Using a multi-task-trained neural network to guide interaction with a query-processing system via useful suggestions
US11853362B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 16, 2020 |
| Grant date | Dec 26, 2023 |
| Priority date | — |
| Expiry date | Oct 29, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A computer-implemented technique is described herein for assisting a user in advancing a task objective. The technique uses a suggestion-generating system (SGS) to provide one or more suggestions to a user in response to at least a last-submitted query provided by the user. The SGS may correspond to a classification-type or generative-type neural network. The SGS uses a machine-trained model that is trained using a multi-task training framework based on plural groups of training examples, which, in turn, are produced using different respective example-generating methods. One such example-generating method constructs a training example from queries in a search session. It operates by identifying the task-related intent the queries, and then identifying at least one sequence of queries in the search session that exhibits a coherent task-related intent. A training example is constructed based on queries in such a sequence.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.