# Patent Analysis Using Bayesian Data Analysis and Network Modeling
## 6 Conclusions, Limitations, and Future Research  
In this paper, we proposed a methodology of patent analysis using keyword visualizations and BART modeling for technology analysis. We selected the drone technology as our target technology. Using word cloud, correlation networks, multiple linear regression, and BART modeling, we built technology scenarios for drone technology development. We constructed three scenarios as representative strategies for developing drone technology and service. The first scenario is the technology to control drone flighting by collecting and analyzing visual data around the drones. We generated this scenario through the technological relations between 20 keywords identified in the results of keyword visualization and BART modeling. We made the second scenario the technology to control the power of drone flighting by collecting surrounding information and using it. Lastly, we built the technological strategy to control efficient battery storage and operation for drone flighting as the third scenario. All scenarios were produced by the analysis results on the basis of the keywords extracted from drone patents. The results of our research can be applied to various fields in MOT for drone technology. Through collaboration with a group of experts in the field of drone technology, we can draw more diverse implications of the research results of this paper. For example, the drone experts can create new technology scenarios using our results. We leave the further applications of this research results to the role of experts in the drone industry.  
We analyzed the drone technology only using keywords extracted from drone patent documents. However, in addition to patent keywords, much more information describing the technology, such as application date, citation information, pictures, and technology classification codes, is contained in the patent document. Therefore, we can expect better analysis results if we perform technology analysis using more information from patent documents. This study has a limitation in that we performed technology analysis using only a portion of the large patent data.  
In our research, we developed keyword visualization using word cloud and correlation networks. We also constructed a patent analysis model based on multiple linear regression and BART. There are many methods for patent data analysis from statistics and machine learning. To construct more advanced models that analyze patent data, we will consider diverse learning algorithms based on deep learning and mathematical statistics. Therefore, in our future research, we will develop a better patent analysis methodology than now through two new trials. First, we will extract more information contained in patent documents as well as technology keywords and use them for analysis. Second, we will develop a new learning model for analysis optimized for patent data. Through this, we will continue to develop new and excellent technology analysis models for technology management.  
[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, wrote the paper, and performed all the research steps. In addition, all authors have cooperated with each other for revising the paper. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Republic of Korea government (MSIT) (no. NRF-2020R1A2C1005918).  
The authors declare no conflict of interest.