Machine-learned models incorporating sequence encoders that operate on bag of words input
US12190209B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 6, 2021 |
| Grant date | Jan 7, 2025 |
| Priority date | — |
| Expiry date | Oct 28, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/0895
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques for incorporating sequence encoders into machine-learned models where the sequence encoders operate on bag of words (BOW) input are provided. Tokens that are associated with online activities of an entity are identified. Machine-learned embeddings that correspond to the tokens are identified. Based on one or more ordering criteria that are independent of the temporal occurrence of the online activities of the entity, an order of the machine-learned embeddings is determined. Based on the order, the machine-learned embeddings are inputted to a sequence encoder that generates output. Based on the output, a machine learned model that includes the sequence encoder generates a score. A content item is selected based on the score. The content item is transmitted over a computer network to a computing device.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.