Patents by Inventor Filip Gralinkski

Filip Gralinkski 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: 20240211691
    Abstract: Systems and methods are disclosed for generating a Natural Language Processing (NLP) model through iterative training. A method involves processing a plurality of real-world documents, each containing text data, layout data, and image data, using at least one hardware processor. An initial prediction for data points within the documents is generated using a neural network. The initial prediction is then validated by comparing extracted values with the information present in the documents and correcting any discrepancies. The quality of the NLP model is evaluated based on the validated predictions, and upon satisfying a quality constraint, the NLP model is configured to process new documents to extract data points without further validation. This method streamlines the extraction of information from diverse document formats, enhancing the efficiency and accuracy of data retrieval in automated systems.
    Type: Application
    Filed: January 31, 2024
    Publication date: June 27, 2024
    Inventors: Adam Dancewicz, Filip Gralinkski, Lukasz Konrad Borchmann
  • Patent number: 11934786
    Abstract: Methods, systems, and computer programs are presented for providing access to a cloud data platform including a machine learning model for performing a plurality of iterations, by at least one hardware processor, to generate a Natural Language Processing (NLP) model. The cloud data platform performs each iteration by receiving real-world documents and enabling information retrieval from the real-world documents without annotated training data. Each iteration includes receiving data comprising text data, layout data, and image data and analyzing the text data, the layout data, and the image data. The cloud data platform generates one or more outputs from the machine learning model by applying the iterative training on new data, based at least in part on the analyzing of the text data, the layout data, and the image data.
    Type: Grant
    Filed: March 28, 2023
    Date of Patent: March 19, 2024
    Assignee: APPLICA SP. Z O.O.
    Inventors: Adam Dancewicz, Filip Gralinkski, Lukasz Konrad Borchmann
  • Publication number: 20230259709
    Abstract: Methods, systems, and computer programs are presented for providing access to a cloud data platform including a machine learning model for performing a plurality of iterations, by at least one hardware processor, to generate a Natural Language Processing (NLP) model. The cloud data platform performs each iteration by receiving real-world documents and enabling information retrieval from the real-world documents without annotated training data. Each iteration includes receiving data comprising text data, layout data, and image data and analyzing the text data, the layout data, and the image data. The cloud data platform generates one or more outputs from the machine learning model by applying the iterative training on new data, based at least in part on the analyzing of the text data, the layout data, and the image data.
    Type: Application
    Filed: March 28, 2023
    Publication date: August 17, 2023
    Inventors: Adam Dancewicz, Filip Gralinkski, Lukasz Konrad Borchmann