  In this paper, we extend some usual techniques of classification resulting
from a large-scale data-mining and network approach. This new technology, which
in particular is designed to be suitable to big data, is used to construct an
open consolidated database from raw data on 4 million patents taken from the US
patent office from 1976 onward. To build the pattern network, not only do we
look at each patent title, but we also examine their full abstract and extract
the relevant keywords accordingly. We refer to this classification as semantic
approach in contrast with the more common technological approach which consists
in taking the topology when considering US Patent office technological classes.
Moreover, we document that both approaches have highly different topological
measures and strong statistical evidence that they feature a different model.
This suggests that our method is a useful tool to extract endogenous
information.
