دانلود رایگان مقاله مدل گرافیکی استنتاج علی و عضو رابط رگرسیون برای کلید واژه اپل توسط متن کاوی

Abstract

Apple is a leading company of technological evolution and innovation. This company founded and produced the Apple I computer in 1976. Since then, based on its innovative technologies, Apple has launched creative and innovative products and services such as the iPod, iTunes, the iPhone, the Apple app store, and the iPad. In many fields of academia and business, diverse studies of Apple’s technological innovation strategy have been performed. In this paper, we analyze Apple’s patents to better understand its technological innovation. We collected all applied patents by Apple until now, and applied statistics and text mining for patent analysis. By using graphical causal inference method, we created the causal relations among Apple keywords preprocessed by text mining, and then we carried out the semiparametric Gaussian copula regression model to see how the target response keyword and the predictor keywords are relating to each other. Furthermore, Gaussian copula partial correlation was applied to Apple keywords to find out the detailed dependence structure. By performing these methods, this paper shows the technological trends and relations between Apple’s technologies. This research could make contributions in finding vacant technology areas and central technologies for Apple’s R&D planning.

6. Conclusions In this paper, we were interested in Apple’s technologies and its technological innovation. To understand and analyze Apple’s technologies, we collected all patent documents applied by Apple in the world. We used Apple’s keywords extracted from searched patent data, because the keywords contain technological aspects of Apple. From this study, we found that how the target response keyword and the predictor keywords were related to each other by copula modeling. Technologies of predictor keywords affect the technological developments of target response keywords. The associations between predictor and response keywords will provide novel information for Apple’s R&D planning. By using Gaussian copula partial correlation, we also found the detailed dependence structure of Apple keywords. By performing these methods, this paper showed the technological trends and relations between Apples technologies. Also expert groups interesting in Apple’s technologies can use our experimental results. This research contributes to efficient R&D planning such as intellectual property R&D strategy of a company as well as Apple