鏡頭產業應用之領域廣泛，且對於許多裝置，如數位相機、手機、監視器等皆為重要元件。藉由專利強度分析，探討公司之研發狀況，以提供未來研發策略以及布局策略之建議。首先，以日本專利資料庫為主，先分析整個產業在日本的現況，並找出日本前十大主要專利申請人，對針對各申請人之專利布局狀況，以F-term日本專利分類號進行專利指標之計算，了解主要申請人間之目前現況以及優劣勢。接著，找出主要申請人向美國申請之取像鏡頭相關專利，分析申請人之申請狀況，再以CPC分類號與被引證數進行專利指標之計算，了解申請人於美國之布局情形。最後，分別分析十大申請人於日本與美國之專利情況，並給予未來研發建議以及國外布局策略之建議。
本研究分析結果可得以下結論：
1.目前在取像鏡頭領域，產業上已經相當成熟，且維持一定之需求量，根據日本專利數量，十大主要申請人依序為Canon、Nikon、Fujifilm、Olympus、Konica Minolta、AAC、Ricoh、Panasonic、Sony、Tamron，其中AAC為中國公司，其餘為日商公司。
2.根據日本專利強度，Canon、Nikon、Fujifilm相對有較強之鏡頭設計能力；Olympus、Konica Minolta、Panasonic、Sony相對布局較廣之技術；Tamron之專利相對涉及較廣之技術；Ricoh相對在專利內容投入較多之創新程度。
3.根據美國專利分析，從美國專利數量與日本專利數量之比例來看，各申請人選案至美國申請的比例差異不大。從美國專利指標來看，美國公司技術能力指標，在日本有布局的技術領域同樣在美國也有布局，但從美國專利案件所涉及的技術廣度來看，日本申請人可能須多注意專利內容之布局。

In this study, through the analysis of patent strength about “Optical objective lens” patent, the research and development status of the company is discussed to provide suggestions for future research and development strategies and layout strategies. First, based on the Japanese patent database, we first analyze the current situation of the entire industry in Japan, and find out the top ten major patent applicants in Japan. It can be understanding the current application situation and advantages and disadvantages among the major patent applicants by using Japanese Patent Classification(F-term) to calculates patent indicators. Next, find out Optical Objectives lens patent of major applicants in USPTO, and analyze the applicant's application status. It can be understanding the application strategy in United States by using CPC classification to calculates patent indicators. Finally, it analyzes the patent application situation of the top ten applicants in Japan and the United States and gives suggestions on future research and development and strategies.
Results of the study are shown below:
1.At present, in the field of optical objectives lens, this industry is quite mature and maintains a certain demand. According to the number of Japanese patents, the top ten major applicants are: Canon, Nikon, Fujifilm, Olympus, Konica Minolta, AAC, Ricoh, Panasonic, Sony, Tamron in order.
2.According to the strength of Japanese patents, Canon, Nikon, and Fujifilm have relatively strong lens design capabilities. Olympus, Konica Minolta, Panasonic, and Sony have relatively wide-ranging technologies. Tamron’s patents involve relatively wide-ranging technologies. Ricoh’s patents are relatively great innovation in content.
3.According to the analysis of U.S. patents, from the ratio of the number of U.S. patents to the number of Japanese patents is similar, it can be derived that the Japanese applicant’s strategies is similar. From the perspective of US patent indicators, Japanese applicants may need to pay more attention to the layout of patent content.

一、 期刊論文
[1] Daejung Kim, & Jieun Kim(2021). Is innovation design- or technology-driven? Citation as a measure of innovation pollination. World Patent Information,64, 102010. https://doi.org/10.1016/j.wpi.2020.102010
[2] Dennis Verhoeven, Jurriën Bakker, & Reinhilde Veugelers(2016). Measuring technological novelty with patent-based indicators. Research Policy,45, 707-723. https://doi.org/10.1016/j.respol.2015.11.010
[3] Eduardo Perez-Molina, & Fernando Loizides(2021). Novel data structure and visualization tool for studying technology evolution based on patent information: The DTFootprint and the TechSpectrogram. World Patent Information,64, 102009. https://doi.org/10.1016/j.wpi.2020.102009
[4] Jeonghun Jee, Hyunjin Shin, Chulhyun Kim , & Sungjoo Lee(2021). Six different approaches to defining and identifying promising technology through patent analysis. Technology Analysis & Strategic Management, 28 May 2021. https://doi.org/10.1080/09537325.2021.1934437
[5] Jiho Lee, Namuk Ko, Janghyeok Yoon, & Changho Son(2021). An approach for discovering firm-specific technology opportunities: Application of link prediction to F-term networks. Technological Forecasting and Social Change, 168, 120746. https://doi.org/10.1016/j.techfore.2021.120746
[6] Junichi Sakata, Katsuhiro Suzuki, & Jun Hosoya(2009). The analysis of research and development efficiency in Japanese companies in the field of fuel cells using patent data. R & D management, 39, 291-304. https://doi.org/10.1111/j.1467-9310.2009.00555.x
[7] Kisik Song, Karp Soo Kim, & Sungjoo Lee, Discovering new technology opportunities based on patents: Text-mining and F-term analysis, Technovation, 60–61, 1-14. https://doi.org/10.1016/j.technovation.2017.03.001
[8] Lafond, François, & Kim, Daniel(2019). Long-run dynamics of the U.S. patent classification system. J Evol Econ 29, 631–664. https://doi.org/10.1007/s00191-018-0603-3
[9] La-yin WANG, & Dong ZHAO(2021). Cross-domain function analysis and trend study in Chinese construction industry based on patent semantic analysis. Technological Forecasting and Social Change,162, 120331. https://doi.org/10.1016/j.techfore.2020.120331
[10] Mahmood H. Shubbak(2019). Advances in solar photovoltaics: Technology review and patent trends. Renewable and Sustainable Energy Reviews, 115, 109383. https://doi.org/10.1016/j.rser.2019.109383
[11] Manuel Baumann et al(2021). Comparative patent analysis for the identification of global research trends for the case of battery storage, hydrogen and bioenergy. Technological Forecasting and Social Change,165, 120505. https://doi.org/10.1016/j.techfore.2020.120505
[12] Matti Karvonen, & Kimmo Klemola(2019). Identifying bioethanol technology generations from the patent data. World Patent Information,57, 25-34. https://doi.org/10.1016/j.wpi.2019.03.004
[13] Mekyung Lee(2020). An analysis of the effects of artificial intelligence on electric vehicle technology innovation using patent data. World Patent Information,63, 102002. https://doi.org/10.1016/j.wpi.2020.102002
[14] Schmoch, U. J. I. j. o. t. M. (1995). Evaluation of technological strategies of companies by means of MDS maps. International journal of technology Management, 10(4-6), 426-440. 10.1504/IJTM.1995.025635.
[15] Serkan Altuntas,Turkay Dereli,& Andrew Kusiak(2015). Forecasting technology success based on patent data. Technological Forecasting & Social Change, 96, 202-214. https://doi.org/10.1016/j.techfore.2015.03.011
[16] Shuying Li, Xian Zhang, Haiyun Xu, Shu Fang, Edwin Garces, & Tugrul Daim(2020). Measuring strategic technological strength :Patent Portfolio Model. Technological Forecasting and Social Change,157, 120119. https://doi.org/10.1016/j.techfore.2020.120119
[17] Xuefeng Wang et al(2019). Measuring patent similarity with SAO semantic analysis. Scientometrics, 121, 1–23. https://doi.org/10.1007/s11192-019-03191-z
[18] Yang X, & Liu X, Song J(2019). A Study on Technology Competition of Graphene Biomedical Technology Based on Patent Analysis. Applied Sciences, 2019, 9(13):2613. https://doi.org/10.3390/app9132613
[19] Yi Zhang et al. (2016). A hybrid similarity measure method for patent portfolio analysis. Journal of Informetrics,10, 1108-1130. https://doi.org/10.1016/j.joi.2016.09.006
[20] Yoshie Ishii et al. (2021). A STUDY ON TECHNOLOGY TREND OF CAMERA BY USING FI CODE IN JAPANESE PATENT, JOURNAL OF ENGINEERING AND MANAGEMENT IN INDUSTRIAL SYSTEM, 9(1). 10.21776/ub.jemis.2021.009.01.7
[21] Youngjin Park, & Janghyeok Yoon(2017). Application technology opportunity discovery from technology portfolios: Use of patent classification and collaborative filtering. Technological Forecasting & Social Change, 118,170-183. https://doi.org/10.1016/j.techfore.2017.02.018
[22] 髙橋 啓治ほか(2018年7月)，特許と論文の複合解析による有望応用分野の予測 －印刷技術を例に－，情報プロフェッショナルシンポジウム，一般社団法人 情報科学技術協会。
[23] 葉士瑋、黃振榮(2017)，合作專利分類(CPC)實施現況之探討與應用，智慧財產權月刊，卷217。

二、 學位論文
[1] 楊采璇(2016)，專利分類號數量與被引用數量關聯性研究，國立臺灣科技大學專利研究所碩士論文。
[2] 傅雅吟(2018)，Google 搜尋核心技術之專利分析，國立臺北科技大學智慧財產權研究所碩士論文。
[3] 廖彥伶(2020)，下肢外骨骼機器人發展趨勢專利分析，國立臺北科技大學智慧財產權研究所碩士論文。
[4] 李怡蓁 (2020) ，競爭公司在光達系統之專利強度研究，國立臺灣科技大學專利研究所碩士論文。
[5] 陳家賀 (2021) ，以專利強度分析探討矽晶圓長晶技術之研發趨勢，國立臺灣科技大學專利研究所碩士論文。

三、 專書
[1] 劉國讚 (2021) ，國際專利分析與布局，元照出版。
[2] 耿繼業、何建娃(2013)，幾何光學，全華圖書。
[3] Malacara-Hernández, Zacarías Malacara-Hernández (2004), Handbook of Optical Design. Cambridge: CRC Press.
[4] Rudolf Kingslake, & Barry Johnson(2009), Lens Design Fundamental [2nd Edition]. Cambridge: Academic Press.
[5] José Sasián(2019), Introduction to Lens Design [1st edition]. Cambridge: Cambridge University Press.
[6] Stephen Adams(2012), Information Sources in Patents. Munich: De Gruyter Saur.