# Patent Keyword Analysis Using Time Series and Copula Models
## 5 Conclusions  
To analyze the patent keyword data over time, we proposed a Gaussian copula directional dependence by using the beta logit model with an integer-valued GARCH model for marginal distributions. We tried to develop the model considering the characteristic of patent data and technology analysis which are time factor and frequency (integer) value. Using the proposed directional dependence with the highest frequency Apple data, we were interested in Apple's technological structure and its technological innovation. The patent keywords extracted from collected patent documents issued by Apple were used in our experiments, because the keywords contain technological aspects of Apple's developments. From this study, we found how much the target response keyword is influenced by the predictor keywords by copula modeling. Technologies of predictor keywords affect the technological developments of target response keywords. The associations between predictor and response keywords provide novel information for Apple's R&D planning. By using a Gaussian copula marginal regression with the beta logit model, we also found the detailed dependence structure of Apple keywords. So, by performing these methods, this paper showed the meaningful trends and relationships among the technologies of Apple.  
This paper is worthy of study from two perspectives: academic and practical. From the first academic point of view, our study presented a statistical analysis method for patent keyword analysis considering time. Next, this paper contributes to the technology management such as research and development planning, technology marketing, new product development, etc. from a practical point of view. For example, Figure 6 in our experimental results shows that Apple's device technology is affected by the detailed technologies of data, user, media, and system. In particular, it can be seen that data and user technologies have more impact on device technology than do media and system technologies. This is because the weight values of data and user are 0.603 and 0.633, respectively, which are relatively larger than the weight values of 0.112 and 0.458 of the media and the system. The other relationships in Figure 6 can also be interpreted in this way. In our future study, we will develop more advanced statistical models that can use more keywords using various copula analyses, generalized linear models, and other integer valued time series models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Keywords** & **2.5\% Quantile** & **50\% Quantile** & **97.5\% Quantile** \\ \hline Device\(\times\)Data & 0.49 & 0.72 & 0.85 \\ Device\(\times\)System & 0.42 & 0.64 & 0.79 \\ Device\(\times\)User & 0.48 & 0.73 & 0.86 \\ Device\(\times\)Media & 0.08 & 0.37 & 0.67 \\ Data\(\times\)System & 0.54 & 0.73 & 0.87 \\ Data\(\times\)User & 0.63 & 0.81 & 0.89 \\ Data\(\times\)Media & \(-\)0.09 & 0.27 & 0.58 \\ System\(\times\)User & 0.39 & 0.67 & 0.81 \\ System\(\times\)Media & \(-\)0.14 & 0.17 & 0.51 \\ User\(\times\)Media & \(-\)0.05 & 0.33 & 0.62 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Posterior quantiles of correlation coefficients.  
**Author Contributions:** [PERSON]-[PERSON] and [PERSON] designed this research and collected the data set for the experiment. [PERSON] and [PERSON] analyzed the data to show the validity of this paper and wrote the paper and performed all the research steps. In addition, all authors have cooperated with each other for revising the paper.  
**Funding:** This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education(NRF-2017R1D1A3B03031152).  
**Conflicts of Interest:** The authors declare no conflict of interest.