System and method for customer journey event representation learning and outcome prediction using neural sequence models
US11568305B2 · kind B2 · utility
Inventors
Key dates
| Filing date | Apr 9, 2019 |
| Grant date | Jan 31, 2023 |
| Priority date | — |
| Expiry date | Dec 1, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.