Dell Products Inc.

International Business Machines Corporation

Emc IP Holding Company LLC

Lenovo (singapore) Pte. Ltd.

Hung The Dinh

Bijan Kumar Mohanty

Sabu K Syed

Teng Hu

Panguluru Vijaya Sekhar

Satish Ranjan Das

Phong Anh Pham

Kannappan Ramu

Dhilip S Kumar

Manikandan Pammal Rathinavelu

Parminder Singh Sethi

Abhijit Mishra

Kulvir Singh Bhogal

Vinod Kumar

Jatin Kamlesh Thakkar

Krishna Mohan Akkinapalli

Seshadri Srinivasan

Tousif Mohammed

Shishir Kumar Parhi

Mandeep Singh Sidhu

Rajesh Krishnan

Reddeppa Kollu

Sashibhusan Panda

Baishali Roy

Sweta Kumari

Vijaya Panguluru Sekhar

Sambasivarao Gaddam

Shubham Gupta

Mansoor Abdulali Lakhdhir

Shivangi Geetanjali

Antarlina Tripathy

Vinay Sathyanarayana

Navin Kumar Neithalath

Sowmya Kumar

Shivangi Maharana

Venkat S Ramachandran

Harikrishna Reyyi

Mohammad Rafey

Sujit Kumar Sahoo

Kiran Kumar Pidugu

Sourav Datta

Craig J Van Der Bogart

Pallavi Jaini

John Kenneth Maxi

Muhammed Mohiuddin

Nikhil Reddy Kota

Nithiyanandham Tamilselvan

Divya Maddi

Nagireddy Bonthu

Pradeepta Ranjan Choudhury

Rômulo Teixeira De Abreu Pinho

Nizamudeen Ishmael, Jr

Tarun Gupta

David John Linsey

Nizam Ishmael, Jr

Ponnayan Sekar

Gnanasambandam Palaniswami

Ankita Rathi

Geetha Venkatesan

Lakshman Kumar Tiwari

Vellore Mohammed Imran

Ranjani Muthyam Venkata

Ramu Kannappan

Chakradhar Kommana

Gnanesh Gowda

Naga Kalyan Kambapu

Madhusudhana Reddy Chilipi

Nilay Mishra

Vivek Chauhan

Reshma Nair

Mohammed Imran V

Farook Shaik

Sijoy Thomas

Pravash Ranjan Panda

Sathish Kumar Bikumala

Sudhir Vijendra

Vishal Agarwal

Puneet Kumar Arora

Anay Kishore

Mohamed Sohail

James S Watt, Jr

Akanksha Goel

Karthik Sethuramalingam

Kanika Kapish

Sean Creedon

Subramanya Padubidri

Gregory Michael Ramsey

Patrick M Goal

Jaganathan Subramanian

William C White

David P Lee

Devipriya Selvaraju

Falaah Arif Khan

Marcio Fragoso Stumpf Lena

Kalyan Ram Palagummi

Ravi R Shastri

Karan Kapoor

Nirmala D'Souza

Lokajit Tikayatray

Prasad Ts Rao

A method comprises collecting data corresponding to a plurality of components in a system, wherein the data comprises information about one or more issues with the plurality of components. The data is analyzed and categorized based at least in part on the analysis. In the method, one or more application programming interfaces (APIs) are selected to monitor respective statuses of the plurality of components, wherein the selection is based at least in part on the categorization of the data, and the data is pushed to the one or more APIs.

Component monitoring framework

A method comprises analyzing a plurality of natural language inputs associated with at least one user, and determining a plurality of contexts for the plurality of natural language inputs based, at least in part, on the analysis. In the method, a plurality of relationships linked to the at least one user are identified based, at least in part, on the analysis, and the at least one user is classified in one or more categories based, at least in part, on the plurality of contexts and the plurality of relationships. At least one of the analyzing, determining, identifying and classifying is performed using one or more machine learning models.

Using machine learning for individual classification

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 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, 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

In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, receiving a message for delivery to a message-oriented middleware (MOM) server and determining whether an anomaly is predicted in the MOM server. The method also includes, by the computing device, responsive to a determination that an anomaly is predicted in the MOM server, identifying an alternate MOM server for delivery of the message, and routing the message to the alternate MOM server. The method may also include, by the computing device, responsive to a determination that an anomaly is not predicted in the MOM server, delivering the message to the MOM server.

Smart failure prediction and seamless processing in messaging systems

Growing community of inventors

Austin, TX, United States of America

Hung The Dinh