International Business Machines Corporation

Kyndryl, Inc.

Lenovo (singapore) Pte. Ltd.

Other

Maplebear Inc.

Lenovo Enterprise Solutions (singapore) Pte. Ltd.

Sarbajit Kumar Rakshit

John Michael Ganci, Jr

James Edward Bostick

Martin G Keen

Jeremy R Fox

Craig M Trim

Venkata Vara Prasad Karri

Eric Vance Kline

Shikhar Kwatra

Barry Alan Kritt

Vijay Ekambaram

Tushar Agrawal

Shailendra Moyal

Sathya Santhar

Raghuveer Prasad Nagar

Sudheesh S Kairali

Saraswathi Sailaja Perumalla

Kimberly Greene Starks

John David Wilson

Akash U Dhoot

Zachary A Silverstein

Mauro Marzorati

Michael Bender

Christopher J Hardee

Cesar Augusto Rodriguez Bravo

Aaron K Baughman

Gregory J Boss

David Bruce Lection

Gary David Cudak

Fang Lu

Mukundan Sundararajan

Mark B Stevens

Robert Huntington Grant

Keith R Walker

John Elbert Moore, Jr

Komminist S Weldemariam

Sushain Pandit

Todd Russell Whitman

Kuntal Dey

Hernan A Cunico

Adam Roberts

Harish Bharti

Vijay Kumar Ananthapur Bache

Hemant Kumar Sivaswamy

Abhishek Jain

Sandeep R Patil

Jana Helton Jenkins

Katsuyuki Sakuma

Soma Shekar Naganna

Liam S Harpur

Gandhi Sivakumar

Maharaj Mukherjee

Brian Samuel Beaman

Edgar Adolfo Zamora Duran

Jeremy A Greenberger

Vittorio R Castelli

Seema Nagar

Manish Anand Bhide

Sasikanth Eda

Randy Allan Rendahl

James R Kozloski

Kulvir Singh Bhogal

Michael S Gordon

Donna Karen Byron

Kelley Anders

Swaminathan Balasubramanian

Chandrasekhar Narayanaswami

Sougata Mukherjea

Clifford A Pickover

Jonathan D Dunne

Peeyush Jaiswal

Nitin Gaur

Nadiya Kochura

Florian Pinel

Romelia H Flores

Vinod A Valecha

Petr Novotny

Marc Henri Coq

Jeronimo Irazabal

Michael K Gschwind

Lisa Seacat DeLuca

Valentina Salapura

Rhonda L Childress

Trudy L Hewitt

Sarvesh S Patel

Manish Satyapal Gupta

Jonathan Lenchner

Kushal S Patel

James Peter Ward

Nataraj Nagaratnam

David Jaramillo

John A Gunnels

Srikanth Govindaraj Tamilselvam

Rajesh Kumar Saxena

Diwesh Pandey

Pankaj Satyanarayan Dayama

Justin David Weisz

Alexandre Polozoff

Michael John Spisak

Vincenzo V Di Luoffo

An embodiment for dynamically converting a virtual reality (VR) classroom into a VR breakout session is provided. The embodiment may include receiving educational content from a VR educational session and profiles of a plurality of users. The embodiment may also include identifying a topic of the educational content. The embodiment may further include extracting a plurality of real-world use cases relevant to the identified topic from a knowledge corpus. The embodiment may also include in response to determining at least two users have at least one overlapping learning preference, dividing the at least two users into a subgroup. The embodiment may further include converting the VR educational session into a smaller VR breakout session containing at least one real-world use case conforming to the at least one overlapping learning preference. The embodiment may also include displaying the converted VR educational session to the at least two users in the subgroup.

Dynamic conversion of VR classroom into VR breakout session

According to one embodiment, a method, computer system, and computer program product for selective dataset encryption is provided. The embodiment may include identifying one or more data items within one or more datasets to be moved from network attached storage to cloud-based storage. The embodiment may also include determining an encryption method to be performed on each data item based on a level of confidentiality or sensitivity of each data item. The embodiment may further include sorting each data item into one or more groups based on the determined encryption method. The embodiment may also include performing each determined encryption method to the corresponding group.

Selective encryption while loading from network attached storage system

A computer-implemented method for displaying an augmented reality (AR) object is disclosed. The computer-implemented method includes identifying a physical object of interest in a user's AR field of view (FOV), determining an amount of time required for the physical object of interest to be displayed within the user's AR FOV, based on the user's relative position and movement with respect to the physical object of interest, determining the physical object of interest is no longer in the user's physical FOV prior to an expiration of the determined amount of time required for the physical object to be displayed within the user's AR FOV, and displaying a digital object corresponding to the physical object of interest in the users AR FOV until the determined amount of time required for the physical object to be displayed within the user's AR FOV has expired.

Velocity based dynamic augmented reality object adjustment

A computer implemented method predicts a next frame. A number of processor units receives a sequence of frames for a live video stream for an activity. The number of processor units predicts a next frame for the live video stream for using the sequence of frames for the activity. The number of processor units determines a set of advertisement spaces in the next frame predicted for live video stream.

Next event and frame prediction system for live video

Mitigating GPS inaccuracy is provided. The method comprises identifying a required level of location accuracy for an activity of an autonomous system and determining, according to GPS coordinates, a location of the autonomous system. A determination is made, according to a knowledge base created by historical learning, whether GPS location accuracy at the location of meets the required level of location accuracy for the activity. Responsive to a determination that the GPS location accuracy at the location of the autonomous system does not meet the required level of accuracy, location determination is handed off to an installed infrastructure within proximity of the autonomous system which provides the required level of location accuracy. The autonomous system then performs the activity according to location information provided by the installed infrastructure.

Proactive mitigation of GPS accuracy errors

A computer-implemented method according to one embodiment includes displaying an avatar associated with a second user on an augmented reality (AR) lens of a first communication device, in response to receiving, on the first communication device worn by a first user, a notification from a second communication device of the second user. The first user is monitored for a predetermined gesture. In response to a determination that the first user performs a first predetermined gesture, an action associated with the first predetermined gesture is performed by the first communication device. The method further includes outputting a response associated with the first predetermined gesture. The response indicates an availability of the first user to communicate with the second user.

Displaying avatars on an augmented reality (AR) lens in response to receiving a communication notification

An embodiment establishes a transmission performance database that comprises performance data related to transmission of data across a plurality of data channels of a network. The embodiment defines an initial data chunk size for a first data object transmitted across a first data channel of the plurality data channels of the network. The embodiment transmits the first data object across the first data channel according to the initial data chunk size. The embodiment collects performance data for the first data channel. The embodiment evaluates transmission performance of the first data channel based at least in part on the performance data of the first data channel. The embodiment generates a recommendation to adjust the initial data chunk size of the first data object based on the transmission performance of the first data channel. The embodiment adjusts the initial data chunk size based at least in part on the recommendation.

Intelligent data ingestion chunk size optimization

A system for layer-by-layer three-dimensional (3D) printing of a 3D printed object. The system includes a printing component configured to print a plurality of layers to form the 3D printed object, a surface modification component configured to be co-operable with the printing component and configured to apply an appropriate degree of surface roughness to at least one of the plurality of layers after being printed, and a controller configured to identify parameters of each of the at least one of the plurality of layers and configured to control operation of the printing component and the surface modification component, wherein the parameters include the appropriate degree of surface roughness.

Method and system of three-dimensional printing to prepare surface for gripping of subsequent layers

Methods for intelligent management of asset workflows are disclosed herein. One method includes predicting that a user is going to perform a predetermined action based on one or more user movements included in a signal received from a wearable sensing device and generating, by a processor, a warning of one or more consequences of the user performing the predetermined action. The method further includes displaying, on augmented reality glass, the warning to the user prior to the user actually performing the predetermined action. Also disclosed herein are systems and apparatus that can include, perform, and/or implement the operations of the methods.

Systems, apparatus, program products, and methods for intelligent management of asset workflows

A computer-implemented method according to one embodiment includes establishing a handshake between a security and compliance center (SCC) and a first operator of a first cluster via a manager component therebetween. An operand mapping of the SCC is updated, and the established handshake is used to cause the update to be communicated to the first operator of the first cluster. A computer program product according to another embodiment includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a computer to cause the computer to perform the foregoing method. A system according to another embodiment includes a processor, and logic integrated with the processor, executable by the processor, or integrated with and executable by the processor. The logic is configured to perform the foregoing method.

Using a handshake to communicate between a security and compliance center (SCC) and at least a first operator

An approach for dynamically restoring light-based network communication (e.g., Li-Fi, etc.) between Li-Fi devices is disclosed. The approach includes identifying environmental situation such as location of Li-Fi devices, automaton robotics with moving parts, determining risk of obstacles by simulating (via machine learning) one or more scenarios that could potentially be created by moving parts. The approach selects the optimal scenario and based on the selected scenario, the approach executes the scenario. The executed action can include optimizing the light path of the Li-Fi devices by instructing moving parts to follow a new path or leverage a new light path created by secondary Li-Fi device and/or mirror assembly to ensure no interruption to the Li-Fi network.

Dynamically restoring Li-Fi communication

Provided is a method, system, and computer program product for using a generative adversarial network to visually generate object deformation predictions. A processor may identify, based on an analysis of image data, a plurality of objects that are in a first stack formation. The processor may determine a load distribution of each object of the plurality of objects in relation to a subset of objects of the plurality of objects in the first stack formation. The processor may generate, using a generative adversarial network (GAN) algorithm and based on the load distribution for each object, a visualization depicting deformation of each object of the plurality of object in relation to the subset of objects of the plurality of objects in the first stack formation. The processor may display the visualization depicting the deformation of each object of the plurality of objects to a user.

Predicting object deformation using a generative adversarial network model

According to one embodiment, a method, computer system, and computer program product for preventing malware propagation. The embodiment may include identifying a malware scan report of an Internet-of-Things (IoT) device within an automated material handling (AMH) environment. The embodiment may include determining whether the IoT device is malware affected. In response to determining that the IoT device is malware affected, the embodiment may include disabling the IoT device. The embodiment may include instructing an industrial robot to transport the IoT device to a destination location within the AMH environment.

Secure material movement to prevent malware propagation

A computer-implemented system and method protect a robot in a hazardous environment. The method comprises detecting, with a hazardous environment detector, the presence of a hazardous gas within a bounded environment defining the boundaries of the hazardous environment, and detecting a wind flow speed and wind flow direction proximate the hazardous environment detector. The method further comprises determining a hazardous gas concentration (HGC) that is at least one of a present HGC and a predicted future HGC, and reading, from a database, a maximum HGC for at least one of the robot and a payload of the robot. Conditioned upon the HGC exceeding a threshold value of the maximum HGC, the method directs the robot to move to a safe location in which the HGC does not exceed the threshold value of the maximum HGC.

Self-protection of robotic vehicles in invisible hazardous gaseous surrounding

Computer technology that receives a query and a desired level of precision and/or accuracy (herein, a 'p/a value') associated with the received query, determines whether the p/a value exceeds a predetermined threshold, if the p/a value exceeds the predetermined threshold, then the virtual assistant system applies digital twin simulation to a digital twin data set in determining the query response to improve precision and/or accuracy of the query response, and outputs the query response to the querying party (for example, output as sound data in a voice assistant system). If the p/a value is below the threshold value, then the digital twin simulation is not performed, which makes the response quicker and puts less computational power demands on the virtual assistant system. In some embodiments, the virtual assistant system calculates the p/a value based on context.

Computer technology for controlling digital twin simulation in voice assistant devices

Growing community of inventors

Kolkata, India

Sarbajit Kumar Rakshit