Semi-supervised, deep-learning approach for removing irrelevant sentences from text in a customer-support system
US11397952B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 29, 2019 |
| Grant date | Jul 26, 2022 |
| Priority date | — |
| Expiry date | Aug 20, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q10/02
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
During operation, the system receives a customer request. Next, the system segments the customer request into customer request sentences. The system then encodes each sentence from the customer request with information sequentially collected from the previously observed sentences. Next, the system translates the encodings to sparse probabilities that measure the importance of sentences from the customer request. The system then extracts relevant sentences from the customer request based on the importance. Next, the system forms an extracted-sentence customer request embedding from embeddings for the extracted relevant customer request sentences. The system then uses the extracted-sentence customer request embedding to select an agent response from a set of possible agent responses based on comparisons between the extracted-sentence customer request embedding and embeddings for the set of possible agent responses. Finally, the system presents the selected agent response to the customer to facilitate resolving the customer request.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.