Amid the quickening rhythms of contemporary life and the steady escalation of stress exposure, concerns surrounding psychological well-being have become increasingly visible. Within this context, music therapy has received increasing attention from both researchers and practitioners because it focuses on emotional regulation. However, current music therapy applications still have some clear limitations. In particular, they often do not provide a satisfying emotional experience for users or fully meet their individual needs. To address these issues, this paper applies emotional design theory and combines it with the Analytic Hierarchy Process (AHP). Based on this approach, it develops a research framework for designing music therapy applications that better support users' emotional needs. First, this study reviews previous research on emotional design theory and the AHP method through a literature review. Based on this review, the overall research process is established. Next, user research is carried out through interviews, questionnaires, and competitor analysis. These methods help identify the main user needs in different music therapy scenarios. Finally, an AHP model is developed to analyze the importance of these needs and determine their priority.

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As artificial intelligence (AI) grows more sophisticated in the modern era, transnational commercial trade involving AI-generated content has become increasingly frequent, giving rise to complex intellectual property frictions among multinational enterprises. This essay focuses on the intellectual property dispute resolution mechanism under the framework of the "Agreement on Trade-Related Aspects of Intellectual Property Rights" (TRIPS), and its applicability in the commercial application of AI. An extraterritorial study is conducted by taking the United States and the European Union as examples. Through case analysis, comparative research, and literature analysis, this study explores the current status and limitations of the dispute resolution mechanism among countries under the TRIPS framework, as well as the practical situation of international commercial arbitration in transnational technical disputes. The research finds that although the TRIPS agreement provides a fundamental multilateral legal framework, in resolving issues which are connected with works generated by artificial intelligence, the existing mechanism faces many challenges, including procedural lag, lack of relief measures at the enterprise level, and substantive uncertainty regarding the copyrightability of the output of artificial intelligence. It is necessary to enhance the flexibility of international commercial arbitration at the enterprise level and promote the modernization of TRIPS provisions to adapt to the technological reality of artificial intelligence.

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As data has become a key factor of production, data property rights confirmation has emerged as a core issue in the development of the digital economy. The data property rights registration system transforms factual control into legal rights through registration, thereby performing the functions of ownership proof, information transparency, transaction security, and corporate compliance. However, the current data registration system faces practical dilemmas such as insufficient normative foundations (due to the absence of specialized high-level legislation) and inconsistencies in object definition (e.g., data resources, products, assets) and review mechanisms (formal vs. substantive). To address these issues, a dual-layer normative system of "legal empowerment and administrative regulation implementation" should be constructed, along with the unification of object classification standards and the introduction of a third-party substantive review mechanism. These reforms will promote the unification and standardization of the data property rights registration system, enhance its legal credibility, and facilitate the healthy development of the digital economy.

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Currently, one of the elements of generative artificial intelligence that is rapidly advancing, is the training of AI using existing resources like text, images, audio, and video. Most of the training data are copyrighted resources that were collected without the rights holder's consent, and using such resources has resulted in an unprecedented and mass infringement of copyright. This has resulted in a global copyright crisis, and the legal and commercial challenges posed by this situation are foremost in the era of artificial intelligence. This paper analyzes the copyright infringement risks and the governance dilemmas of AI training data. Within this scope, it examines the lawsuits of media companies, content creators, and AI technology companies. The author employs a combination of methodologies such as case study, comparison, documentation, and normative legal study. Accordingly, the paper aims to identify the fundamental issues of copyright infringement in AI training data, the negative effects of such infringement on the copyright owner and the digital content value chain, and provide specific regulations for AI companies, copyright regulators, and governments.

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