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Explore technology innovation and intelligence for IoT (Internet of Things) based eyewear technology

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  • Wang, Yu-Hui
  • Hsieh, Chia-Ching

Abstract

Advances in wireless Internet and mobile communications devices have driven significant development in the Internet of Things (IoT), bringing a stream of innovative technologies and services. This study explores the technology innovation and intelligence for IoT (internet of things) based eyewear technology. This study proposes a two-stage patent analysis based on the quality function development (QFD) method which adopts customer requirement and technology viewpoints to explore key technologies. This methodology can recognize the specific technologies with development potential in the eyewear industry, and identify holders of key relevant patents. This study finds that consumers value functions including motion tracking, reminders, eye state detection, and non-eye disease detection. Key technologies with development potential for satisfying customer demand include eyewear, communications protocols, and sensors. Thus, embedding micron-scale sensors directly into contact lenses to monitor user physiological data can satisfy customer demand and is considered as emerging technology in the smart eyewear industry. Furthermore, patent portfolios of these technologies vary among different countries and regions, with the US and EU focusing on eye tracking, motion tracking, and identity verification, while China focuses on eye fatigue detection, distance measurement, and wireless frequency technologies. Visualizations of overall research results can benefit eyewear-related patent holders, eyewear manufacturers and smart wearable manufacturers to build their patent portfolio strategies on the basis of regional or country considerations.

Suggested Citation

  • Wang, Yu-Hui & Hsieh, Chia-Ching, 2018. "Explore technology innovation and intelligence for IoT (Internet of Things) based eyewear technology," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 281-290.
    • Handle: RePEc:eee:tefoso:v:127:y:2018:i:c:p:281-290
    DOI: 10.1016/j.techfore.2017.10.001
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    References listed on IDEAS

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    1. Lee, Changyong & Kim, Juram & Kwon, Ohjin & Woo, Han-Gyun, 2016. "Stochastic technology life cycle analysis using multiple patent indicators," Technological Forecasting and Social Change, Elsevier, vol. 106(C), pages 53-64.
    2. Ju, Yonghan & Sohn, So Young, 2015. "Patent-based QFD framework development for identification of emerging technologies and related business models: A case of robot technology in Korea," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 44-64.
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    Cited by:

    1. Ly, Pham Thi Minh & Lai, Wen-Hsiang & Hsu, Chiung-Wen & Shih, Fang-Yin, 2018. "Fuzzy AHP analysis of Internet of Things (IoT) in enterprises," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 1-13.
    2. Federica Murmura & Laura Bravi & Gilberto Santos, 2021. "Sustainable Process and Product Innovation in the Eyewear Sector: The Role of Industry 4.0 Enabling Technologies," Sustainability, MDPI, vol. 13(1), pages 1-16, January.
    3. Arfi, Wissal Ben & Nasr, Imed Ben & Kondrateva, Galina & Hikkerova, Lubica, 2021. "The role of trust in intention to use the IoT in eHealth: Application of the modified UTAUT in a consumer context," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    4. Magni, Domitilla & Scuotto, Veronica & Pezzi, Alberto & Giudice, Manlio Del, 2021. "Employees’ acceptance of wearable devices: Towards a predictive model," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    5. Lo, Fang-Yi & Campos, Nayara, 2018. "Blending Internet-of-Things (IoT) solutions into relationship marketing strategies," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 10-18.

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