Patents by Inventor João António Sobral Leite Veiga

João António Sobral Leite 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).

  • Obtaining a generated dataset with a predetermined bias for evaluating algorithmic fairness of a machine learning model
    Patent number: 11734612
    Abstract: In various embodiments, a process for obtaining a generated dataset with a predetermined bias for evaluating algorithmic fairness of a machine learning model includes receiving an input dataset and generating an anonymized reconstructed dataset based at least on the input dataset. The process includes introducing a predetermined bias into the generated dataset, forming an evaluation dataset based at least on the generated dataset with the predetermined bias, and outputting the evaluation dataset. In various embodiments, a process for training a generative model includes configuring a generative model and receiving training data, where the training data includes a tabular dataset. The process includes using computer processor(s) and the received training data to train the generative model, where the generative model is sampled to generate a dataset with a predetermined bias.
    Type: Grant
    Filed: June 30, 2022
    Date of Patent: August 22, 2023
    Inventors: Sérgio Gabriel Pontes Jesus, Duarte Miguel Rodrigues dos Santos Marques Alves, José Maria Pereira Rosa Correia Pombal, André Miguel Ferreira Da Cruz, Joäo António Sobral Leite Veiga, Joäo Guilherme Simöes Bravo Ferreira, Catarina Garcia Belém, Marco Oliveira Pena Sampaio, Pedro Dos Santos Saleiro, Pedro Gustavo Santos Rodrigues Bizarro
  • OBTAINING A GENERATED DATASET WITH A PREDETERMINED BIAS FOR EVALUATING ALGORITHMIC FAIRNESS OF A MACHINE LEARNING MODEL
    Publication number: 20230074606
    Abstract: In various embodiments, a process for obtaining a generated dataset with a predetermined bias for evaluating algorithmic fairness of a machine learning model includes receiving an input dataset and generating an anonymized reconstructed dataset based at least on the input dataset. The process includes introducing a predetermined bias into the generated dataset, forming an evaluation dataset based at least on the generated dataset with the predetermined bias, and outputting the evaluation dataset. In various embodiments, a process for training a generative model includes configuring a generative model and receiving training data, where the training data includes a tabular dataset. The process includes using computer processor(s) and the received training data to train the generative model, where the generative model is sampled to generate a dataset with a predetermined bias.
    Type: Application
    Filed: June 30, 2022
    Publication date: March 9, 2023
    Inventors: Sérgio Gabriel Pontes Jesus, Duarte Miguel Rodrigues dos Santos Marques Alves, José Maria Pereira Rosa Correia Pombal, André Miguel Ferreira da Cruz, João António Sobral Leite Veiga, João Guilherme Simões Bravo Ferreira, Catarina Garcia Belém, Marco Oliveira Pena Sampaio, Pedro dos Santos Saleiro, Pedro Gustavo Santos Rodrigues Bizarro