# Study on the Technology Trend Screening Framework Using Unsupervised Learning
## 5 Conclusions and Future Research  
This study proposes a technology trend screening framework using outliers. Previous studies have presented various methods for selecting keywords from patent documents, aiming at technology forecasting, the discovery of promising technologies, and the search for vacant technologies. However, the previous keyword-based method risks information loss during the pre-processing step. This study, to overcome these limitations, presents a method for interpreting outliers as a new pattern, from a technological viewpoint. DTM is generated by pre-processing text from the collected patent documents by minimizing the amount of information loss. The latest three-year data based on the time series are assigned to the testing dataset, while the remaining data are assigned to the training dataset to train the Doc2 vec model. The trained model is utilized to reduce each embedded document to two dimensions. The inner and outlier parts are distinguished by applying outlier detection techniques, IQR, the three-sigma rule, and iForest, to the dimensionally reduced patent document data. The outliers extracted through each technique are used to construct an ensemble outlier extraction model proposed through voting. The final inner and outlier sets are determined through the constructed model, and the topic model is further applied to identify the technical description included in each set. According to the results of analyzing the drone technology field using the method proposed in this study, research was done regarding the technical fields related to the hardware and functions of the drone during the period included in the training set. In contrast, there is a lack of research on drone systems. The latest three-year technological change revealed that technology development has been related mainly to network, fuel, and drone control. However, the flight control technology  
Figure 10: The perplexity graph for topic model (**a**) training set—inner set (**b**) training set—outlier set (**c**) testing set—inner set (**d**) testing set—outlier set. field was determined to be an outlier, which may be attributed to the recent research on autonomous flight. Therefore, the autonomous flight field will be a vacant or promising technology for future studies in the drone field. This study is significant in that it proposed a technology trend screening framework that offers a guide to considering outliers as new patterns, or new or vacant technologies. In our research, we use unsupervised learning to identify detailed technology in each inner and outlier set. Due to depending on text information only, there is a limitation that the performance of the model cannot be measured. In future research, there is a need to consider an outlier detection method through supervised learning to secure the reliability of the model. Also, we try to apply the XAI (explainable AI) techniques to explain in more detail why a patent has novelty or not. These efforts are expected to help technology-based corporates establish more effective strategies in the future. [PERSON]. ([PERSON]) designed this research and conducted the experiment as described. [PERSON] collected the data set for the experiment. [PERSON]. ([PERSON]) analyzed the data to show the validity of this paper. In addition, all authors cooperated with each other in revising the paper. All authors have read and agreed to the published version of the manuscripts. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2020R1A2C1005918). This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (No. NRF-2022R1I1A1A01069422). This research was supported by the MOTIE (Ministry of Trade, Industry, and Energy) in Korea, under the Human Resource Development Program for Industrial Innovation (Global) (P0017311) supervised by the Korea Institute for Advancement of Technology (KIAT). Not applicable.