Patents by Inventor Zhaleh Feizollahi
Zhaleh Feizollahi 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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Publication number: 20220100972Abstract: Examples of the present disclosure describe systems and methods of configuring generic language understanding models. In aspects, one or more previously configured schemas for various applications may be identified and collected. A generic schema may be generated using the collected schemas. The collected schemas may be programmatically mapped to the generic schema. The generic schema may be used to train on ore more models. An interface may be provided to allow browsing the models. The interface may include a configuration mechanism that provides for selecting on or more of the models. The selected models may be bundled programmatically, such that the information and instructions needed to implement the models are configured programmatically. The bundled models may then be provided to a requestor.Type: ApplicationFiled: December 14, 2021Publication date: March 31, 2022Applicant: Microsoft Technology Licensing, LLCInventors: Ruhi Sarikaya, Asli Celikyilmaz, Young-Bum Kim, Zhaleh Feizollahi, Nikhil Ramesh, Hisami Suzuki, Alexandre Rochette
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Patent number: 9886958Abstract: A universal model-based approach for item disambiguation and selection is provided. An utterance may be received by a computing device in response to a list of items for selection. In aspects, the list of items may be displayed on a display screen. The universal disambiguation model may then be applied to the utterance. The universal disambiguation model may be utilized to determine whether the utterance is directed to at least one of the list of items and identify an item from the list corresponding to the utterance, based on identified language and/or domain independent referential features. The computing device may then perform an action which may include selecting the identified item associated with utterance.Type: GrantFiled: December 11, 2015Date of Patent: February 6, 2018Assignee: Microsoft Technology Licensing, LLCInventors: Fethiye Asli Celikyilmaz, Zhaleh Feizollahi, Dilek Hakkani-Tur, Ruhi Sarikaya
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Patent number: 9875237Abstract: An understanding model is trained to account for human perception of the perceived relative importance of different tagged items (e.g. slot/intent/domain). Instead of treating each tagged item as equally important, human perception is used to adjust the training of the understanding model by associating a perceived weight with each of the different predicted items. The relative perceptual importance of the different items may be modeled using different methods (e.g. as a simple weight vector, a model trained using features (lexical, knowledge, slot type, . . . ), and the like). The perceptual weight vector and/or or model are incorporated into the understanding model training process where items that are perceptually more important are weighted more heavily as compared to the items that are determined by human perception as less important.Type: GrantFiled: March 14, 2013Date of Patent: January 23, 2018Assignee: MICROSFOT TECHNOLOGY LICENSING, LLCInventors: Ruhi Sarikaya, Anoop Deoras, Fethiye Asli Celikyilmaz, Zhaleh Feizollahi
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Patent number: 9747279Abstract: Systems and methods for determining a user intent or goal for contextual language understanding by utilizing information from one or more previous user natural language inputs and one or more previous system generated responses to the user natural language inputs are provided. More specifically, the systems and methods utilize a common schema for determining features from the responses and natural language inputs and provide carryover tracking between responses and the natural language inputs. Accordingly, the systems and methods for contextual language understanding provide for a more accurate, a more reliable, and a more efficient context carryover and goal tracking system when compared to systems and methods that do not utilized the responses in determining the user goal/intent.Type: GrantFiled: April 28, 2015Date of Patent: August 29, 2017Assignee: Microsoft Technology Licensing, LLCInventors: Daniel Boies, Ruhi Sarikaya, Zhaleh Feizollahi, Puyang Xu
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Publication number: 20170212886Abstract: Examples of the present disclosure describe systems and methods of configuring generic language understanding models. In aspects, one or more previously configured schemas for various applications may be identified and collected. A generic schema may be generated using the collected schemas. The collected schemas may be programmatically mapped to the generic schema. The generic schema may be used to train on ore more models. An interface may be provided to allow browsing the models. The interface may include a configuration mechanism that provides for selecting on or more of the models. The selected models may be bundled programmatically, such that the information and instructions needed to implement the models are configured programmatically. The bundled models may then be provided to a requestor.Type: ApplicationFiled: January 22, 2016Publication date: July 27, 2017Applicant: Microsoft Technology Licensing, LLCInventors: Ruhi Sarikaya, Asli Celikyilmaz, Young-Bum Kim, Zhaleh Feizollahi, Nikhil Ramesh, Hisami Suzuki, Alexandre Rochette
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Publication number: 20170169829Abstract: A universal model-based approach for item disambiguation and selection is provided. An utterance may be received by a computing device in response to a list of items for selection. In aspects, the list of items may be displayed on a display screen. The universal disambiguation model may then be applied to the utterance. The universal disambiguation model may be utilized to determine whether the utterance is directed to at least one of the list of items and identify an item from the list corresponding to the utterance, based on identified language and/or domain independent referential features. The computing device may then perform an action which may include selecting the identified item associated with utterance.Type: ApplicationFiled: December 11, 2015Publication date: June 15, 2017Applicant: Microsoft Technology Licensing, LLCInventors: Fethiye Asli Celikyilmaz, Zhaleh Feizollahi, Dilek Hakkani-Tur, Ruhi Sarikaya
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Publication number: 20160307567Abstract: Systems and methods for determining a user intent or goal for contextual language understanding by utilizing information from one or more previous user natural language inputs and one or more previous system generated responses to the user natural language inputs are provided. More specifically, the systems and methods utilize a common schema for determining features from the responses and natural language inputs and provide carryover tracking between responses and the natural language inputs. Accordingly, the systems and methods for contextual language understanding provide for a more accurate, a more reliable, and a more efficient context carryover and goal tracking system when compared to systems and methods that do not utilized the responses in determining the user goal/intent.Type: ApplicationFiled: April 28, 2015Publication date: October 20, 2016Applicant: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Daniel Boies, Ruhi Sarikaya, Zhaleh Feizollahi, Puyang Xu
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Patent number: 9412363Abstract: A model-based approach for on-screen item selection and disambiguation is provided. An utterance may be received by a computing device in response to a display of a list of items for selection on a display screen. A disambiguation model may then be applied to the utterance. The disambiguation model may be utilized to determine whether the utterance is directed to at least one of the list of displayed items, extract referential features from the utterance and identify an item from the list corresponding to the utterance, based on the extracted referential features. The computing device may then perform an action which includes selecting the identified item associated with utterance.Type: GrantFiled: March 3, 2014Date of Patent: August 9, 2016Assignee: Microsoft Technology Licensing, LLCInventors: Ruhi Sarikaya, Fethiye Asli Celikyilmaz, Zhaleh Feizollahi, Larry Paul Heck, Dilek Z. Hakkani-Tur
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Patent number: 9318109Abstract: Embodiments provide for tracking a partial dialog state as part of managing a dialog state space, but the embodiments are not so limited. A method of an embodiment jointly models partial state update and named entity recognition using a sequence-based classification or other model, wherein recognition of named entities and a partial state update can be performed in a single processing stage at runtime to generate a distribution over partial dialog states. A system of an embodiment is configured to generate a distribution over partial dialog states at runtime in part using a sequence classification decoding or other algorithm to generate one or more partial dialog state hypothesis and/or a confidence score or measure associated with each hypothesis. Other embodiments are included.Type: GrantFiled: October 2, 2013Date of Patent: April 19, 2016Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Daniel Boies, Ruhi Sarikaya, Alexandre Rochette, Zhaleh Feizollahi, Nikhil Ramesh
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Publication number: 20150248886Abstract: A model-based approach for on-screen item selection and disambiguation is provided. An utterance may be received by a computing device in response to a display of a list of items for selection on a display screen. A disambiguation model may then be applied to the utterance. The disambiguation model may be utilized to determine whether the utterance is directed to at least one of the list of displayed items, extract referential features from the utterance and identify an item from the list corresponding to the utterance, based on the extracted referential features. The computing device may then perform an action which includes selecting the identified item associated with utterance.Type: ApplicationFiled: March 3, 2014Publication date: September 3, 2015Applicant: Microsoft CorporationInventors: Ruhi Sarikaya, Fethiye Asli Celikyilmaz, Zhaleh Feizollahi, Larry Paul Heck, Dilek Z. Hakkani-Tur
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Publication number: 20150095033Abstract: Embodiments provide for tracking a partial dialog state as part of managing a dialog state space, but the embodiments are not so limited. A method of an embodiment jointly models partial state update and named entity recognition using a sequence-based classification or other model, wherein recognition of named entities and a partial state update can be performed in a single processing stage at runtime to generate a distribution over partial dialog states. A system of an embodiment is configured to generate a distribution over partial dialog states at runtime in part using a sequence classification decoding or other algorithm to generate one or more partial dialog state hypothesis and/or a confidence score or measure associated with each hypothesis. Other embodiments are included.Type: ApplicationFiled: October 2, 2013Publication date: April 2, 2015Applicant: MICROSOFT CORPORATIONInventors: Daniel Boies, Ruhi Sarikaya, Alexandre Rochette, Zhaleh Feizollahi, Nikhil Ramesh
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Publication number: 20140278355Abstract: An understanding model is trained to account for human perception of the perceived relative importance of different tagged items (e.g. slot/intent/domain). Instead of treating each tagged item as equally important, human perception is used to adjust the training of the understanding model by associating a perceived weight with each of the different predicted items. The relative perceptual importance of the different items may be modeled using different methods (e.g. as a simple weight vector, a model trained using features (lexical, knowledge, slot type, . . . ), and the like). The perceptual weight vector and/or or model are incorporated into the understanding model training process where items that are perceptually more important are weighted more heavily as compared to the items that are determined by human perception as less important.Type: ApplicationFiled: March 14, 2013Publication date: September 18, 2014Applicant: MICROSOFT CORPORATIONInventors: Ruhi Sarikaya, Anoop Deoras, Fethiye Asli Celikyilmaz, Zhaleh Feizollahi