Patents by Inventor Hugo Miguel Ferrão Casal da Veiga

Hugo Miguel Ferrão Casal da Veiga 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: 20240143285
    Abstract: A program code component module implementing a portion of a program application is received. A trained machine learning model is used to automatically predict to which one among a plurality of program architecture layer classifications the program code component module belongs. An automatic analysis option is selected based on the predicted program architecture layer classification for the program code component module. The selected automatic analysis option is performed on the program code component module.
    Type: Application
    Filed: January 9, 2024
    Publication date: May 2, 2024
    Inventors: Hugo Miguel Ferrão Casal da Veiga, António Manuel de Carvalho dos Santos Alegria, Rui Valdemar Pereira Madaleno
  • Patent number: 11922137
    Abstract: A specification of a program code component module implementing a portion of a program application is received. A trained machine learning model is used to automatically predict to which one among a plurality of program architecture layer classifications the program code component module belongs. An automatic analysis option is selected based on the predicted program architecture layer classification for the program code component module. The selected automatic analysis option is performed on the program code component module.
    Type: Grant
    Filed: August 11, 2021
    Date of Patent: March 5, 2024
    Assignee: OutSystems—Software em Rede, S.A.
    Inventors: Hugo Miguel Ferrão Casal da Veiga, António Manuel de Carvalho dos Santos Alegria, Rui Valdemar Pereira Madaleno
  • Publication number: 20230325154
    Abstract: In various embodiments, a process for constrained decoding and ranking of language models for code generation includes receiving a natural language input specifying a desired computer task. The process includes using a machine learning trained converter to convert the natural language input to an output in a computer language, including by, based on a specified grammar for the computer language, limiting eligible options for a token to include in the output in the computer language. The process includes providing the output in the computer language for computer execution.
    Type: Application
    Filed: June 15, 2023
    Publication date: October 12, 2023
    Inventors: Samuel David Pelaio Arcadinho, João Pedro Gonçalves Lages, João Pedro Nunes Nadkarni, Mariana Rodrigues Lourenço, Ana Sofia Aparício da Costa, David Oliveira Aparício, António Manuel de Carvalho dos Santos Alegria, Paulo Jorge Abreu Duarte Ferreira, Catarina Pina de Almeida Coelho, Ângelo Filipe da Silva dos Santos, Hugo Miguel Ferrão Casal da Veiga, Magda Almeida Lopes Pereira
  • Patent number: 11726750
    Abstract: In various embodiments, a process for constrained decoding and ranking of language models for code generation includes receiving a natural language input specifying a desired computer task. The process includes using a machine learning trained converter to convert the natural language input to an output in a computer language, including by, based on a specified grammar for the computer language, limiting eligible options for a token to include in the output in the computer language. The process includes providing the output in the computer language for computer execution.
    Type: Grant
    Filed: November 15, 2022
    Date of Patent: August 15, 2023
    Inventors: Samuel David Pelaio Arcadinho, Joäo Pedro Gonçalves Lages, Joäo Pedro Nunes Nadkarni, Mariana Rodrigues Lourenço, Ana Sofia Aparicio da Costa, David Oliveira Aparício, António Manuel de Carvalho dos Santos Alegria, Paulo Jorge Abreu Duarte Ferreira, Catarina Pina de Almeida Coelho, Ângelo Filipe da Silva dos Santos, Hugo Miguel Ferrão Casal da Veiga, Magda Almeida Lopes Pereira