International Business Machines Corporation

Nuance Communications, Inc.

Kyndryl, Inc.

Baiju Dhirajlal Mandalia

Wendi L Nusbickel

Victor S Moore

William V Da Palma

Gordon Taylor Davis

John F Hollingsworth

Pradeep Parsram Mansey

John C Sinibaldi

Douglas Morgan Freimuth

Vicki L Colson

Mark V Chitti

Lawrence P Andrews

Thomas Edward Creamer

Mahesh Viswanathan

Robert K Floyd, Iii

Robert P Monaco

Charles Henry Sederholm

Carl Louis Thomson

Robert J Sundstrom

Joseph Gary Rusnak

Himanshu Chandrakant Parikh

Gaby J Salem

Edward J Dahmus

Oscar E Ortega

William R Robinson, Jr

Carl Louis Thomson, Jr

Wasim Joseph Shomar

Charles Clifford Hallock

David M Lubensky

Carl Kraenzel

Vijay Dheap

Judith Marie Linger

Joseph D Robinson

Robert E Landa

Dilip V Hardikar

Tommy R Alcendor

Bertus Eggen

Girish Dhanakshirur

Cheng Wu

Brett J Gavagni

Zygmunt A Lozinski

Richard Daniel Gunjal

William M Zevin

Ran Cohen

Kevin Bradley Williams

Harish Rajagopal

Jan W Van Den Berg

Tung OuYang

Aimee Silva

Madhusudan Ramakrishna

Derrick L Arias

Joe Fletcher Jones, Jr

Ann-Marie W Hoher

Zygmunt Anthony Boleslaw Gerard Lozinski

Sebastian Theodore Ventrone

Ciprian Agapi

Vanessa V Michelini

Brent L Davis

Dilip Dinkar Kandlur

John J Reilly

Alexander Sorin

Shay Ben-David

Pnina A Vortman

Laurence Victor Marks

Uzi Shvadron

Zohar Sivan

David Curtis Van Voorhis

Aleksas Joseph Vitenas

Paul G Ringhof

Philip D Tetlow

Michael John Lyons

Susan Renee Wallenborn

Ronald D Swan

Leo A Sharp, Jr

Douglas H Cox

Pravin Bernard Phadte

Willie James Johnson

William C Souhrada

Joseph Dawson Davis, Iii

Fernando Matos

Juan F Caraballo

Roland J Millas

Michael G Ho Lung

Loran R Queen

Rafael J Picon

Richard H Robinson

Michael G Holung

Brien H Muschette

Charles C Hallock

Robert E Biehl

Christopher D Touch

Karl-Heinz Ziegenhain

Michael George Ho-Lung

Lee H House

Dave B Lindquist

Uzi U Shavdron

Elinor R Williams

Scott S Joffe

Zygmunt Anthony Bloeslaw Gerard Lozinski

Zygmunt A Lozinskie

A computing device detects that another computing device has connected to a network. The computing device determines whether the other computing device is valid and whether the computing device is being utilized for one or more suspicious activities. Based on determining that the other computing device is being utilized for one or more suspicious activities, the computing device determines a location of the other computing device, determines whether a user associated with the other computing device can be identified, and based on determining that the user associated with the other computing device cannot be identified, disables the other computing device, and transmits an alert to security personnel.

Security control for an enterprise network

Methods and systems for medicine disposition in hospitals are disclosed. A method includes: receiving, by a computing device, an electronic health record (EHR) of a patient; receiving, by the computing device, an order to dispense a medicine to the patient; receiving, by the computing device, information from a wearable device of the patient and updating the EHR of the patient based on the information from the wearable device of the patient; determining, by the computing device, that a health concern is not identified based on the information from the wearable device of the patient; and dispensing, by the computing device, the medicine to the patient in accordance with the order.

Internet of things communication and social network for medicine disposition in hospitals

A method can include obtaining a first weight measurement of a receptacle and obtaining a second weight measurement of the receptacle. The method can further include calculating a first difference between the first weight measurement and the second weight measurement, resulting in a reference dispensation value. The method can further include obtaining dispensation data, the dispensation data including a measured dispensation value. The measured dispensation value can correspond to a quantity of a substance dispensed from the receptacle, the quantity of the substance passing through a dispensation monitor coupled to the receptacle. The method can further include calculating a second difference between the reference dispensation value and the measured dispensation value. The method can further include determining that the second difference exceeds a threshold and adjusting the dispensation monitor in response to determining that the second difference exceeds the threshold.

Monitoring dispensation of a substance

A computer-implemented method and system for workload scheduling based on localizing virtual computing resources. The computer implemented method includes, in one embodiment, localizing a first computing resource having a workload under execution, the first computing resource being associated with a set of virtual computing resources. An embodiment includes rescheduling, responsive to the localizing, the workload for execution at least partially at a second computing resource in the set of virtual computing resources.

Workload scheduling with localized virtual network resources

The present invention discloses a solution for handling text exchange input in a composite service environment. The method can included a step of conveying text exchange input to an ACM engine. The ACM engine can map the text exchange input to field and variable values of a component services application. A memory of the composite service environment can be updated using the mapping results. Specifically, field and variable values can be updated that are stored in the memory for a session of the component services application for which the text exchange input was provided.

Solution for adding context to a text exchange modality during interactions with a composite services application

Resource configuration to exploit elastic network capability is provided by establishing an elastic network bandwidth allocation level to provide for an application for transfer of data between the application and an elastic network, the application hosted at a data center, then dynamically configuring, for the application, elastic network bandwidth allocation from the network service provider in accordance with the established elastic network bandwidth allocation level, and allocating storage resources of the data center for the application and processing resources of the data center for the application, the allocating being based on the established elastic network bandwidth allocation level and providing storage resources and processing resources to operate at a level commensurate with the established elastic network bandwidth allocation level.

Configuring resources to exploit elastic network capability

An approach is provided for managing resiliency of an application provided by a first service provider (SP). A fail condition indicating an outage of a first service of the first SP is detected. Based on a topology mapping the first SP to a second SP and the first service to a second service of the second SP, a first API provided by the first SP is determined to have provided the first service prior to the fail condition, and a second API provided by the second SP is determined to provide the second service. The first API is disabled and the second API is activated, thereby continuing to provide a functionality of the application that had been provided by the first service prior to the fail condition being detected, even though the first SP is experiencing the fail condition.

Application resiliency using APIs

Dynamic configuration of network features is provided by performing real-time traffic analysis on network traffic flowing between an elastic cloud computing network and an edge appliance, evaluating effects of modifying elastic network bandwidth allocation and applying network traffic optimizations in routing traffic flowing between the elastic cloud computing network and the edge appliance, and dynamically configuring, based on the real-time traffic analysis and on the evaluating, one or more of (i) elastic network bandwidth allocation from the network service provider or (ii) at least one of the network traffic optimizations for performance by the edge appliance in routing traffic flowing between the elastic cloud computing network and the edge appliance.

Dynamic configuration of network features

An approach for regional firewall clustering for optimal state-sharing of different sites in a virtualized/networked (e.g., cloud) computing environment is provided. In a typical embodiment, each firewall in a given region is informed of its peer firewalls via a registration process with a centralized server. Each firewall opens up an Internet protocol (IP)-based communication channel to each of its peers in the region to share state table information. This allows for asymmetrical firewall flows through the network and allows routing protocols to ascertain the best path to a given destination without having to take firewall placement into consideration.

Regional firewall clustering in a networked computing environment

Dynamic quality of service for storage I/O port allocation is provided by obtaining indications of anticipated demand on a plurality of storage ports of a storage device of a data center, the anticipated demand being at least in part for storage input/output supporting transfer of data to or from a network, and dynamically adjusting storage port allocations based on the indications of anticipated demand and on real-time evaluation of workloads being serviced by the plurality of storage ports.

Dynamic quality of service for storage I/O port allocation

Event-based data transfer scheduling using elastic network optimization criteria is provided. Bandwidth optimization criteria corresponding to different types of data transfer event scenarios are maintained, and based on recognizing a data transfer event scenario, a bandwidth optimization criteria is selected based on a type of the data transfer event scenario. A schedule for transferring data from a source to a target across the elastic network is determined according to the selected bandwidth optimization criteria, and the elastic network is used in transferring the data to the target storage location, where the using includes dynamically configuring elastic network bandwidth allocation from an elastic network service provider and initiating transfer of the data to the target storage location according to the schedule.

Event-based data transfer scheduling using elastic network optimization criteria

A method and associated systems for automatic redistribution of virtual machines. A cloud-optimization module selects parameters, such as bandwidth requirements, that characterize an efficiency of a virtual network. It assigns weightings to these parameters based on relative importance of each parameter to the proper operation of the network, where the weightings may be determined as functions of captured network-performance statistics. The module translates the network's topology into a graph in which each node represents a network entity, such as a virtual machine or an application, and each edge represents a connection between two such entities. The module then uses a growing neural gas algorithm to revise the graph and the weightings, and translates the revised graph to a more optimal topology that has redistributed the network entities to operate more efficiently, as measured by the weighted parameters.

Automatic redistribution of virtual machines as a growing neural gas

A method and associated systems for a resource-utilization monitor with self-adjusting sample size. A processor monitors availability of a resource-limited entity by analyzing a set of samples that each identify, at the time the sample was recorded, an unused amount of resource available to the entity. The processor computes a Chi-square statistic of the sample set. If the statistic reveals that the number of samples in the sample set is too small to produce valid results, the processor adjusts the sample size to specify a larger number of samples and repeats these steps. If the number of samples is large enough to produce statistically valid results, the processor analyzes the sample set to determine whether the current amount of remaining resource is undesirably low, is likely to become undesirably low during a short-term period of time, or is likely to become undesirably low during a longer-term period of time.

Predicting a likelihood of a critical storage problem

Growing community of inventors

Boca Raton, FL, United States of America

Baiju Dhirajlal Mandalia