# Emerging Perspectives of Blockchains in Food Supply Chain Traceability Based on Patent Analysis
## 3 Results
### Modeling of Portfolio Technology Space Based on the LDA  
Based on the LDA, patents from the portfolio were found to be distributed across four latent topics. The first step in the analysis of obtained results was the visualization of relations among formed topic clusters in a scatter plot presented in the factorial plane using PCA (Figure 5). It is obvious that patents from the analyzed patent portfolio are distributed in four mutually distinguished topics. Topic A, placed in the lower right quadrant of the factorial plane, is distinguished from the other three topics in relation to being the first, most influential dimension of principal component analysis, explaining 42.21% of the total variability. Topic C, positioned in the lower left quadrant, is quite similar to topic A, in terms of being the second PCA dimension, while topic D, and particularly topic B, are differentiated from topics A and C in that this PCA dimension explains 35.83% of the total variability. Analysis of the content of identified latent topics was performed based on analysis of the most frequent words revealed from the LDA, and the most frequent IPC codes attributed to the patents. Frequencies of occurrence of the most frequent words in abstracts of patent applications, allocated to four topics (Figure 6), provide a step forward in understanding the underlying trends in issues of patenting activities in which hidden topics occur. The most frequent words revealed for each topic from LDA were further classified into three groups of words and are presented in Figure 6 with different colors. The first group of words are verbs, which most probably indicate which operation, action, or activity in FSCs are being addressed in the patents (colored in green). The second group is nouns, which most probably indicate the subject or object in FSCs targeted by the patent (colored in dark gray). The third group of words are revealed as the most frequent and most common for all  
Figure 5: PCA-based distribution of patents in four LDA topics in the factorial plane. topics, i.e., the words from the patent database search pattern, or the adverbs that have not been taken into consideration in the analysis of issues targeted within topic clusters of the patent portfolio. Verbs and nouns identified as the most frequent in each topic are further systematized in Table 2, in which data used for revelation of hidden topics are presented. The second step forward toward understanding the underlying trends regarding the occurrence of issues that are latent topics of patenting activities, was the analysis of the most frequently assigned IPC codes within topics (Table 1). Table 1 also contains explanations of the most frequent IPC subgroups in the portfolio, together with the frequency of their appearance in the patents assigned to each hidden topic. However, within all topics, the most frequently assigned IPC codes were, as expected based on patent portfolio search criteria, with the IPC group G06Q, described as \"Data processing systems or methods, specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes; systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes, not otherwise provided for\". Further analysis was directed toward understanding the focus of each of the four identified hidden topics in the analyzed patent portfolio. Besides data from Table 2, including the most common words in the abstracts and the most frequently assigned IPC codes, the analysis also included an in-depth review of the patents assigned to each topic by LDA, which we performed manually. Topic A: The IPC code most frequently assigned to patents on topic A is G06Q10 (Data processing systems: administration, management), followed by G06Q30 (Data processing systems: commerce), and G06Q50 (Systems or methods specially adapted for a specific business sector) (Table 2).