Dell Products Inc.

Emc IP Holding Company LLC

Kannappan Ramu

Hung The Dinh

Sabu K Syed

Parminder Singh Sethi

Satish Ranjan Das

Panguluru Vijaya Sekhar

Manikandan Pammal Rathinavelu

Pradeepta Ranjan Choudhury

Bijan Kumar Mohanty

Abhijit Mishra

Anay Kishore

James S Watt, Jr

Akanksha Goel

Rajesh Krishnan

Krishna Mohan Akkinapalli

Jatin Kamlesh Thakkar

Reddeppa Kollu

Vijaya Panguluru Sekhar

Venkat S Ramachandran

Craig J Van Der Bogart

Muhammed Mohiuddin

Geetha Venkatesan

Nikhil Reddy Kota

Matthew Christopher Frank

Naga Kalyan Kambapu

Ranjani Muthyam Venkata

Sandeep Govindraj

Sethukarasi Sockalingam

Jonathan Cornish Byerly

A method includes generating a model for a content taxonomy using one or more machine learning (ML) techniques. The model comprises a plurality of metadata tags for electronic content. In the method, a plurality of electronic content items are received from a plurality of content management systems, and are analyzed using the one or more ML techniques. The method also includes assigning one or more of the plurality of metadata tags to each of the plurality of electronic content items based on the analysis, and transmitting to the plurality of content management systems via one or more application programming interfaces, a plurality of recommendations comprising which of the plurality metadata tags to apply to the plurality of electronic content items.

Enterprise taxonomy management framework for digital content marketing platform

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

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 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

A method includes receiving a plurality of performance metrics from a data streaming infrastructure. The data streaming infrastructure comprises one or more message producers, one or more message brokers and one or more message consumers. In the method, the plurality of performance metrics are analyzed in a machine learning model, and one or more performance issues with the data streaming infrastructure are predicted based on the analyzing.

Streaming and event management infrastructure performance prediction

Growing community of inventors

Frisco, TX, United States of America

Kannappan Ramu