公开(公告)号：US11381651B2

公开(公告)日：2022-07-05

申请号：US16424949

申请日：2019-05-29

Applicant: ADOBE INC.

Inventor： Handong Zhao ,  Zhiqiang Tao ,  Zhaowen Wang ,  Sheng Li ,  Chen Fang

IPC: H04L29/08 ,  G06N3/08 ,  G06N3/04 ,  H04L67/50 ,  H04L67/306

Abstract: Methods and systems are provided for generating interpretable user modeling system. The interpretable user modeling system can use an intent neural network to implement one or more tasks. The intent neural network can bridge a semantic gap between log data and human language by leveraging tutorial data to understand user logs in a semantically meaningful way. A memory unit of the intent neural network can capture information from the tutorial data. Such a memory unit can be queried to identify human readable sentences related to actions received by the intent neural network. The human readable sentences can be used to interpret the user log data in a semantically meaningful way.

公开(公告)号：US20200382612A1

公开(公告)日：2020-12-03

申请号：US16424949

申请日：2019-05-29

Applicant: ADOBE INC.

Inventor： Handong Zhao ,  Zhiqiang Tao ,  Zhaowen Wang ,  Sheng Li ,  Chen Fang

IPC: H04L29/08 ,  G06N3/08 ,  G06N3/04

Abstract: Methods and systems are provided for generating interpretable user modeling system. The interpretable user modeling system can use an intent neural network to implement one or more tasks. The intent neural network can bridge a semantic gap between log data and human language by leveraging tutorial data to understand user logs in a semantically meaningful way. A memory unit of the intent neural network can capture information from the tutorial data. Such a memory unit can be queried to identify human readable sentences related to actions received by the intent neural network. The human readable sentences can be used to interpret the user log data in a semantically meaningful way.