公开(公告)号：US20220108334A1

公开(公告)日：2022-04-07

申请号：US17060723

申请日：2020-10-01

Applicant: ADOBE INC.

Inventor： AYUSH CHAUHAN ,  Aditya Anand ,  Sunny Dhamnani ,  Shaddy Garg ,  Shiv Kumar Saini

IPC: G06Q30/02 ,  G06N3/04 ,  G06N3/08 ,  G06Q10/06

Abstract: Systems and methods for data analytics are described. The systems and methods include receiving attribute data for at least one user, identifying a plurality of precursor events causally related to an observable target interaction with the at least one user, wherein at least one of the precursor events comprises a marketing event, predicting a probability for each of the precursor events based on the attribute data using a neural network trained with a first loss function comparing individual level training data for the observable target interaction, and performing the marketing event directed to the at least one user based at least in part on the predicted probabilities.

公开(公告)号：US20230153195A1

公开(公告)日：2023-05-18

申请号：US17455364

申请日：2021-11-17

Applicant: ADOBE INC.

Inventor： SUBRATA MITRA ,  AYUSH CHAUHAN ,  SUNAV CHOUDHARY

IPC: G06F11/10

CPC classification number: G06F11/1004 ,  G06F11/1088

Abstract: A method performed by one or more processors that preserves a machine learning model comprises accessing model parameters associated with a machine learning model. The model parameters are determined responsive to training the machine learning model. The method comprises generating a plurality of model parameter sets, where each of the plurality of model parameter sets comprises a separate portion of the set of model parameters. The method comprises determining one or more parity sets comprising values calculated from the plurality of model parameter sets. The method comprises distributing the plurality of model parameter sets and the one or more parity sets among a plurality of computing devices, where each of the plurality of computing devices stores a model parameter set of the plurality of model parameter sets or a parity set of the one or more parity sets. The method comprises accessing, from the plurality of computing devices, a number of sets comprising model parameter sets and at least one parity set. The method comprises reconstructing the machine learning model from the number of sets accessed from the plurality of computing devices.