Patents by Inventor Stefan Iliev STEFANOV

Stefan Iliev STEFANOV 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: 20240071067
    Abstract: A non-transitory processor-readable medium stores instructions to be executed by a processor. The instructions cause the processor to receive a first trained machine learning model that generates a transcription based on a document. The instructions cause the processor to execute the first trained machine learning model and a second trained machine learning model to generate a refined transcription based on the transcription. The instructions cause the processor to execute a quality assurance program to generate a transcription score based on the document and the transcription. The instructions cause the processor to execute the quality assurance program to generate a refined transcription score based on the refined transcription and at least one of the document or the transcription. The at least one refined transcription score indicates an automation performance better than an automation performance for the at least one transcription score.
    Type: Application
    Filed: July 25, 2023
    Publication date: February 29, 2024
    Inventors: Stefan Iliev STEFANOV, Boris Nikolaev DASKALOV, Akhil LOHCHAB
  • Patent number: 11854251
    Abstract: A non-transitory processor-readable medium stores instructions to be executed by a processor. The instructions cause the processor to receive a first trained machine learning model that generates a transcription based on a document. The instructions cause the processor to execute the first trained machine learning model and a second trained machine learning model to generate a refined transcription based on the transcription. The instructions cause the processor to execute a quality assurance program to generate a transcription score based on the document and the transcription. The instructions cause the processor to execute the quality assurance program to generate a refined transcription score based on the refined transcription and at least one of the document or the transcription. The at least one refined transcription score indicates an automation performance better than an automation performance for the at least one transcription score.
    Type: Grant
    Filed: October 20, 2022
    Date of Patent: December 26, 2023
    Assignee: Hyper Labs, Inc.
    Inventors: Stefan Iliev Stefanov, Boris Nikolaev Daskalov, Akhil Lohchab
  • Publication number: 20230050829
    Abstract: A non-transitory processor-readable medium stores instructions to be executed by a processor. The instructions cause the processor to receive a first trained machine learning model that generates a transcription based on a document. The instructions cause the processor to execute the first trained machine learning model and a second trained machine learning model to generate a refined transcription based on the transcription. The instructions cause the processor to execute a quality assurance program to generate a transcription score based on the document and the transcription. The instructions cause the processor to execute the quality assurance program to generate a refined transcription score based on the refined transcription and at least one of the document or the transcription. The at least one refined transcription score indicates an automation performance better than an automation performance for the at least one transcription score.
    Type: Application
    Filed: October 20, 2022
    Publication date: February 16, 2023
    Applicant: Hyper Labs, Inc.
    Inventors: Stefan Iliev STEFANOV, Boris Nikolaev DASKALOV, Akhil LOHCHAB
  • Patent number: 11481691
    Abstract: A non-transitory processor-readable medium stores instructions to be executed by a processor. The instructions cause the processor to receive a first trained machine learning model that generates a transcription based on a document. The instructions cause the processor to execute the first trained machine learning model and a second trained machine learning model to generate a refined transcription based on the transcription. The instructions cause the processor to execute a quality assurance program to generate a transcription score based on the document and the transcription. The instructions cause the processor to execute the quality assurance program to generate a refined transcription score based on the refined transcription and at least one of the document or the transcription. The at least one refined transcription score indicates an automation performance better than an automation performance for the at least one transcription score.
    Type: Grant
    Filed: January 16, 2020
    Date of Patent: October 25, 2022
    Assignee: Hyper Labs, Inc.
    Inventors: Stefan Iliev Stefanov, Boris Nikolaev Daskalov, Akhil Lohchab
  • Publication number: 20210224695
    Abstract: A non-transitory processor-readable medium stores instructions to be executed by a processor. The instructions cause the processor to receive a first trained machine learning model that generates a transcription based on a document. The instructions cause the processor to execute the first trained machine learning model and a second trained machine learning model to generate a refined transcription based on the transcription. The instructions cause the processor to execute a quality assurance program to generate a transcription score based on the document and the transcription. The instructions cause the processor to execute the quality assurance program to generate a refined transcription score based on the refined transcription and at least one of the document or the transcription. The at least one refined transcription score indicates an automation performance better than an automation performance for the at least one transcription score.
    Type: Application
    Filed: January 16, 2020
    Publication date: July 22, 2021
    Inventors: Stefan Iliev STEFANOV, Boris Nikolaev DASKALOV, Akhil LOHCHAB