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

Sudheesh S Kairali

Sathya Santhar

Shailendra Moyal

Raghuveer Prasad Nagar

Saraswathi Sailaja Perumalla

Kimberly Greene Starks

John David Wilson

Akash U Dhoot

Zachary A Silverstein

Mauro Marzorati

Michael Bender

Christopher J Hardee

Aaron K Baughman

Cesar Augusto Rodriguez Bravo

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

Abhishek Jain

Adam Roberts

Harish Bharti

Hemant Kumar Sivaswamy

Vijay Kumar Ananthapur Bache

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

Marc Henri Coq

Clifford A Pickover

Jonathan D Dunne

Peeyush Jaiswal

Nitin Gaur

Nadiya Kochura

Florian Pinel

Romelia H Flores

Vinod A Valecha

Petr Novotny

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

Distributed queries are analyzed to identify repeated queries. Pattern of the repeated queries is learned, where the pattern provides information associated with at least availability of data sources used for responding to the repeated queries and response times of the data sources. For a data source in the data sources, a time window during which the data source has a response time that is slower than a response time criterion is determined; prior to beginning of the time window, data is prefetched from the data source and storing the data in a cache storage; responsive to receiving a distributed query that uses the data source during the time window, the data is fetched from the cache storage, where at least a part of a response to the distributed query is sourced from the cache storage; and after end of the time window, the data is removed from the cache storage.

Distributed query optimization

A computer implemented method for managing data in a mobile computing device. A number of processor units determine a geographic location of a mobile computing device. The number of processor units predict a predicted data residency issue with meeting data residency requirements for data stored in the mobile computing device based on the geographic location of the mobile computing device and a set of movement patterns for the mobile computing device. The number of processor units identify a number of recipient devices that meet the data residency requirements for moving the data from the mobile computing device to the number of recipient devices in response to predicting the predicted data residency issue. The number of processor units move the data to the number of recipient devices from the mobile computing device in response to identifying the number of recipient devices.

Intelligent data movement for data residency restrictions

Aspects of this disclosure include a method for proactive cooling, a vehicle, and a computer program product for a proactive cooling system. One embodiment of the method may comprise creating a machine learning model of a physical object, and instrumenting the physical object with sensors to generate real-time data about the physical object and its operational environment. The method may further comprise generating, with the machine learning model, a predicted future temperature for the physical object using the real-time data about the physical object and its operational environment, and in response to the predicted future temperature exceeding a threshold temperature within a threshold time period, proactively cooling the physical object.

Proactive cooling system

An embodiment selects, from a set of candidate locations within a power generation site, a first power generation location. An embodiment causes generation of electricity, by a first power generation unit, at the first power generation location. An embodiment forecasts, during the generation of electricity, a future power generation status of the first power generation unit at the first power generation location. An embodiment causes relocating, responsive to the future power generation status, of the first power generation unit to a second power generation location, the relocating altering the future power generation status.

Location optimization for an autonomous power generation unit

An embodiment includes selecting, using a first attribute of a first object, the first object in a first volumetric video. The embodiment also includes selecting, using a second attribute of a second object, the second object in a second volumetric video, where the first attribute and the second attribute satisfy an aggregation rule. The embodiment also includes generating an aggregated volumetric video from the first volumetric video and the second volumetric video, where the generating of the aggregated video comprises rendering the first object and the second object simultaneously in the aggregated volumetric video based on the aggregation rule.

Personalized aggregation of volumetric videos

A data-analysis-based process of controlling a floating solar array location is provided. The process includes providing data analysis-based control of location of the floating solar array on water within a geographical area. The floating solar array has a propulsion system coupled to the floating solar array to facilitate relocating of the floating solar array. The control is configured to identify, using one or more machine learning prediction models, a region of a plurality of regions of the geographical area which meets, for a forecasted time period, a predefined criteria for harvesting energy from the floating solar array. Further, the control is configured to initiate, using the propulsion system, dynamic relocating of the floating solar array to the identified region of the geographical area to facilitate solar energy harvesting from the floating solar array for the forecasted time period.

Data-analysis-based control of floating solar array location

A method comprising establishing a local communication network among and between a plurality of onboard vehicle computers respectively located in a plurality of vehicles, polling the vicinity of each vehicle to determine a set of qualified mobile device(s) that meet the following criteria: (i) the qualified mobile device is located within one of the vehicles of the plurality of vehicles, (ii) the qualified mobile device is configured and equipped to join the local network, and (iii) the qualified mobile device consents to join the local network, and adding the set of qualified mobile device(s) to the local network.

Context aware involving mobile phone with vehicle to enhance vehicle to vehicle communication network strength

An energy generation control process is provided for a wind turbine with shadow-effect energy generator(s). The control process includes determining, by an energy generation controller, an adjustment to a blade angle of a wind turbine to increase cumulative energy generation over a forecast time interval from the wind turbine. The wind turbine generates wind-based energy, and the wind turbine includes one or more shadow-effect energy generators to generate shadow-effect energy. The cumulative energy generation includes any wind-based energy generation and shadow-effect energy generation over the forecast time interval. The control process further includes initiating, by the energy generation controller, dynamically adjusting of the blade angle of the wind turbine using the determined adjustment to the blade angle to increase the cumulative energy generation over the forecast time interval from the wind turbine and the one or more shadow-effect energy generators.

Energy generation controller for wind turbine with shadow-effect energy generator(s)

A method of optimizing parameter data passing between microservices in a service mesh includes receiving, by a vertical reduction interceptor, a data reduction condition of a target microservice. The data reduction condition is provided by a vertical reduction database. The method further includes intercepting, by the vertical reduction interceptor, a sender microservice that sends the parameter data to the target microservice and reducing, upon identifying that the parameter data meets the data reduction condition of the target microservice, a dimension of the parameter data based on the data reduction condition. The parameter data is sent to the target microservice. The data reduction condition is based on a specified user profile.

Intelligent dimensionality reduction

Techniques are described with respect to a system, method, and computer product for managing a risk-free walkway. An associated method includes defining a virtual space in augmented reality and determining a geofence between at least a contaminated area and the risk-free walkway that avoids the contaminated area. The method further includes rendering augmented reality content in the virtual space with the risk-free walkway being associated with a user.

Managing a risk-free walkway using augmented reality

A method, computer program product, and computer system for optimizing locations of micro-warehouses that minimize an aggregate carbon footprint within a geographical region. A first trained artificial intelligence model is used to extract, for a geographical region, a product demand of users located in the geographical region over a fixed time period, and delivery location clusters within the geographical region. The delivery location clusters output by the first trained artificial intelligence model is input into a regression model, the regression model outputting recommended locations of temporary micro-warehouses within the geographical region. A second trained artificial intelligence model is used to modify the recommended locations and output optimized locations based on reducing an aggregate carbon footprint caused by emissions resulting from transportation to and from the temporary micro-warehouses.

Minimizing aggregate carbon footprint within geographical region

Provided is a computer-implemented method, system, and computer program product for displaying augmented reality (AR) visualization of a user action on an identified and/or recognized object. A processor may determine, for a user, an object interest level for each object of a plurality of known objects. The processor may detect one or more objects in an environment. The processor may identify, based on the object interest level, a first object of the one or more objects in the environment that is similar to at least one object of the plurality of known objects. The processor may determine, based on properties of the first object and the object interest level, an action for interacting with the first object. The processor may display, using an AR display, an avatar performing the determined action for interacting with the first object.

Augmented reality visualization of an action on an identified object

A computer resource disparity is detected. The computer resource disparity is related to performing a computing task. The computer resource disparity is located proximate to a first location. A set of one or more autonomous vehicles capable of being adjacent to the first location is identified. An autonomous vehicle computing inquiry is generated. The inquiry is generated based on the first location and based on the computer resource disparity. The autonomous vehicle computing inquiry is transmitted based on the first location. An autonomous vehicle status is received in response to the autonomous vehicle computing inquiry that includes a set of one or more computing resources of the set of autonomous vehicles. A first autonomous vehicle of the set of autonomous vehicles is assigned to perform the computing task. The assignment is based on the set of computing resources of the set of autonomous vehicles.

Edge computing autonomous vehicle infrastructure

The present inventive provides for a method of enhancing photosynthesis using IoT. The method includes obtaining data related to at least one crop and at least one location. Respective crop features and location features are extracted from the obtained data. At least one of photosynthetic rate potential, photosynthetic rate limiting conditions, and potential loss for the at least one crop at the location is determined based on the extracted crop features and location features. Ameliorative actions are calculated based on the determined at least one of photosynthetic rate potential, photosynthetic rate limiting conditions, and potential loss for the at least one crop at the location.

Enhancing photosynthesis using IoT

The illustrative embodiments provide for adaptation of reference architecture documents across clouds. An embodiment includes loading a reference architecture document. The embodiment also includes populating a knowledge repository corpus where the knowledge repository corpus includes data from information technology (IT) products, cloud services, and integrations stored with associated metadata about functionalities. The IT products, the cloud services, and the integrations are stored with metadata. The embodiment also includes classifying, using an incident analysis module, the service incidents based on the reference architecture document. The embodiment also includes detecting flaws in the reference architecture document based on the classified service incidents. The embodiment also includes creating a weighted graph of the cloud services. The embodiment also includes traversing the weighted graph and creating an adapted reference architecture based on the traversed weighted graph.

Adaptation of reference architecture documents across clouds

Growing community of inventors

Kolkata, India

Sarbajit Kumar Rakshit