Preliminary landscape analysis of deep tomographic imaging patents

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,...

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Detalles Bibliográficos
Autores principales: Yang, Qingsong, Lizotte, Donna L., Cong, Wenxiang, Wang, Ge
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Nature Singapore 2023
Materias:
Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9868030/
https://www.ncbi.nlm.nih.gov/pubmed/36683096
http://dx.doi.org/10.1186/s42492-023-00130-x
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author Yang, Qingsong
Lizotte, Donna L.
Cong, Wenxiang
Wang, Ge
author_facet Yang, Qingsong
Lizotte, Donna L.
Cong, Wenxiang
Wang, Ge
author_sort Yang, Qingsong
collection PubMed
description 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.
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spelling pubmed-98680302023-01-24 Preliminary landscape analysis of deep tomographic imaging patents Yang, Qingsong Lizotte, Donna L. Cong, Wenxiang Wang, Ge Vis Comput Ind Biomed Art Review 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. Springer Nature Singapore 2023-01-23 /pmc/articles/PMC9868030/ /pubmed/36683096 http://dx.doi.org/10.1186/s42492-023-00130-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Review
Yang, Qingsong
Lizotte, Donna L.
Cong, Wenxiang
Wang, Ge
Preliminary landscape analysis of deep tomographic imaging patents
title Preliminary landscape analysis of deep tomographic imaging patents
title_full Preliminary landscape analysis of deep tomographic imaging patents
title_fullStr Preliminary landscape analysis of deep tomographic imaging patents
title_full_unstemmed Preliminary landscape analysis of deep tomographic imaging patents
title_short Preliminary landscape analysis of deep tomographic imaging patents
title_sort preliminary landscape analysis of deep tomographic imaging patents
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9868030/
https://www.ncbi.nlm.nih.gov/pubmed/36683096
http://dx.doi.org/10.1186/s42492-023-00130-x
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