Patents by Inventor Svyatoslav Vergun

Svyatoslav Vergun 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: 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: 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