Unsupervised learning of local-aware attribute relevance for device classification and clustering
US11416522B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 26, 2020 |
| Grant date | Aug 16, 2022 |
| Priority date | — |
| Expiry date | Sep 29, 2040 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L41/16
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
In various embodiments, a device classification service obtains data indicative of device attributes of a plurality of devices. The device classification service forms, based on the obtained data indicative of the device attributes, a concept graph that comprises nodes that represent different sets of the device attributes. The device classification service determines, by analyzing the concept graph, a relevance score for each of the device attributes that quantifies how relevant that attribute is to classifying a device by its device type. The device classification service uses the relevance scores for the device attributes to cluster the plurality of devices into device type clusters by their device attributes.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.