公开(公告)号：US20200327444A1

公开(公告)日：2020-10-15

申请号：US16379110

申请日：2019-04-09

Applicant: Genesys Telecommunications Laboratories, Inc.

Inventor： Sapna Negi ,  Maciej Dabrowski ,  Aravind Ganapathiraju ,  Emir Munoz ,  Veera Elluru Raghavendra ,  Felix Immanuel Wyss

IPC: G06N20/00 ,  G06F21/60 ,  G06N3/08 ,  G06N3/04 ,  G06Q30/06

Abstract: A system and method are presented for customer journey event representation learning and outcome prediction using neural sequence models. A plurality of events are input into a module where each event has a schema comprising characteristics of the events and their modalities (web clicks, calls, emails, chats, etc.). The events of different modalities can be captured using different schemas and therefore embodiments described herein are schema-agnostic. Each event is represented as a vector of some number of numbers by the module with a plurality of vectors being generated in total for each customer visit. The vectors are then used in sequence learning to predict real-time next best actions or outcome probabilities in a customer journey using machine learning algorithms such as recurrent neural networks.

公开(公告)号：US11568305B2

公开(公告)日：2023-01-31

申请号：US16379110

申请日：2019-04-09

Applicant: Genesys Telecommunications Laboratories, Inc.

Inventor： Sapna Negi ,  Maciej Dabrowski ,  Aravind Ganapathiraju ,  Emir Munoz ,  Veera Elluru Raghavendra ,  Felix Immanuel Wyss

IPC: G06Q20/00 ,  G06N20/00 ,  G06F21/60 ,  G06N3/04 ,  G06N3/08 ,  G06Q30/06

Abstract: A system and method are presented for customer journey event representation learning and outcome prediction using neural sequence models. A plurality of events are input into a module where each event has a schema comprising characteristics of the events and their modalities (web clicks, calls, emails, chats, etc.). The events of different modalities can be captured using different schemas and therefore embodiments described herein are schema-agnostic. Each event is represented as a vector of some number of numbers by the module with a plurality of vectors being generated in total for each customer visit. The vectors are then used in sequence learning to predict real-time next best actions or outcome probabilities in a customer journey using machine learning algorithms such as recurrent neural networks.