Nuance Communications, Inc.

Cgi Information Systems and Management Consultants Inc.

Simona Gandrabur

Eric Buist

David Andrew Mauro

Real Tremblay

Jean-Francois Lavallee

Andrei Dragoi

Alireza Salimi

Jan Kleindienst

Elizabeth Ann Dykstra-Erickson

Aimee Piercy

Mark Fanty

Matthieu Hebert

Robert Douglas Sharp

Jan Curin

Susan Dawnstarr Daniel

Réal Tremblay

Henri Bouvier

Jiri Havelka

Jean-Francois Lavallée

Christophe Faucher-courchesne

Nada Naji

Pierre Laroche

Karine Yelle

Roger Miller

Eric-Olivier Savoie

Multiple natural language understanding (NLU) interpretation selection models may be generated. The NLU interpretation selection models may include a generic NLU interpretation selection model that is not specialized for a specific set of NLU interpretations type and one or more specialized NLU interpretation selection models, each of which may be specific to a particular set of NLU interpretations type. The specialized NLU interpretation selection model(s) may be utilized to process natural language input data comprising data corresponding to their respective sets of NLU interpretations type(s). The generic NLU interpretation selection model may be utilized to process natural language input data comprising data that does not correspond to the sets of NLU interpretations type(s) associated with the specialized NLU interpretation selection model(s).

Semantic re-ranking of NLU results in conversational dialogue applications

An intelligent assistant can be used to facilitate an end user's participation in a virtual meeting. The intelligent assistant can receive recognized speech from a speech recognition engine, and then forward the recognized speech to a natural language understanding engine which in turn ascribes a meaning to the recognized speech. The ascribed meaning can include a semantic representation of the recognized speech and thereby a semantic representation of voice commands enshrined within the recognized speech. Using the ascribed meaning, the intelligent assistant then searches for meeting information associated with the virtual meeting and uses that meeting information to join the end user to the virtual meeting. Meeting information can include a conference call number, a participant code, a moderator code or a link to an online meeting.

Facilitating participation in a virtual meeting using an intelligent assistant

A method of providing a task assistant is described. The task assistant is designed to receive input from a user through multimodal input including a plurality of speech input, typing input, and touch input, determine the meaning of the input, and determining whether there is a context based on prior interactions with the user. The method further to generate an interpreted input based on a combination of the input and the context, and providing a formatted query to an application. The method further to receive data from the application in response to the formatted query, and provide a response to the user through multimodal output including a plurality of: speech output, text output, non-speech audio output, haptic output, and visual non-text output. The method further to update the context based on the interpreted input.

Task assistant utilizing context for improved interaction

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

Automated user-machine interaction is gaining attraction in many applications and services. However, implementing and offering smart automated user-machine interaction services still present technical challenges. According to at least one example embodiment, a dialogue manager is configured to handle multiple dialogue applications independent of the language, the input modalities, or output modalities used. The dialogue manager employs generic semantic representation of user-input data. At a step of a dialogue, the dialogue manager determines whether the user-input data is indicative of a new request or a refinement request based on the generic semantic representation and at least one of a maintained state of the dialogue, general knowledge data representing one or more concepts, and data representing history of the dialogue. The dialogue manager then responds to determined user-request with multi-facet output data to a client dialogue application indicating action(s) to be performed.

Method and apparatus for a multi I/O modality language independent user-interaction platform

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 from the human user including speech inputs. 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 NLU-ranked semantic interpretations. A semantic re-ranking module re-ranks the NLU-ranked semantic interpretations based on at least one of dialog context information and world knowledge information. A dialog manager responds to the re-ranked semantic interpretations and generates the output prompts so as to manage a dialog process with the human user.

An automatic conversational system has multiple computer-implemented dialogue components for conducting an automated dialogue process with a human user. A user client delivers dialogue output prompts to the human user and receives dialogue input responses from the human user including speech inputs. An automatic speech recognition engine processes the speech inputs to determine corresponding sequences of representative text words. A natural language understanding (NLU) processing arrangement processes the dialogue input responses and the text words to determine corresponding semantic interpretations. The NLU processing arrangement includes an anaphora processor that accesses different information sources characterizing dialogue context, linguistic features, and NLU features to identify unresolved anaphora in the text words needing resolution in order to determine a semantic interpretation. A dialogue manager manages the dialogue process with the human user based on the semantic interpretations.

Anaphora resolution using linguisitic cues, dialogue context, and general knowledge

Current human-to-machine interfaces enable users to interact with a company's database and enter into a series of transactions (e.g., purchasing products/services and paying bills). Each transaction may require several operations or stages requiring user input or interaction. Some systems enable a user to enter a voice input parameter providing multiple operations of instruction (e.g., single natural language command). However, users of such a system do not know what types of commands the system is capable of accepting. Embodiments of the present invention facilitate communications for user transactions by determining a user's goal transaction and presenting a visual representation of a voice input parameter for the goal transaction. The use of visual representations notifies the user of the system's capability of accepting single natural language commands and the types of commands the system is capable of accepting, thereby enabling a user to complete a transaction in a shorter period of time.

Method and system for facilitating communications for a user transaction

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Verdun, Canada

Simona Gandrabur