Patents by Inventor Sankaranarayanan Ananthakrishnan

Sankaranarayanan Ananthakrishnan 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).

  • Patent number: 11574637
    Abstract: Techniques for using a federated learning framework to update machine learning models for spoken language understanding (SLU) system are described. The system determines which labeled data is needed to update the models based on the models generating an undesired response to an input. The system identifies users to solicit labeled data from, and sends a request to a user device to speak an input. The device generates labeled data using the spoken input, and updates the on-device models using the spoken input and the labeled data. The updated model data is provided to the system to enable the system to update the system-level (global) models.
    Type: Grant
    Filed: September 8, 2020
    Date of Patent: February 7, 2023
    Assignee: Amazon Technologies, Inc.
    Inventors: Anoop Kumar, Anil K Ramakrishna, Sriram Venkatapathy, Rahul Gupta, Sankaranarayanan Ananthakrishnan, Premkumar Natarajan
  • Patent number: 11335346
    Abstract: Techniques for processing a user input are described. Text data representing a user input is processed with respect to at least one finite state transducer (FST) to generate at least one FST hypothesis. Context information may be required to traverse one or more paths of the at least one FST. The text data is also processed using at least one statistical model (e.g., perform intent classification, named entity recognition, and/or domain classification processing) to generate at least one statistical model hypothesis. The at least one FST hypothesis and the at least one statistical model hypothesis are input to a reranker that determines a most likely interpretation of the user input.
    Type: Grant
    Filed: December 10, 2018
    Date of Patent: May 17, 2022
    Assignee: Amazon Technologies, Inc.
    Inventors: Chengwei Su, Spyridon Matsoukas, Sankaranarayanan Ananthakrishnan, Shirin Saleem, Chungnam Chan, Yugang Li, Mallory McManamon, Rahul Gupta, Luca Soldaini
  • Patent number: 11081104
    Abstract: A natural language understanding system that can determine an overall score for a natural language hypothesis using hypothesis-specific component scores from different aspects of NLU processing as well as context data describing the context surrounding the utterance corresponding to the natural language hypotheses. The individual component scores may be input into a feature vector at a location corresponding to a type of a device captured by the utterance. Other locations in the feature vector corresponding to other device types may be populated with zero values. The feature vector may also be populated with other values represent other context data. The feature vector may then be multiplied by a weight vector comprising trained weights corresponding to the feature vector positions to determine a new overall score for each hypothesis, where the overall score incorporates the impact of the context data. Natural language hypotheses can be ranked using their respective new overall scores.
    Type: Grant
    Filed: December 12, 2017
    Date of Patent: August 3, 2021
    Assignee: Amazon Technologies, Inc.
    Inventors: Chengwei Su, Sankaranarayanan Ananthakrishnan, Spyridon Matsoukas, Shirin Saleem, Rahul Gupta, Kavya Ravikumar, John Will Crimmins, Kelly James Vanee, John Pelak, Melanie Chie Bomke Gens
  • Patent number: 11043205
    Abstract: A natural language processing system that can determine an overall score for a natural language hypothesis using hypothesis-specific component scores from different aspects of NLU processing. The individual component scores may be weighted by weights trained to optimize the overall scores relative to each other. Each domain of the system may be configured with a separate component that determines the overall score with respect to the domain. Natural language hypotheses can be ranked using the overall score either within a specific domain or on a cross-domain basis.
    Type: Grant
    Filed: December 12, 2017
    Date of Patent: June 22, 2021
    Assignee: Amazon Technologies, Inc.
    Inventors: Chengwei Su, Sankaranarayanan Ananthakrishnan, Spyridon Matsoukas, Rahul Gupta, Kelly James Vanee
  • Patent number: 9710463
    Abstract: A two-way speech-to-speech (S2S) translation system actively detects a wide variety of common error types and resolves them through user-friendly dialog with the user(s). Examples include features including one or more of detecting out-of-vocabulary (OOV) named entities and terms, sensing ambiguities, homophones, idioms, ill-formed input, etc. and interactive strategies for recovering from such errors. In some examples, different error types are prioritized and systems implementing the approach can include an extensible architecture for implementing these decisions.
    Type: Grant
    Filed: December 6, 2013
    Date of Patent: July 18, 2017
    Assignee: Raytheon BBN Technologies Corp.
    Inventors: Rohit Prasad, Rohit Kumar, Sankaranarayanan Ananthakrishnan, Sanjika Hewavitharana, Matthew Roy, Frederick Choi
  • Patent number: 9412361
    Abstract: A system that configures a device's operation based on the device's environment. The system may receive scene data describing a scene in which the device will operate. The scene data may include image data, audio data, or other data. A feature vector comprising the scene data may be processed to identify one or more categories to be associated with the scene. Various processing techniques, such as using Bayesian nonparametric models, may be used to categorize the scene data. The device may then adjust its operation based on the one or more selected categories.
    Type: Grant
    Filed: September 30, 2014
    Date of Patent: August 9, 2016
    Assignee: Amazon Technologies, Inc.
    Inventors: Alborz Geramifard, Sankaranarayanan Ananthakrishnan
  • Publication number: 20140297252
    Abstract: A two-way speech-to-speech (S2S) translation system actively detects a wide variety of common error types and resolves them through user-friendly dialog with the user(s). Examples include features including one or more of detecting out-of-vocabulary (OOV) named entities and terms, sensing ambiguities, homophones, idioms, ill-formed input, etc. and interactive strategies for recovering from such errors. In some examples, different error types are prioritized and systems implementing the approach can include an extensible architecture for implementing these decisions.
    Type: Application
    Filed: December 6, 2013
    Publication date: October 2, 2014
    Inventors: Rohit Prasad, Rohit Kumar, Sankaranarayanan Ananthakrishnan, Sanjika Hewavitharana, Matthew Roy, Frederick Choi
  • Patent number: 8655640
    Abstract: An unsupervised boosting strategy is applied to refining automatic word alignment. In some examples, the strategy improves the quality of automatic word alignment, for example for resource poor language pairs, thus improving Statistical Machine Translation (SMT) performance.
    Type: Grant
    Filed: March 2, 2011
    Date of Patent: February 18, 2014
    Assignee: Raytheon BBN Technologies Corp.
    Inventor: Sankaranarayanan Ananthakrishnan
  • Publication number: 20120226489
    Abstract: An unsupervised boosting strategy is applied to refining automatic word alignment. In some examples, the strategy improves the quality of automatic word alignment, for example for resource poor language pairs, thus improving Statistical Machine Translation (SMT) performance.
    Type: Application
    Filed: March 2, 2011
    Publication date: September 6, 2012
    Applicant: BBN Technologies Corp.
    Inventor: Sankaranarayanan Ananthakrishnan