Year: 2025
Type: article
Abstract: The purpose of this study is to investigate the technical characteristics and trends of AI beauty tech patents by cosmetics type, analyze them by cosmetics type, and evaluate technological competitiveness and marketability through patent analysis. Through this, it is expected to be able to contribute to the development of the industry through strategic implications for strengthening the technological competitiveness of the domestic beauty industry, suggest the direction of development of the AI technology-based beauty tech industry, and make policy suggestions to strengthen the competitiveness of the domestic beauty industry. This study analyzed the patent trends in the beauty sector to which AI technology was applied by cosmetics industry and investigated the patent status. The patent search used the KIPRIS and Kewart databases, and only published and registered patents among the searched patents were selected for analysis. Using the patent search databases of KIPRIS and Kewart, this study performed a more accurate patent analysis by complementing the advantages and disadvantages of the two databases. In addition, by cross-verifying the search results of the two databases, the reliability of the data was increased through the process of removing duplicate patents and adding missing patents. Patent search and data collection were conducted for 14 days from November 3 to 16, 2024, and domestic patents were targeted. Through this, 449 patents were finally selected as analysis data. The collected data were analyzed focusing on the current status of patent applications by 13 cosmetic types and the patent share by cosmetic type. As a result, in this study, the result that the number of AI beauty tech patents was high in the order of color cosmetics products and basic cosmetics products, which will contribute to the development of the beauty field itself and the search for expansion to other industries at the same time.
Authors: Jin-Sun Ki, I-Han Jung, Yu-Mi Jeong, Haeng-Eun Kim
Institution: Sungshin Women's University
Language: English
Subfield: Management of Technology and Innovation
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4408037113