design targeted protections

This study addresses the lack of a comprehensive, cross-national metric for assessing the overall strength of patent systems, focusing on legal design, administrative examination, and judicial enforcement. Method: We develop a novel multidimensional dynamic evaluation framework that integrates statutory provision indices, procedural friendliness proxies, and empirically grounded measures of enforcement effectiveness. Leveraging multinational “twin patent” matching data, we conduct regression analyses of grant-rate differentials, comparative institutional econometrics, and quantitative scoring of administrative procedures. Contribution/Results: We identify significant divergence across major patent offices—particularly in the U.S., Europe, China, and Japan—in terms of substantive grant thresholds and enforcement consistency. The framework yields a comparable, empirically validated benchmark for evaluating how patent institutions shape innovation incentives and global knowledge diffusion. Findings inform evidence-based innovation policy design and strengthen multilateral intellectual property governance.

本文针对欧盟数字服务法案下大型平台和搜索引擎需评估基本权利影响的问题，提出了一种基于决策树的工具包，以系统化的方式识别、评估并记录相关风险。

This study addresses the practical challenge of translating Article 16 of the Digital Services Act (DSA)—an abstract legal obligation—into user-friendly, legally compliant content reporting mechanisms. Bridging disciplinary gaps among law, technology, and design, the paper introduces a “legal design” paradigm, employing expert workshops, multi-case qualitative analysis, and a compliance-driven UX evaluation framework in an interdisciplinary, participatory design process. Findings demonstrate that UX decisions—including interface prompts and reporting workflows—significantly shape user reporting behavior and regulatory compliance outcomes, empirically confirming that design functions not merely as an implementation tool but as a critical mediator in constructing legal meaning. The study contributes a reusable methodological pathway for operationalizing digital regulation and advances a human-centered, compliance-by-design paradigm.

This study addresses core conceptual challenges in requirements engineering (RE) concerning legal requirements (LRs)—including definitional ambiguity, inconsistent conceptualization, ill-defined attributes, and weak empirical grounding. Adopting a rapid literature review methodology, we systematically coded and analyzed how LRs are defined, classified, assigned functional or non-functional status, and characterized with respect to dynamism, overlap, and implementability across RE literature. Our analysis reveals, for the first time, that LRs are routinely reduced to static compliance baselines; suffer from definitional inconsistency, insufficient operationalization, and limited empirical validation; and lack consensus on theoretical positioning. We thus propose reconceptualizing LRs as a distinct requirement type characterized by normative bindingness, dynamic evolution, and cross-domain dependency. This work establishes a rigorous conceptual foundation and empirical basis for modeling, verifying, and governing LRs in RE practice.

This paper addresses the interpretable encoding of legal texts into deontic defeasible logic rules. We propose a novel method wherein interpretability arises intrinsically from the encoding process itself: normative text fragments are systematically translated into formal logical rules, and multi-dimensional test scenarios are designed to empirically validate semantic correctness. A key innovation is the introduction of a “depth” metric—quantifying the hierarchical complexity of legal citations—integrated with empirical experiments and regression modeling to build a predictive model of encoding time. Our study provides the first empirical evidence that text length, domain expertise, coder experience, and citation depth significantly impact encoding efficiency. The approach ensures logical fidelity while unifying transparency and efficiency in legal knowledge engineering. It establishes a reproducible, evaluable technical pathway for explainable AI–driven legal automation.

This work addresses the unpredictable interpretive choices often implicit in large language model (LLM) formalizations of legal provisions, which undermine the comparability and explainability of reasoning outcomes. The authors propose a systematic approach that integrates graph node matching with SAT solvers to enumerate divergent inferences arising from alternative formalizations when applied to identical legal cases. These divergences are then rendered into natural-language scenarios amenable to expert legal review. For the first time, this method maps formalization discrepancies onto intelligible edge cases, revealing their qualitative connection to real-world legal disputes. Experiments on ten EU legal provisions demonstrate that structural similarity among formalizations correlates poorly with behavioral agreement, whereas the generated divergence cases effectively capture actual conflicts in legal interpretation.

This study addresses the challenges posed by the proliferation, complexity, and expanding scope of regulatory requirements in software engineering, which hinder their systematic integration into development processes. To tackle this issue, the paper proposes a viewpoint-centered, artifact-based approach to regulatory requirements engineering. The approach innovatively integrates viewpoint analysis with artifact modeling to develop the AM4RRE (Artifact Modeling for Regulatory Requirements Engineering) framework, which facilitates cross-functional collaboration and ensures consistency in compliance-driven design. Preliminary validation demonstrates that AM4RRE effectively bridges the gap between organizational regulatory processes and software development practices, enabling a shift from ad hoc compliance responses toward systematic integration. This foundational work paves the way for further empirical investigation into scalable and sustainable regulatory compliance in software engineering.

为解决法律文档复杂性问题，通过用户意图分类设计了Lexplorer系统，支持法律文本的探索、导航与分析，并在欧盟法律背景下验证其有效性。

This study addresses the challenge posed by the high adaptability of medical AI systems, which renders legal risks difficult to anticipate, compounded by the general lack of technical understanding among legal professionals despite their legal expertise—limiting their capacity to effectively guide AI development and deployment. To bridge this gap, the project innovatively integrates lawyers into the governance process of medical AI through a two-year interdisciplinary co-design initiative. Combining participatory workshops with customized visualization techniques, the work constructs a practical bridge between legal judgment and AI practice. The resulting outputs include an actionable suite of visualization tools and a systematic risk management framework, significantly enhancing organizations’ ability to identify, anticipate, and respond to legal uncertainties associated with medical AI.

本文提出了一种通过双向反转验证的条款级模型和构建协议，以解决法律交叉引用中的准确性和一致性问题，应用于欧盟AI法案相关法规。