Nuance Communications, Inc.

Matthieu Hebert

Jean-Philippe Robichaud

Christopher M Parisien

Paul Joseph Vozila

Donald E Owen

Mehmet Mert Öz

Guido Remi Marcel Gallopyn

Simona Gandrabur

Nicolae Duta

Jerome Tremblay

Eric Buist

Amjad Almahairi

Lakshmish Kaushik

Maryse Boisvert

Jean-Phillipe Robichaud

Andre Gillet

A method, computer program product, and computing system for obtaining encounter information during a patient encounter; processing the encounter information to detect the execution of a physical event during the patient encounter, thus defining a detected physical event; and deriving information for the detected physical event.

Ambient clinical intelligence system and method

Methods and apparatus for natural language understanding (NLU) processing based on user-specified interests. Information specifying a weight for each of a plurality of domains is received via a user interface. The plurality of domains each relates to a potential area of interest for the user, and the weight for a domain from among the plurality of domains indicates a level of interest for the user in the domain. A ranking classifier used to rank NLU hypotheses generated by an NLU engine is trained using training data from which features are, at least in part, based on the information specifying a weight for each of the plurality of domains.

Natural language understanding (NLU) processing based on user-specified interests

An arrangement is described for conducting natural language dialogs with a user on a mobile device using automatic speech recognition (ASR) and multiple different dialog applications. A user interface provides for user interaction with the dialogue applications in natural language dialogs. An ASR engine processes unknown speech inputs from the user to produce corresponding speech recognition results. A dialog concept module develops dialog concept items from the speech recognition results and stores the dialog concept items and additional dialog information in a dialog concept database. A dialog processor accesses dialog concept database information and coordinates operation of the ASR engine and the dialog applications to conduct with the user a plurality of separate parallel natural language dialogs in the dialog applications.

Multiple parallel dialogs in smart phone applications

Disclosed methods and systems are directed to concept identification and capture. The methods and systems may include receiving, by a device, a first natural language input comprising one or more terms, and analyzing the first natural language input via a natural language processing engine to identify one or more named entities associated with the one or more terms, wherein each of the one or more named entities is associated with at least one category of a plurality of categories. The methods and systems may also include detecting a text field configured to receive text, the text field being associated with one of the plurality of categories, and inputting into the text field one of the one or more identified named entities based on the text field being associated with a same category as the one of the one or more named entities.

Concept identification and capture

An arrangement and corresponding method are described for multi-domain natural language processing. Multiple parallel domain pipelines are used for processing a natural language input. Each domain pipeline represents a different specific subject domain of related concepts. Each domain pipeline includes a mention module that processes the natural language input using natural language understanding (NLU) to determine a corresponding list of mentions, and an interpretation generator that receives the list of mentions and produces a rank-ordered domain output set of sentence-level interpretation candidates. A global evidence ranker receives the domain output sets from the domain pipelines and produces an overall rank-ordered final output set of sentence-level interpretations.

Multi-domain natural language processing architecture

Disclosed methods and systems are directed to natural language understanding cache usage. The methods and systems may include receiving a first natural language input comprising a set of one or more terms, and parsing the first natural language input to determine a first pretag result comprising a first string comprising at least one term from the set of one or more terms. The methods and systems may also determine that if the first pretag result corresponds to a key stored in a cache, then retrieve one or more cached NLU results corresponding to the at least one key. The methods and systems may also determine that if the first pretag result does not correspond to a key stored in the cache, then determine, based on the set of one or more terms, a first NLU result corresponding to the first natural language input.

Natural language understanding cache

An arrangement and corresponding method are described for natural language processing. A natural language understanding (NLU) arrangement processes a natural language input to determine a corresponding sentence-level interpretation. A user state component maintains user context data that characterizes an operating context of the NLU arrangement. Operation of the NLU arrangement is biased by the user context data.

Situation aware NLU/NLP

An automated arrangement is described for conducting natural language interactions with a human user. A user interface is provided for user communication in a given active natural language interaction with a natural language application during an automated dialog session. An automatic speech recognition (ASR) engine processes unknown user speech inputs from the user interface to produce corresponding speech recognition results. A natural language concept module processes the speech recognition results to develop corresponding natural language concept items. A concept item storage holds selected concept items for reuse in a subsequent natural language interaction with the user during the automated dialog session.

Concept cloud in smart phone applications

A human-machine dialog system is described which has multiple computer-implemented dialog components. A user client delivers output prompts to a human user and receives dialog inputs including speech inputs from the human user. An automatic speech recognition (ASR) engine processes the speech inputs to determine corresponding sequences of representative text words. A natural language understanding (NLU) engine processes the text words to determine corresponding semantic interpretations. A dialog manager (DM) generates the output prompts and responds to the semantic interpretations so as to manage a dialog process with the human user. The dialog components share context information with each other using a common context sharing mechanism such that the operation of each dialog component reflects available context information.

Communicating context across different components of multi-modal dialog applications

Some embodiments of the invention provide techniques for receiving a query from a user of a mobile device, and for conveying to the user not only search results, but also feedback relating to query. For example, the user may be prompted to elicit supplemental information relating to the query, or provided other feedback. The feedback may be conveyed in a manner which minimizes how much of the mobile device's display screen is dedicated to presenting the feedback.

Receipt and processing of user-specified queries

An arrangement and corresponding method are described for distributed natural language processing. A set of local data sources is stored on a mobile device. A local natural language understanding (NLU) match module on the mobile device performs natural language processing of a natural language input with respect to the local data sources to determine one or more local interpretation candidates. A local NLU ranking module on the mobile device processes the local interpretation candidates and one or more remote interpretation candidates from a remote NLU server to determine a final output interpretation corresponding to the natural language input.

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Melocheville, Canada

Matthieu Hebert