Patent ID: 11922290
Assignee: VISA INTERNATIONAL SERVICE ASSOCIATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 3:
4. The system of claim 3, wherein the at least one processor is further programmed or configured to:
generate the real input portion of the input to the dual-attention component of the machine learning model; and
generate the imaginary input portion of the input to the dual-attention component of the machine learning model;
wherein, when generating the real input portion of the input to the dual-attention component of the machine learning model, the at least one processor is programmed or configured to:
provide the output of the one-dimensional feature extraction convolutional layer for the combined real part of the first plurality of features as an input to a dropout layer to generate a real contemporary transaction metric feature vector as the first real input;
provide the output of the one-dimensional feature extraction convolutional layer for the combined real part of the second plurality of features as an input to the dropout layer to generate a real historical transaction metric feature vector as the second real input; and
provide the output of the one-dimensional feature extraction convolutional layer for the combined real part of the third plurality of features as an input to the dropout layer to generate a real historical target transaction metric feature vector as the third real input; and

wherein, when, generating the imaginary input portion of the input to the dual-attention component of the machine learning model, the at least one processor is programmed or configured to:
provide the output of the one-dimensional feature extraction convolutional layer for the combined imaginary part of the first plurality of features as an input to the dropout layer to generate an imaginary contemporary transaction metric feature vector as the first imaginary input;
provide the output of the one-dimensional feature extraction convolutional layer for the combined imaginary part of the second plurality of features as an input to the dropout layer to generate an imaginary historical transaction metric feature vector as the second imaginary input; and
provide the output of the one-dimensional feature extraction convolutional layer for the combined imaginary part of the third plurality of features as an input to the dropout layer to generate an imaginary historical target transaction metric feature vector as the third imaginary input.