  This article presents our generative model for rhythm action games together
with applications in business operations. Rhythm action games are video games
in which the player is challenged to issue commands at the right timings during
a music session. The timings are rendered in the chart, which consists of
visual symbols, called notes, flying through the screen. We introduce our deep
generative model, Gen\'eLive!, which outperforms the state-of-the-art model by
taking into account musical structures through beats and temporal scales.
Thanks to its favorable performance, Gen\'eLive! was put into operation at KLab
Inc., a Japan-based video game developer, and reduced the business cost of
chart generation by as much as half. The application target included the
phenomenal "Love Live!," which has more than 10 million users across Asia and
beyond, and is one of the few rhythm action franchises that has led the online
era of the genre. In this article, we evaluate the generative performance of
Gen\'eLive! using production datasets at KLab as well as open datasets for
reproducibility, while the model continues to operate in their business. Our
code and the model, tuned and trained using a supercomputer, are publicly
available.
