深层层析成像专利的初步前景分析。

Preliminary landscape analysis of deep tomographic imaging patents.

作者信息

Yang Qingsong, Lizotte Donna L, Cong Wenxiang, Wang Ge

机构信息

Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.

Murtha Cullina LLP, Boston, MA, 02110, USA.

出版信息

Vis Comput Ind Biomed Art. 2023 Jan 23;6(1):3. doi: 10.1186/s42492-023-00130-x.

DOI:10.1186/s42492-023-00130-x

PMID:36683096

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9868030/

Abstract

Over recent years, the importance of the patent literature has become increasingly more recognized in the academic setting. In the context of artificial intelligence, deep learning, and data sciences, patents are relevant to not only industry but also academe and other communities. In this article, we focus on deep tomographic imaging and perform a preliminary landscape analysis of the related patent literature. Our search tool is PatSeer. Our patent bibliometric data is summarized in various figures and tables. In particular, we qualitatively analyze key deep tomographic patent literature.

摘要

近年来，专利文献在学术环境中的重要性日益得到认可。在人工智能、深度学习和数据科学的背景下，专利不仅与产业相关，也与学术界及其他群体相关。在本文中，我们聚焦于深度断层成像，并对相关专利文献进行初步的全景分析。我们的搜索工具是PatSeer。我们的专利文献计量数据在各种图表中进行了总结。特别是，我们对关键的深度断层专利文献进行了定性分析。