Dell Products Inc.

Emc IP Holding Company LLC

Navin Kumar Neithalath

Hung The Dinh

Bijan Kumar Mohanty

Nithiyanandham Tamilselvan

Tanvi Korlam

Girish Murthy

Sristirupa Tripathy

Vijayasri Chikmagalur Shivakumar

Ashok Nitin

Narendra Buwade

Hariharan Suresh Babu

Damodaran Sivaraman

Venkata Kishore Kumar Reddy Chintham

Rohit Das

Mahesh Reddy Nagaiah Reddy

Sampath Kumar Kalyana Sundaram

Sivaraman Damodaran

Girish VenkateshaMurthy

Navnit Varu

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

In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, determining a source platform code for migration from a source platform to a target platform and determining one or more attributes of the source platform code. The method also includes determining, using a machine learning (ML) model, one or more existing templates based on the one or more attributes of the source platform code, and recommending the one or more existing templates for use in generating a template for migration of the source platform code to the target platform. The template for the source platform code is configured to convert the source platform code to a target platform code suitable for the target platform. The one or more existing template can then be used to generate a template for migrating the source platform code to a target platform code suitable for the target platform.

Code migration framework

A method comprises extracting first task data from a first data source corresponding to a first application and second task data from a second data source corresponding to a second application, and comparing the first task data to the second task data using one or more natural language processing techniques. In the method, one or more matching tasks between the first task data and the second task data are identified based at least in part on the comparing. Code of at least one of the first application and the second application is analyzed to determine whether the code of at least one of the first application and the second application implements the one or more matching tasks.

Task correlation framework

Methods, apparatus, and processor-readable storage media for implementing a message management framework using machine learning techniques are provided herein. An example computer-implemented method includes processing a message comprising identifying at least one message type of the message; determining, based at least in part on the at least one identified message type, one or more message failures by applying one or more machine learning-based rules to at least a portion of the message; determining one or more remedial actions by processing, using one or more machine learning techniques, the at least one identified message type, the one or more determined message failures, and multiple attributes of the message; and performing one or more automated actions based at least in part on the one or more determined remedial actions.

Message management using machine learning techniques

Growing community of inventors

Bengaluru, India

Navin Kumar Neithalath