International Business Machines Corporation

Kyndryl, Inc.

Zachary A Silverstein

Jeremy R Fox

Shikhar Kwatra

Robert Huntington Grant

Jacob Ryan Jepperson

Sarbajit Kumar Rakshit

Spencer Thomas Reynolds

Clement Decrop

Craig M Trim

Logan Bailey

Martin G Keen

Trudy L Hewitt

Melanie Dauber

Lisa Seacat DeLuca

Jacob Thomas Covell

Jonathan D Dunne

Tiberiu (Jim) Suto

Michael Seth Silverstein

Michael Bender

Kelley Anders

Fang Lu

Cesar Augusto Rodriguez Bravo

Liam S Harpur

Su Liu

Hemant Kumar Sivaswamy

Gregory J Boss

Charles Evan Beller

Todd Russell Whitman

Indervir Singh Banipal

Thomas Jefferson Sandridge

Suman Patra

Natalie Watkins

John Michael Ganci, Jr

Mauro Marzorati

Jennifer M Hatfield

Kelly Camus

Alan Chung

Daniel Ruiz

Alvin Zhang

Madeleine Eve Barker

Ruchika Bengani

Naga ManojKasyap Chilakamarthy Venkata

Brian M O'Connell

Andrew R Jones

Komminist S Weldemariam

Qi Cheng Li

Mark Patrick Delaney

Nadiya Kochura

Raghuveer Prasad Nagar

Tushar Agrawal

Adam Lee Griffin

Uri Kartoun

Victor Povar

Hamid Majdabadi

Melissa Restrepo Conde

Caleb Miles

Jayanth Krishnan

Raden Tonev

Neerju Neerju

Juan C Lopez

Paul Bergen

Maruthi P Koiloth

Aaron K Baughman

Vijay Ekambaram

Rhonda L Childress

Jana Helton Jenkins

Manish Anand Bhide

Sushain Pandit

Gary Francis Diamanti

Munish Goyal

Smitkumar Narotambhai Marvaniya

Shubhadip Ray

Pratyush Kumar

Barton Wayne Emanuel

Krishnan Sugavanam

Christian Compton

William Robert Taylor

Jill S Dhillon

Stefan Van Der Stockt

Samir Nasser

Jennifer L Szkatulski

Richard Daniel Gunjal

Ravithej Chikkala

Saswati Dana

Cedric D Cook

Pritesh Patel

Sudhir Alluri

Rodrigo Goulart Silva

Carlos E Aguilera

Mukesh Muraleedharan Nair

Nicholas Bokun

Neil Delima

Mayoore Selvarasa Jaiswal

John Edward Martin

Norton Samuel Augustus Stanley

Clarissa Ho

Weixin Xu

Iranna Dharmaraya Ankad

Vyacheslav Zheltonogov

Angel Marie Smith

Embodiments detect a task keyword related to an offloaded task of a task-giver, train a machine learning (ML) model using the task keyword, determine that the task keyword is similar to a historical task by utilizing the trained ML model, record a user input in response to determining that the task keyword is similar to the historical task, freeze an action execution during a pseudo action before a finality action is executed, provide the recorded user input to the task-giver and requesting approval from the task-giver, and execute the offloaded task using robotic process automation (RPA) in response to receiving approval from the task-giver.

Ad-hoc pseudo completion task

A computer-implemented method for document concatenation and ontological structuring. The method includes identifying a downloaded file and determining whether the downloaded file is associated with one or more saved files. Based on determining that the downloaded file is associated with the one or more saved files, developing an ontological structure storing the downloaded file and the one or more saved files.

Document concatenation and ontological structuring based on similarities

A computer-implemented method, according to one embodiment, includes identifying a plurality of user devices determined to run a predetermined software version and a predetermined application. A first of the identified user devices is assigned to be a primary user device, and a second of the identified user devices is assigned to be a first backup user device. In response to a determination that the first identified user device launches the predetermined application, the second identified user device is caused to launch the predetermined application. In response to a determination that the first identified user device experiences a predetermined interruption event while using the predetermined application, the second identified user device is assigned to be the primary user device and the first identified user device is assigned to be the first backup user device.

Selective failover between a plurality of devices concurrently running an application

Systems and methods synchronize content of a virtual environment with a state of a physical environment. In aspects, a method includes obtaining sensor data from a network of remote sensors measuring a physical state of a location at a time; generating context specific parameter data based on the sensor data; obtaining context data from a remote virtual reality (VR) system, wherein the context data reflects a current state of virtual content in a virtual environment displayed by the remote VR system; selecting virtual content to be displayed in the virtual environment by the remote VR system based on the context specific parameter data, the context data, and rules; and sending the virtual content to the remote VR system to be displayed to a user, wherein the virtual content reflects a state of the physical location at the time.

Physical and virtual environment synchronization

Resource parity scaling includes receiving in real time a plurality of machine-generated input signals from a computer network, the machine-generated signals generated during simultaneous real-time executions of multiple processes using human and compute resources. Based on analyzing the plurality of machine-generated signals, a performance level of each process is determined. The performance level of each process is compared with one or more predetermined key performance indicator (KPI) metrics corresponding to each process. Responsive to the comparing different combinations of the human and compute resources are allocated for performing different ones of the multiple processes. The allocating is based on resource-specific incremental conversion factors corresponding to the compute and human resources. One or more output signals are conveyed to the computer network indicating the different combinations.

Resource parity scaling using key performance indicator metrics

In an approach for providing a user with a set of information on a feature of a comparable product and enabling the user to assess the feature by overlaying the user's product with a digital twin of the comparable product, a processor identifies a first product in a surrounding of the user by comparing an image of the first product to one or more images stored in a product corpus. A processor searches, based on the first product, the product corpus for one or more products within a family of the first product. A processor selects a second product from the product corpus based in part on a set of product comparison preferences. A processor overlays the first product with a digital twin of the second product. A processor simulates a feature associated with the second product that is different from a feature associated with the first product.

Augmented reality overlay of feature and versioning improvement

In several approaches, a processor feeds an interaction by a user into a recurrent neural network (RNN) model. A processor identifies, using the RNN model trained to identify a specific workflow based on the interaction, an in-process workflow of the user. A processor predicts, using the RNN model, a next interaction of the user based on a next expected step in the in-process workflow. A processor performs at least one optimization function related to the next interaction of the user including at least one of autofocusing on a next user interface (UI) element associated with the next expected step in the in-process workflow, snapping a text cursor to the next UI element associated with the next expected step in the in-process workflow, and providing a UI prompt with a link to the next UI element associated with the next expected step in the in-process workflow.

UI workflow optimization based on expected next UI interaction

The illustrative embodiments provide for signature discourse transformation. An embodiment includes detecting a signature discourse of a user by analyzing the user's discourse using corpus linguistics. The embodiment also includes generating computing a collection of words distinct to the user. The embodiment also includes sensing, using tone analysis and a natural language process model, a tone of the collection of words distinct to the user in the signature discourse utterance. The embodiment also includes mapping a cartesian value of the collection of words distinct to the user. The embodiment also includes deriving a signature discourse transformation (SDT) model from the signature discourse utterances, the collection of words, the tone, and the cartesian value. The embodiment also includes transforming, using the signature discourse transformation, an original discourse by the user into an anonymized text.

Signature discourse transformation

A method, computer system, and a computer program product for multi-content interactions is provided. The present invention may include detecting a user engaging with a first content and a second content simultaneously. The present invention may also include determining that the user is more engaged with the second content relative to the first content. The present invention may further include executing a content preserving action to enable the user to reengage with the first content without missing any segments of the first content.

Multi-content interactions

Aspects of the present disclosure relate to save context capturing. An indication of a save of a version of software can be received. Contextual data associated with a user managing the version of the software can be associated with the save of the version of software, the contextual data captured during a timeframe temporally related to the save. The association between the contextual data and the save of the version of the software can be stored in a software versioning repository. The save of the version of the software and the associated contextual data can be retrieved in response to a predetermined condition or a request.

Save context capturing

A method, computer system, and a computer program product for translating a classifier construction into a graphical representation is provided. The present invention may include observing a classifier handshape by an augmented reality device. The present invention may include analyzing the observed classifier handshape according to an object recognition algorithm to determine a contextual meaning of the classifier handshape. The present invention may include converting the contextual meaning of the observed classifier handshape into a graphical representation. The present invention may include displaying the graphical representation alongside the observed classifier handshape on the augmented reality device.

Augmented reality translation of sign language classifier constructions

According to one embodiment, a method, computer system, and computer program product for importing player settings is provided. The present invention may include requesting one or more player settings responsive to receiving a command from a user to import one or more player settings associated with a player; receiving the one or more player settings; performing a hardware check to determine if the player settings are compatible with one or more user hardware elements; overwriting one or more user settings with the revised player settings based on the hardware check; and displaying a message to the user with one or more results of the overwrite.

Multiplayer E-gaming importable controls and settings

An embodiment includes classifying an incoming communication message as having an instruction classification based on a natural language processing (NLP) analysis of the incoming communication message. The embodiment extracts, responsive to classifying the incoming communication message as having the instruction classification, user operations as a workflow dataset. The embodiment clusters the workflow dataset into a task cluster with other similar workflow datasets representative of previously-extracted user operations. The embodiment designates the instruction classification as a trigger event based at least in part on the NLP analysis of the incoming communication message. The embodiment generates a robotic process automation (RPA) bot script that comprises program instructions executable by a processor to cause the processor to perform operations that automate the user operations in response to the trigger event.

Automatic detection of robotic process automation trigger events

An embodiment intercepts a first notification intended for a user. An embodiment identifies, by analyzing a currently occurring interaction including the user, a lull in the interaction. An embodiment delivers during the lull, the first notification to the user.

Context based user notification adjustment and management

Optimizing a user experience during transition to an updated user interface includes using a computer processor to capture a user experience of a user's interaction with a program based on migration of the user interface from a current version of a page layout associated with the user to a new version of the page layout associated with the user. Disparity events experienced by the user while transitioning from the current version of the page layout to the new version of the page layout are analyzed, and a recommendation report of changes to the user interface is generated and sent to a developer associated with the new version of the page layout. An updated page layout reflecting modifications may then be received based on the recommendation report.

Software user experience transition assistance

Growing community of inventors

Jacksonville, FL, United States of America

Zachary A Silverstein