Automated conversation goal discovery using neural networks and deep multi-view clustering
US11687730B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | May 13, 2021 |
| Grant date | Jun 27, 2023 |
| Priority date | — |
| Expiry date | Feb 10, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure describes a system, method, and computer program for automatically discovering goals from conversations using neural networks and deep multi-view clustering. A dataset of conversations is partitioned into two views. Vector representations of each view are then generated and clustered in an alternating fashion between views for a number of iterations (i.e., the system alternates between views in generating and clustering vector representations of a view). A first neural network encoder generates the vector representations for the first view, and a second neural network encoder generates the vector representations for the second view. With each semi-iteration, cluster assignments from one view are used to update the encoder for the other view, thus encouraging the two neural network encoders to yield similar cluster assignments. After all the iterations are complete, a user interface enables a user to label each first-view cluster with a goal, where a subset of example utterances is displayed for each cluster.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.