Patents by Inventor Ruhi Sarikaya

Ruhi Sarikaya 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).

  • Publication number: 20220246149
    Abstract: Techniques for determining a command or intent likely to be subsequently invoked by a user of a system are described. A user inputs a command (either via a spoken utterance or textual input) to a system. The system determines content responsive to the command. The system also determines a second command or corresponding intent likely to be invoked by the user subsequent to the previous command. Such determination may involve analyzing pairs of intents, with each pair being associated with a probability that one intent of the pair will be invoked by a user subsequent to a second intent of the pair. The system then outputs first content responsive to the first command and second content soliciting the user as to whether the system to execute the second command.
    Type: Application
    Filed: February 28, 2022
    Publication date: August 4, 2022
    Inventors: Anjishnu Kumar, Xing Fan, Arpit Gupta, Ruhi Sarikaya
  • Patent number: 11386268
    Abstract: Methods and systems are provided for discriminating ambiguous expressions to enhance user experience. For example, a natural language expression may be received by a speech recognition component. The natural language expression may include at least one of words, terms, and phrases of text. A dialog hypothesis set from the natural language expression may be created by using contextual information. In some cases, the dialog hypothesis set has at least two dialog hypotheses. A plurality of dialog responses may be generated for the dialog hypothesis set. The dialog hypothesis set may be ranked based on an analysis of the plurality of the dialog responses. An action may be performed based on ranking the dialog hypothesis set.
    Type: Grant
    Filed: December 4, 2017
    Date of Patent: July 12, 2022
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Jean-Philippe Robichaud, Ruhi Sarikaya
  • Patent number: 11380308
    Abstract: Devices and techniques are generally described for using user feedback to determine routing decisions in a speech processing system. In various examples, first data representing a first utterance may be received. Second data representing a first semantic interpretation of the first utterance may be determined. A first intent data processing application may be selected for processing the second data. Feedback data may be determined related to the first intent data processing application processing the second data. Third data representing a semantic interpretation of a second utterance may be received, wherein the first semantic interpretation is the same as the second semantic interpretation. A second intent data processing application may be determined for processing the third data based at least in part on the feedback data.
    Type: Grant
    Filed: December 13, 2019
    Date of Patent: July 5, 2022
    Assignee: AMAZON TECHNOLOGIES, INC.
    Inventors: Rajesh Kumar Pandey, Ruhi Sarikaya, Shubham Katiyar, Arun Kumar Thenappan, Isaac Joseph Madwed, Jihwan Lee, David Thomas, Julia Kennedy Nemer, Mohamed Farouk AbdelHady, Joe Pemberton, Young-Bum Kim, Prasha Shrestha, Hao Yuan
  • Publication number: 20220100972
    Abstract: 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: Application
    Filed: December 14, 2021
    Publication date: March 31, 2022
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Ruhi Sarikaya, Asli Celikyilmaz, Young-Bum Kim, Zhaleh Feizollahi, Nikhil Ramesh, Hisami Suzuki, Alexandre Rochette
  • Patent number: 11289075
    Abstract: Devices and techniques are generally described for using user feedback to determine routing decisions in a speech processing system. In various examples, first data representing a first utterance may be received. Second data representing a first semantic interpretation of the first utterance may be determined. A first intent data processing application may be selected for processing the second data. Feedback data may be determined related to the first intent data processing application processing the second data. Third data representing a semantic interpretation of a second utterance may be received, wherein the first semantic interpretation is the same as the second semantic interpretation. A second intent data processing application may be determined for processing the third data based at least in part on the feedback data.
    Type: Grant
    Filed: December 13, 2019
    Date of Patent: March 29, 2022
    Assignee: Amazon Technologies, Inc.
    Inventors: Rajesh Kumar Pandey, Ruhi Sarikaya, Shubham Katiyar, Arun Kumar Thenappan, Isaac Joseph Madwed, Jihwan Lee, David Thomas, Julia Kennedy Nemer, Mohamed Farouk AbdelHady, Joe Pemberton, Young-Bum Kim, Hao Yuan, Prasha Shrestha
  • Patent number: 11276403
    Abstract: Techniques for limiting natural language processing performed on input data are described. A system receives input data from a device. The input data corresponds to a command to be executed by the system. The system determines applications likely configured to execute the command. The system performs named entity recognition and intent classification with respect to only the applications likely configured to execute the command.
    Type: Grant
    Filed: November 25, 2019
    Date of Patent: March 15, 2022
    Assignee: Amazon Technologies, Inc.
    Inventors: Ruhi Sarikaya, Rohit Prasad, Kerry Hammil, Spyridon Matsoukas, Nikko Strom, Frédéric Johan Georges Deramat, Stephen Frederick Potter, Young-Bum Kim
  • Patent number: 11270698
    Abstract: Techniques for determining a command or intent likely to be subsequently invoked by a user of a system are described. A user inputs a command (either via a spoken utterance or textual input) to a system. The system determines content responsive to the command. The system also determines a second command or corresponding intent likely to be invoked by the user subsequent to the previous command. Such determination may involve analyzing pairs of intents, with each pair being associated with a probability that one intent of the pair will be invoked by a user subsequent to a second intent of the pair. The system then outputs first content responsive to the first command and second content soliciting the user as to whether the system to execute the second command.
    Type: Grant
    Filed: August 26, 2019
    Date of Patent: March 8, 2022
    Assignee: Amazon Technologies, Inc.
    Inventors: Anjishnu Kumar, Xing Fan, Arpit Gupta, Ruhi Sarikaya
  • Patent number: 11250218
    Abstract: Examples of the present disclosure describe systems and methods of personalizing natural language systems. In aspects, personal data may be uploaded to a personalization server. Upon receiving a data request, a server device may query the personalization server using a user's login information. The login data and the associated personal data may be paired and provided to the personalization server. The paired data may then be provided to a language understanding model to generate a response to the data request. The data in the response may be used to train the language understanding model.
    Type: Grant
    Filed: December 11, 2015
    Date of Patent: February 15, 2022
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Ruhi Sarikaya, Xiaohu Liu
  • Patent number: 11062228
    Abstract: Examples of the present disclosure describe systems and methods of transfer learning techniques for disparate label sets. In aspects, a data set may be accessed on a server device. The data set may comprise labels and word sets associated with the labels. The server device may induce label embedding within the data set. The embedded labels may be represented by multi-dimensional vectors that correspond to particular labels. The vectors may be used to construct label mappings for the data set. The label mappings may be used to train a model to perform domain adaptation or transfer learning techniques. The model may be used to provide results to a statement/query or to train a different model.
    Type: Grant
    Filed: July 6, 2015
    Date of Patent: July 13, 2021
    Assignee: Microsoft Technoiogy Licensing, LLC
    Inventors: Young-Bum Kim, Ruhi Sarikaya
  • Patent number: 11061550
    Abstract: Aspects herein provide third party application authors with a user interface authoring platform that automates and simplifies a task definition process while also providing the ability to leverage pre-existing language understanding models and canonicalization and resolution modules that are provided by the operating system on which the CU system resides or as provided by other third parties. In particular, the present disclosure provides a method and system for authoring a task using a user interface authoring platform.
    Type: Grant
    Filed: March 26, 2020
    Date of Patent: July 13, 2021
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Marius Alexandru Marin, Paul Anthony Crook, Nikhil Holenarsipur Ramesh, Vipul Agarwal, Omar Zia Khan, Alexandre Rochette, Jean-Philippe Robichaud, Ruhi Sarikaya
  • Publication number: 20210193116
    Abstract: Techniques for optimizing a system to improve an overall user satisfaction in a speech controlled system are described. A user speaks an utterance and the system compares an expected sum of user satisfaction values for each action to make a decision as to how best to process the utterance. As a result, the system may make a decision that decreases user satisfaction in the short term but increases user satisfaction in the long term. The system may estimate a user satisfaction value and associate the estimated user satisfaction value with a current dialog state. By tracking user satisfaction values over time, the system may train machine learning models to optimize the expected sum of user satisfaction values. This improves how the system selects an action or application to which to dispatch the dialog state and how a specific application selects an action or intent corresponding to the command.
    Type: Application
    Filed: November 30, 2020
    Publication date: June 24, 2021
    Inventors: Alborz Geramifard, Shiladitya Roy, Ruhi Sarikaya
  • Publication number: 20210142794
    Abstract: A system for processing user utterances and/or text based queries that tracks entities and other context data of a current dialog between the system and the user and can fill slots for new intents of the dialog by performing statistical processing on previously mentioned entities with respect to current slots to be filled. The system may compare a previously mentioned entity to a current slot to be filled using vector representations, such as word embeddings, of the current utterance, dialog history, current intent, name of an entity under consideration, category of the current slot to be filled, distance between the current dialog turn and the dialog turn that mentioned the entity, and other considerations. The individual vectors may be weighted according to an attention operation and processed by a trained decoder to output a score indicating whether the entity in consideration is relevant to the particular slot.
    Type: Application
    Filed: November 17, 2020
    Publication date: May 13, 2021
    Inventors: Lambert Leo Mathias, Bala Murali Krishna Ummaneni, Ryan Scott Aldrich, Diamond Bishop, Ruhi Sarikaya, Chetan Nagaraj Naik
  • Patent number: 10957313
    Abstract: Techniques for performing command processing are described. A system receives, from a device, input data corresponding to a command. The input data may originate as audio data, as text data, or as other data. The system determines NLU processing results corresponding to the input data. The NLU processing results may be associated with multiple speechlets. The system also determines NLU confidences for the NLU processing results for each speechlet. The system sends NLU processing results and an indication to provide potential results to a portion of the multiple speechlets, and receives potential results from the portion of the speechlets. The system also receives indications whether the speechlets need to be re-called if the speechlets are selected to execute with respect to the command. The system ranks the portion of the speechlets based at least in part on the NLU processing results as well as the potential results provided by the portion of the speechlets.
    Type: Grant
    Filed: November 22, 2017
    Date of Patent: March 23, 2021
    Assignee: Amazon Technologies, Inc.
    Inventors: Ruhi Sarikaya, Zheng Ma, Simon Peter Reavely, Kerry Hammil, Huinan Ren, Bradford Jason Snow, Jerrin Thomas Elanjikal
  • Patent number: 10909325
    Abstract: Multi-turn cross-domain natural language understanding (NLU) systems and platforms for building the multi-turn cross-domain NLU system are provided. Further, methods for using and building the multi-turn cross-domain NLU system are provided. More specifically, the multi-turn cross-domain NLU system supports multi-turn bot/agent/application scenarios for new domains without having to select a task definition and/or define a new schema during the building of the NLU system. Accordingly, the platform for building the multi-turn cross-domain NLU system that does not require the builder to select a task and/or build a schema for a selected task provides an easy to use, cost effective, and efficient service for building a NLU system. Further, the multi-turn cross-domain NLU system provides a more versatile NLU system than previously utilized NLU systems that were trained for and limited to a selected task and/or domain.
    Type: Grant
    Filed: June 12, 2019
    Date of Patent: February 2, 2021
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Ruhi Sarikaya, Young-Bum Kim, Alexandre Rochette
  • Publication number: 20210020182
    Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for providing personalized experiences to a computing device based on user input such as voice, text and gesture input are provided. Acoustic patterns associated with voice input, speech patterns, language patterns and natural language processing may be used to identify a specific user providing input from a plurality of users, identify user background characteristics and traits for the specific user, and topically categorize user input in a tiered hierarchical index. Topically categorized user input may be supplemented with user data and world knowledge and personalized responses and feedback for an identified specific user may be provided reactively and proactively.
    Type: Application
    Filed: October 6, 2020
    Publication date: January 21, 2021
    Applicant: Microsoft Technology Licensing, LLC
    Inventor: Ruhi Sarikaya
  • Patent number: 10878808
    Abstract: A system for processing user utterances and/or text based queries that tracks entities and other context data of a current dialog between the system and the user and can fill slots for new intents of the dialog by performing statistical processing on previously mentioned entities with respect to current slots to be filled. The system may compare a previously mentioned entity to a current slot to be filled using vector representations, such as word embeddings, of the current utterance, dialog history, current intent, name of an entity under consideration, category of the current slot to be filled, distance between the current dialog turn and the dialog turn that mentioned the entity, and other considerations. The individual vectors may be weighted according to an attention operation and processed by a trained decoder to output a score indicating whether the entity in consideration is relevant to the particular slot.
    Type: Grant
    Filed: March 23, 2018
    Date of Patent: December 29, 2020
    Inventors: Lambert Leo Mathias, Bala Murali Krishna Ummaneni, Ryan Scott Aldrich, Diamond Bishop, Ruhi Sarikaya, Chetan Nagaraj Naik
  • Patent number: 10867597
    Abstract: Technologies pertaining to slot filling are described herein. A deep neural network, a recurrent neural network, and/or a spatio-temporally deep neural network are configured to assign labels to words in a word sequence set forth in natural language. At least one label is a semantic label that is assigned to at least one word in the word sequence.
    Type: Grant
    Filed: September 2, 2013
    Date of Patent: December 15, 2020
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Anoop Deoras, Kaisheng Yao, Xiaodong He, Li Deng, Geoffrey Gerson Zweig, Ruhi Sarikaya, Dong Yu, Mei-Yuh Hwang, Gregoire Mesnil
  • Patent number: 10854191
    Abstract: Techniques for optimizing a system to improve an overall user satisfaction in a speech controlled system are described. A user speaks an utterance and the system compares an expected sum of user satisfaction values for each action to make a decision as to how best to process the utterance. As a result, the system may make a decision that decreases user satisfaction in the short term but increases user satisfaction in the long term. The system may estimate a user satisfaction value and associate the estimated user satisfaction value with a current dialog state. By tracking user satisfaction values over time, the system may train machine learning models to optimize the expected sum of user satisfaction values. This improves how the system selects an action or application to which to dispatch the dialog state and how a specific application selects an action or intent corresponding to the command.
    Type: Grant
    Filed: September 20, 2017
    Date of Patent: December 1, 2020
    Assignee: Amazon Technologies, Inc.
    Inventors: Alborz Geramifard, Shiladitya Roy, Ruhi Sarikaya
  • Patent number: 10832684
    Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for providing personalized experiences to a computing device based on user input such as voice, text and gesture input are provided. Acoustic patterns associated with voice input, speech patterns, language patterns and natural language processing may be used to identify a specific user providing input from a plurality of users, identify user background characteristics and traits for the specific user, and topically categorize user input in a tiered hierarchical index. Topically categorized user input may be supplemented with user data and world knowledge and personalized responses and feedback for an identified specific user may be provided reactively and proactively.
    Type: Grant
    Filed: August 31, 2016
    Date of Patent: November 10, 2020
    Assignee: Microsoft Technology Licensing, LLC
    Inventor: Ruhi Sarikaya
  • Publication number: 20200334420
    Abstract: Technology is provided for improving digital assistant performance by generating and presenting suggestions to users for completing a task or a session. To generate the suggestions, a machine learned language prediction model is trained with features extracted from multiple sources, such as log data and session context. When input is received from a user, the trained machine learned language prediction model is used to determine the most likely suggestion to present to the user to lead to successful task completion. In generating the suggestion, intermediate suggestion data, such as a domain, intent, and/or slot, is generated for the suggestion. From the generated intermediate suggestion data for the suggestion, a surface form of the suggestion is generated that can be presented to the user. The resulting suggestion and related context may further be used to continue training the machine learned language prediction model.
    Type: Application
    Filed: July 1, 2020
    Publication date: October 22, 2020
    Applicant: Microsoft Technology Licensing, LLC
    Inventor: Ruhi SARIKAYA