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

Thailand’s Board of Investment (BOI) has significantly revised its post-approval compliance framework for projects that receive investment promotion incentives, replacing the previous semiannual reporting system for project progress with a new quarterly reporting regime. The initial report is due by July 30, 2026, covering the second-quarter reporting period of April to June 2026. The new requirements—implemented through BOI Announcement No. 8/2569 and Office of the BOI Notification No. Por. 8/2569, both of which became effective on March 30, 2026—apply both to newly promoted projects and to existing promoted projects that remain in the implementation stage. Background Under the previous reporting framework, BOI-promoted companies that had not yet commenced full operations were generally required to submit reports on project progress to the BOI twice a year (February and July) through the BOI’s e-Monitoring system. By adopting a quarterly reporting regime, the BOI seeks to strengthen monitoring and evaluation of investment progress and project implementation. Reporting Requirements Under the new regulations, BOI-promoted companies must submit project progress reports on a quarterly basis during the implementation phase of a promoted project. The reporting periods and submission deadlines are: Q1 (January–March): April 30 Q2 (April–June): July 30 Q3 (July–September): October 30 Q4 (October–December): January 30 of the following year The quarterly reporting obligation runs from the date the BOI promotion certificate is issued until the BOI grants approval for commencement of full operations. For newly promoted projects, no quarterly report is required for the quarter in which the BOI promotion certificate is issued—the first reporting obligation arises in the immediately following reporting period. All project progress reports must be submitted electronically through the BOI’s e-Monitoring system. The existing annual reporting requirement also remains in effect, requiring promoted companies to submit an annual operating results report through the e-Monitoring system by July 31 of

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