associated with participants of the group communication session; processing, using a second set of artificial intelligence techniques, audio data associated with participants of the group communication session; identifying at least one of the participants of the group communication session based on the processing of the image data, the processing of the video data, and/or the processing of the audio data; augmenting, in connection with at least one interface associated with the group communication session, identifying metadata attributed to the at least one identified participant; and performing one or more automated actions based on the augmenting of the identifying metadata.

Augmenting identifying metadata related to group communication session participants using artificial intelligence techniques

A method comprises collecting message-oriented-middleware system parameters from a plurality of message-oriented-middleware clusters, analyzing the parameters using one or more machine learning algorithms, and predicting, based at least in part on the analyzing, at least one anomaly in a message-oriented-middleware cluster of the plurality of message-oriented-middleware clusters. In the method, message metadata is collected from the message-oriented-middleware cluster, and at least part of the message metadata is transmitted to one or more remaining ones of the plurality of message-oriented-middleware clusters. At least the part of the message metadata corresponds to messaging operations to be transferred from the message-oriented-middleware cluster to the one or more remaining ones of the plurality of message-oriented-middleware clusters.

Message oriented middleware cluster synchronization

A method comprises collecting data corresponding to a plurality of components in a system, wherein the data comprises information about at least one of respective protocols and respective interfaces associated with respective ones of the plurality of components. The data is analyzed to determine at least one of the respective protocols and the respective interfaces associated with the respective ones of the plurality of components. In the method, operations of one or more components of the plurality of components are tested based at least in part on the determination of the at least one of the respective protocols and the respective interfaces. The method further includes outputting respective statuses of the one or more components, wherein the respective statuses are derived at least in part from the testing.

Testing framework with load forecasting

A method comprises receiving a request to log at least one operation of a plurality operations, wherein the request includes one or more features of the at least one operation. The one or more features are analyzed using one or more machine learning algorithms. The method further comprises selecting, based at least in part on the analyzing, a log provider of a plurality of log providers to log the at least one operation, and interfacing with the log provider to enable logging of the at least one operation.

Log provider recommendation using machine learning

Methods, apparatus, and processor-readable storage media for automatically prioritizing supply chain-related demand using artificial intelligence techniques are provided herein. An example computer-implemented method includes processing supply-chain related data using a first set of artificial intelligence techniques trained based at least in part on historical demand availability data; processing supply-chain related data using a second set of artificial intelligence techniques trained based at least in part on historical supply availability data; processing supply-chain related data using a third set of artificial intelligence techniques trained based at least in part on historical production availability data; prioritizing multiple orders within a supply chain environment by processing, using a fourth set of artificial intelligence techniques, results from the first set of artificial intelligence techniques, the second set of artificial intelligence techniques, and the third set of artificial intelligence techniques; and performing one or more automated actions based on the prioritization of the multiple orders.

Automatically prioritizing supply chain-related demand using artificial intelligence techniques

In one aspect, an example methodology implementing the disclosed techniques includes receiving information regarding a customer to onboard to a managed service and determining one or more relevant features from the information regarding the customer, the one or more relevant features correlated with historical onboarding times. The method also includes determining, using a machine learning (ML) model, an expected time to onboard the customer to the managed service based on the one or more relevant features.

Intelligent estimation of onboarding times for managed services

In one aspect, an example methodology implementing the disclosed techniques includes, by a product subscription service, receiving information regarding a hardware asset being returned at an end of a subscription and predicting, using a first machine learning (ML) model, whether the hardware asset has reached EOL. The method also includes, responsive to predicting that the hardware asset has reached EOL, creating, by the product subscription service, a work order to dispatch an eco-partner. The method may further include, by the product subscription service, responsive to predicting that the hardware asset has not reached EOL, predicting, using a second ML model, one or more new subscription orders matching the hardware asset and recommending the one or more matching new subscription orders as possible fits for the hardware asset.

Optimized hardware product returns for subscription services

Growing community of inventors

Austin, TX, United States of America

Bijan Kumar Mohanty