Customs recordation is an enforcement mechanism in Myanmar that enables intellectual property (IP) rights holders to seek prevention of the cross-border movement of infringing goods. The enactment of Myanmar’s IP laws in 2019 has enabled customs recordation for registered marks and copyrights under the Trademark Law 2019 and the Copyright Law 2019. By contrast, the Patent Law 2019 and the Industrial Design Law 2019 do not provide a practical framework for customs recordation, and accordingly such rights are not subject to the customs recordation regime. Under the Trademark Law 2019, rights holders may apply for customs recordation and may also ask the Customs Department to suspend the release of goods suspected of bearing counterfeit marks. Likewise, the Copyright Law 2019 allows for customs intervention in relation to pirated works. These provisions reflect Myanmar’s gradual alignment with international standards on border measures, although the implementation framework remains at a relatively early stage of development. Customs Recordation Pursuant to the Trademark Law 2019 and the Copyright Law 2019, the relevant authorities have issued customs rules concerning the protection of registered marks and copyrights. In practice, the process generally begins with the submission of an application to the Customs Department together with supporting documentation. This typically includes proof of registration in Myanmar; details of the rights holder, applicant, and any authorized representative; and a comprehensive description of the genuine goods. Product identification materials—such as photographs, packaging samples, and distinguishing features—are particularly important in helping customs officers identify suspected infringing goods. A recordation remains valid for two years from the date of approval. It may be renewed for additional two-year terms, provided that the renewal application is filed within the thirty days prior to expiry for marks and up to thirty days in advance of the expiry date for copyrights, in accordance with

Patent enablement requirements are provided under Article 102 of Vietnam’s Law on Intellectual Property (IP Law). In particular, a patent specification must “fully and clearly disclose the nature of the invention to such an extent that, based on the specification, a person having ordinary skill in the relevant art can implement the invention.” In pharmaceutical and biotechnology patents, this requirement is more complicated and subject to more rigorous assessment. The Patent Examination Guidelines (Guidelines) of the Intellectual Property Office of Vietnam (IP Office) were amended in March 2026 to introduce Annexes III and IV for the pharmaceutical and biotechnology sectors, in which Annex III provides detailed guidelines on the assessment of specification requirements. These amendments were made under a project for strengthening capacity in industrial property examination between the Japan International Cooperation Agency (JICA) and the IP Office. Annex III provides detailed instructions on how examiners assess enablement in a pharmaceutical or biotechnology application, and offers examples of acceptable and unacceptable descriptions with regard to the enablement aspect. Enablement Requirements in Pharma and Biotech Patents Article 12.7 of Circular 10/2026/TT-BKHCN (Circular 10) adds to the requirements of Article 102 of the IP Law that the description must demonstrate the novelty, inventive step, and industrial applicability of the technical solution. For pharmaceutical composition subject matters, Article 12.9 of Circular 10 sets out that the description must present the results of clinical trials and/or the pharmacological effects of the claimed pharmaceutical composition, and must include at least the following information: Substance/mixture used. Testing method (system) employed. Information on the test results. Correlation between the pharmacological effects obtained from the tests and the application of the pharmaceutical product in the prevention, diagnosis, and treatment of diseases. The Guidelines note that pharmacological study results should be presented in a quantified manner, and pharmacological

Since the implementation of the Trademark Law 2019 on April 1, 2023, Myanmar has operated under a modern first-to-file trademark system that brings its registration framework closer to international practice. As the new regime continues to develop in practice, applicants are increasingly required to navigate formal examination requirements, substantive objections, and procedural deadlines with greater precision. This article provides a high-level review of the trademark examination process in Myanmar, focusing on the principal stages from initial review to approval, the types of objections commonly raised by the Intellectual Property Department (IPD), and the key considerations for responding effectively. A clear understanding of these issues is essential for applicants seeking to secure registration efficiently and to mitigate avoidable delays or refusals. Examination Process: Key Stages Trademark applications filed with the IPD undergo two stages of review. Formality Examination The IPD first verifies compliance with procedural requirements, including: Correct Nice Classification Clear mark representation Accurate applicant details Clearly defined goods or services Representative details, if the application is filed by a representative Other formality requirements cover translation and transliteration of any non-English or non-Myanmar elements in the mark, color claim details, applicable disclaimers, and payment of official fees. Deficiencies result in an office action requiring correction within 30 days, which may be extended upon request. Registrability Examination The IPD also assesses registrability. A mark may be refused if it: Lacks distinctiveness Is descriptive or generic Misleads the public or violates public order/morality Contains prohibited state symbols Only compliant applications proceed to publication. Responding to Office Actions Applicants must respond within 30 days of notification from the IPD. Depending on the nature of the objection, strategies may include submitting legal arguments for distinctiveness, providing evidence of acquired distinctiveness, filing appropriate disclaimers, clarifying descriptions such as color claims, or amending the listed goods

The surge in AI development has led to a desperate demand for large, high-quality training data. However, real-world data can be expensive to collect, difficult to access, and often subject to strict privacy and regulatory constraints. Synthetic data, which consists of artificially generated records that replicate the statistical properties of real-world data without reproducing specific individuals’ information, provides an appealing solution by generating artificial datasets at scale without relying on identifiable personal information. It combines speed, cost efficiency, and regulatory compliance, making it a sensible alternative for organizations seeking to reduce risks while maintaining data utility. When properly anonymized, synthetic datasets may fall outside the scope of laws such as the EU’s General Data Protection Regulation (GDPR) or Thailand’s Personal Data Protection Act (PDPA), reducing compliance burdens while still supporting high-quality model training. However, relying on synthetic data without rigorous legal due diligence could be a strategic mistake. It replaces one set of known risks (scraping, direct privacy liability) with a new set of complex liabilities. The narrative that synthetic data is a “silver bullet” for privacy and IP compliance is dangerous and could be misleading. While synthetic data addresses data scarcity, it also introduces new legal uncertainties. Legal counsel should anticipate downstream risks arising from compromised data sources. Models trained on unlawfully obtained data may need to be decommissioned, even if their outputs appear lawful. What is synthetic data? Synthetic data refers to artificially generated information created using AI techniques such as deep learning and generative models. Instead of copying real records, it reproduces the statistical patterns and relationships found in the original dataset. Synthetic data generally falls into three categories: Fully synthetic data – Entirely new data points generated from learned patterns. The model studies the structure of the original data and produces records that resemble real-world