Keyword bids determined from sparse data
US11494810B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 29, 2019 |
| Grant date | Nov 8, 2022 |
| Priority date | — |
| Expiry date | Feb 10, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F16/9535
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Keyword bids determined from sparse data are described. Initially, a portfolio optimization platform identifies which keywords included in a portfolio of keywords are low-impression keywords. This platform trains a machine learning model to generate bids for the low-impression keywords with historical data from a search engine. In particular, the platform trains this machine learning model according to an algorithm suited for training with sparse amounts of data, e.g., a temporal difference learning algorithm. In contrast, the platform uses different models, trained according to different algorithms than the low-impression keyword model, to generate bids for keywords determined not to be low-impression keywords. Once the low-impression keyword model is trained offline, the platform deploys the model for use online to generate actual bids for the low-impression keywords and submits them to the search engine. The platform continues to update the low-impression keyword model while deployed according to the sparse-data algorithm.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.