US 12,468,922 B2
Automatic profile extraction in data streams using recurrent neural networks
Bernardo José Amaral Nunes de Almeida Branco, Lisbon (PT); Jacopo Bono, Esposende (PT); João Tiago Barriga Negra Ascensão, Lisbon (PT); and Pedro Gustavo Santos Rodrigues Bizarro, Lisbon (PT)
Assigned to Feedzai - Consultadoria e Inovação Tecnológica, S.A., (PT)
Filed by Feedzai - Consultadoria e Inovação Tecnológica, S.A., Coimbra (PT)
Filed on Jan. 27, 2022, as Appl. No. 17/586,461.
Claims priority of provisional application 63/143,253, filed on Jan. 29, 2021.
Claims priority of application No. 117712 (PT), filed on Dec. 29, 2021; and application No. 117759 (PT), filed on Jan. 25, 2022.
Prior Publication US 2022/0245426 A1, Aug. 4, 2022
Int. Cl. G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01)
CPC G06N 3/044 (2023.01) [G06N 3/045 (2023.01); G06N 3/08 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A method comprising:
receiving input sequence data associated with a stream of events;
using a plurality of trained recurrent neural network machine learning models at least in part in parallel to determine different embedding output sets that represent at least a portion of the input sequence data in a plurality of different embedding spaces;
providing the different embedding output sets to one or more classifier machine learning models to determine one or more classifier results, wherein:
the one or more classifier machine learning models includes a respective classifier for a respective one of the trained recurrent neural network machine learning models;
each of the one or more classifier machine learning models is configured to determine a score; and
using the one or more classifier results to provide a prediction output including by combining the score determined by each of the one or more classifier machine learning models.