Patents by Inventor Daniel Biser Balchev

Daniel Biser Balchev 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).

  • Apparatuses, methods, and systems for 3-channel dynamic contextual script recognition using neural network image analytics and 4-tuple machine learning with enhanced templates and context data
    Patent number: 11610084
    Abstract: In some embodiments, a method includes training a first machine learning model based on multiple documents and multiple templates associated with the multiple documents. The method further includes executing the first machine learning model to generate multiple relevancy masks, the multiple relevancy masks to remove a visual structure of the multiple templates from a visual structure of the multiple documents. The method further includes generating multiple multichannel field images to include the multiple relevancy masks and at least one of the multiple documents or the multiple templates. The method further includes training a second machine learning model based on the multiple multichannel field images and multiple non-native texts associated with the multiple documents. The method further includes executing the second machine learning model to generate multiple non-native texts from the multiple multichannel field images.
    Type: Grant
    Filed: June 1, 2020
    Date of Patent: March 21, 2023
    Assignee: Hyper Labs, Inc.
    Inventors: Boris Nikolaev Daskalov, Daniel Biser Balchev
  • Apparatuses, methods, and systems for 3-channel dynamic contextual script recognition using neural network image analytics and 4-tuple machine learning with enhanced templates and context data
    Patent number: 10671892
    Abstract: In some embodiments, a method includes training a first machine learning model based on multiple documents and multiple templates associated with the multiple documents. The method further includes executing the first machine learning model to generate multiple relevancy masks, the multiple relevancy masks to remove a visual structure of the multiple templates from a visual structure of the multiple documents. The method further includes generating multiple multichannel field images to include the multiple relevancy masks and at least one of the multiple documents or the multiple templates. The method further includes training a second machine learning model based on the multiple multichannel field images and multiple non-native texts associated with the multiple documents. The method further includes executing the second machine learning model to generate multiple non-native texts from the multiple multichannel field images.
    Type: Grant
    Filed: November 5, 2019
    Date of Patent: June 2, 2020
    Assignee: Hyper Labs, Inc.
    Inventors: Boris Nikolaev Daskalov, Daniel Biser Balchev