Preliminary landscape analysis of deep tomographic imaging patents

Area of Science:

• Medical Imaging
• Computer Science
• Data Science

Background:

• Patent literature is increasingly recognized in academia.
• Artificial intelligence (AI), deep learning, and data sciences have significant patent activity.
• Deep tomographic imaging is a rapidly evolving field with growing patent interest.

Purpose of the Study:

• To conduct a preliminary landscape analysis of patent literature in deep tomographic imaging.
• To identify key trends and influential patents in the field.
• To bridge the gap between academic research and industrial innovation in deep tomographic imaging.

Main Methods:

• Utilized PatSeer as the primary search tool for patent literature.
• Performed bibliometric analysis of patent data.
• Conducted a qualitative analysis of key deep tomographic imaging patents.

Main Results:

• Summarized patent bibliometric data in figures and tables.
• Identified significant patenting activity in deep tomographic imaging.
• Highlighted key innovations and trends within the analyzed patent literature.

Conclusions:

• The patent landscape of deep tomographic imaging is dynamic and warrants further investigation.
• Understanding patent trends is essential for academic researchers and industry professionals.
• This analysis provides a foundation for future research and development in deep tomographic imaging.