Abstract: A computer-implemented method, system and computer program product for sharing and utilizing healthcare data, by: providing one or more computer-implemented machine learning models for analyzing the healthcare data; and recording transactions involving the machine learning models using a blockchain as a distributed ledger that is shared, replicated and synchronized. Healthcare data is also used to train the machine learning models. The healthcare data comprises research data or patient data such as Electronic Medical Records (EMRs). A smart contract that is a computer-implemented protocol is used to facilitate, verify or enforce negotiation of the transactions involving the machine learning models or healthcare data.

Abstract: A mechanism is provided for implementing an anticipatory analytics processing mechanism for processing electronic medical records (EMRs) of a set of patients using anticipatory analytics to provide patient insights. Responsive to receiving a set of EMRs for a set of patients, each EMR in the set of EMRs is queued into one of a plurality of queues based on a set of work types. Responsive to receiving a request for work by a processing service indicating a work type to be provided, an EMR is identified from the plurality of queues based on the work type to be provided and a predetermined order of work. The identified EMR is dispatched to the processing service. Responsive to receiving a completion indication from the processing service, the EMR is either advanced to a queue associated with a next work type category or indicated that patient insight for a patient associated with the EMR is complete.

Abstract: A method of managing an electronic medical record comprises receiving patient data associated with the medical record. First encounter information in the patient data is identified and compared with second encounter information in a first encounter bundle in the medical record. Based on the comparing, the first encounter information and second encounter information are determined to be associated with the same encounter. The patient data is added to the first encounter bundle based on the determining. The first encounter bundle is analyzed, resulting in a computer-readable artifact for the first encounter bundle. A summary of the first encounter bundle is updated based in part on the computer-readable artifact.

Abstract: A computer-implemented method, system and computer program product for sharing and utilizing healthcare data, by: providing one or more computer-implemented machine learning models for analyzing the healthcare data; and recording transactions involving the machine learning models using a blockchain as a distributed ledger that is shared, replicated and synchronized. Healthcare data is also used to train the machine learning models. The healthcare data comprises research data or patient data such as Electronic Medical Records (EMRs). A smart contract that is a computer-implemented protocol is used to facilitate, verify or enforce negotiation of the transactions involving the machine learning models or healthcare data.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate scheduling processing and summarization of an electronic medical record based on a summarization deadline are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a task manager component that can determine a processing priority of one or more data bundles of an electronic medical record based on a summarization deadline of the electronic medical record. The computer executable components can further comprise a cognitive analysis component that can employ an artificial intelligence model to process the one or more data bundles based on the processing priority.

Abstract: A system for processing analytics on a cluster of computing resources may receive a user request to process a Job, Service or Reservation, and may include an Orchestrator, Resource Manager, Process Manager, and one or more Agents and Job Drivers, which together deploy the Job onto one or more nodes in the cluster for parallelized processing of Jobs and their associated work items.

Abstract: A system for processing analytics on a cluster of computing resources may receive a user request to process a Job, Service or Reservation, and may include an Orchestrator, Resource Manager, Process Manager, and one or more Agents and Job Drivers, which together deploy the Job onto one or more nodes in the cluster for parallelized processing of Jobs and their associated work items.

Abstract: A method for conversational computing includes executing code embodying a conversational virtual machine, registering a plurality of input/output resources with a conversational kernel, providing an interface between a plurality of active applications and the conversational kernel processing input/output data, receiving input queries and input events of a multi-modal dialog across a plurality of user interface modalities of the plurality of active applications, generating output messages and output events of the multi-modal dialog in connection with the plurality of active applications, managing, by the conversational kernel, a context stack associated with the plurality of active applications and the multi-modal dialog to transform the input queries into application calls for the plurality of active applications and convert the output messages into speech, wherein the context stack accumulates a context of each of the plurality of active applications.

Abstract: A conversational computing system that provides a universal coordinated multi-modal conversational user interface (CUI) 10 across a plurality of conversationally aware applications (11) (i.e., applications that “speak” conversational protocols) and conventional applications (12). The conversationally aware applications (11) communicate with a conversational kernel (14) via conversational application APIs (13). The conversational kernel 14 controls the dialog across applications and devices (local and networked) on the basis of their registered conversational capabilities and requirements and provides a unified conversational user interface and conversational services and behaviors. The conversational computing system may be built on top of a conventional operating system and APIs (15) and conventional device hardware (16). The conversational kernel (14) handles all I/O processing and controls conversational engines (18).

Abstract: A conversational computing system that provides a universal coordinated multi-modal conversational user interface (CUI) 10 across a plurality of conversationally aware applications (11) (i.e., applications that “speak” conversational protocols) and conventional applications (12). The conversationally aware applications (11) communicate with a conversational kernel (14) via conversational application APIs (13). The conversational kernel 14 controls the dialog across applications and devices (local and networked) on the basis of their registered conversational capabilities and requirements and provides a unified conversational user interface and conversational services and behaviors. The conversational computing system may be built on top of a conventional operating system and APIs (15) and conventional device hardware (16). The conversational kernel (14) handles all I/O processing and controls conversational engines (18).

Abstract: A conversational computing system that provides a universal coordinated multi-modal conversational user interface (CUI) (10) across a plurality of conversationally aware applications (11) (i.e., applications that “speak” conversational protocols) and conventional applications (12). The conversationally aware maps, applications (11) communicate with a conversational kernel (14) via conversational application APIs (13). The conversational kernel (14) controls the dialog across applications and devices (local and networked) on the basis of their registered conversational capabilities and requirements and provides a unified conversational user interface and conversational services and behaviors. The conversational computing system may be built on top of a conventional operating system and APIs (15) and conventional device hardware (16). The conversational kernel (14) handles all I/O processing and controls conversational engines (18).

Abstract: In a text-to-speech system, a method of converting text-to-speech can include receiving a text input and comparing the received text input to at least one entry in a text-to-speech cache memory. Each entry in the text-to-speech cache memory can specify a corresponding spoken output. If the text input matches one of the entries in the text-to-speech cache memory, the cached speech output specified by the matching entry can be provided.

Abstract: In a text-to-speech system, a method of converting text-to-speech can include receiving a text input and comparing the received text input to at least one entry in a text-to-speech cache memory. Each entry in the text-to-speech cache memory can specify a corresponding spoken output. If the text input matches one of the entries in the text-to-speech cache memory, the cached speech output specified by the matching entry can be provided.

Abstract: A speech recognizer that selects a command model for a current sound if the best match score for the current sound exceeds its corresponding threshold score. The threshold score is assigned a confidence score based on the best match score and recognition threshold of a prior sound.

Abstract: In speech recognition and speech coding, the values of at least two features of an utterance are measured during a series of time intervals to produce a series of feature vector signals. A plurality of single-dimension prototype vector signals having only one parameter value are stored. At least two single-dimension prototype vector signals having parameter values representing first feature values, and at least two other single-dimension prototype vector signals have parameter values representing second feature values. A plurality of compound-dimension prototype vector signals have unique identification values and comprise one first-dimension and one second-dimension prototype vector signal. At least two compound-dimension prototype vector signals comprise the same first-dimension prototype vector signal. The feature values of each feature vector signal are compared to the parameter values of the compound-dimension prototype vector signals to obtain prototype match scores.

Abstract: New and improved method and apparatus for the correction of coincident errors attendant the automated detection and counting of mixed particles having detectable characteristics of different levels in particle counting applications wherein the detection of "dominant" particles under coincident particle conditions, renders undetectable the "dominated" particles, with resulting inaccuracy in the "dominated" particle count. Such inaccuracy is corrected by modifying the "dominated" particle count in accordance with the time duration of the signals which are generated attendant the detection of the "dominated" particles.