# Patent Portfolio Analysis of the Synergy between Machine Learning and Photonics
## 4 Results
### Patent Portfolio Positioning Analysis  
This study used correspondence analysis to perform patent portfolio positioning analysis. Past scholars have used correspondence analysis to study the distribution of ecosystems [32], brand positioning [33], applications of medical management [34], and innovation management strategies [35]. This study used assignees and IPC numbers as categorical variables and selected data of the top ten assignees in machine learning in photonics patents; these patents were distributed over 77 classes of the three-level IPC patents and are related to diverse technological fields. Sometimes the positioning figure of the correspondence analysis cannot completely explain the relationship between the variables, particularly when too many variables exist and overlap or blur the figure, resulting in difficulty in understanding the figure. Therefore, this study used correspondence-cluster analysis to clarify the relationship and connection between multiple variables and classes; the original data were quantified with correspondence analysis, and cluster analysis was used to conduct research [36]. This study used a two-stage clustering method by splitting the clustering calculations into two stages. The first stage involved using the hierarchical clustering method to collect the agglomeration schedule of the samples during merging to observe the clustering process. Next, the cluster distance coefficient was used as the standard to determine the number of clusters. After the optimal number of clusters was decided, the second stage used the nonhierarchical clustering method to perform clustering. The results are displayed in Figure 2. Figure 2 reveals the cluster distance coefficients for different numbers of clusters. \begin{table}
\begin{tabular}{c c} \hline \hline
**Relevance to Machine Learning and Photonics** & **Assignee** \\ \hline Optical communication equipment & AT\&T Intellectual Property (81, 9.87\%), Samsung Electronics Co., Ltd. (Suwon, Korea) (13, 1.58\%) \\  & Intel Corporation (56, 6.82\%), Magic Leap (40, 4.87\%), International \\ Smart computing & Business Machines Corporation (35, 4.26\%), Microsoft Technology \\  & Licensing (20, 2.44\%) \\ Medical applications & HeartFlow, Inc. (15, 1.83\%), Align Technology Inc. (13, 1.58\%) \\ Academic institutions using machine learning and photonics & California Institute of Technology (15, 1.83\%), Leland Stanford \\  & Junior University (13, 1.58\%) \\ \hline \hline \end{tabular} Note: The first number in the bracket represents the number of patents. The second number in the bracket represents the patent share.  
\end{table} 
Table 2: Number of granted patents of the top ten assignees. The cluster distance coefficients were used to determine the number of clusters. The coefficients underwent the most considerable change when the number of clusters changed from three groups to two groups, which signifies that more effort is required to merge three groups into two groups. Therefore, the optimal number of groups was determined to be three groups based on the coefficient changes. Next, the K-means cluster method divided the top ten assignees and 77 IPC patent classes into three groups. The grouped patent portfolios of assignees and IPC numbers of machine learning in photonics are illustrated in Figure 3. It reveals the visual results of correspondence analysis and cluster analysis. The axes represent the two main factors determined through principal component analysis, and the axis scores are presented as \(x\) and \(y\) coordinates on a 2-dimensional plane. Table 3 shows the coordinates of each assignee in Figure 3. Table 4 demonstrates the main members of each group. Figure 3, Tables 3 and 4 indicate that most assignees are in Group III, which signifies that most assignees have achieved similar developments in machine learning in photonics technologies. These main developments include image data processing (G06T), optical elements, systems, or apparatuses (G02B), healthcare informatics and diagnosis (G16H, A61B), and recognition of data (G16H).