While numerous studies have explored the field of research and development (R&D) landscaping, the preponderance of these investigations has emphasized predictive analysis based on R&D outcomes, specifically patents, and academic literature. However, the value of research proposals and novelty analysis has seldom been addressed. This study proposes a systematic approach to constructing and navigating the R&D landscape that can be utilized to guide organizations to respond in a reproducible and timely manner to the challenges presented by increasing number of research proposals. At the heart of the proposed approach is the composite use of the transformer-based language model and the local outlier factor (LOF). The semantic meaning of the research proposals is captured with our further-trained transformers, thereby constructing a comprehensive R&D landscape. Subsequently, the novelty of the newly selected research proposals within the annual landscape is quantified on a numerical scale utilizing the LOF by assessing the dissimilarity of each proposal to others preceding and within the same year. A case study examining research proposals in the energy and resource sector in South Korea is presented. The systematic process and quantitative outcomes are expected to be useful decision-support tools, providing future insights regarding R&D planning and roadmapping.

翻译：尽管已有大量研究探索研发（R&D）格局分析领域，但绝大多数现有研究侧重于基于研发成果（特别是专利和学术文献）的预测性分析。然而，研究提案的价值与新颖性分析却鲜少得到关注。本研究提出一种系统性构建与导航研发格局的方法，可用于指导各组织以可复现且及时的方式应对日益增长的研究提案所带来的挑战。该方法的核心理念是结合使用基于Transformer的语言模型与局部离群因子（LOF）。我们通过进一步训练的Transformer模型捕捉研究提案的语义信息，从而构建全面的研发格局。随后，利用LOF通过评估每份提案与同年及先前提案的差异度，在数值尺度上量化年度格局中新入选研究提案的新颖性。本文提供了一个针对韩国能源与资源领域研究提案的案例研究。该系统性流程与量化结果有望成为有用的决策支持工具，为未来的研发规划与技术路线图制定提供前瞻性见解。