International Business Machines Corporation

Technicon Instruments Corporation

Edward Adam Epstein

Burn Lewin Lewis

David Nahamoo

Stephane Herman Maes

Liam David Comerford

Lalit R Bahl

Ponani S Gopalakrishnan

Daniel Mark Coffman

Steven V DeGennaro

Dimitri Kanevsky

Sara H Basson

Michael Alan Picheny

Louis Degenaro

Steven V De Gennaro

Peter Vincent deSouza

Jean-Michel Le Roux

Claire Waast-Richard

Alexander Zlatsin

Peter Gustav Fairweather

Maria Elena Smith

Etienne Marcheret

Raimo Bakis

Abraham P Ittycheriah

Abhishek Malvankar

Gregory A Farrell

Jerome R Bellegarda

James R Challenger

John M Lucassen

Charles T Rutherfoord

Stephen Graham Lawrence

Steven J Friedland

Saurabh Pujar

Hari Chittaluru

Jaroslaw Cwiklik

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.

System and method to share and utilize healthcare data

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.

Distributed UIMA cluster computing (DUCC) facility

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.

Conversational computing via conversational virtual machine

A conversational computing system that provides a universal coordinated multi-modal conversational user interface (CUI)across a plurality of conversationally aware applications () (i.e., applications that 'speak' conversational protocols) and conventional applications (). The conversationally aware applications () communicate with a conversational kernel () via conversational application APIs (). The conversational kernelcontrols 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 () and conventional device hardware (). The conversational kernel () handles all I/O processing and controls conversational engines (). The conversational kernel () converts voice requests into queries and converts outputs and results into spoken messages using conversational engines () and conversational arguments (). The conversational application API () conveys all the information for the conversational kernel () to transform queries into application calls and conversely convert output into speech, appropriately sorted before being provided to the user.

A conversational computing system that provides a universal coordinated multi-modal conversational user interface (CUI) () across a plurality of conversationally aware applications () (i.e., applications that 'speak' conversational protocols) and conventional applications (). The conversationally aware maps, applications () communicate with a conversational kernel () via conversational application APIs (). The conversational kernel () 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 () and conventional device hardware (). The conversational kernel () handles all I/O processing and controls conversational engines (). The conversational kernel () converts voice requests into queries and converts outputs and results into spoken messages using conversational engines () and conversational arguments (). The conversational application API () conveys all the information for the conversational kernel () to transform queries into application calls and conversely convert output into speech, appropriately sorted before being provided to the user.

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.

Method and system for text-to-speech caching

A system and method is provided for real time transcription or translation services. In one aspect of the system and method, a user requests transcription/translation service with certain service parameters. It is determined what resources can be used for such service, and, if all the service parameters can be met, the service is performed. Resources include live stenographers or translators, and computer processing power. If all the service parameters can not be met, it is determined whether to perform the service by meeting only some of the service parameters. These determinations may be programmed into the system beforehand. In another aspect of the system and method, a user makes a request for transcription/translation service, and the request is displayed so that stenographers or translators may make bids to perform the transcription/translation service. In some embodiments, the request is only displayed to those stenographers or translators who are determined to be able to perform the service. Bids are received and stored. The user chooses whether or not to accept a bid.

Real-time remote transcription or translation service

Methods and apparatus for providing speech recognition in noisy environments. An energy level associated with audio input is ascertained, and a decision is rendered on whether to accept the at least one word as valid speech input, based on the ascertained energy level.

Speech recognition in noisy environments

A system and method is provided for real time teleconferencing, where one of the participants is deaf or hearing-impaired. In one aspect of the system and method, each participant has an Automatic Speech Recognition (ASR) system and a chat service system, such as AOL Instant Messenger&trade;. Each participant may have a different type of ASR system, as well as a different type of chat service system. It is not necessary that the deaf or hearing-impaired participant have an ASR system. For each participant, the participant's ASR system transcribes the speech of the participant and provides it to the participant's chat service system, which translates the transcribed text into the chat service message in the format of the participant's chat service system. An Integration Server receives all the participant's chat messages, which have various formats, and translates them into the format used by the chat service system of the deaf or hearing-impaired participant, thus allowing her to see the transcribed text of the conversation between the other participants. In addition, the deaf or hearing-impaired participant inputs text to her chat service system, which creates a formatted message text. The Integration Server receives this formatted chat message and translates it into the formats of the remaining participant's chat service systems, so that the remaining participants see the comments of the deaf or hearing-impaired participant. In other aspects, the teleconferencing system and method is used to connect disparate chat service systems, without necessarily including a deaf or hearing-impaired participant.

System and method of teleconferencing with the deaf or hearing-impaired

A system and method for recognizing spoken liaisoned words. The method and system identify each word in the vocabulary as a liaison generator and/or liaison receptor. If the word is a liaison receptor, and if the word is preceded by a liaison generator, the most probable recognition result for the word will be the liaison generated by the preceding word plus the word. Liaisons are identified on an immediately preceding word in accordance with rules in a language. A word that ends with an unpronounced consonant phoneme, when followed by a word beginning with a consonant phoneme, and ends with a pronounced phoneme, when followed by a word with a vowel-like phoneme, causes a match list for the current word to be amended with words having liaisons added at their beginnings.

Method for modeling and recognizing speech including word liaisons

A method and system of recognizing speech. The method and system perform a fast match on a word in the string of speech to be recognized which generates a fast match list representing words in a system vocabulary that most likely match a current word to be recognized. Next, the method and system perform a detailed match on the words in the fast match list and generate a detailed match list representing words that most likely match the current word to be recognized. Then for each word in the detailed match list that can accept a liaison phoneme from a preceding word, where each word is a liaison receptor, adding to the detailed match list a form of the liaison receptor, where the form represents an addition of a liaison phoneme to the liaison receptor, creating a modified detailed match list which is inclusive of the forms of the liaison receptors added to the detailed match list. Finally the method and system outputs a word in the modified detailed match list that has the highest probability of matching the word to be recognized.

Recognizing speech having word liaisons by adding a phoneme to reference

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. When the best match score for the current sound exceeds a 'poor' confidence score but is less than a 'good' confidence score: (a) the word corresponding to the acoustic model having the best match score is accepted as highly likely to correspond to the measured sound if the previously recognized word was accepted as having a high likelihood of corresponding to the previous sound; (b) the word corresponding to the acoustic model having the best match score is rejected as highly unlikely to correspond to the measured sound if the previously recognized word was rejected as having a low likelihood of corresponding to the previous sound; or (c) if there is sufficient intervening silence between a previously rejected word and the current word, then the current word is also accepted as having a high likelihood of corresponding to the measured current sound.

Speech recognition system with improved rejection of words and sounds

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. The identification values of the compound-dimension prototype vector signals having the best prototype match scores for the feature vectors signals are output as a sequence of coded representations of an utterance to be recognized. A match score, comprising an estimate of the closeness of a match between a speech unit and the sequence of coded representations of the utterance, is generated for each of a plurality of speech units. At least one speech subunit, of one or more best candidate speech units having the best match scores, is displayed.

Speech coding apparatus with single-dimension acoustic prototypes for a

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.

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Putnam Valley, NY, United States of America

Edward Adam Epstein