Unsupervised technique for training an engagement classifier in chat-based group conversation
US10949454B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 22, 2018 |
| Grant date | Mar 16, 2021 |
| Priority date | — |
| Expiry date | Apr 11, 2039 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L51/04
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
An engagement classifier for a group chatbot is trained by leveraging the implicit dataset generated by humans engaging in both direct messages as well as group conversations. Human-to-human direct messages are used as an approximate representation of the domain knowledge and expertise of each user. The decision to engage in a group conversation is assumed to be based on that domain knowledge. The knowledge representations and instances of engagements in group conversations yields an effective set of features and labels which can be used to model the engagement decision. The same transfer learning technique is used to generate a knowledge representation for the group chatbot. Given this representation of the domain knowledge of the chatbot, the classifier can predict whether it should engage in any particular group conversation.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.