Patents by Inventor Tejas Shastry

Tejas Shastry 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: 20220189501
    Abstract: Machine natural language processing to analyze language in apparatus, systems, and methods of using are provided. Audio from camera footage can be transcribed in one exemplary method includes extracting at least one audio segment from a body camera video track, detecting voice activity to identify starting and ending timestamps of voice, transcribing the at least one audio segment to identify and separate audio of at least a first speaker, and scoring the audio of the first speaker to identify interactions of interest. Audio could be analyzed and scored to record verbal performance, respectfulness, wellness, etc. and speakers from the audio can be detected.
    Type: Application
    Filed: December 16, 2021
    Publication date: June 16, 2022
    Applicant: Truleo, Inc.
    Inventors: Tejas SHASTRY, Svyatoslav VERGUN, Colin BROCHTRUP, Matthew GOLDEY
  • Publication number: 20220092268
    Abstract: Systems and methods herein provide for establishing a subjective viewpoint in text. In one embodiment, a method includes identifying intents and metrics in each of a plurality of texts, calculating a sentiment score for each text based on the identified intents and metrics of each text, and calculating a disfluency score for each text to weight the sentiment score of each text. The method also includes training the machine learning model with the texts, and processing a subsequent text through the trained machine learning model to determine a sentiment score of the subsequent text.
    Type: Application
    Filed: September 22, 2021
    Publication date: March 24, 2022
    Applicant: Green Key Technologies, Inc.
    Inventors: Anthony TASSONE, Tejas SHASTRY
  • Publication number: 20220092693
    Abstract: Systems and methods herein provide for understanding the context of multiple conversation events and accurately linking them together. Such may allow for fewer financial transaction opportunities to be missed and enable sell side institutions to book more trades. In one embodiment, a method of classifying financial transaction messages with a trained machine learning model includes identifying entities in a financial transaction message, identifying subsequent passages relating to the financial transaction message, and classifying intent as valid or invalid in the financial transaction message. The method also includes linking events within a specific thread by sequentially processing the passages of the financial transaction message.
    Type: Application
    Filed: September 22, 2021
    Publication date: March 24, 2022
    Applicant: Green Key Technologies, Inc.
    Inventors: Amy ZUEND, Tejas SHASTRY
  • Publication number: 20210375266
    Abstract: In some embodiments, the present invention provides for an exemplary computer system which includes at least the following components: an adaptive self-trained computer engine programmed, during a training stage, to electronically receive an initial speech audio data generated by a microphone of a computing device; dynamically segment the initial speech audio data and the corresponding initial text into a plurality of user phonemes; dynamically associate a plurality of first timestamps with the plurality of user-specific subject-specific phonemes; and, during a transcription stage, electronically receive to-be-transcribed speech audio data of at least one user; dynamically split the to-be transcribed speech audio data into a plurality of to-be-transcribed speech audio segments; dynamically assigning each timestamped to-be-transcribed speech audio segment to a particular core of the multi-core processor; and dynamically transcribing, in parallel, the plurality of timestamped to-be-transcribed speech audio segm
    Type: Application
    Filed: August 9, 2021
    Publication date: December 2, 2021
    Inventors: Tejas Shastry, Anthony Tassone, Patrick Kuca, Svyatoslav Vergun
  • Patent number: 11114088
    Abstract: In some embodiments, the present invention provides for an exemplary computer system which includes at least the following components: an adaptive self-trained computer engine programmed, during a training stage, to electronically receive an initial speech audio data generated by a microphone of a computing device; dynamically segment the initial speech audio data and the corresponding initial text into a plurality of user phonemes; dynamically associate a plurality of first timestamps with the plurality of user-specific subject-specific phonemes; and, during a transcription stage, electronically receive to-be-transcribed speech audio data of at least one user; dynamically split the to-be transcribed speech audio data into a plurality of to-be-transcribed speech audio segments; dynamically assigning each timestamped to-be-transcribed speech audio segment to a particular core of the multi-core processor; and dynamically transcribing, in parallel, the plurality of timestamped to-be-transcribed speech audio segm
    Type: Grant
    Filed: April 13, 2018
    Date of Patent: September 7, 2021
    Assignee: Green Key Technologies, Inc.
    Inventors: Tejas Shastry, Anthony Tassone, Patrick Kuca, Svyatoslav Vergun
  • Publication number: 20210142805
    Abstract: In some embodiments, an exemplary inventive system for improving computer speed and accuracy of automatic speech transcription includes at least components of: a computer processor configured to perform: generating a recognition model specification for a plurality of distinct speech-to-text transcription engines; where each distinct speech-to-text transcription engine corresponds to a respective distinct speech recognition model; receiving at least one audio recording representing a speech of a person; segmenting the audio recording into a plurality of audio segments; determining a respective distinct speech-to-text transcription engine to transcribe a respective audio segment; receiving, from the respective transcription engine, a hypothesis for the respective audio segment; accepting the hypothesis to remove a need to submit the respective audio segment to another distinct speech-to-text transcription engine, resulting in the improved computer speed and the accuracy of automatic speech transcription and gen
    Type: Application
    Filed: January 20, 2021
    Publication date: May 13, 2021
    Applicant: GREEN KEY TECHNOLOGIES, INC.
    Inventors: Tejas Shastry, Matthew Goldey, Svyat Vergun
  • Patent number: 10930287
    Abstract: In some embodiments, an exemplary inventive system for improving computer speed and accuracy of automatic speech transcription includes at least components of: a computer processor configured to perform: generating a recognition model specification for a plurality of distinct speech-to-text transcription engines; where each distinct speech-to-text transcription engine corresponds to a respective distinct speech recognition model; receiving at least one audio recording representing a speech of a person; segmenting the audio recording into a plurality of audio segments; determining a respective distinct speech-to-text transcription engine to transcribe a respective audio segment; receiving, from the respective transcription engine, a hypothesis for the respective audio segment; accepting the hypothesis to remove a need to submit the respective audio segment to another distinct speech-to-text transcription engine, resulting in the improved computer speed and the accuracy of automatic speech transcription; and ge
    Type: Grant
    Filed: December 3, 2018
    Date of Patent: February 23, 2021
    Inventors: Tejas Shastry, Matthew Goldey, Svyat Vergun
  • Patent number: 10720560
    Abstract: A transverse thermoelectric includes a first array of hole-conducting nanowires and a second array of electron-conducting nanowires positioned orthogonal to the first array of nanowires. A substrate provides structure to the first array of nanowires and the second array of nanowires.
    Type: Grant
    Filed: September 10, 2015
    Date of Patent: July 21, 2020
    Assignee: Northwestern University
    Inventors: Matthew Grayson, Mark C Hersam, Chuanle Zhou, Yang Tang, Tejas Shastry
  • Publication number: 20190371335
    Abstract: In some embodiments, an exemplary inventive system for improving computer speed and accuracy of automatic speech transcription includes at least components of: a computer processor configured to perform: generating a recognition model specification for a plurality of distinct speech-to-text transcription engines; where each distinct speech-to-text transcription engine corresponds to a respective distinct speech recognition model; receiving at least one audio recording representing a speech of a person; segmenting the audio recording into a plurality of audio segments; determining a respective distinct speech-to-text transcription engine to transcribe a respective audio segment; receiving, from the respective transcription engine, a hypothesis for the respective audio segment; accepting the hypothesis to remove a need to submit the respective audio segment to another distinct speech-to-text transcription engine, resulting in the improved computer speed and the accuracy of automatic speech transcription; and ge
    Type: Application
    Filed: December 3, 2018
    Publication date: December 5, 2019
    Inventors: Tejas Shastry, Matthew Goldey, Svyat Vergun
  • Patent number: 10147428
    Abstract: In some embodiments, an exemplary inventive system for improving computer speed and accuracy of automatic speech transcription includes at least components of: a computer processor configured to perform: generating a recognition model specification for a plurality of distinct speech-to-text transcription engines; where each distinct speech-to-text transcription engine corresponds to a respective distinct speech recognition model; receiving at least one audio recording representing a speech of a person; segmenting the audio recording into a plurality of audio segments; determining a respective distinct speech-to-text transcription engine to transcribe a respective audio segment; receiving, from the respective transcription engine, a hypothesis for the respective audio segment; accepting the hypothesis to remove a need to submit the respective audio segment to another distinct speech-to-text transcription engine, resulting in the improved computer speed and the accuracy of automatic speech transcription; and ge
    Type: Grant
    Filed: May 30, 2018
    Date of Patent: December 4, 2018
    Assignee: Green Key Technologies LLC
    Inventors: Tejas Shastry, Matthew Goldey, Svyat Vergun
  • Publication number: 20180301143
    Abstract: In some embodiments, the present invention provides for an exemplary computer system which includes at least the following components: an adaptive self-trained computer engine programmed, during a training stage, to electronically receive an initial speech audio data generated by a microphone of a computing device; dynamically segment the initial speech audio data and the corresponding initial text into a plurality of user phonemes; dynamically associate a plurality of first timestamps with the plurality of user-specific subject-specific phonemes; and, during a transcription stage, electronically receive to-be-transcribed speech audio data of at least one user; dynamically split the to-be transcribed speech audio data into a plurality of to-be-transcribed speech audio segments; dynamically assigning each timestamped to-be-transcribed speech audio segment to a particular core of the multi-core processor; and dynamically transcribing, in parallel, the plurality of timestamped to-be-transcribed speech audio segm
    Type: Application
    Filed: April 13, 2018
    Publication date: October 18, 2018
    Inventors: Tejas Shastry, Anthony Tassone, Patrick Kuca, Svyatoslav Vergun
  • Patent number: 9741337
    Abstract: In some embodiments, the present invention provides for an exemplary computer system which includes at least the following components: an adaptive self-trained computer engine programmed, during a training stage, to electronically receive an initial speech audio data generated by a microphone of a computing device; dynamically segment the initial speech audio data and the corresponding initial text into a plurality of user phonemes; dynamically associate a plurality of first timestamps with the plurality of user-specific subject-specific phonemes; and, during a transcription stage, electronically receive to-be-transcribed speech audio data of at least one user; dynamically split the to-be transcribed speech audio data into a plurality of to-be-transcribed speech audio segments; dynamically assigning each timestamped to-be-transcribed speech audio segment to a particular core of the multi-core processor; and dynamically transcribing, in parallel, the plurality of timestamped to-be-transcribed speech audio segm
    Type: Grant
    Filed: April 3, 2017
    Date of Patent: August 22, 2017
    Assignee: Green Key Technologies LLC
    Inventors: Tejas Shastry, Anthony Tassone, Patrick Kuca, Svyatoslav Vergun
  • Publication number: 20160079509
    Abstract: A transverse thermoelectric includes a first array of hole-conducting nanowires and a second array of electron-conducting nanowires positioned orthogonal to the first array of nanowires. A substrate provides structure to the first array of nanowires and the second array of nanowires.
    Type: Application
    Filed: September 10, 2015
    Publication date: March 17, 2016
    Inventors: Matthew Grayson, Mark C. Hersam, Chuanle Zhou, Yang Tang, Tejas Shastry
  • Patent number: D766820
    Type: Grant
    Filed: May 21, 2015
    Date of Patent: September 20, 2016
    Assignee: STRYDE TECHNOLOGIES, INC.
    Inventors: Michael Geier, Michael Mannhard, Tejas Shastry, Alex Smith
  • Patent number: D778235
    Type: Grant
    Filed: May 25, 2016
    Date of Patent: February 7, 2017
    Assignee: STYRDE TECHNOLOGIES, INC.
    Inventors: Michael Geier, Michael Mannhard, Tejas Shastry, Alex Smith