Context-sensitive search using a deep learning model
US9535960B2 · kind B2 · utility
Inventors
Key dates
| Filing date | Apr 14, 2014 |
| Grant date | Jan 3, 2017 |
| Priority date | — |
| Expiry date | Oct 31, 2034 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/09
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A search engine is described herein for providing search results based on a context in which a query has been submitted, as expressed by context information. The search engine operates by ranking a plurality of documents based on a consideration of the query, and based, in part, on a context concept vector and a plurality of document concept vectors, both generated using a deep learning model (such as a deep neural network). The context concept vector is formed by a projection of the context information into a semantic space using the deep learning model. Each document concept vector is formed by a projection of document information, associated with a particular document, into the same semantic space using the deep learning model. The ranking operates by favoring documents that are relevant to the context within the semantic space, and disfavoring documents that are not relevant to the context.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.