Dell Products Inc.

Emc IP Holding Company LLC

Emc IP Company LLC

Shubham Gupta

Rohan Sharma

Rangan Basu

Hung The Dinh

Gyanendra Kumar Patro

Sathish Kumar Bikumala

Sabu K Syed

Vivek Bhargava

Ramu Kannappan

Rajesh Krishnan

Jatin Kamlesh Thakkar

Reddeppa Kollu

Rishav Sethia

Tousif Mohammed

Venkat S Ramachandran

Nikhil Reddy Kota

Nithiyanandham Tamilselvan

Falaah Arif Khan

Pallavi Jaini

Naga Kalyan Kambapu

Hithesh Sekhar Bathala

Puttaraju Chikkanna

Madhu Chilipi

Navin Kumar

Desai Yarlagadda

Lakshmi Prasad Banala

Anubhab Mohanty

Vibhor Sharma

Tirumala Rao Sangapu

Md Shadab Ali

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

Methods, apparatus, and processor-readable storage media for automatically validating design parameters for enterprise applications are provided herein. An example computer-implemented method includes obtaining image data and code data of a given application associated with an enterprise; processing at least a portion of the image data against at least a portion of design parameters of the enterprise by processing the at least a portion of the image data and a set of design-compliant image data using similarity algorithms; processing at least a portion of the code data against at least a portion of the design parameters by performing an automated scan of the at least a portion of the code data against at least one code-related portion of the design parameters; and performing automated actions based on the processing of the image data and/or the processing of the code data.

Automatically validating design parameters for enterprise applications

A method includes retrieving information regarding a candidate from a plurality of sources, and analyzing the information regarding the candidate using one or more machine learning techniques. A plurality of questions for the candidate are generated based on the analysis. The method further includes receiving and analyzing a plurality of natural language responses to the plurality of questions from the candidate, and computing a plurality of confidence scores for the plurality of natural language responses using the one or more machine learning techniques. The plurality of questions and the plurality of confidence scores are provided to a user via a user interface.

Artificial intelligence-based resource selection

A method, system and computer-usable medium are disclosed for executing a comparative analysis of microservice profiles and recommending modifications to a microservice. In at least one embodiment, a plurality of microservice analysis daemons and a plurality of microservices are deployed to nodes of a network. Run-time execution characteristic of the plurality of microservices are monitored and gathered by the analysis daemons for microservices executed at the node at which the microservice analysis daemon is deployed. Microservice profiles are generated using the run-time execution characteristics data and data retrieved from a service registry. Recommendations regarding modification of one or more microservices are made based on the results of a microservice profile comparative analysis.

Microservice management system for recommending modifications to optimize operation of microservice-based systems

Methods and systems are disclosed for allocating tasks between apparatus in an IoT system in a manner to generally minimize the total amount of time to execute the tasks. At least one embodiment includes a computer-implemented method for allocating task processing between an internet of things (IoT) device and an edge device. The computer-implemented method includes collecting data from one or more sensors to execute a task having data size Xt; predicting a space complexity data size Xc for the task based on data size Xt, and allocating data for processing between the IoT device and edge device as a function of Xc. In at least one embodiment, the space complexity data size Xc is determined by applying Xt to the input of a long short-term memory neural network.

System for allocating task processing between an IoT device and an edge device

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

As an example, a computing device may broadcast, via a software application, a message to multiple computing devices requesting devices to participate in an activity occurring at a particular time at a particular location. A subset of the multiple computing devices may gather at the particular location prior to the particular time to participate in the activity. Prior to the particular time, the computing device may discover the subset and create a peer-to-peer network. Based on a set of attributes associated with each computing device in the subset, the computing device may assign individual computing devices in the subset a role and a position. Each of the computing devices in the subset may be positioned in the particular location according to the position and perform the role at the particular time to collectively perform the activity.

Crowdsourcing and organizing multiple devices to perform an activity

In some examples, a security application may monitor data received from one or more of sensors of a computing device, one or more input devices of the computing device, or one or more biometric sensors of a smartwatch that is connected to the computing device. If the user is not logged in and the user that is carrying the computing device has an uneven gait or is stumbling, the security app may, when the user initiates a login process, gather additional data, such as, from the sensors, the smartwatch, or both. If the user makes too many errors, based on data from the input devices, when attempting to login or after logging in, the security application may gather additional data. If the additional data indicates that the user may be under the influence of prescription or recreational drugs or alcohol, the user may be prevented from logging in.

Increasing security of a computing device based on determining a user's state of consciousness

At least one embodiment of the disclosed system is directed to a computer-implemented method for operating a light projection technology keyboard of an Information Handling System. The computer-implemented method may include receiving electronic environmental condition data at a keyboard configuration system, and processing the electronic environmental condition data through a neural network of an adaptive configuration engine. The neural network may output one or more functions and/or visual settings to be used in configuring operation of the light projection technology keyboard. The light projection technology keyboard may be controlled based on the one or more functions and/or visual settings output by the neural network. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

Adaptive intelligence enabled software providing extensibility and configuration for light projection technology based keyboards

An apparatus comprises a processing device configured to select a given notification to be delivered from a first computing device to a second computing device and to determine (i) first notification metrics associated with one or more previous notifications delivered to a set of one or more computing devices including the second computing device and (ii) second notification metrics associated with a current status of the second computing device. The processing device is also configured to provide the first and second notification metrics to one or more machine learning models, to identify recommendations for modifying delivery of the given notification from the first computing device to the second computing device based on output of the one or more machine learning models, and to modify the delivery of the given notification from the first computing device to the second computing device based on the identified recommendations.

Machine learning-based recommendations for modifying delivery of notifications

Methods and systems are disclosed for migrating tasks between edge devices in an Internet of Things (IoT) system in a manner to generally minimize the total amount of time to execute and migrate the tasks. At least one embodiment includes a computer-implemented method for receiving a task for execution at an edge device; detecting whether a task migration condition exists at the edge device; dividing the task into a plurality of subtasks of equal data size b; and migrating at least some of the subtasks of data size b to a further edge device for execution. At least one embodiment designates 1&#x2dc;n subtasks of size b for execution by the edge device and n+1&#x2dc;N subtasks of data size b for execution by the further edge device and optimizes the total time for execution of the subtasks is optimized as a function of n.

System for migrating tasks between edge devices of an IoT system

Methods, apparatus, and processor-readable storage media for generating context-based question-answer pairs by applying artificial intelligence techniques to context-related data are provided herein. An example computer-implemented method includes obtaining multi-modal data pertaining to a given event, and removing noise from the multi-modal data by applying filtering techniques to the multi-modal data, thereby generating filtered multi-modal data; creating a comprehensive set of multi-modal data pertaining to at least a portion of the given event by aggregating the filtered multi-modal data in accordance with topic modelling techniques and removing any items of duplicate filtered multi-modal data; dynamically generating question-answer pairs related to the given event by applying machine reading comprehension-based artificial intelligence models to the comprehensive set of multi-modal data; and outputting at least a portion of the questions from the question-answer pairs to one or more participants of the given event.

Dynamic evaluation of event participants using a smart context-based quiz system

Systems and methods for prioritizing enhancement and/or improvements of features of a user application are disclosed. In at least one embodiment, a method includes retrieving analytics data generated by an analytics engine, where the analytics data includes data relating to user interactions with a feature of the user application. A plurality of vectors is generated from the analytics data. The plurality of vectors include vectors corresponding to user interactions with the feature. A priority is assigned to enhancing and/or improving the feature of the user application based on a weighted sum of the plurality of vectors.

Autonomous intelligent system for feature enhancement and improvement prioritization

As an example, a server hosting a search engine may receive a search query and determine a searched time interval, a searched object, and a searched event. The server may select, based on the searched time interval, a portion of an object-event bipartite graph that was created using information gathered from social media sites. The server may compare attributes of individual events in the portion with attributes of the searched event to identify a set of relevant events. The server may determine objects associated with the relevant events and compare attributes of individual objects with the attributes of the searched object to identify a set of relevant objects. The search engine may provide search results that include the set of relevant objects ordered according to their similarity to the searched object.

Event-based search engine

A system and method automatically refactor mature program code having interdependent features to remove instructions pertaining to features that are no longer used. To facilitate reduction of the number of feature dependencies to test, instrumentation data are analyzed to determine which of the available features are in actual use. A graph of feature dependencies is built based on the program configuration, and the program code is simulated, according to existing testing protocols, with various combinations of features disabled to determine whether the program continues to function without error. When features are found that can be safely removed, the codebase is automatically refactored to eliminate the implementing code corresponding to the features. The refactored code then may be further automatically retested and deployed into the production environment.

Growing community of inventors

Jaipur, India

Shubham Gupta