Webinterface production and deployment using artificial neural networks
US10783429B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 5, 2017 |
| Grant date | Sep 22, 2020 |
| Priority date | — |
| Expiry date | Feb 9, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/06
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Roughly described, the technology disclosed provides a so-called machine-learned conversion optimization (MLCO) system that uses artificial neural networks and evolutionary computations to efficiently identify most successful webpage designs in a search space without testing all possible webpage designs in the search space. The search space is defined based on webpage designs provided by marketers. Neural networks are represented as genomes. Neural networks map user attributes from live user traffic to different dimensions and dimension values of output funnels that are presented to the users in real time. The genomes are subjected to evolutionary operations like initialization, testing, competition, and procreation to identify parent genomes that perform well and offspring genomes that are likely to perform well.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.