Selecting dialog acts using controlled randomness and offline optimization
US11776542B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 30, 2021 |
| Grant date | Oct 3, 2023 |
| Priority date | — |
| Expiry date | Jun 22, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L2015/223
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Dialog acts (e.g., questions) are selected for voice browsing by a machine learning model trained to identify a dialog act that is most likely to lead to a desired outcome. When an invocation is received from a user, a context of the invocation is determined, and a pool of dialog acts is scored based on the context by a machine learning model. Dialog acts are selected from the pool and presented to the user in accordance with a randomization policy. Data regarding the dialog acts and their success in achieving a desired outcome is used to train one or more machine learning models to select dialog acts in response to invocations.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.