公开(公告)号：US12130841B2

公开(公告)日：2024-10-29

申请号：US16933361

申请日：2020-07-20

申请人： Adobe Inc.

发明人： Karan Aggarwal ,  Georgios Theocharous ,  Anup Rao

IPC分类号： G06F16/28 ,  G05B23/02 ,  G06F16/35 ,  G06F16/45 ,  G06F18/23213 ,  G06N3/02 ,  G06N3/044 ,  G06N3/0442 ,  G06N3/08 ,  G06V30/19 ,  G06F9/54 ,  G06N3/043 ,  G06N7/04

CPC分类号： G06F16/285 ,  G05B23/0281 ,  G06F16/35 ,  G06F16/45 ,  G06F18/23213 ,  G06N3/02 ,  G06N3/044 ,  G06N3/0442 ,  G06N3/08 ,  G06V30/19107 ,  G06F9/542 ,  G06N3/043 ,  G06N7/046

摘要： A single unified machine learning model (e.g., a neural network) is trained to perform both supervised event predictions and unsupervised time-varying clustering for a sequence of events (e.g., a sequence representing a user behavior) using sequences of events for multiple users using a combined loss function. The unified model can then be used for, given a sequence of events as input, predict a next event to occur after the last event in the sequence and generate a clustering result by performing a clustering operation on the sequence of events. As part of predicting the next event, the unified model is trained to predict an event type for the next event and a time of occurrence for the next event. In certain embodiments, the unified model is a neural network comprising a recurrent neural network (RNN) such as an Long Short Term Memory (LSTM) network.

公开(公告)号：US20220019888A1

公开(公告)日：2022-01-20

申请号：US16933361

申请日：2020-07-20

申请人： Adobe Inc.

发明人： Karan Aggarwal ,  Georgios Theocharous ,  Anup Rao

IPC分类号： G06N3/08 ,  G06F9/54

摘要： A single unified machine learning model (e.g., a neural network) is trained to perform both supervised event predictions and unsupervised time-varying clustering for a sequence of events (e.g., a sequence representing a user behavior) using sequences of events for multiple users using a combined loss function. The unified model can then be used for, given a sequence of events as input, predict a next event to occur after the last event in the sequence and generate a clustering result by performing a clustering operation on the sequence of events. As part of predicting the next event, the unified model is trained to predict an event type for the next event and a time of occurrence for the next event. In certain embodiments, the unified model is a neural network comprising a recurrent neural network (RNN) such as an Long Short Term Memory (LSTM) network.