# Zero-Inflated Patent Data Analysis Using Generating Synthetic Samples
## 5 Conclusions  
A new method is studied to solve the zero-inflated problem in the patent data analysis. We considered an approach based on synthetic data generation to zero-inflated data analysis. In particular, we focused on the patent data analysis with the zero-inflated problem. Using text mining techniques, the patent documents are preprocessed to a patent-keyword matrix for patent analysis based on statistics and machine learning algorithms. Each element of this matrix represents the frequency value of a keyword occurring in each patent document. In general, many elements are zeros because the number of keywords is much larger than that of patents. This is the zero-inflated problem. If there are too many zero values in the given data, the performance of the predictive model deteriorates. To solve this problem, we proposed a method of synthetic sample generation from an original given patent-keyword matrix. We compared the proposed method with other common methods such as zero-inflated Poisson and negative binomial models using the AIC as a criterion of performance evaluation between comparative methods. We conducted experiments using the patent documents related to disaster prevention technology and zero-inflated data simulated from multivariate Poisson distribution. From the experimental results, we found that our proposed method has an improved performance and validity, comparing the AICs among the comparison models. The zero-inflated problem appears in various big data analysis fields, and it causes performance degradation of predictive models based on statistics and machine learning algorithms. In our research, we applied the synthetic data generation by CART and random sampling and confirmed the improved performance of this method using real patent documents and simulation data. Furthermore, we can consider more advanced approaches to synthetic data generation using generative deep learning algorithms such as VAE or GAN. However, most previous applications of VAE and GAN focused on image data rather than count data. The practical advantage of our proposed method is that we propose a new method to solve the zero-inflated problem that occurs in the patent keyword analysis process. Using the proposed method, a technology analysis model with improved explanatory power can be built, and the analysis results will be used for technology management such as R&D \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Parameter** & **X2** & **X3** & **X4** & **AIC** \\ \hline \multirow{2}{*}{Original} & b & 0.1809 & \(-\)0.2232 & \(-\)0.0008 & \multirow{2}{*}{54,081.8} \\  & SE (b) & 0.0065 & 0.0046 & 0.0047 & \\ \hline \multirow{2}{*}{With noise (sigma = 0.1)} & b & 0.1796 & \(-\)0.2215 & \(-\)0.0013 & \multirow{2}{*}{54,329.7} \\  & SE (b) & 0.0066 & 0.0047 & 0.0047 & \\ \hline \multirow{2}{*}{With noise (sigma = 0.5)} & b & 0.1450 & \(-\)0.1965 & 0.0021 & \multirow{2}{*}{59,454.2} \\  & SE (b) & 0.0067 & 0.0050 & 0.0050 & \\ \hline \multirow{2}{*}{With noise (sigma = 1)} & b & 0.0915 & \(-\)0.1469 & 0.0035 & \multirow{2}{*}{69,674.9} \\  & SE (b) & 0.0068 & 0.0056 & 0.0056 & \\ \hline \multirow{2}{*}{Zero-inflated Negative Binomial} & b & 0.1515 & \(-\)0.2418 & \(-\)0.0010 & \multirow{2}{*}{49,648.4} \\  & SE (b) & 0.0074 & 0.0060 & 0.0054 & \\ \hline \multirow{2}{*}{Zero-inflated Poisson} & b & 0.1515 & \(-\)0.2418 & \(-\)0.0010 & \multirow{2}{*}{49,646.4} \\  & SE (b) & 0.0074 & 0.0060 & 0.0054 & \\ \hline \multirow{2}{*}{Synthetic sample} & b & \(-\)0.0278 & \(-\)0.0174 & 0.0190 & \multirow{2}{*}{2772.0} \\  & SE (b) & 0.0301 & 0.0219 & 0.0210 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance evaluation between comparative methods (simulation data 2 with low correlation). planning and technology roadmapping. In this paper, we analyzed the patent keywords for technology analysis. We can also consider the technological phrases as well as patent keywords for technology analysis. We think this is an important task related to our research.