## 2 Theoretical Background
### Patent Map  
Patent maps describe relationships with patents through visual elements such as charts, graphs, bars, and tables [26; 27]. A patent map is an effective method in that it provides both practical and intuitive information [28]. Although various types of patent maps have been developed, what the patent maps that utilize information such as author and citations have in common is that they are simple and intuitive. However, since such maps do not use a description of the patent document that contains a definition of the technology, an interpretation of a specific technical field is limited [29]. As machine learning-based tools have advanced, the concept of forecasting vacant technical fields has been employed by developing patent maps. In previous studies, principal component analysis (PCA), self-organizing maps (SOMs), and generative topographic mapping (GTM) have been employed as representative techniques. They can represent patent vacuum areas on a map by analyzing the information extracted from patent data [30]. These patent vacuum areas are regarded as vacant technical fields; therefore, patent maps are suitable to identify potential technologies that have not been identified or developed. PCA is a dimensionality reduction and feature extraction algorithm that is accomplished by converting multiple variables into several linear combinations [31]. PCA can indicate the significant information in a two-dimensional space; therefore, by using it, researchers can simply identify patent vacuum areas. However, a PCA-based patent map has some limits in that the map has difficulty grasping the boundaries of the patent vacuum areas. If the patent vacuum area is defined differently for each researcher, the results can lead to various interpretations and can consequently be evaluated as invalid. An SOM is an artificial neural network that maps multidimensional data into two-dimensional topology grids [32]. SOM-based patent maps can concentrate patents with similar characteristics on individual nodes, and the similarities and the differences of the nodes can be described by color contrasts [33; 34]. However, an SOM-based patent map has limitations in identifying and interpreting patent vacuum areas because there is no specific rule to define the boundaries of the contrasting colors. A GTM is a mathematical model for density modeling and visualization [35]. GTMs can project multidimensional data to lower dimensions and vice versa based on the inverse mapping. More importantly, a GTM-based patent map can overcome the limitations of PCA and SOMs [36; 37]. Researchers can automatically identify patent vacuum areas and interpret vacant technology fields using an inverse mapping algorithm in the GTM-based patent map. Comparing the characteristics of the aforementioned three patent maps, unlike the PCA and SOM-based patent maps that require qualitative decision-making concerning their results, a GTM-based patent map can objectively interpret the results [37]. [PERSON] et al. (2020) conducted a patent analysis study by developing a GTM-based patent map. However, the study has some limitations in that keyword vectors according to text mining were employed to develop the GTM-based patent map; technical domain and expert opinion are required to extract the significant keywords that construct keyword vectors. As mentioned above, analyses depending on experts require subjective decisions. Consequently, the keyword vectors are involved in determining whether the results are valid or not. The scope of the data can also be restricted according to the keyword vectors. The paper also analyzed the results of GTM-based patent maps into individual elements. The study did not take advantage of the GTM based patent map's results. Therefore, this study aims to identify patent vacuum areas in the field of TFIFT systems by applying a GTM-based patent map based on classification information. In this study, IPC codes and keywords were used together to analyze the patent vacuum to improve the accuracy of the results.