# Zero-Inflated Patent Data Analysis Using Generating Synthetic Samples
## 3 Methods  
In this paper, a new method is proposed to overcome the zero-inflated problem in patent data analysis. The problem mainly occurs during the preprocessing of patent document data. Figure 2 shows a process of patent data preprocessing and analysis. The left flow chart shows the general text mining process and the corresponding procedure of our patent data preprocessing and analysis is on the right. First, we collected patent data related to target technology such as artificial intelligence (AI) or internet of things (IoT) from various patent databases in the world. Patent is a text document containing the results of developed technology such as title, abstract, claims, international patent classification (IPC) codes, etc. [7,8]. Using the patent title and abstract, we built a corpus that was a collection of patent documents. Next, we extracted terms from the patent corpus and selected patent keywords in the terms. Consequently, we built structured data that are a patent-keyword matrix. Its rows and columns represent the patent and keyword, respectively. Each element of this matrix is an occurring frequent value of a keyword in a patent document. This matrix has been used for the patent keyword analysis by various methods based on statistics and machine learning algorithms. In general, the number of keywords is much larger than the number of patent documents. Since we might have the zero-inflated problem in the matrix, we propose a method to overcome the problem in the patent-keyword matrix. We provide an approach to the management and analysis of zero-inflated data using synthetic sample generation. The synthetic data have been used to create data that mimic the characteristics of original data while protecting personal information in medical data [15]. In our research, we provide a method of synthetic sample generation for zero-inflated data analysis. There are diverse methods to generate synthetic data. Variational autoencoder (VAE) and generative adversarial network (GAN) are popular deep learning algorithms for generating synthetic data [16]. Since the VAE and GAN have been mainly used to generate the synthetic data in a field of imaging, these methods are hardly applied to synthetic data generation for  
Figure 2: Patent data preprocessing and analysis. ponent keyword analysis. In particular, because image data consist of pixel values ranging from 0 to 255, most of the data are not zero values. In contrast, since most values of patent keyword data are zeros, data learning of VAE or GAN may be distorted by zero values. If we use VAE or GAN to generate synthetic data from patent keyword data, we need an additional effort to solve the problem of the distortion of data learning by zero value. Therefore, we consider another method for generating synthetic data rather than the VAE and GAN. Figure 3 shows our proposed method for the analysis of zero-inflated data as a whole. In Figure 3, patent documents are transformed to a patent-keyword matrix by the text mining techniques. The matrix could be zero-inflated data because most of its elements are zeros. We generate synthetic data from the matrix to overcome the zero-inflated problem. Synthetic data generation is performed by two approaches, parametric and nonparametric models [15]. The parametric models generate synthetic data by considering all variables at the same time. We have difficulties in generating synthetic data using the parametric models because the number of keywords used as variables in the patent data is very large. Moreover, in the parametric method, it is necessary to obtain the posterior distribution that generates the synthetic data, but in some cases, it is difficult to obtain the posterior distribution. Because of this problem, recently, research on a nonparametric method is being conducted. So, we also study a nonparametric model for synthetic data generation. A classification and regression trees (CART) model is a nonparametric method to generate synthetic data.