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

Craig M Trim

Jeremy R Fox

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

Mukundan Sundararajan

Mark B Stevens

Robert Huntington Grant

Keith R Walker

John Elbert Moore, Jr

Komminist S Weldemariam

Sushain Pandit

Todd Russell Whitman

Fang Lu

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

Sasikanth Eda

Randy Allan Rendahl

James R Kozloski

Kulvir Singh Bhogal

Michael S Gordon

Donna Karen Byron

Seema Nagar

Kelley Anders

Manish Anand Bhide

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

Pankaj Satyanarayan Dayama

Diwesh Pandey

Alexandre Polozoff

Justin David Weisz

Michael John Spisak

Vincenzo V Di Luoffo

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

A computer-implemented method performs dynamic filtering within a service mesh. While transferring data from one microservice to another microservice, each microservice evaluates a level of trust with each upstream and downstream microservice based on the level of trust score. Each microservice evaluates which data should be filtered while transferring data to downstream microservice and which data should be filtered while receiving data from upstream microservice.

Dynamic data filtering based on trust score within service mesh

According to one embodiment, a method, computer system, and computer program product for iteratively improving a virtual model of an object to be printed in a mixed reality environment is provided. The present invention may include rendering the virtual model within the mixed-reality environment; monitoring interactions between a user and the virtual model within the mixed-reality environment; predicting one or more deficiencies in the physical object based on the monitoring; suggesting one or more edits of the virtual model to the user to address the one or more predicted deficiencies; and integrating the one or more edits into the virtual model.

Iterative virtual reality modeling amelioration for three-dimensional to-be-printed objects and virtual reality filament sections

Allocating processing activities among multiple computing devices can include identifying multiple computing activities of a computer-executable process and, for each computing activity identified, estimating in real time the computing resources needed. The identifying can be in response to detecting a computer-executable instruction executed by one multiple communicatively coupled computing devices, and the computer-executable instruction can be associate with the computer-executable process. A current condition and configuration of each of the computing devices can be determined in real time. For each computing device an effect induced by executing one or more of the plurality of activities can be predicted, the predicting based each computing device's current condition and configuration and performed by a machine learning model trained using data collected from prior real-time processing of example process activities. Based on the predicting, computing activities can be allocated in real time among the computing devices.

Multi-device processing activity allocation

A capture operation is identified. The capture operation is performed by a first capture device. The capture operation is of a video steam of a subject that is located in an environment. The video stream includes a perspective of the subject. A capture limitation of the first capture device, related to capturing the subject, is determined. In response to the capture limitation one or more candidate capture devices in the environment are detected. A second capture device is selected as a selected capture device, from the one or more candidate capture devices. The second capture device is instructed to perform the capture operation. The video stream is transferred from the first capture device to the second capture device; the transfer maintains the perspective.

Multiple entity video capture coordination

According to one embodiment, a method, computer system, and computer program product for precise alignment of one or more augmented reality objects is provided. The present invention may include identifying one or more physical objects in a physical environment that will be overlaid with the one or more AR objects; monitoring positions of the one or more identified physical objects with an AR device and one or more connected computing devices; analyzing data of the one or more identified physical objects; and aligning the one or more AR objects precisely to the one or more identified physical objects based on the analyzed data of the one or more physical objects.

Precise alignment of augmented reality objects with physical objects

An augmented reality (AR) container orchestration system includes computer hardware including an AR system and a container orchestration platform. The AR system is configured to perform identifying a container to be deployed, displaying, to a user, a grid having a plurality of cells each of which are configured to receive a representation of the container, and redisplaying, using the AR system and to the user, the grid based upon a movement of the representation of the container to a particular position within the grid. The container orchestration platform is configured to perform identifying possible configurations for the container, and configuring the container based upon the movement of the representation of the container to the particular position. The includes a plurality of axes, and each axis of the plurality of axis represents a different configuration-related parameter of the configurations for the container.

Intelligent container configuring using augmented reality

According to one embodiment, a method, computer system, and computer program product for volumetric video modification is provided. The embodiment may include generating a volumetric video from a multi-camera feed. The embodiment may also include presenting the generated volumetric video to a user. The embodiment may further include receiving one or more user inputs to the presented volumetric video. The embodiment may also include modifying the volumetric video based on the one or more user inputs. The embodiment may further include presenting the modified volumetric video to the user.

Volumetric video modification using a virtual reality system

Systems and methods are provided for detecting and repelling bird species approaching a wind farm, using targeted ultrasonic/sonic frequencies. A zone of vulnerability boundary is set around the wind farm at which bird repellers start responding to instructions to broadcast ultrasonic/sonic. Based on the received plurality of images, at least one bird species is identified as approaching the zone of vulnerability boundary. The bird repellers receive instructions to broadcast beginning with the calculated starting ultrasonic/sonic frequency, and incrementally increasing or decreasing the starting ultrasonic/sonic frequency in response to the at least one identified bird species continuing to approach or to move away from the zone of vulnerability boundary. A learning model is updated with statistics, which the identified plurality of bird species and the ultrasonic/sonic frequency receiving the optimal response from the identified plurality of bird species.

Automated bird detection and targeted activation of ultrasonic bird repeller for wind turbines

A computer-implemented method for multimedia modification is disclosed. The computer-implemented method includes classifying one or more objects detected within a user's field of view through an augmented reality environment. The computer-implemented method further includes determining a context of the user based, at least in part, on the one or more classified objects detected within the user's field of view. The computer-implemented method further includes generating a priority score for the one or more classified objects based, at least in part, on the context of the user. The computer-implemented method further includes modifying an object detected within the user's field of view based, at least in part, on the priority score of the object.

Contextual priority based multimedia modification

A computer-implemented method according to one embodiment includes identifying a load that will be applied to a robotic structure, and determining whether the load, when applied to the robotic structure, would exceed a structural threshold of the robotic structure. In response to determining that the load, when applied to the robotic structure, would exceed the structural threshold, a position of one or more swarm robots with respect to the robotic structure is determined that would increase a structural strength of the robotic structure, and the one or more swarm robots are caused to assume the determined position. 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.

Increasing strength of a robotic structure using swarm robot(s)

Growing community of inventors

Kolkata, India

Sarbajit Kumar Rakshit