面向设计创新的技术适应度景观：基于专利数据的深度神经嵌入方法
Technology Fitness Landscape for Design Innovation: A Deep Neural Embedding Approach Based on Patent Data
摘要：
技术对于创新和经济繁荣至关重要。理解技术变革可以引导创新者找到设计创新的新方向，从而取得突破。在这项工作中，我们通过专利数据的深度神经嵌入构建了一个技术适应度景观。该景观由1，757个技术领域及其各自的改进率组成。在景观中，我们发现了一个与信息和通信技术（ICT）相关的高山和一个巨大的低平原。景观呈现了整个技术空间结构的鸟瞰图，为创新者提供了一种新的方式，用生物类比来解释技术进化，并对下一次创新进行生物启发的推断。
英文摘要：
Technology is essential to innovation and economic prosperity. Understanding technological changes can guide innovators to find new directions of design innovation and thus make breakthroughs. In this work, we construct a technology fitness landscape via deep neural embeddings of patent data. The landscape consists of 1,757 technology domains and their respective improvement rates. In the landscape, we found a high hill related to information and communication technologies (ICT) and a vast low plain of the remaining domains. The landscape presents a bird's eye view of the structure of the total technology space, providing a new way for innovators to interpret technology evolution with a biological analogy, and a biologically-inspired inference to the next innovation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
