本研究以電梯為觀察對象透過資料處理進行主路徑分析(Main Path Analysis)之相關研究，電梯設備是現代建築中不可或缺的設施之一，也是人們生活中必不可少的交通工具，伴隨著高齡化及城市化的不斷加劇，各種無障礙設施的增設，人們對電梯的需求也更加多樣化，進一步加劇了電梯的研究發展程度。因此，在電梯設計、使用與管理方面也產生了各種多樣性的專利活動，電梯專利也在2020年達到專利申請數量的巔峰，在漫長的時間積累下，豐富的專利數量清理後為本研究以不同方式進行主路徑分析有不可或缺的因素。
本研究同時針對檢索標的之域內網路及向外延伸的跨域網路進行主路徑分析，主要採用較完整的全域搜尋(Global Search)以及可以產出不只一條的關鍵路徑搜尋(Key-Route Search)，也會針對相同網路中整體與較大群體產出的主路徑是否相同進行觀察，這可以作為專利檢索出的資料是否具有代表性的依據，倘若大群體中的主路徑與整體網路的主路徑不同，就必須向節點數量較少之群集尋找，而域內網路因專利活動區間定為近十年之相關發明，主路徑的長度也因此受限，但域內網路也的專利技術鎖定在電梯控制系統、監控、安全的領域中，這也相對應了相對於傳統的升降設備中，在達到足夠的功效之後，便在安全及舒適度上面提升研發力度，當全球高齡化的過程中，對於這類無障礙設施的需求品質會越來越高，而在跨域網路中的主路徑分析中，當資料的基數延伸，可以得到較長的主路徑。
總之，針對電梯的設計、系統和管理需要進行的專利活動、主路徑分析和權重分析等方法的應用，以觀察電梯的研究發展及知識傳遞路徑。未來，隨著科技的不斷發展，相信不論是本研究中針對資料處理方法和工具也會不斷升級和完善，經過本研究結合相同領域內的不同網路、不同的主路徑分析方法或是不同的權重計算方式，總結出主路徑分析在檢索資料的型態完整的情形下，可以對其專利發展路線快速的彙整以利觀察，而跨域網路分析則是更優於域內網路針對知識傳遞的觀察方式，因為具備了更長的時間軸及引用的關聯性，適合專利研究更加深入的引用關係，藉此了解專利發展的路線。

This study conducted Main Path Analysis (MPA) on elevators through data processing. Elevators are an essential facility in modern buildings and a necessary means of transportation in people's daily lives. With the aging of the population and urbanization, the demand for elevators has become more diverse, leading to further research and development. Therefore, various types of patents have emerged in elevator design, use, and management, and the number of elevator patents reached its peak in 2020. The accumulated number of patents is an indispensable factor for MPA in this study after cleaning up the abundant patent data in different ways.
This study conducted MPA on both the domain network and the cross-domain network of the retrieval targets. It mainly used a more comprehensive global search and a key-route search that can produce more than one critical path. It also observed whether the overall and larger groups produced the same main paths in the same network, which can serve as a basis for determining the representativeness of the patent retrieval data. If the main path in the larger group is different from that in the overall network, the search must be directed towards clusters with fewer nodes. The domain network is limited in the length of the main path because it focuses on relevant inventions in the past decade. However, patents related to elevator control systems, monitoring, and safety are more targeted in the domain network, corresponding to the traditional lifting equipment that focuses on safety and comfort after achieving sufficient efficacy. As the global population ages, the demand for such accessible facilities will become higher. When the cardinality of the data is extended in cross-domain network MPA, a longer main path can be obtained.
In summary, this study applied methods such as patent activity, MPA, and weight analysis to observe the research and development of elevators and knowledge transfer paths. In the future, with the continuous development of technology, the data processing methods and tools used in this study are expected to be upgraded and improved continuously. By combining different networks in the same field, different MPA methods, or different weight calculation methods, this study concludes that MPA can quickly summarize the patent development route in complete data retrieval type cases to facilitate observation. Cross-domain network analysis is a better way to observe knowledge transfer than domain network analysis because it has a longer time frame.

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