Patents by Inventor Réal Tremblay
Réal Tremblay has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11593572Abstract: A system and method incorporate prior knowledge into the optimization and regularization of a classification and regression model. The optimization may be a regularization process and the prior knowledge may be incorporated through adjustment of a cost function. A method of at least one processor developing a classification and regression model may be provided. The method may be implemented by at least one processor that implements classification and regression model functionality, including receiving training data and adjusting the model according to the training data; testing the classification and regression model; and employing prior knowledge during an optimization of the classification and regression model. The regularizing can include adjusting feature weights according to prior knowledge. In various embodiments, such systems and methods can be used in the processing of language inputs, e.g., speech and/or text inputs, to achieve greater interpretation accuracy.Type: GrantFiled: August 26, 2020Date of Patent: February 28, 2023Assignee: Nuance Communications, Inc.Inventors: Jean-François Lavallée, Jean-Michel Attendu, Réal Tremblay
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Publication number: 20210064829Abstract: A system and method incorporate prior knowledge into the optimization and regularization of a classification and regression model. The optimization may be a regularization process and the prior knowledge may be incorporated through adjustment of a cost function. A method of at least one processor developing a classification and regression model may be provided. The method may be implemented by at least one processor that implements classification and regression model functionality, including receiving training data and adjusting the model according to the training data; testing the classification and regression model; and employing prior knowledge during an optimization of the classification and regression model. The regularizing can include adjusting feature weights according to prior knowledge. In various embodiments, such systems and methods can be used in the processing of language inputs, e.g., speech and/or text inputs, to achieve greater interpretation accuracy.Type: ApplicationFiled: August 26, 2020Publication date: March 4, 2021Inventors: Jean-François Lavallée, Jean-Michel Attendu, Réal Tremblay
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Patent number: 10811004Abstract: An ontology stores information about a domain of an automatic speech recognition (ASR) application program. The ontology is augmented with information that enables subsequent automatic generation of a speech understanding grammar for use by the ASR application program. The information includes hints about how a human might talk about objects in the domain, such as preludes (phrases that introduce an identification of the object) and postludes (phrases that follow an identification of the object).Type: GrantFiled: March 28, 2013Date of Patent: October 20, 2020Assignee: Nuance Communications, Inc.Inventors: Stephen Douglas Peters, Réal Tremblay
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Patent number: 10540965Abstract: 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).Type: GrantFiled: September 11, 2017Date of Patent: January 21, 2020Assignee: Nuance Communications, Inc.Inventors: Simona Gandrabur, Jean-Francois Lavallee, Real Tremblay
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Patent number: 10339217Abstract: Aspects described herein provide quality assurance checks for improving the construction of natural language understanding grammars. An annotation module may obtain a set of annotations for a set of text samples based, at least in part, on an ontology and a grammar. A quality assurance module may automatically perform one or more quality assurance checks on the set of annotations, the ontology, the grammar, or combinations thereof. The quality assurance module may generate a list of flagged annotations during performance of a quality assurance check. The list of flagged annotations may be presented at an annotation review interface displayed at a display device. One of the flagged annotations may be selected and presented at an annotation interface displayed at the display device. Responsive to presentation of the flagged annotation, the ontology, the grammar, the flagged annotation selected, or combinations thereof may be updated based on user input received.Type: GrantFiled: June 26, 2017Date of Patent: July 2, 2019Assignee: Nuance Communications, Inc.Inventors: Real Tremblay, Jerome Tremblay, Serge Robillard, Jackson Liscombe, Alina Andreevskaia, Tagyoung Chung
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Patent number: 10235359Abstract: Inferring a natural language grammar is based on providing natural language understanding (NLU) data with concept annotations according to an application ontology characterizing a relationship structure between application-related concepts for a given NLU application. An application grammar is then inferred from the concept annotations and the application ontology.Type: GrantFiled: July 15, 2013Date of Patent: March 19, 2019Assignee: Nuance Communications, Inc.Inventors: Réal Tremblay, Jerome Tremblay, Stephen Douglas Peters, Serge Robillard
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Publication number: 20180143962Abstract: Aspects described herein provide quality assurance checks for improving the construction of natural language understanding grammars. An annotation module may obtain a set of annotations for a set of text samples based, at least in part, on an ontology and a grammar. A quality assurance module may automatically perform one or more quality assurance checks on the set of annotations, the ontology, the grammar, or combinations thereof. The quality assurance module may generate a list of flagged annotations during performance of a quality assurance check. The list of flagged annotations may be presented at an annotation review interface displayed at a display device. One of the flagged annotations may be selected and presented at an annotation interface displayed at the display device. Responsive to presentation of the flagged annotation, the ontology, the grammar, the flagged annotation selected, or combinations thereof may be updated based on user input received.Type: ApplicationFiled: June 26, 2017Publication date: May 24, 2018Inventors: Real Tremblay, Jerome Tremblay, Serge Robillard, Jackson Liscombe, Alina Andreevskaia, Tagyoung Chung
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Publication number: 20180075846Abstract: 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).Type: ApplicationFiled: September 11, 2017Publication date: March 15, 2018Inventors: Simona Gandrabur, Jean-Francois Lavallee, Real Tremblay
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Patent number: 9767093Abstract: Natural language understanding (NLU) engines perform better when they are trained with large amounts of data. However, a large amount of data is not always available. Embodiments of the present invention overcome this problem by generating annotated data for use in a NLU system. An example embodiment generates annotated data by parsing an input annotated phrase, generating a syntactic tree reflecting a grammatical structure of the parsed phrase, and generating one or more alternative versions of the input annotated phrase based on the syntactic tree. Alignment between expressions and corresponding annotations in the annotated phrase are preserved in the one or more alternative versions generated to ensure intention of the input annotated phrase is maintained.Type: GrantFiled: June 19, 2014Date of Patent: September 19, 2017Assignee: Nuance Communications, Inc.Inventors: Real Tremblay, Gabriel Forgues, Tagyoung Chung
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Patent number: 9761225Abstract: 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).Type: GrantFiled: June 25, 2014Date of Patent: September 12, 2017Assignee: Nuance Communications, Inc.Inventors: Simona Gandrabur, Jean-Francois Lavallee, Real Tremblay
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Patent number: 9690771Abstract: Aspects described herein provide quality assurance checks for improving the construction of natural language understanding grammars. An annotation module may obtain a set of annotations for a set of text samples based, at least in part, on an ontology and a grammar. A quality assurance module may automatically perform one or more quality assurance checks on the set of annotations, the ontology, the grammar, or combinations thereof. The quality assurance module may generate a list of flagged annotations during performance of a quality assurance check. The list of flagged annotations may be presented at an annotation review interface displayed at a display device. One of the flagged annotations may be selected and presented at an annotation interface displayed at the display device. Responsive to presentation of the flagged annotation, the ontology, the grammar, the flagged annotation selected, or combinations thereof may be updated based on user input received.Type: GrantFiled: August 6, 2014Date of Patent: June 27, 2017Assignee: Nuance Communications, Inc.Inventors: Real Tremblay, Jerome Tremblay, Serge Robillard, Jackson Liscombe, Alina Andreevskaia, Tagyoung Chung
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Patent number: 9646001Abstract: Operation of an automated dialog system is described using a source language to conduct a real time human machine dialog process with a human user using a target language. A user query in the target language is received and automatically machine translated into the source language. An automated reply of the dialog process is then delivered to the user in the target language. If the dialog process reaches an initial assistance state, a first human agent using the source language is provided to interact in real time with the user in the target language by machine translation to continue the dialog process. Then if the dialog process reaches a further assistance state, a second human agent using the target language is provided to interact in real time with the user in the target language to continue the dialog process.Type: GrantFiled: September 19, 2011Date of Patent: May 9, 2017Assignee: Nuance Communications, Inc.Inventors: Ruhi Sarikaya, Vaibhava Goel, David Nahamoo, Rèal Tremblay, Bhuvana Ramabhadran, Osamuyimen Stewart
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Patent number: 9620110Abstract: An automated method is described for developing an automated speech input semantic classification system such as a call routing system. A set of semantic classifications is defined for classification of input speech utterances, where each semantic classification represents a specific semantic classification of the speech input. The semantic classification system is trained from training data from training data substantially without manually transcribed in-domain training data, and then operated to assign input speech utterances to the defined semantic classifications. Adaptation training data based on input speech utterances is collected with manually assigned semantic labels from at least one source of already collected language data. When the adaptation training data satisfies a pre-determined adaptation criteria, the semantic classification system is automatically retrained based on the adaptation training data.Type: GrantFiled: April 28, 2014Date of Patent: April 11, 2017Assignee: Nuance Communications, Inc.Inventors: Nicolae Duta, Réal Tremblay, Andrew D. Mauro, S. Douglas Peters
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Publication number: 20160140957Abstract: An automated method is described for developing an automated speech input semantic classification system such as a call routing system. A set of semantic classifications is defined for classification of input speech utterances, where each semantic classification represents a specific semantic classification of the speech input. The semantic classification system is trained from training data from training data substantially without manually transcribed in-domain training data, and then operated to assign input speech utterances to the defined semantic classifications. Adaptation training data based on input speech utterances is collected with manually assigned semantic labels from at least one source of already collected language data. When the adaptation training data satisfies a pre-determined adaptation criteria, the semantic classification system is automatically retrained based on the adaptation training data.Type: ApplicationFiled: April 28, 2014Publication date: May 19, 2016Applicant: Nuance Communications, Inc.Inventors: Nicolae Duta, Réal Tremblay, Andrew D. Mauro, S. Douglas Peters
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Patent number: 9269354Abstract: 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.Type: GrantFiled: March 11, 2013Date of Patent: February 23, 2016Assignee: Nuance Communications, Inc.Inventors: Simona Gandrabur, Jean-Francois Lavallée, Réal Tremblay
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Patent number: 9251785Abstract: A system and method for providing an easy-to-use interface for verifying semantic tags in a steering application in order to generate a natural language grammar. The method includes obtaining user responses to open-ended steering questions, automatically grouping the user responses into groups based on their semantic meaning, and automatically assigning preliminary semantic tags to each of the groups. The user interface enables the user to validate the content of the groups to ensure that all responses within a group have the same semantic meaning and to add or edit semantic tags associated with the groups. The system and method may be applied to interactive voice response (IVR) systems, as well as customer service systems that can communicate with a user via a text or written interface.Type: GrantFiled: November 26, 2014Date of Patent: February 2, 2016Assignee: Nuance Communications, Inc.Inventors: Real Tremblay, Jerome Tremblay, Amy E. Ulug, Jean-Francois Fortier, Francois Berleur, Jeffrey N. Marcus, David Andrew Mauro
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Publication number: 20160026608Abstract: Designing a dialog application is a difficult task that typically requires a complete understanding of the dialog framework and a high level of expertise to map system requirements to the actual implementations. In contrast, determining the logic of the dialog application via sample interaction is typically very simple and efficient. A developer can describe via speech or text what the operations of the application are, effectively writing dialog samples. Methods described herein reverse the way dialog applications are designed by obtaining annotated dialog samples and defined concepts related to a requested dialog application; analyzing the annotated dialog samples, defined concepts, and one or more relationships between or among the defined concepts; and generating an executable dialog application based on the analysis of the annotated dialog samples and the defined concepts.Type: ApplicationFiled: July 22, 2014Publication date: January 28, 2016Inventors: Jan Curin, Jacques-Olivier Goussard, Real Tremblay, Richard J. Beaufort, Jan Kleindienst, Jiri Havelka, Raimo Bakis
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Publication number: 20150370778Abstract: Natural language understanding (NLU) engines perform better when they are trained with large amounts of data. However, a large amount of data is not always available. Embodiments of the present invention overcome this problem by generating annotated data for use in a NLU system. An example embodiment generates annotated data by parsing an input annotated phrase, generating a syntactic tree reflecting a grammatical structure of the parsed phrase, and generating one or more alternative versions of the input annotated phrase based on the syntactic tree. Alignment between expressions and corresponding annotations in the annotated phrase are preserved in the one or more alternative versions generated to ensure intention of the input annotated phrase is maintained.Type: ApplicationFiled: June 19, 2014Publication date: December 24, 2015Inventors: Real Tremblay, Gabriel Forgues, Tagyoung Chung
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Publication number: 20150347375Abstract: Aspects described herein provide quality assurance checks for improving the construction of natural language understanding grammars. An annotation module may obtain a set of annotations for a set of text samples based, at least in part, on an ontology and a grammar. A quality assurance module may automatically perform one or more quality assurance checks on the set of annotations, the ontology, the grammar, or combinations thereof. The quality assurance module may generate a list of flagged annotations during performance of a quality assurance check. The list of flagged annotations may be presented at an annotation review interface displayed at a display device. One of the flagged annotations may be selected and presented at an annotation interface displayed at the display device. Responsive to presentation of the flagged annotation, the ontology, the grammar, the flagged annotation selected, or combinations thereof may be updated based on user input received.Type: ApplicationFiled: August 6, 2014Publication date: December 3, 2015Inventors: Real Tremblay, Jerome Tremblay, Serge Robillard, Jackson Liscombe, Alina Andreevskaia, Tagyoung Chung
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Patent number: 9064001Abstract: In FAQ based systems, associating questions with answers can be a time consuming task if performed manually. In one embodiment, a method of building a frequently-asked questions (FAQ) portal can include creating cluster labels. The labels can include predefined universal semantic labels and application-specific labels. The method can further include applying the cluster labels to clusters of queries within an FAQ application. The method can additionally include adjusting the application-specific labels to support combined and newly created clusters of queries based on application-specific queries within the FAQ application on an ongoing basis and reapplying the universal semantic labels and the adjusted application-specific labels to the combined and newly created clusters of queries. The method and system proposed herein allow for the automated clustering of queries and association with applicable answers, which leads to higher efficiencies for a faster response time for a user.Type: GrantFiled: March 15, 2013Date of Patent: June 23, 2015Assignee: Nuance Communications, Inc.Inventors: Ding Liu, Real Tremblay, Jerome Tremblay, Serge Robillard