Telecommunications network predictions based on machine learning using aggregated network key performance indicators
US12356220B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 29, 2021 |
| Grant date | Jul 8, 2025 |
| Priority date | — |
| Expiry date | Mar 29, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods for making telecommunication network predictions on a granular geographic area-scale. The method can include receiving network key performance indicators (KPIs) associated with a plurality of user equipment (UEs) and then parsing the network KPIs into a plurality of geographic area-based categories corresponding to network KPIs obtained by cell towers or UEs located within a given geographic area, like a zip code. The method can further include combining the geographic area-based categories into clusters determined based on one or more similarities in the network KPIs of each, and predicting a most-influential one of the network KPIs within at least one of the clusters via a machine learning (ML) model. In response, the method can then cause an indicator to be generated at a user interface, the indicator indicating the most-influential one of the network KPIs within the at least one of the clusters.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.