Patents by Inventor Geoffrey Zweig
Geoffrey Zweig 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: 10949748Abstract: Embodiments include methods and systems for using, creating and maintaining goal-oriented, dialog systems (i.e., transactional bots) that provide interfaces to application functionality. The methods and systems of the embodiments provide a bot that may learn in supervised learning and reinforcement learning from conversational examples provided by domain experts and from interaction with users. Conversational bots may be created to interact using both text and/or application programming interface (API) calls. A developer may configure a bot that interfaces with an application back-end where behavior of the bot may be controlled by use of masking actions. A specification for the bot may be flexibly designed to specify how developer code may be organized, for example, as masking operations on the possible actions the bot may execute. Additionally, the methods and systems may automatically infer the best state representation during a dialog so a state variable need not be predefined.Type: GrantFiled: May 13, 2016Date of Patent: March 16, 2021Assignee: Microsoft Technology Licensing, LLCInventors: Jason Williams, Geoffrey Zweig
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Publication number: 20170330077Abstract: Embodiments include methods and systems for using, creating and maintaining goal-oriented, dialog systems (i.e., transactional bots) that provide interfaces to application functionality. The methods and systems of the embodiments provide a bot that may learn in supervised learning and reinforcement learning from conversational examples provided by domain experts and from interaction with users. Conversational bots may be created to interact using both text and/or application programming interface (API) calls. A developer may configure a bot that interfaces with an application back-end where behavior of the bot may be controlled by use of masking actions. A specification for the bot may be flexibly designed to specify how developer code may be organized, for example, as masking operations on the possible actions the bot may execute. Additionally, the methods and systems may automatically infer the best state representation during a dialog so a state variable need not be predefined.Type: ApplicationFiled: May 13, 2016Publication date: November 16, 2017Applicant: Microsoft Technology Licensing, LLCInventors: Jason Williams, Geoffrey Zweig
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Patent number: 9684741Abstract: A query may be applied against search engines that respectively return a set of search results relating to various items discovered in the searched data sets. However, presenting numerous and varied search results may be difficult on mobile devices with small displays and limited computational resources. Instead, search results may be associated with search domains representing various information types (e.g., contacts, public figures, places, projects, movies, music, and books) and presented by grouping search results with associated query domains, e.g., in a tabbed user interface. The query may be received through an input device associated with a particular input domain, and may be transitioned to the query domain of a particular search engine (e.g., by recognizing phonemes of a voice query using an acoustic model; matching phonemes with query terms according to a pronunciation model; and generating a recognition result according to a vocabulary of an n-gram language model.Type: GrantFiled: June 5, 2009Date of Patent: June 20, 2017Assignee: Microsoft Technology Licensing, LLCInventors: Xiao Li, Patrick Nguyen, Geoffrey Zweig, Alejandro Acero
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Patent number: 9583105Abstract: Technologies described herein relate to modifying visual content for presentment on a display to facilitate improving performance of an automatic speech recognition (ASR) system. The visual content is modified to move elements further away from one another, wherein the moved elements give rise to ambiguity from the perspective of the ASR system. The visual content is modified to take into consideration accuracy of gaze tracking. When a user views an element in the modified visual content, the ASR system is customized as a function of the element being viewed by the user.Type: GrantFiled: June 6, 2014Date of Patent: February 28, 2017Assignee: Microsoft Technology Licensing, LLCInventors: Andreas Stolcke, Geoffrey Zweig, Malcolm Slaney
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Patent number: 9547471Abstract: Conversational interactions between humans and computer systems can be provided by a computer system that classifies an input by conversation type, and provides human authored responses for conversation types. The input classification can be performed using trained binary classifiers. Training can be performed by labeling inputs as either positive or negative examples of a conversation type. Conversational responses can be authored by the same individuals that label the inputs used in training the classifiers. In some cases, the process of training classifiers can result in a suggestion of a new conversation type, for which human authors can label inputs for a new classifier and write content for responses for that new conversation type.Type: GrantFiled: July 3, 2014Date of Patent: January 17, 2017Assignee: Microsoft Technology Licensing, LLCInventors: Jason Williams, Geoffrey Zweig, Aparna Lakshmiratan, Carlos Garcia Jurado Suarez
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Patent number: 9460708Abstract: The described implementations relate to automated data cleanup. One system includes a language model generated from language model seed text and a dictionary of possible data substitutions. This system also includes a transducer configured to cleanse a corpus utilizing the language model and the dictionary. The transducer can process speech recognition data in some cases by substituting a second word for a first word which shares pronunciation with the first word but is spelled differently. In some cases, this can be accomplished by establishing corresponding probabilities of the first word and second word based on a third word that appears in sequence with the first word.Type: GrantFiled: September 17, 2009Date of Patent: October 4, 2016Assignee: Microsoft Technology Licensing, LLCInventors: Geoffrey Zweig, Yun-Cheng Ju
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Publication number: 20160091965Abstract: A “Natural Motion Controller” identifies various motions of one or more parts of a user's body to interact with electronic devices, thereby enabling various natural user interface (NUI) scenarios. The Natural Motion Controller constructs composite motion recognition windows by concatenating an adjustable number of sequential periods of inertial sensor data received from a plurality of separate sets of inertial sensors. Each of these separate sets of inertial sensors are coupled to, or otherwise provide sensor data relating to, a separate user worn, carried, or held mobile computing device. Each composite motion recognition window is then passed to a motion recognition model trained by one or more machine-based deep learning processes. This motion recognition model is then applied to the composite motion recognition windows to identify a sequence of one or more predefined motions. Identified motions are then used as the basis for triggering execution of one or more application commands.Type: ApplicationFiled: September 30, 2014Publication date: March 31, 2016Inventors: Jiaping Wang, Yujia Li, Xuedong Huang, Lingfeng Wu, Wei Xiong, Kaisheng Yao, Geoffrey Zweig
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Publication number: 20160005395Abstract: Conversational interactions between humans and computer systems can be provided by a computer system that classifies an input by conversation type, and provides human authored responses for conversation types. The input classification can be performed using trained binary classifiers. Training can be performed by labeling inputs as either positive or negative examples of a conversation type. Conversational responses can be authored by the same individuals that label the inputs used in training the classifiers. In some cases, the process of training classifiers can result in a suggestion of a new conversation type, for which human authors can label inputs for a new classifier and write content for responses for that new conversation type.Type: ApplicationFiled: July 3, 2014Publication date: January 7, 2016Inventors: Jason Williams, Geoffrey Zweig, Aparna Lakshmiratan, Carlos Garcia Jurado Suarez
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Publication number: 20150364127Abstract: The technology relates to performing letter-to-sound conversion utilizing recurrent neural networks (RNNs). The RNNs may be implemented as RNN modules for letter-to-sound conversion. The RNN modules receive text input and convert the text to corresponding phonemes. In determining the corresponding phonemes, the RNN modules may analyze the letters of the text and the letters surrounding the text being analyzed. The RNN modules may also analyze the letters of the text in reverse order. The RNN modules may also receive contextual information about the input text. The letter-to-sound conversion may then also be based on the contextual information that is received. The determined phonemes may be utilized to generate synthesized speech from the input text.Type: ApplicationFiled: June 13, 2014Publication date: December 17, 2015Applicant: MICROSOFT CORPORATIONInventors: Pei Zhao, Kaisheng Yao, Max Leung, Mei-Yuh Hwang, Sheng Zhao, Bo Yan, Geoffrey Zweig, Fileno A. Alleva
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Publication number: 20150356971Abstract: Technologies described herein relate to modifying visual content for presentment on a display to facilitate improving performance of an automatic speech recognition (ASR) system. The visual content is modified to move elements further away from one another, wherein the moved elements give rise to ambiguity from the perspective of the ASR system. The visual content is modified to take into consideration accuracy of gaze tracking. When a user views an element in the modified visual content, the ASR system is customized as a function of the element being viewed by the user.Type: ApplicationFiled: June 6, 2014Publication date: December 10, 2015Inventors: Andreas Stolcke, Geoffrey Zweig, Malcolm Slaney
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Patent number: 8938391Abstract: A dynamic exponential, feature-based, language model is continually adjusted per utterance by a user, based on the user's usage history. This adjustment of the model is done incrementally per user, over a large number of users, each with a unique history. The user history can include previously recognized utterances, text queries, and other user inputs. The history data for a user is processed to derive features. These features are then added into the language model dynamically for that user.Type: GrantFiled: June 12, 2011Date of Patent: January 20, 2015Assignee: Microsoft CorporationInventors: Geoffrey Zweig, Shuangyu Chang
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Publication number: 20140249799Abstract: Relational similarity measuring embodiments are presented that generally involve creating a relational similarity model that, given two pairs of words, is used to measure a degree of relational similarity between the two relations respectively exhibited by these word pairs. In one exemplary embodiment this involves creating a combined relational similarity model from a plurality of relational similarity models. This is generally accomplished by first selecting a plurality of relational similarity models, each of which measures relational similarity between two pairs of words, and each of which is trained or created using a different method or linguistic/textual resource. The selected models are then combined to form the combined relational similarity model. The combined model inputs two pairs of words and outputs a relational similarity indicator representing a measure the degree of relational similarity between the word pairs.Type: ApplicationFiled: March 4, 2013Publication date: September 4, 2014Applicant: Microsoft CorporationInventors: Wen-tau Yih, Geoffrey Zweig, Christopher Meek, Alisa Zhila, Tomas Mikolov
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Patent number: 8615388Abstract: Training data may be provided, the training data including pairs of source phrases and target phrases. The pairs may be used to train an intra-language statistical machine translation model, where the intra-language statistical machine translation model, when given an input phrase of text in the human language, can compute probabilities of semantic equivalence of the input phrase to possible translations of the input phrase in the human language. The statistical machine translation model may be used to translate between queries and listings. The queries may be text strings in the human language submitted to a search engine. The listing strings may be text strings of formal names of real world entities that are to be searched by the search engine to find matches for the query strings.Type: GrantFiled: March 28, 2008Date of Patent: December 24, 2013Assignee: Microsoft CorporationInventors: Xiao Li, Yun-Cheng Ju, Geoffrey Zweig, Alex Aero
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Publication number: 20120316877Abstract: A dynamic exponential, feature-based, language model is continually adjusted per utterance by a user, based on the user's usage history. This adjustment of the model is done incrementally per user, over a large number of users, each with a unique history. The user history can include previously recognized utterances, text queries, and other user inputs. The history data for a user is processed to derive features. These features are then added into the language model dynamically for that user.Type: ApplicationFiled: June 12, 2011Publication date: December 13, 2012Applicant: MICROSOFT CORPORATIONInventors: Geoffrey Zweig, Shuangyu Chang
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Patent number: 8145484Abstract: The described implementations relate to speech spelling by a user. One method identifies one or more symbols that may match a user utterance and displays an individual symbol for confirmation by the user.Type: GrantFiled: November 11, 2008Date of Patent: March 27, 2012Assignee: Microsoft CorporationInventor: Geoffrey Zweig
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Patent number: 7949526Abstract: A voice interaction system is configured to analyze an utterance and identify inherent attributes that are indicative of a demographic characteristic of the system user that spoke the utterance. The system then selects and presents a personalized response to the user, the response being selected based at least in part on the identified demographic characteristic. In one embodiment, the demographic characteristic is one or more of the caller's age, gender, ethnicity, education level, emotional state, health status and geographic group. In another embodiment, the selection of the response is further based on consideration of corroborative caller data.Type: GrantFiled: June 4, 2007Date of Patent: May 24, 2011Assignee: Microsoft CorporationInventors: Yun-Cheng Ju, Alejandro Acero, Neal Bernstein, Geoffrey Zweig
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Publication number: 20100312782Abstract: A query may be applied against search engines that respectively return a set of search results relating to various items discovered in the searched data sets. However, presenting numerous and varied search results may be difficult on mobile devices with small displays and limited computational resources. Instead, search results may be associated with search domains representing various information types (e.g., contacts, public figures, places, projects, movies, music, and books) and presented by grouping search results with associated query domains, e.g., in a tabbed user interface. The query may be received through an input device associated with a particular input domain, and may be transitioned to the query domain of a particular search engine (e.g., by recognizing phonemes of a voice query using an acoustic model; matching phonemes with query terms according to a pronunciation model; and generating a recognition result according to a vocabulary of an n-gram language model.Type: ApplicationFiled: June 5, 2009Publication date: December 9, 2010Applicant: Microsoft CorporationInventors: Xiao Li, Patrick Nguyen, Geoffrey Zweig, Alejandro Acero
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Publication number: 20100121639Abstract: The described implementations relate to speech spelling by a user. One method identifies one or more symbols that may match a user utterance and displays an individual symbol for confirmation by the user.Type: ApplicationFiled: November 11, 2008Publication date: May 13, 2010Applicant: Microsoft CorporationInventor: Geoffrey Zweig
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Publication number: 20090248422Abstract: Training data may be provided, the training data including pairs of source phrases and target phrases. The pairs may be used to train an intra-language statistical machine translation model, where the intra-language statistical machine translation model, when given an input phrase of text in the human language, can compute probabilities of semantic equivalence of the input phrase to possible translations of the input phrase in the human language. The statistical machine translation model may be used to translate between queries and listings. The queries may be text strings in the human language submitted to a search engine. The listing strings may be text strings of formal names of real world entities that are to be searched by the search engine to find matches for the query strings.Type: ApplicationFiled: March 28, 2008Publication date: October 1, 2009Applicant: MICROSOFT CORPORATIONInventors: Xiao Li, Yun-Cheng Ju, Geoffrey Zweig, Alex Acero
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Publication number: 20050119885Abstract: In a speech recognition system, the combination of a log-linear model with a multitude of speech features is provided to recognize unknown speech utterances. The speech recognition system models the posterior probability of linguistic units relevant to speech recognition using a log-linear model. The posterior model captures the probability of the linguistic unit given the observed speech features and the parameters of the posterior model. The posterior model may be determined using the probability of the word sequence hypotheses given a multitude of speech features. Log-linear models are used with features derived from sparse or incomplete data. The speech features that are utilized may include asynchronous, overlapping, and statistically non-independent speech features. Not all features used in training need to appear in testing/recognition.Type: ApplicationFiled: November 28, 2003Publication date: June 2, 2005Inventors: Scott Axelrod, Sreeram Balakrishnan, Stanley Chen, Yuging Gao, Ramesh Gopinath, Hong-Kwang Kuo, Benoit Maison, David Nahamoo, Michael Picheny, George Saon, Geoffrey Zweig