## 5 Conclusions  
This study performed thorough data analytics of the patent databases of the nine technological power countries over a time span of nine years. Through this work, a set of empirical knowledge providing key insights to investors and DMs for/in the high-tech industries all over the world was mined. First, after suitable data pre-processing (Section 3.1) in terms of the IPCs, the trends of the patenting activities in the high-tech patent sections (i.e., G and H) were analysed for each country (Section 3.2). The popularities of the technological domains (areas) were also analysed with regard to each IPC code prefix (until the subclass level) in terms of volume, and the top 10 'hottest technologies' were identified (Section 3.3). The spectrum of technical strengths (STS) of each technological power was calculated as a vector. This vector is an element-wise ordered vector in which each element represented the strength of the country in a certain technological area. The correlation matrix analysis method was used to identify the correlations between all the STS pairs when each STS vector was treated as a statistical variable, while knowledge about the homogeneity and heterogeneity in the industry or market among these countries was exploited (Section 3.4). Second, in terms of PCA modelling, it is understood that up to 92% of the total variance which is present in the intermediate data (i.e., the STSs) can be resolved by merely introducing two principal components (Section 4.1). As further explorations drew many implications pertaining to the global patenting strategies of the high-tech companies, and the markets and industries in the technological power countries were analysed in more detail through the angular analysis (Section 4.2), PCA modelling and the subsequent angular-based analysis were shown to be the effective methods when utilising the cross-national patent databases. Third, the set of discovered knowledge may support the optimal yet precise decisions of investors and company DMs, or even the policy makers of a country. For example, the analysis in Section 4.2.3 revealed that in the study period, the patented areas were closely related to the development of smartphones. A major part of these technological areas patented by the countries in group (i) is related to the market of 'touchable mobile device with panels', which is, in other words, the'smartphone' market. This observation also applied to the clustering analysis in Section 4.3, wherein wireless communications and networking, digital image transmission, cellular battery module, optical lens and fin-tech technologies, all of which are relevant to smartphones, were identified as the'most important technologies'. In any case, by referencing these findings, a cogent yet plausible decision process is guaranteed for either making investment decisions in the stock market or for parsimoniously allocating the limited resources of a company within the emphasised technological domains. As this study also applied the K-means method to conduct a clustering analysis for all the patents in Sections G and H (Section 4.3), the results showed that partitioning the relevant areas into 7 clusters is the optimal status. However, this was not as important as the other finding that the non-ordered set of the top 10'most important technologies' identified using this recently popular unsupervised machine learning method totally concurs with the set of the top 10 'hottest technologies' identified previously using the traditional statistical method. This finding is surprising and also critical, in that although the two methods are intrinsically dissimilar, their results nonetheless appear to cross-validate each other. With these cogent results, the interdisciplinary application of the methods may form a confident yet systematic methodological flow for future applications. This can be a safe ground for mining similar matters in other patent sections that are irrelevant to electronics or IT.