International Business Machines Corporation

Globalfoundries Inc.

Nuance Communications, Inc.

Robert R Friedlander

James R Kraemer

Robert Lee Angell

Samuel Scott Adams

Richard D Hennessy

Jeb R Linton

Timothy Durniak

Anwer Mujahid Khan

Joanna Lynn Fueyo

Richard Harold Boivie

John Kenyon Gerken, Iii

Elizabeth Vera Woodward

Justyna M Nowak

David M Ungar

Suparna Bhattacharya

David W Cosby

Kirill M Osipov

Josko Silobrcic

Wendy Ann Belluomini

William Philip Shaouy

Sergio Borger

Yashodhara M Patnaik

Robin Lougee

Thomas L Hanks

Philip R Varker

Tony H Chow

Jennifer B Brown

Paul T Dimarzio

Richard D Dettinger

Maja Vukovic

Edward Stanely Suffern

Shunguo Yan

Carlos Henrique Cardonha

Julio Nogima

Erik H Katzen

David Charles Martin

German Sergio Goldszmidt

Harold Leon Ossher

Vinod A Bijlani

John Baxter Rollins

Christopher M Poulin

Robin Lougee-Heimer

Kelly Grant Lee

Ricardo G Herrmann

Linda Susan Erlanger

Michael Harold Eubank

Meredith Keating Moore

Paul L Brackett

Anne O Jackson

Jack Chen

Daniel J Byrne

Le Gang Wu

Jay A Goodwyn

Mark W Byrne

Anthony W Young

John D Hill, Iii

Richard J Dalle-Molle

Edward J Macko

A method securely manages smart card transactions. A processing entity receives a smart card identifier from a smart card, where the smart card is a virtual card on a mobile computing device that comprises a processor, where the smart card identifier is a transaction-specific identifier for a transaction. A protected application is received at the mobile computing device, where a received protected application initially cannot be utilized by an operating system for execution by the processor. A security object is received at the mobile computing device, where the security object is used to convert the received protected application into an executable application that can be utilized by the operating system for execution by the processor. The processor executes the executable application to act as the virtual card, where the virtual card provides a functionality of a predefined physical electronic card.

Secure management of transactions using a smart/virtual card

A method, system, and/or computer program product optimize a display of messages on a public electronic video display. A video camera captures images of multiple mobile vehicles that are traversing a vehicular pathway, which is physically proximate to a public electronic video display. The multiple mobile vehicles are categorized into a specific vehicle category based on captured images of the multiple mobile vehicles. The specific vehicle category is matched to a particular set of multiple video display messages. A video display message is displayed on the public electronic video display based on the specific vehicle category.

Electronic messaging display optimization

A computer-implemented method, system, and/or computer program product generates and utilizes synthetic context-based objects. One or more processors define a context object, where the context object provides a context that identifies a specific subject-matter, from multiple subject-matters, of a non-contextual data object. The processor(s) associate the non-contextual data object with the context object to define a synthetic context-based object and the synthetic context-based object with at least one specific data store. A request is received from a requester for data from said at least one specific data store that is associated with the synthetic context-based object, where said at least one specific data store is within a database of multiple data stores. Data is returned to the requester from said at least one specific data store that is associated with the synthetic context-based object.

Generation of synthetic context objects using bounded context objects

A computer implemented method modifies a unit of medical equipment based on never-event cohorts, where a never-event is a preventable error experienced by a patient while receiving medical care. One or more processors process patient care data to form digital patient care data. The processor(s) analyze the digital patient care data using cohort criteria to identify a set of never-event attributes from the set of patient care patterns. The processor(s) receive, from a set of sensors, facility event data that describe an electrical fault in a unit of medical equipment used on patients in the patient care facility in order to modify the digital patient care data, and then modify the unit of medical equipment in order to correct the electrical fault based on the modified digital patient care data.

Controlling equipment in a patient care facility based on never-event cohorts from patient care data

A method prevents a cascading failure in a complex stream computer system. The method includes receiving binary data that identifies multiple subcomponents in a complex stream computer system. These identified multiple subcomponents include upstream subcomponents that generate multiple outputs and a downstream subcomponent that executes a downstream computational process that uses the multiple outputs. The method dynamically adjusts which of multiple inputs are used by the downstream subcomponent in an attempt to generate an output from the downstream subcomponent that meets a predefined trustworthiness level for making a first type of prediction. If no variations of execution of one or more functions used by the downstream subcomponent ever produce an output that meets the predefined trustworthiness level for making a first type of prediction, then computer hardware executes a new downstream computational process that produces a different second type of prediction.

Preventing cascade failures in computer systems

A sensor system comprises: an energy storage device electrically coupled to an intermittent energy release device that causes the energy storage device to release stored energy intermittently; a sensor electrically coupled to the energy storage device, where the sensor detects physical events occurring at a physical device and is intermittently powered by electrical energy received from the energy storage device; a synaptic neural network core electrically coupled to the sensor, where the synaptic neural network core converts sensor readings into an object that describes the physical events occurring at the physical device; a transponder electrically coupled to the synaptic neural network core; and a storage buffer within the transponder, where the storage buffer stores the object for transmission from the transponder to a monitoring system, where the intermittent energy release device provides power to the sensor in response to the transponder transmitting the object to the monitoring system.

Synaptic neural network core based sensor system

Methods and systems for event detection include defining a plurality of conditions that represent one or more synthetic events. Data from a plurality of data sources is aggregated across a period of time, multiple attack surfaces, and geographically distinct locations. The aggregated data is matched to the conditions to determine whether a synthetic event has occurred. A response to the synthetic event is formed to resist an attack.

Probabilistically detecting low-intensity, multi-modal threats using synthetic events

An invention for comparing utility and warranty of services in an information (IT) stack is provided. Embodiments of this invention are directed to comparing utility and warranty of services in an information technology (IT) stack comprising a plurality of layers, at least one of the plurality of layers having a set of services. In these embodiments, a utility and warranty tool provides this capability. Specifically, the utility and warranty tool comprises a determination component configured to determine, at each layer of the IT stack, a required utility and warranty (RUW) value for each of the set of services, wherein the RUW value represents a desired solution for implementing a business process; and determine, at each layer of the IT stack, an available utility and warranty (AUW) value for each of the set of services, wherein the AUW value is a measure of an ability of each of the set of services to satisfy the RUW at each layer of the IT stack. The utility and warranty tool further comprises a comparison component configured to compare the RUW value against the AUW value at each layer of the IT stack to determine whether each of the set of services satisfies the desired solution for implementing the business process.

Comparing utility and warranty of services

A computer system includes processors that execute instructions stored on storage media to sort data using a data gravity well membrane. When executed, the program instructions: convert raw data into a first logical address that describes metadata about a first payload data; compare the first logical address to a second logical address for a second payload data to derive a Hamming distance between the first and second logical addresses; create a data vector for the second payload data that includes the Hamming distance between the first and second logical addresses; sort data vectors into specific data gravity wells on a data gravity wells membrane according to the Hamming distance stored in the data vector; incorporate a context object and a non-contextual data object into the data vector for the second payload data; and sort the second payload data into specific data gravity wells on the data gravity wells membrane.

Population of context-based data gravity wells

A method, system, and/or computer program product securely manages smart card transactions. A processing entity receives a smart card identifier from a smart card. The smart card identifier is a transaction-specific identifier for a transaction, and contains no financial data about a holder of the smart card. The smart card identifier is hidden from a second party to the transaction. The processing entity receives transaction data for the transaction from the second party, where the transaction data is blocked from the smart card. In response to determining that the transaction is valid, the processing entity issues a transaction confirmation to the smart card and a transaction approval to the second party to the transaction.

A method, computer program product and system for generating and maintaining synthetic context events. The steps include searching a data structure of synthetic context-based objects and associated data for a pattern of context exhibited at a first specified frequency within a first specified time period; combining the synthetic context-based objects and associated data exhibiting the pattern of context exhibited at the first specified frequency within the first specified time period into a synthetic context event; and optimizing and maintaining the synthetic context event by searching the data structure for additional synthetic context-based objects and associated data exhibiting a same pattern of context at a second specified time period different than the first specified time period and adding the additional synthetic context-based objects and associated data to the synthetic context event.

Generation and maintenance of synthetic context events from synthetic context objects

A method, computer product, and computer system of minimizing surprisal data comprising: at a source, reading and identifying characteristics of a genetic sequence of an organism; receiving an input of rank of at least two identified characteristics of the genetic sequence of the organism; generating a hierarchy of ranked, identified characteristics based on the rank of the at least two identified characteristics of the genetic sequence of the organism; comparing the hierarchy of ranked, identified characteristics to a repository of reference genomes; and if at least one reference genome from the repository matches the hierarchy of ranked, identified characteristics, breaking the matched reference genomes into pieces, combining pieces associated with the identified characteristics from at least one matched reference genome to form a filter pattern to be compared to the nucleotides of the genetic sequence of the organism, to obtain differences and create surprisal data.

Minimization of surprisal data through application of hierarchy filter pattern

Automatically create abstractions of large sets of data and then probabilistic inferences based on the abstractions. The probabilistic inference is derived from the logical hierarchy using Bayesian statistics to infer a probabilistic event based upon a characteristic of the data in a hierarchy of synthetic events. The logical hierarchy of a set of a plurality of synthetic events is related by at least one characteristic of data is built by accessing a first set of data. The first set of data is organized based on a first characteristic. A second set of data different than the first set of data is accessed. A second set of data based is organized based on a second characteristic. The first characteristic and the second characteristic are processed to generate a synthetic event. The synthetic event is a third set of data representing a result of a mathematical computation defined by an operation S(p)==&#x3e;F(p).

Probabilistic inference engine based on synthetic events from measured data

A computer product and system of minimizing surprisal data comprising: at a source, reading and identifying characteristics of an organism's background associated with a genetic sequence of the organism; receiving an input of rank of at least two identified characteristics of the genetic sequence; generating a hierarchy of ranked, identified characteristics based on the rank of the identified characteristics; comparing the hierarchy of ranked, identified characteristics to a repository of reference genomes; and if at least one reference genome from the repository matches the ranked characteristics, breaking the matched reference genomes into pieces, combining pieces associated with the identified characteristics from the matched reference genome to form a filter pattern to be compared to the nucleotides of the genetic sequence of the organism. The differences from the comparison are used to create surprisal data representing an entire genome of the organism.

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Southbury, CT, United States of America

Robert R Friedlander