Causal analysis system
US11468348B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 11, 2020 |
| Grant date | Oct 11, 2022 |
| Priority date | — |
| Expiry date | Apr 15, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2201/865
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods and apparatus for identifying features that may have a high potential impact on key application metrics. These methods rely on observational data to estimate the importance of application features, and use causal inference tools such as Double Machine Learning (double ML) or Recurrent Neural Networks (RNN) to estimate the impacts of treatment features on key metrics. These methods may allow developers to estimate the effectiveness of features without running online experiments. These methods may, for example, be used to effectively plan and prioritize online experiments. Results of the online experiments may be used to optimize key metrics of mobile applications, web applications, websites, and other web-based programs.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.