International Business Machines Corporation

Kyndryl, Inc.

Nadiya Kochura

Fang Lu

Shikhar Kwatra

Tiberiu (Jim) Suto

Janani Janakiraman

Indervir Singh Banipal

Susan Marie Cox

Sourav Mazumder

Jonathan D Dunne

Erik Rueger

Hemant Kumar Sivaswamy

Su Liu

Alphonse Joseph Wojtas

Timothy B Snow

Donald H Randall, Jr

Derek M Reedy

Anh Uong

Jeremy R Fox

Vinodkumar Raghavan

Sarbajit Kumar Rakshit

Craig M Trim

Zachary A Silverstein

Donna Karen Byron

Kelley Anders

Vinod A Valecha

Barton Wayne Emanuel

Daniel Ramirez

Alex Xingqi Casella

Nicolo′ Sgobba

Nicolo' Sgobba

Kevin R Giles

Darian Christian Shane Springer

Ivy Malao

Daniel Manning

Michael Donati

Scott L Sachs

Lisa Seacat DeLuca

Paul R Bastide

Itzhack Goldberg

Sharathchandra UmapathiRao Pankanti

Raghuveer Prasad Nagar

Rasit Onur Topaloglu

Jeronimo Irazabal

Grzegorz Piotr Szczepanik

Adam Lee Griffin

Michal S Paluch

Abhishek Malvankar

Schuyler Bruce Matthews

Richard Daniel Gunjal

Tomas Krojzl

Melissa Restrepo Conde

Venkatesh S Gopal

Nicolò Sgobba

Jingwei Yang

Vibha Anand

Zia K Mohammad

Sneha Palarapu

Tejaswini K Ranadive

Lincoln A Alexander

VinodKumar Raghavan Nair

Tetsuya Shimada

Joan Mccarthy-Griffin

Prakalathan Nirmalakumaran

Aline Wilkins

Pedro Andres Guzman Benavides

Brajesh Pandey

Anupriya Ray

Hanna Yang

Esther Kim

Alexandre Bonstrom

A method for tuning software container interdependent systems includes receiving, by a computer processor operating a prediction engine, data from one or more software containers. The data includes operations from one or more software applications in the software containers. The prediction engine identifies a plurality of dependencies in the operations. The prediction engine develops a model of interdependencies based on the identified dependencies. The prediction engine determines one or more failure candidates in the identified dependencies. The prediction engine generates a software architecture template based on the identification of failure candidates.

Rebuilding container event logic from secondary and tertiary systems

Embodiments receive a data metrics input which corresponds with a microservice issue, determine a final outcome by multiplying each metric of the data metrics input by a weight, determine a link output based on the final outcome, determine a cost function based on the link output, evaluate that a cost output of the cost function has a minimized error, output a data metrics output based on the cost output of the cost function having the minimized error, train at least one machine learning (ML) model using the data metrics output, receive a new data metrics input which corresponds with another microservice issue, and provide a recommendation corresponding to the new data metrics input using the trained at least one ML model.

Alert management

An embodiment trains a machine-learning model using a first training corpus of general items indicative of varying levels of confidence. The embodiment also prepares a second training corpus that includes domain-specific items indicative of varying levels of confidence extracted from communications from members of a project group associated with a project. The embodiment retrains the machine-learning model using the second training corpus and generates a confidence score for the project based on confidence values assigned by the machine-learning model to each of a plurality of project-related communication items from members of the project group. The embodiment also detects that the confidence score is below a predetermined threshold confidence level and, in response, initiates a communication to members of the project group conveying information regarding an automated remedial action for the project.

Assessing project quality using confidence analysis of project communications

An approach for directing a chat agent for an online discussion is provided. A processor retrieves message data from a messaging program. A processor identifies at least one topic present in the message data. A processor, responsive to a determination that the at least one topic is a primary topic, updates the chat agent with the primary topic. A processor modifies message data based on the determined primary topic.

Primary topic identification in text data

A method for dynamically rearranging one or more packages for delivery on a delivery vehicle in response to unforeseen changes in a pre-planned delivery route. The method detects, via one or more sensor-enabled shelves in a delivery vehicle, an address and package details of a respective delivery package. The method further integrates the one or more sensor-enabled shelves with a route map that depicts the pre-planned delivery route. The method further assigns new values to the one or more sensor-enabled shelves, in response to a change in the pre-planned delivery route, indicating a new sequence of delivery for each respective delivery package. The method further notifies a delivery person which delivery package is to be delivered at a current stop by illuminating the one or more sensor-enabled shelves containing the respective delivery package.

Dynamic display of stacked items in a delivery vehicle based on a delivery route change

Method and apparatus for optimized data partition selection for related queries. A query to be executed over a plurality of data records is received. The plurality of data records comprises a first set of data records stored in a first database distributed across a plurality of partitions and a second set of data records stored in a second database. An input vector is generated based on the query, the first and second databases. The input vector is processed using a machine learning (ML) model to predict a cost for executing the query if one or more data records in the second set of data records are loaded to a first partition of the first database. A plan is selected for loading the one or more data records from the second set of data records to the first partition based on the predicted cost for executing the query.

System and method for efficient query processing

An embodiment includes parsing an input dataset associated with a first node of a decision tree, where the input dataset includes a set of profile values for a set of projected usage parameters for a computing environment. The embodiment identifies a structure of the dataset using a recursive neural network that predicts a question sequence in a hierarchical tree format. The embodiment calculates a first deviation from the predicted question sequence and determines whether the deviation exceeds a threshold value. The embodiment generates a modified input dataset using a disambiguation rule and calculates a second deviation of the modified structure from the predicted question sequence and determines whether the deviation exceeds the threshold value. The embodiment assembles a customized hierarchical path using a generative model and assembles the customized hierarchical path by performing iterations of generating a series of candidate questions until a leaf node is reached.

Cognitive recommendation of computing environment attributes

Aspects of the present disclosure relate generally to software development environments and, more particularly, to systems, computer program products, and methods of automating software development, security, and operations (DevSecOps). For example, a computer-implemented method includes receiving, by a processor, a plurality of infrastructure as code files specifying a configuration of a runtime environment for a deployable image of source code in a continuous integration and continuous delivery pipeline for a cloud platform; generating, by the processor, compliance code for at least one file of the plurality of infrastructure as code files; building, by the processor, the deployable image of the source code in the continuous integration and continuous delivery pipeline according to the configuration specified by the plurality of infrastructure as code files and the compliance code; and deploying, by the processor, an instance of the image in the runtime environment.

Optimized build environment on cloud platforms for software development, security, and operations

Static parameters of a software container are identified that relate to metadata of the software container itself. The software container is assigned to a selected runtime environment based on the static parameters using a first machine learning model. Runtime parameters for the software container are identified by analyzing the software container at runtime. The runtime parameters relate to operations that the software container requires during runtime. Using a second machine learning model, it is determined whether the selected runtime environment matches the runtime parameters. Where the runtime environment matches, the software container continues to run in this environment. Where the runtime environment does not match, the software container is run in a different runtime environment that matches both the static and runtime parameters.

Runtime environment determination for software containers

A computer-implemented method, a computer system and a computer program product enhance machine translation of a document. The method includes capturing an image of the document. The document includes a plurality of characters that are arranged in a character layout. The method also includes classifying the image by a document type based on the character layout. The method further includes determining a strategy for an intelligent character recognition (ICR) algorithm with the image based on the character layout of the image. Lastly, the method includes generating a translated document by applying the intelligent character recognition (ICR) algorithm to the plurality of characters in the image using the strategy. The translated document includes a plurality of translated characters that are arranged in the character layout.

Enhancing machine translation of handwritten documents

A method, computer system, and a computer program product for environment mapping is provided. The present invention may include generating a digital twin, wherein the digital twin is a digital representation of a smart building. The present invention may include applying the digital twin to a second building. The present invention may include providing an implementation assessment, wherein the implementation assessment includes at least the environmental impact of preferences of a user in the second building.

Generating digital building representations and mapping to different environments

A computer implemented method and system are disclosed for dynamically enabling a first user device operated by a first user to provide wireless hotspot capability for a wireless hotspot session to a second user device operated by a second user. The wireless hotspot session is accepted based at least on urgency and bandwidth requirement of the second user device. When the first user device accepts a connection to the second user device to provision a wireless hotspot for the wireless hotspot session, the first user restricts use of the second user device in the wireless hotspot session, based on bandwidth and context of use of the second user device. The first user device may refuse a connection by rejecting the wireless hotspot session request. wireless hotspot session transactions information is provided as blockchain ledgers.

Cognitive hotspot provisioning and network prioritization

A computer-implemented method according to one embodiment includes building a knowledge graph of resource vendors. The method further includes identifying resources that are in relatively short supply in a host system, and determining, using a matching algorithm, a first of the resource vendors in the knowledge graph from which to temporarily lease the identified resources to the host system. The first resource vendor includes the identified resources and satisfies policy prerequisites of the host system. A lease is caused to be issued, from the first resource vendor to the host system, for the identified resources. 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.

Knowledge graph for determining a resource vendor from which to lease resources

A computer-implemented method for avatar rendering of virtual presentations is disclosed. The computer-implemented method includes extracting visual content from a presentation. The computer-implemented method further includes extracting audio content from the presentation. The computer-implemented method includes correlating the visual content with the audio content of the presentation. The computer-implemented method includes generating a virtual avatar to dynamically render a virtual presentation to a viewer, based at least in part, on the correlated visual content and audio content of the presentation.

Avatar rendering of presentations

The present invention may have a computer that monitors conversation between a user and a first virtual assistant from a plurality of virtual assistants, for a confidence score value being below a confidence threshold value, where the confidence score value is associated with each answer of the first virtual assistant. When the computer determines the confidence score value is below the confidence threshold value, the computer generates a dialog flow for answering by a second virtual assistant, where the dialog flow is related to an answer associated with the confidence score value being below the confidence threshold value. Then, based on determining the second virtual assistant having a second confidence score value that is higher than the confidence score value, the computer transfers a knowledge base data of a topic associated with the dialog flow from the first virtual assistant to the second virtual assistant.

Data amalgamation management between multiple digital personal assistants

Growing community of inventors

Bolton, MA, United States of America

Nadiya Kochura