Patents by Inventor Abhinav Sethy

Abhinav Sethy 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: 11527237
    Abstract: Techniques for recommending a skill experience to a user after a user-system dialog session has ended are described. Upon a dialog session ending, the system uses a first machine learning model to determine potential intents to recommend to a user. The system then uses a second machine learning model to determine a particular skill and intent to recommend. The system then prompts the user to accept the recommended skill and intent. If the user accepts, the system calls the recommended skill to execute. As part of calling the skill, the system sends to the skill at least one entity provided in a natural language user input of the ended dialog session. This enables the skill to skip welcome prompts, and initiate processing to output a response based on the intent and the at least one entity of the ended dialog session.
    Type: Grant
    Filed: September 18, 2020
    Date of Patent: December 13, 2022
    Assignee: Amazon Technologies, Inc.
    Inventors: Ruhi Sarikaya, Hung Tuan Pham, Savas Parastatidis, Dean Curtis, Pushpendre Rastogi, Nitin Ashok Jain, John Arland Nave, Abhinav Sethy, Arpit Gupta, Mayank Kumar, Nakul Dahiwade, Arshdeep Singh, Nikhil Reddy Kortha, Rohit Prasad
  • Publication number: 20220059086
    Abstract: Techniques for decreasing (or eliminating) the possibility of a skill performing an action that is not responsive to a corresponding user input are described. A system may train one or more machine learning models with respect to user inputs, which resulted in incorrect actions being performed by skills, and corresponding user inputs, which resulted in the correct action being performed. The system may use the trained machine learning model(s) to rewrite user inputs that, if not rewritten, may result in incorrect actions being performed. The system may implement the trained machine learning model(s) with respect to ASR output text data to determine if the ASR output text data corresponds (or substantially corresponds) to previous ASR output text data that resulted in an incorrect action being performed.
    Type: Application
    Filed: September 2, 2021
    Publication date: February 24, 2022
    Inventors: Bigyan Rajbhandari, Praveen Kumar Bodigutla, Zhenxiang Zhou, Karen Catelyn Stabile, Chenlei Guo, Abhinav Sethy, Alireza Roshan Ghias, Pragaash Ponnusamy, Kevin Quinn
  • Patent number: 11151986
    Abstract: Techniques for decreasing (or eliminating) the possibility of a skill performing an action that is not responsive to a corresponding user input are described. A system may train one or more machine learning models with respect to user inputs, which resulted in incorrect actions being performed by skills, and corresponding user inputs, which resulted in the correct action being performed. The system may use the trained machine learning model(s) to rewrite user inputs that, if not rewritten, may result in incorrect actions being performed. The system may implement the trained machine learning model(s) with respect to ASR output text data to determine if the ASR output text data corresponds (or substantially corresponds) to previous ASR output text data that resulted in an incorrect action being performed.
    Type: Grant
    Filed: September 21, 2018
    Date of Patent: October 19, 2021
    Assignee: Amazon Technologies, Inc.
    Inventors: Bigyan Rajbhandari, Praveen Kumar Bodigutla, Zhenxiang Zhou, Karen Catelyn Stabile, Chenlei Guo, Abhinav Sethy, Alireza Roshan Ghias, Pragaash Ponnusamy, Kevin Quinn
  • Patent number: 11145308
    Abstract: Symbol sequences are estimated using a computer-implemented method including detecting one or more candidates of a target symbol sequence from a speech-to-text data, extracting a related portion of each candidate from the speech-to-text data, detecting repetition of at least a partial sequence of each candidate within the related portion of the corresponding candidate, labeling the detected repetition with a repetition indication, and estimating whether each candidate is the target symbol sequence, using the corresponding related portion including the repetition indication of each of the candidates.
    Type: Grant
    Filed: September 20, 2019
    Date of Patent: October 12, 2021
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Kenneth W. Church, Gakuto Kurata, Bhuvana Ramabhadran, Abhinav Sethy, Masayuki Suzuki, Ryuki Tachibana
  • Patent number: 11019306
    Abstract: A method of combining data streams from fixed audio-visual sensors with data streams from personal mobile devices including, forming a communication link with at least one of one or more personal mobile devices; receiving at least one of an audio data stream and/or a video data stream from the at least one of the one or more personal mobile devices; determining the quality of the at least one of the audio data stream and/or the video data stream, wherein the audio data stream and/or the video data stream having a quality above a threshold quality is retained; and combining the retained audio data stream and/or the video data stream with the data streams from the fixed audio-visual sensors.
    Type: Grant
    Filed: January 9, 2019
    Date of Patent: May 25, 2021
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Stanley Chen, Kenneth W. Church, Vaibhava Goel, Lidia L. Mangu, Etienne Marcheret, Bhuvana Ramabhadran, Laurence P. Sansone, Abhinav Sethy, Samuel Thomas
  • Patent number: 10692488
    Abstract: A computer selects a test set of sentences from among sentences applied to train a whole sentence recurrent neural network language model to estimate the probability of likelihood of each whole sentence processed by natural language processing being correct. The computer generates imposter sentences from among the test set of sentences by substituting one word in each sentence of the test set of sentences. The computer generates, through the whole sentence recurrent neural network language model, a first score for each sentence of the test set of sentences and at least one additional score for each of the imposter sentences. The computer evaluates an accuracy of the natural language processing system in performing sequential classification tasks based on an accuracy value of the first score in reflecting a correct sentence and the at least one additional score in reflecting an incorrect sentence.
    Type: Grant
    Filed: August 23, 2019
    Date of Patent: June 23, 2020
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Yinghui Huang, Abhinav Sethy, Kartik Audhkhasi, Bhuvana Ramabhadran
  • Publication number: 20200013393
    Abstract: A computer selects a test set of sentences from among sentences applied to train a whole sentence recurrent neural network language model to estimate the probability of likelihood of each whole sentence processed by natural language processing being correct. The computer generates imposter sentences from among the test set of sentences by substituting one word in each sentence of the test set of sentences. The computer generates, through the whole sentence recurrent neural network language model, a first score for each sentence of the test set of sentences and at least one additional score for each of the imposter sentences. The computer evaluates an accuracy of the natural language processing system in performing sequential classification tasks based on an accuracy value of the first score in reflecting a correct sentence and the at least one additional score in reflecting an incorrect sentence.
    Type: Application
    Filed: August 23, 2019
    Publication date: January 9, 2020
    Inventors: YINGHUI HUANG, Abhinav Sethy, Kartik Audhkhasi, Bhuvana Ramabhadran
  • Publication number: 20200013408
    Abstract: Symbol sequences are estimated using a computer-implemented method including detecting one or more candidates of a target symbol sequence from a speech-to-text data, extracting a related portion of each candidate from the speech-to-text data, detecting repetition of at least a partial sequence of each candidate within the related portion of the corresponding candidate, labeling the detected repetition with a repetition indication, and estimating whether each candidate is the target symbol sequence, using the corresponding related portion including the repetition indication of each of the candidates.
    Type: Application
    Filed: September 20, 2019
    Publication date: January 9, 2020
    Inventors: Kenneth W. Church, Gakuto Kurata, Bhuvana Ramabhadran, Abhinav Sethy, Masayuki Suzuki, Ryuki Tachibana
  • Patent number: 10529337
    Abstract: Symbol sequences are estimated using a computer-implemented method including detecting one or more candidates of a target symbol sequence from a speech-to-text data, extracting a related portion of each candidate from the speech-to-text data, detecting repetition of at least a partial sequence of each candidate within the related portion of the corresponding candidate, labeling the detected repetition with a repetition indication, and estimating whether each candidate is the target symbol sequence, using the corresponding related portion including the repetition indication of each of the candidates.
    Type: Grant
    Filed: January 7, 2019
    Date of Patent: January 7, 2020
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Kenneth W. Church, Gakuto Kurata, Bhuvana Ramabhadran, Abhinav Sethy, Masayuki Suzuki, Ryuki Tachibana
  • Publication number: 20190318732
    Abstract: A whole sentence recurrent neural network (RNN) language model (LM) is provided for for estimating a probability of likelihood of each whole sentence processed by natural language processing being correct. A noise contrastive estimation sampler is applied against at least one entire sentence from a corpus of multiple sentences to generate at least one incorrect sentence. The whole sentence RNN LN is trained, using the at least one entire sentence from the corpus and the at least one incorrect sentence, to distinguish the at least one entire sentence as correct. The whole sentence recurrent neural network language model is applied to estimate the probability of likelihood of each whole sentence processed by natural language processing being correct.
    Type: Application
    Filed: April 16, 2018
    Publication date: October 17, 2019
    Inventors: Yinghui Huang, Abhinav Sethy, Kartik Audhkhasi, Bhuvana Ramabhadran
  • Patent number: 10431210
    Abstract: A whole sentence recurrent neural network (RNN) language model (LM) is provided for for estimating a probability of likelihood of each whole sentence processed by natural language processing being correct. A noise contrastive estimation sampler is applied against at least one entire sentence from a corpus of multiple sentences to generate at least one incorrect sentence. The whole sentence RNN LN is trained, using the at least one entire sentence from the corpus and the at least one incorrect sentence, to distinguish the at least one entire sentence as correct. The whole sentence recurrent neural network language model is applied to estimate the probability of likelihood of each whole sentence processed by natural language processing being correct.
    Type: Grant
    Filed: April 16, 2018
    Date of Patent: October 1, 2019
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Yinghui Huang, Abhinav Sethy, Kartik Audhkhasi, Bhuvana Ramabhadran
  • Publication number: 20190149769
    Abstract: A method of combining data streams from fixed audio-visual sensors with data streams from personal mobile devices including, forming a communication link with at least one of one or more personal mobile devices; receiving at least one of an audio data stream and/or a video data stream from the at least one of the one or more personal mobile devices; determining the quality of the at least one of the audio data stream and/or the video data stream, wherein the audio data stream and/or the video data stream having a quality above a threshold quality is retained; and combining the retained audio data stream and/or the video data stream with the data streams from the fixed audio-visual sensors.
    Type: Application
    Filed: January 9, 2019
    Publication date: May 16, 2019
    Inventors: STANLEY CHEN, KENNETH W. CHURCH, VAIBHAVA GOEL, LIDIA L. MANGU, ETIENNE MARCHERET, BHUVANA RAMABHADRAN, LAURENCE P. SANSONE, ABHINAV SETHY, SAMUEL THOMAS
  • Publication number: 20190139550
    Abstract: Symbol sequences are estimated using a computer-implemented method including detecting one or more candidates of a target symbol sequence from a speech-to-text data, extracting a related portion of each candidate from the speech-to-text data, detecting repetition of at least a partial sequence of each candidate within the related portion of the corresponding candidate, labeling the detected repetition with a repetition indication, and estimating whether each candidate is the target symbol sequence, using the corresponding related portion including the repetition indication of each of the candidates.
    Type: Application
    Filed: January 7, 2019
    Publication date: May 9, 2019
    Inventors: Kenneth W. Church, Gakuto Kurata, Bhuvana Ramabhadran, Abhinav Sethy, Masayuki Suzuki, Ryuki Tachibana
  • Patent number: 10230922
    Abstract: A method of combining data streams from fixed audio-visual sensors with data streams from personal mobile devices including, forming a communication link with at least one of one or more personal mobile devices; receiving at least one of an audio data stream and/or a video data stream from the at least one of the one or more personal mobile devices; determining the quality of the at least one of the audio data stream and/or the video data stream, wherein the audio data stream and/or the video data stream having a quality above a threshold quality is retained; and combining the retained audio data stream and/or the video data stream with the data streams from the fixed audio-visual sensors.
    Type: Grant
    Filed: October 2, 2017
    Date of Patent: March 12, 2019
    Assignee: International Business Machines Corporation
    Inventors: Stanley Chen, Kenneth W. Church, Vaibhava Goel, Lidia L. Mangu, Etienne Marcheret, Bhuvana Ramabhadran, Laurence P. Sansone, Abhinav Sethy, Samuel Thomas
  • Patent number: 10229685
    Abstract: Symbol sequences are estimated using a computer-implemented method including detecting one or more candidates of a target symbol sequence from a speech-to-text data, extracting a related portion of each candidate from the speech-to-text data, detecting repetition of at least a partial sequence of each candidate within the related portion of the corresponding candidate, labeling the detected repetition with a repetition indication, and estimating whether each candidate is the target symbol sequence, using the corresponding related portion including the repetition indication of each of the candidates.
    Type: Grant
    Filed: January 18, 2017
    Date of Patent: March 12, 2019
    Assignee: International Business Machines Corporation
    Inventors: Kenneth W. Church, Gakuto Kurata, Bhuvana Ramabhadran, Abhinav Sethy, Masayuki Suzuki, Ryuki Tachibana
  • Publication number: 20180204567
    Abstract: Symbol sequences are estimated using a computer-implemented method including detecting one or more candidates of a target symbol sequence from a speech-to-text data, extracting a related portion of each candidate from the speech-to-text data, detecting repetition of at least a partial sequence of each candidate within the related portion of the corresponding candidate, labeling the detected repetition with a repetition indication, and estimating whether each candidate is the target symbol sequence, using the corresponding related portion including the repetition indication of each of the candidates.
    Type: Application
    Filed: January 18, 2017
    Publication date: July 19, 2018
    Inventors: Kenneth W. Church, Gakuto Kurata, Bhuvana Ramabhadran, Abhinav Sethy, Masayuki Suzuki, Ryuki Tachibana
  • Patent number: 10019438
    Abstract: A mechanism is provided in a data processing system for external word embedding neural network language models. The mechanism configures the data processing system with an external word embedding neural network language model that accepts as input a sequence of words and predicts a current word based on the sequence of words. The external word embedding neural network language model combines an external embedding matrix to a history word embedding matrix and a prediction word embedding matrix of the external word embedding neural network language model. The mechanism receives a sequence of input words by the data processing system. The mechanism applies a plurality of previous words in the sequence of input words as inputs to the external word embedding neural network language model. The external word embedding neural network language model generates a predicted current word based on the plurality of previous words.
    Type: Grant
    Filed: March 18, 2016
    Date of Patent: July 10, 2018
    Assignee: International Business Machines Corporation
    Inventors: Kartik Audhkhasi, Bhuvana Ramabhadran, Abhinav Sethy
  • Patent number: 9934778
    Abstract: Techniques for conversion of non-back-off language models for use in speech decoders. For example, an apparatus for conversion of non-back-off language models for use in speech decoders. For example, an apparatus is configured convert a non-back-off language model to a back-off language model. The converted back-off language model is pruned. The converted back-off language model is usable for decoding speech.
    Type: Grant
    Filed: August 1, 2016
    Date of Patent: April 3, 2018
    Assignee: International Business Machines Corporation
    Inventors: Ebru Arisoy, Bhuvana Ramabhadran, Abhinav Sethy, Stanley Chen
  • Patent number: 9912909
    Abstract: A method of combining data streams from fixed audio-visual sensors with data streams from personal mobile devices including, forming a communication link with at least one of one or more personal mobile devices; receiving at least one of an audio data stream and/or a video data stream from the at least one of the one or more personal mobile devices; determining the quality of the at least one of the audio data stream and/or the video data stream, wherein the audio data stream and/or the video data stream having a quality above a threshold quality is retained; and combining the retained audio data stream and/or the video data stream with the data streams from the fixed audio-visual sensors.
    Type: Grant
    Filed: December 6, 2016
    Date of Patent: March 6, 2018
    Assignee: International Business Machines Corporation
    Inventors: Stanley Chen, Kenneth W. Church, Vaibhava Goel, Lidia L. Mangu, Etienne Marcheret, Bhuvana Ramabhadran, Laurence P. Sansone, Abhinav Sethy, Samuel Thomas
  • Publication number: 20180027213
    Abstract: A method of combining data streams from fixed audio-visual sensors with data streams from personal mobile devices including, forming a communication link with at least one of one or more personal mobile devices; receiving at least one of an audio data stream and/or a video data stream from the at least one of the one or more personal mobile devices; determining the quality of the at least one of the audio data stream and/or the video data stream, wherein the audio data stream and/or the video data stream having a quality above a threshold quality is retained; and combining the retained audio data stream and/or the video data stream with the data streams from the fixed audio-visual sensors.
    Type: Application
    Filed: October 2, 2017
    Publication date: January 25, 2018
    Inventors: STANLEY CHEN, KENNETH W. CHURCH, VAIBHAVA GOEL, LIDIA L. MANGU, ETIENNE MARCHERET, BHUVANA RAMABHADRAN, LAURENCE P. SANSONE, ABHINAV SETHY, SAMUEL THOMAS