  Japanese patents are assigned a patent classification code, FI (File Index),
that is unique to Japan. FI is a subdivision of the IPC, an international
patent classification code, that is related to Japanese technology. FIs are
revised to keep up with technological developments. These revisions have
already established more than 30,000 new FIs since 2006. However, these
revisions require a lot of time and workload. Moreover, these revisions are not
automated and are thus inefficient. Therefore, using machine learning to assist
in the revision of patent classification codes (FI) will lead to improved
accuracy and efficiency. This study analyzes patent documents from this new
perspective of assisting in the revision of patent classification codes with
machine learning. To analyze time-series changes in patents, we used the
dynamic topic model (DTM), which is an extension of the latent Dirichlet
allocation (LDA). Also, unlike English, the Japanese language requires
morphological analysis. Patents contain many technical words that are not used
in everyday life, so morphological analysis using a common dictionary is not
sufficient. Therefore, we used a technique for extracting technical terms from
text. After extracting technical terms, we applied them to DTM. In this study,
we determined the technological progress of the lighting class F21 for 14 years
and compared it with the actual revision of patent classification codes. In
other words, we extracted technical terms from Japanese patents and applied DTM
to determine the progress of Japanese technology. Then, we analyzed the results
from the new perspective of revising patent classification codes with machine
learning. As a result, it was found that those whose topics were on the rise
were judged to be new technologies.
