## 3 Proposed Method
### Overall Procedure
#### 3.2.3 Unstructured Data Analysis  
To determine the technical problems solved by the patent set and obtain their values of difficulty (VOD) effectively, natural language processing, K-means cluster analysis, and network analysis are recommended, to process the unstructured data of the patent set. A five-step process is proposed, as shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Application Date** & **IPC1 = G01C** & **IPC2 = F16M** & **IPC3 = G03B** & **IPC4 = F16F** &... \\ \hline Y1 = 2010 & 2 & 0 & 0 & 0 &... \\ Y2 = 2011 & 2 & 3 & 0 & 0 &... \\ Y3 = 2012 & 0 & 0 & 0 & 0 &... \\ Y4 = 2013 & 1 & 7 & 1 & 1 &... \\... &... &... &... &... &... \\ \hline \end{tabular}
\end{table}
Table 1: IPC/date matrix.  
The details are as follows:  
1. Data collection and preprocessing: This includes loading a user dictionary, removing stop words and text segmentation (used to indicate boundaries of words, such as Chinese and Japanese, etc. those languages without any spaces between words), and tagging words, etc., to obtain a tokenized patent corpus. Nltk (or jieba, to segment a Chinese sentence) in Python is used to implement preprocessing, with the aim of simplifying the process and improving the analysis results.
2. Text feature extraction: This uses term frequency-inverse document frequency (TF-IDF) [53] to highlight words that differ greatly from one patent to another, to extract the technical features of a patent. The proprietary technical features of the patent reflect its R&D theme, which to a certain extent is the technical problem solved by the patent. Each patent is represented by a vector, which is constructed from the TF-IDF value of each keyword. Then, the vector normalization features of all the patents are built in a characteristic matrix.
3. Calculation of degree of similarity: The cosine similarity is decided between patents and constructed using a cosine distance matrix.
4. Cluster analysis: K-means clustering is used to obtain the classified R&D themes of the patent set. The number of clusters should be determined before using the K-means clustering. The latent Dirichlet allocation's perplexity is selected, to estimate the appropriate number of clusters, since it is the most extensively used and well-developed process. Scikit-learn in Python is used to implement the K-means cluster analysis, with the aim of simplifying the process and improving the analysis results. Figure 6 shows the Python implementation codes for the previous four steps.  
Figure 5: Workflow of the natural language process and cluster analysis of patent specifications.  
5. Calculating the centrality of R&D themes: An IPC network is built to measure the value of difficulty (VOD) of R&D themes. This research developed an IPC and R&D theme matrix, as shown in Figure 7, and used this matrix as the input to the IPC network analysis.  
As shown in Figure 7, the nodes represent each IPC and R&D theme of the patent set. More IPC symbols connected to a R&D theme node means that the technology used to solve the problem is more inclined to cross-domain characteristics. For each R&D theme node, network indicators are calculated. The degree of centrality represents the sum of the R&D theme nodes connected to IPC nodes. That is, the bigger the centrality of a R&D theme node, the higher the VOD of the R&D theme. In this way, R&D themes are finally sorted by difficulty. The degree of centrality of R&D theme \(p\) in the IPC network can be measured as:  
\[Den_{Con}\big{(}T_{p}\big{)}=\sum_{k=1}^{\text{N+X}}a\big{(}t_{p},t_{k}\big{)}, \tag{2}\]