International Business Machines Corporation

Kyndryl, Inc.

Erik Rueger

Thorsten Muehge

Christof Schmitt

Frank Krick

Ole Asmussen

Robert Beiderbeck

Michael Diederich

Tim Uwe Scheideler

Itzhack Goldberg

Stefan Ravizza

Neil Sondhi

Thomas Allen Snellgrove

Markus Schäfer

Tomas Krojzl

Sharathchandra UmapathiRao Pankanti

Marcus Breuer

Frederik Frank Flöther

Stefan Wiedemann

Nadiya Kochura

Lance Warren Russell

Rainer Wolafka

Florian Graf

Rolf Schaefer

Andrea Giovannini

John Thomas Olson

Matthias Seul

Tiberiu (Jim) Suto

Laurentiu Gabriel Ghergu

Markus M Schaefer

Daniel Wendler

Cesar Augusto Rodriguez Bravo

Michael L Taylor

Ralf Eberhard

Markus Schafer

Nicolo′ Sgobba

Nicolo' Sgobba

Frank Bernhard Schmuck

Markus Schmidt

Michal S Paluch

Ulf Troppens

Hans-Juergen Eickelmann

Stefan Lehmann

Moncef Benboubakeur

Uwe Fiebrich-Kandler

Heiko Schloesser

Nicolò Sgobba

Stefan Mayer

Thomas Doerk

Brian Gerald Goodman

Dominic Mueller-Wicke

Christian Mueller

Jeffrey Alan Calcaterra

Bernd Freitag

Dany Moshkovich

Marcel Butucea Panait

Holger Martens

Mathias Defiebre

Johannes Bort

Emil O Gemes

Nicolo Sgobba

Filis Omer

Adina Pana

Iasmina Boldea

Guillermo Rodriguez De Vera Beltri

Amit Anil Nanavati

Ying Li

Andreas Nauerz

Grzegorz Piotr Szczepanik

Markus Ettl

Nikhil Khandelwal

Anuradha Bhamidipaty

George Andrei Saon

William W Owen

Sharathchandra Umapathirao Pankanti

Michael Starkey

Rajeev Puri

Julija Narodicka

Stefan A G Van Der Stockt

Henry Zheng Liu

Stefan Neff

Kevin Jimenez Mendez

Jeffrey M Ceason

Monty Christoph Poppe

Peter R Kimmel

Matthias Biniok

Stefan Schmidt

Dorota Buresova

Kai Jehnen

Thomas K Doerk

Oliver Roehrsheim

Thomas Bueck

Heiko Sick

Bernd Beicht

Antonello Izzi

Frank Broede

Christoph Roth

Mehmet-Zafer Inal

Peter Minig

Jiri Nehybka

Boris Gulbadamov

The present disclosure relates to a method for classifying a non-monetary donation. The method comprises: training a machine learning model comprising input neurons arranged in an input layer, each input neuron being operable for inputting an input value (X) of a parameter of the donation, and at least one hidden layer comprising multiple hidden neurons, each hidden neuron being operable for calculating a hidden layer value based on at least one input value and weights (w(X, Y)) associated with the respective input neurons. The number of input parameters may be reduced based on ranking the weights (w(X, Y)) related to the individual input parameters. The number of neurons of the hidden layer may be reduced based on ranking the weights related to the individual neurons of the hidden layer. The trained model, the reduced input parameters, and reduced hidden neurons may be provided for enabling the classification using the trained model.

Classification of a non-monetary donation based on machine learning

A computer implemented method, computer readable storage medium, and computer system of operating a robotic process automation organization network comprising multiple software robots. The method includes receiving a task by a main robot via the network. The main robot is one of the multiple software robots. The task comprises a code package. The method also includes broadcasting a multicast request for services by the main robot to multiple software robots. Each robot is implemented as a computer program on individual computer systems of multiple computer systems via the network. The method further includes receiving a response by at least a portion of the multiple software robots via the network and selecting at least one worker robot from the multiple robots by the main robot and distributing at least a portion of the code package to the at least one worker robot by the main robot.

Robotic process automation organization

Techniques regarding adaptive data recovery schemes are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a data management component that can modify a data recovery scheme based on performance data exhibited by a network of data centers and a data recovery requirement. The data recovery scheme can direct a relocation of data within the network.

Data recovery modification based on performance data exhibited by a network of data centers and data recovery requirement

In an approach to improve data migration in application performance monitoring, embodiments execute a synthetic transaction to monitor an application, and monitor network traffic during the execution of the synthetic transaction. Further, embodiments extract a plurality of parameters indicative of the synthetic transaction from the network traffic, and generate a script, based on the extracted plurality of parameters and a predetermined template, that generates the synthetic transaction.

Data migration in application performance monitoring

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 includes: connecting, by a computing device, to a database using an outbound connection, wherein the computing device is an information technology (IT) product in a private network and the database is outside the private network; receiving, by the computing device, a response from the database, the response including a command; executing, by the computing device, the command; and sending, by the computing device, result data to the database, wherein the result data is data that results from executing the command on the computing device.

Secure remote support of systems deployed in a private network

Systems, methods, and computer programming products leveraging recurrent neural network architectures to proactively predict workload demand of container orchestration platforms. The platform continuously collects metric data from clusters of the platform and train multiple parallel neural networks with different architectures to predict future platform workload demands. At periodic intervals, the registered neural networks in consideration for controlling the scaling operations of the platform are compared against one another to identify the neural network demonstrating the highest performance and/or most accurate workload prediction strategy for scaling the orchestration platform. The selected neural network is enforced as controller for the platform to implement the workload prediction strategy. The neural network controller enforced by the platform predictively scales up or down the number of pods within nodes of the platform and/or the number of clusters providing computational resources to the platform, in anticipation of future increased or decreased end user demand.

Predictive scaling of container orchestration platforms

One or more processors receive resource type and capability information and activity information of workloads of a domain. A first model is generated and trained to map the resource information to the activity information of domain workloads. The activity information is decomposed into a set of activity core elements (ACEs). The one or more processors generate a second model, wherein the second model is trained to predict a set of resource types and resource capabilities of the respective resource types, based on an input of the first set of ACEs decomposed from the activity information of the workloads of the domain. The one or more processors receive a second set of ACEs that are decomposed from activities associated with an unprecedented workload, and the one or more processors generate a predicted set of resources to perform the second set of ACEs.

Prediction of proficient resources for performing unprecedented workloads using models

Described are techniques for testing robotic process automation (RPA) bots. The techniques including a system comprising a computational environment configured to present a graphical user interface (GUI) on a display. The system further comprises an application configured to run on the computational environment. The system further comprises a robotic process automation (RPA) bot configured to execute tasks on the application using the GUI. The system further comprises an autonomous robot tester (ART) bot configured to execute a plurality of tests to disrupt the RPA bot by manipulating the GUI.

Autonomous testing of software robots

A system may receive a query for a patient evaluation. The system may then receive historical patient information or hospital data, current medical information, patient information, and testing information. The system may then use the received information to generate a set of rules using a neural network and uses the rules to process an evaluation for the patient.

Enhancement of patient outcome forecasting

A computer-implemented method for managing updates to a knowledge graph with incoming change requests is disclosed. The method comprises creating a snapshot of the knowledge graph, building sub-regions of the knowledge graph, classifying each incoming change to the knowledge graph so that it relates to one of the sub-regions, creating at least one change layer, wherein each of the at least one change layer relates to one of the sub-regions, wherein each of the at least one change layer comprises a portion of the incoming change requests, closing the at least one change layer upon reaching a predetermined threshold value for the at least one change layer, and integrating the at least one change layer into the knowledge graph, thereby generating a updated knowledge graph.

Knowledge graph update using change layers

A tool for network-based package monitoring and recovery. The tool scans a database to identify a gap in network traffic data. The tool determines whether at least one gap in the network traffic data is identified in the database, wherein the at least one gap is a gap in communication between a target device and a communication serve. Responsive to a determination that at least one gap in the network traffic data is identified, the tool determines a missing package from a package archive. The tool determines whether there is active network traffic between the target device and the communication server. Responsive to a determination that there is active network traffic between the target device and the communication server, the tool replays the missing package in the active network traffic between the target device and the communication server.

Non-intrusive disaster recovery and resilience

A tape drive includes a plurality of rollers, a head, a tension sensor, and control circuitry. The rollers are spatially arranged within the tape drive to define a route for tape of a received data cartridge to travel from the received data cartridge to a tape reel. The head includes read elements and write elements configured to read from and write to the tape as tape of the received data cartridge travels along the route. The tension sensor is configured to measure tension of the tape as it travels along the route. The control circuitry is configured to cause a motor to impact the tension to keep the tape within a tension threshold.

Tape drive tension management

Intelligent management of stub files in hierarchical storage is provided by: in response to identifying a file to migrate from a file system to offline storage, providing metadata for the file to a machine learning engine; receiving a stub profile for the file from the machine learning engine that indicates an offset from a beginning of the file and a length from the offset for previewing the file; and migrating the portion of the file from the file system to an offline storage based on the stub profile. In some embodiments this further comprises: monitoring file system operations; in response to detecting a read operation of the portion of the file: determining a file type; providing file data to the machine learning engine; and performing a supervised learning operation based on the file type and the file data to update the machine learning engine.

Intelligent management of stub files in hierarchical storage

Growing community of inventors

Ockenheim, Germany

Erik Rueger