Patents by Inventor Patrick Pantel
Patrick Pantel 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: 11556776Abstract: A task agnostic framework for neural model transfer from a first language to a second language, that can minimize computational and monetary costs by accurately forming predictions in a model of the second language by relying on only a labeled data set in the first language, a parallel data set between both languages, a labeled loss function, and an unlabeled loss function. The models may be trained jointly or in a two-stage process.Type: GrantFiled: October 18, 2018Date of Patent: January 17, 2023Assignee: Microsoft Technology Licensing, LLCInventors: Sujay Kumar Jauhar, Michael Gamon, Patrick Pantel
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Patent number: 11385914Abstract: A content creation application can include a feature that receives an inline note within a document and communicates the content of the inline note and a user identifier associated with an author of the inline note to an intelligence service. The intelligence service can identify, from the content of the inline note, one or more agents and a request, the identified one or more agents being the author, one or more person agents, one or more bot agents, or a combination thereof. Based on the identified agent (or lack thereof), the intelligence service can generate a message to each of the one or more agents and communicate the message to the each of the one or more agents over a communication channel. A person agent or the author can receive the message and view the message using the appropriate communication application without accessing the original document.Type: GrantFiled: January 2, 2018Date of Patent: July 12, 2022Assignee: Microsoft Technology Licensing, LLCInventors: Bernhard S. J. Kohlmeier, Luis Carlos Vargas Herring, Mark J. Encarnacion, Patrick Pantel, Jaime Brooks Teevan, Victor Poznanski, Woon Kiat Wong
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Patent number: 11080468Abstract: This disclosure describes techniques and architectures that involve a latent activity model for workplace emails. Such a model is based, at least in part, on a concept that communications, such as email at a workplace, are purposeful and organized by activities. An activity is a set of interrelated actions and events around a common goal, involving a particular group of people, set of resources, and time framework, for example. The latent activity model involves a probabilistic inference in graphical models that jointly captures the interplay between latent activities and the email contexts governed by the emails. Such contexts may be email recipients, subject and body of the email, and so on.Type: GrantFiled: December 20, 2019Date of Patent: August 3, 2021Assignee: Microsoft Technology Licensing, LLCInventors: Ashequl Qadir, Michael Gamon, Patrick Pantel, Ahmed Hassan Awadallah
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Patent number: 11003858Abstract: A method includes receiving an email addressed to a recipient user, processing the received email using a reparametrized recurrent neural network model to identify an action based on the received email, and wherein the reparametrized recurrent neural network model has been trained on an email dataset annotated with recipient corresponding actions and reparametrized on unannotated conversation data having structures similar to email data.Type: GrantFiled: May 30, 2018Date of Patent: May 11, 2021Assignee: Microsoft Technology Licensing, LLCInventors: Chu-Cheng Lin, Michael Gamon, Dongyeop Kang, Patrick Pantel, Madian Khabsa, Ahmed Hassan Awadallah
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Patent number: 10963318Abstract: Subject matter involves using natural language to Web application program interfaces (API), which map natural language commands into API calls, or API commands. This mapping enables an average user with little or no programming expertise to access Web services that use API calls using natural language. An API schema is accessed and using a specialized grammar, with the help of application programmers, canonical commands associated with the API calls are generated. A hierarchical probabilistic distribution may be applied to a semantic mesh associated with the canonical commands to identify elements of the commands that require labeling. The identified elements may be sent to annotators, for labeling with NL phrases. Labeled elements may be applied to the semantic mesh and probabilities, or weights updated. Labeled elements may be mapped to the canonical commands with machine learning to generate a natural language to API interface. Other embodiments are described and claimed.Type: GrantFiled: October 16, 2019Date of Patent: March 30, 2021Assignee: Microsoft Technology Licensing, LLCInventors: Ahmed Hassan Awadallah, Mark Encarnacion, Michael Gamon, Madian Khabsa, Patrick Pantel, Yu Su
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Patent number: 10818287Abstract: Aspects of the technology described herein provide an efficient user interface that enables users to respond to tasks quickly by providing automated quick task notifications via an audio channel. An audio channel quick task system includes components for recognizing and extracting quick tasks from content (e.g., interpersonal communications, composed content, line of business (LOB) application documents), and for prioritizing and routing the quick tasks to the user via an audio channel at an appropriate and relevant time. The system is enabled to process a user response, determine an action for handling the quick task, and execute the action on behalf of the user (e.g., pass a reply to a requestor, pass an instruction to an application or service, queue the quick task notification, delegate the quick task to another user or bot, forward the quick task to a companion device, or launch an application on a companion device).Type: GrantFiled: January 22, 2018Date of Patent: October 27, 2020Assignee: Microsoft Technology Licensing, LLCInventors: Ryen William White, Mathieu Etienne Jacques Audouin, Patrick Pantel, Nikrouz Ghotbi, Anantha Deepthi Uppala, Vanessa Graham Murdock, Mark James Encarnacion, Nirupama Chandrasekaran
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Publication number: 20200125944Abstract: A task agnostic framework for neural model transfer from a first language to a second language, that can minimize computational and monetary costs by accurately forming predictions in a model of the second language by relying on only a labeled data set in the first language, a parallel data set between both languages, a labeled loss function, and an unlabeled loss function. The models may be trained jointly or in a two-stage process.Type: ApplicationFiled: October 18, 2018Publication date: April 23, 2020Applicant: Microsoft Technology Licensing, LLCInventors: Sujay Kumar JAUHAR, Michael GAMON, Patrick PANTEL
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Publication number: 20200125793Abstract: This disclosure describes techniques and architectures that involve a latent activity model for workplace emails. Such a model is based, at least in part, on a concept that communications, such as email at a workplace, are purposeful and organized by activities. An activity is a set of interrelated actions and events around a common goal, involving a particular group of people, set of resources, and time framework, for example. The latent activity model involves a probabilistic inference in graphical models that jointly captures the interplay between latent activities and the email contexts governed by the emails. Such contexts may be email recipients, subject and body of the email, and so on.Type: ApplicationFiled: December 20, 2019Publication date: April 23, 2020Inventors: Ashequl Qadir, Michael Gamon, Patrick Pantel, Ahmed Hassan Awadallah
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Publication number: 20200050500Abstract: Subject matter involves using natural language to Web application program interfaces (API), which map natural language commands into API calls, or API commands. This mapping enables an average user with little or no programming expertise to access Web services that use API calls using natural language. An API schema is accessed and using a specialized grammar, with the help of application programmers, canonical commands associated with the API calls are generated. A hierarchical probabilistic distribution may be applied to a semantic mesh associated with the canonical commands to identify elements of the commands that require labeling. The identified elements may be sent to annotators, for labeling with NL phrases. Labeled elements may be applied to the semantic mesh and probabilities, or weights updated. Labeled elements may be mapped to the canonical commands with machine learning to generate a natural language to API interface. Other embodiments are described and claimed.Type: ApplicationFiled: October 16, 2019Publication date: February 13, 2020Inventors: Ahmed Hassan Awadallah, Mark Encarnacion, Michael Gamon, Madian Khabsa, Patrick Pantel, Yu Su
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Patent number: 10534848Abstract: This disclosure describes techniques and architectures that involve a latent activity model for workplace emails. Such a model is based, at least in part, on a concept that communications, such as email at a workplace, are purposeful and organized by activities. An activity is a set of interrelated actions and events around a common goal, involving a particular group of people, set of resources, and time framework, for example. The latent activity model involves a probabilistic inference in graphical models that jointly captures the interplay between latent activities and the email contexts governed by the emails. Such contexts may be email recipients, subject and body of the email, and so on.Type: GrantFiled: January 14, 2019Date of Patent: January 14, 2020Assignee: Microsoft Technology Licensing, LLCInventors: Ashequl Qadir, Michael Gamon, Patrick Pantel, Ahmed Hassan Awadallah
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Patent number: 10509837Abstract: In one embodiment, a web service engine server 104 may predict a successive action by a user based on an entity reference 302. The web service engine server 104 may identify an entity reference 302 in a data transmission caused by a user. The web service engine server 104 may determine from the data transmission a user intention towards the entity reference 302 using an intention model based on a transmission log. The web service engine server 104 may predict a related successive web action option 522 for the entity reference 302 based on the user intention.Type: GrantFiled: August 22, 2017Date of Patent: December 17, 2019Assignee: Microsoft Technology Licensing, LLCInventors: Patrick Pantel, Michael Gamon, Anitha Kannan, Ariel Fuxman, Thomas Lin
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Patent number: 10496452Abstract: Subject matter involves using natural language to Web application program interfaces (API), which map natural language commands into API calls, or API commands. This mapping enables an average user with little or no programming expertise to access Web services that use API calls using natural language. An API schema is accessed and using a specialized grammar, with the help of application programmers, canonical commands associated with the API calls are generated. A hierarchical probabilistic distribution may be applied to a semantic mesh associated with the canonical commands to identify elements of the commands that require labeling. The identified elements may be sent to annotators, for labeling with NL phrases. Labeled elements may be applied to the semantic mesh and probabilities, or weights updated. Labeled elements may be mapped to the canonical commands with machine learning to generate a natural language to API interface. Other embodiments are described and claimed.Type: GrantFiled: April 28, 2017Date of Patent: December 3, 2019Assignee: Microsoft Technology Licensing, LLCInventors: Michael Gamon, Mark Encarnacion, Patrick Pantel, Ahmed Hassan Awadallah, Madian Khabsa, Yu Su
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Publication number: 20190228766Abstract: Aspects of the technology described herein provide an efficient user interface that enables users to respond to tasks quickly by providing automated quick task notifications via an audio channel. An audio channel quick task system includes components for recognizing and extracting quick tasks from content (e.g., interpersonal communications, composed content, line of business (LOB) application documents), and for prioritizing and routing the quick tasks to the user via an audio channel at an appropriate and relevant time. The system is enabled to process a user response, determine an action for handling the quick task, and execute the action on behalf of the user (e.g., pass a reply to a requestor, pass an instruction to an application or service, queue the quick task notification, delegate the quick task to another user or bot, forward the quick task to a companion device, or launch an application on a companion device).Type: ApplicationFiled: January 22, 2018Publication date: July 25, 2019Applicant: Microsoft Technology Licensing, LLCInventors: Ryen William White, Mathieu Etienne Jacques Audouin, Patrick Pantel, Nikrouz Ghotbi, Anantha Deepthi Uppala, Vanessa Graham Murdock, Mark James Encarnacion, Nirupama Chandrasekaran
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Publication number: 20190205772Abstract: A content creation application can include a feature that receives an inline note within a document and communicates the content of the inline note and a user identifier associated with an author of the inline note to an intelligence service. The intelligence service can identify, from the content of the inline note, one or more agents and a request, the identified one or more agents being the author, one or more person agents, one or more bot agents, or a combination thereof. Based on the identified agent (or lack thereof), the intelligence service can generate a message to each of the one or more agents and communicate the message to the each of the one or more agents over a communication channel. A person agent or the author can receive the message and view the message using the appropriate communication application without accessing the original document.Type: ApplicationFiled: January 2, 2018Publication date: July 4, 2019Inventors: Bernhard S.J. Kohlmeier, Luis Carlos Vargas Herring, Mark J. Encarnacion, Patrick Pantel, Jaime Brooks Teevan, Victor Poznanski, Woon Kiat Wong
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Publication number: 20190197107Abstract: A method includes receiving an email addressed to a recipient user, processing the received email using a reparametrized recurrent neural network model to identify an action based on the received email, and wherein the reparametrized recurrent neural network model has been trained on an email dataset annotated with recipient corresponding actions and reparametrized on unannotated conversation data having structures similar to email data.Type: ApplicationFiled: May 30, 2018Publication date: June 27, 2019Inventors: Chu-Cheng Lin, Michael Gamon, Dongyeop Kang, Patrick Pantel, Madian Khabsa, Ahmed Hassan Awadallah
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Publication number: 20190147019Abstract: This disclosure describes techniques and architectures that involve a latent activity model for workplace emails. Such a model is based, at least in part, on a concept that communications, such as email at a workplace, are purposeful and organized by activities. An activity is a set of interrelated actions and events around a common goal, involving a particular group of people, set of resources, and time framework, for example. The latent activity model involves a probabilistic inference in graphical models that jointly captures the interplay between latent activities and the email contexts governed by the emails. Such contexts may be email recipients, subject and body of the email, and so on.Type: ApplicationFiled: January 14, 2019Publication date: May 16, 2019Inventors: Ashequl Qadir, Michael Gamon, Patrick Pantel, Ahmed Hassan Awadallah
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Patent number: 10290125Abstract: Various technologies pertaining to exploratory suggestions are described herein. A computer-implemented graph is constructed, where the graph includes nodes that are representative of aspects and edges that are representative of associations between aspects. An aspect is representative of a sub-topic of a topic or a sub-task of a task. The computer-implemented graph is learned based upon content of search logs, and is used to output exploratory suggestions, where a user is exploring a topic or attempting to complete a multi-step task.Type: GrantFiled: July 2, 2014Date of Patent: May 14, 2019Assignee: Microsoft Technology Licensing, LLCInventors: Ahmed Hassan Awadallah, Ryen White, Patrick Pantel, Susan Dumais, Yi-Min Wang
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Patent number: 10217058Abstract: An “Engagement Predictor” provides various techniques for predicting whether things and concepts (i.e., “nuggets”) in content will be engaging or interesting to a user in arbitrary content being consumed by the user. More specifically, the Engagement Predictor provides a notion of interestingness, i.e., an interestingness score, of a nugget on a page that is grounded in observable behavior during content consumption. This interestingness score is determined by evaluating arbitrary documents using a learned transition model. Training of the transition model combines web browsing log data and latent semantic features in training data (i.e., source and destination documents) automatically derived by a Joint Topic Transition (JTT) Model. The interestingness scores are then used for highlighting one or more nuggets, inserting one or more hyperlinks relating to one or more nuggets, importing content relating to one or more nuggets, predicting user clicks, etc.Type: GrantFiled: January 30, 2014Date of Patent: February 26, 2019Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Michael Gamon, Patrick Pantel, Arjun Mukherjee
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Patent number: 10204084Abstract: This disclosure describes techniques and architectures that involve a latent activity model for workplace emails. Such a model is based, at least in part, on a concept that communications, such as email at a workplace, are purposeful and organized by activities. An activity is a set of interrelated actions and events around a common goal, involving a particular group of people, set of resources, and time framework, for example. The latent activity model involves a probabilistic inference in graphical models that jointly captures the interplay between latent activities and the email contexts governed by the emails. Such contexts may be email recipients, subject and body of the email, and so on.Type: GrantFiled: October 10, 2016Date of Patent: February 12, 2019Assignee: Microsoft Technology Licensing, LLCInventors: Ashequl Qadir, Michael Gamon, Patrick Pantel, Ahmed Hassan Awadallah
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Publication number: 20180285170Abstract: Subject matter involves using natural language to Web application program interfaces (API), which map natural language commands into API calls, or API commands. This mapping enables an average user with little or no programming expertise to access Web services that use API calls using natural language. An API schema is accessed and using a specialized grammar, with the help of application programmers, canonical commands associated with the API calls are generated. A hierarchical probabilistic distribution may be applied to a semantic mesh associated with the canonical commands to identify elements of the commands that require labeling. The identified elements may be sent to annotators, for labeling with NL phrases. Labeled elements may be applied to the semantic mesh and probabilities, or weights updated. Labeled elements may be mapped to the canonical commands with machine learning to generate a natural language to API interface. Other embodiments are described and claimed.Type: ApplicationFiled: April 28, 2017Publication date: October 4, 2018Inventors: Michael Gamon, Mark Encarnacion, Patrick Pantel, Ahmed Hassan Awadallah, Madian Khabsa, Yu Su