Patents by Inventor Vasiliki Arvaniti

Vasiliki Arvaniti 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).

  • AUTOMATED CONTENT TAGGING WITH LATENT DIRICHLET ALLOCATION OF CONTEXTUAL WORD EMBEDDINGS
    Publication number: 20230376695
    Abstract: Dynamic content tags are generated as content is received by a dynamic content tagging system. A natural language processor (NLP) tokenizes the content and extracts contextual N-grams based on local or global context for the tokens in each document in the content. The contextual N-grams are used as input to a generative model that computes a weighted vector of likelihood values that each contextual N-gram corresponds to one of a set of unlabeled topics. A tag is generated for each unlabeled topic comprising the contextual N-gram having a highest likelihood to correspond to that unlabeled topic. Topic-based deep learning models having tag predictions below a threshold confidence level are retrained using the generated tags, and the retrained topic-based deep learning models dynamically tag the content.
    Type: Application
    Filed: August 1, 2023
    Publication date: November 23, 2023
    Inventors: Nandan Gautam Thor, Vasiliki Arvaniti, Jere Armas Michael Helenius, Erik Michael Bower
  • Automated content tagging with latent dirichlet allocation of contextual word embeddings
    Patent number: 11763091
    Abstract: Dynamic content tags are generated as content is received by a dynamic content tagging system. A natural language processor (NLP) tokenizes the content and extracts contextual N-grams based on local or global context for the tokens in each document in the content. The contextual N-grams are used as input to a generative model that computes a weighted vector of likelihood values that each contextual N-gram corresponds to one of a set of unlabeled topics. A tag is generated for each unlabeled topic comprising the contextual N-gram having a highest likelihood to correspond to that unlabeled topic. Topic-based deep learning models having tag predictions below a threshold confidence level are retrained using the generated tags, and the retrained topic-based deep learning models dynamically tag the content.
    Type: Grant
    Filed: February 25, 2020
    Date of Patent: September 19, 2023
    Assignee: Palo Alto Networks, Inc.
    Inventors: Nandan Gautam Thor, Vasiliki Arvaniti, Jere Armas Michael Helenius, Erik Michael Bower
  • AUTOMATED CONTENT TAGGING WITH LATENT DIRICHLET ALLOCATION OF CONTEXTUAL WORD EMBEDDINGS
    Publication number: 20210264116
    Abstract: Dynamic content tags are generated as content is received by a dynamic content tagging system. A natural language processor (NLP) tokenizes the content and extracts contextual N-grams based on local or global context for the tokens in each document in the content. The contextual N-grams are used as input to a generative model that computes a weighted vector of likelihood values that each contextual N-gram corresponds to one of a set of unlabeled topics. A tag is generated for each unlabeled topic comprising the contextual N-gram having a highest likelihood to correspond to that unlabeled topic. Topic-based deep learning models having tag predictions below a threshold confidence level are retrained using the generated tags, and the retrained topic-based deep learning models dynamically tag the content.
    Type: Application
    Filed: February 25, 2020
    Publication date: August 26, 2021
    Inventors: Nandan Gautam Thor, Vasiliki Arvaniti, Jere Armas Michael Helenius, Erik Michael Bower