Computer program product that implements a machine learning process using a random forest model for predicting advertisement spending
US11030646B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 21, 2020 |
| Grant date | Jun 8, 2021 |
| Priority date | — |
| Expiry date | Sep 21, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q30/0276
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A computer program product is provided for predicting ad spend for a specific media program aired or streamed on a specific network at a specific date and time using a database of media program data that includes known ad spend for a subset of media programs, and viewership data for each of the media programs, including total viewership and viewership ratings. Each of the media programs is identified by its respective network, and date and time of airing or streaming. A random forest model is trained to predict ad spend using variables that are identified as being correlated with ad spend. The trained random forest model is then used to predict ad spend for a specific media program that is aired or streamed on a specific network at a specific date and time, and which has an unknown ad spend.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.