公开(公告)号：US20220327432A1

公开(公告)日：2022-10-13

申请号：US17715861

申请日：2022-04-07

Applicant: The Toronto-Dominion Bank

Inventor： Jahir Mauricio GUTIERREZ BUGARIN ,  Tingke Shen ,  Ka Ho Yenson Lau ,  Maksims Volkovs ,  Tomi Johan Poutanen

IPC: G06N20/20

Abstract: The disclosed embodiments relate to computer-implemented systems and processes that facilitate a prediction of occurrences of product-specific events during targeted temporal intervals using trained artificial intelligence processes. For example, an apparatus may generate an input dataset based on elements of first interaction data associated with an occurrence of a first event. Based on an application of a trained artificial intelligence process to the input dataset, the apparatus may generate an element of output data representative of a predicted likelihood of an occurrence of each of a plurality of second events during a target temporal interval associated with the first event. The apparatus may also transmit the elements of output data to a computing system, which may perform operations that are consistent with the elements of output data.

公开(公告)号：US20230385694A1

公开(公告)日：2023-11-30

申请号：US18202459

申请日：2023-05-26

Applicant: THE TORONTO-DOMINION BANK

Inventor： Jesse Cole Cresswell ,  Brendan Leigh Ross ,  Ka Ho Yenson Lau ,  Junfeng Wen ,  Yi Sui

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Model training systems collaborate on model training without revealing respective private data sets. Each private data set learns a set of client weights for a set of computer models that are also learned during training. Inference for a particular private data set is determined as a mixture of the computer model parameters according to the client weights. During training, at each iteration, the client weights are updated in one step based on how well sampled models represent the private data set. In another step, gradients are determined for each sampled model and may be weighed according to the client weight for that model, relatively increasing the gradient contribution of a private data set for model parameters that correspond more highly to that private data set.