In my PhD thesis, I tried to apply traditional bibliometrics to patents. This is not a very large field, unlike Altmetrics, for example, but I find it very interesting. One of the papers I read during that time is the subject of today’s post. It contrast traditional bibliometrics with patent analysis and provide a framework for how patents can serve as indicators for technological evolution. It is quite perfect to feed curiosity =)
Jürgensa, B., & Herrero-Solanab, V. (2016). Patent bibliometrics and its use for technology watch. Journal of Intelligence Studies in Business, 7(2), 17-26.
https://doi.org/10.37380/jisib.v7i2.236
Intro:
The paper starts by defining technology watch, also known as technology intelligence or patent intelligence. It’s framed as a methodology organisations employ to monitor technical developments for competitive advantage, originally rooted within the broader field of competitive intelligence. Since patent databases offer structured, globally accessible, and standardised information on inventions, they form a solid foundation for such analysis.
The authors position patent bibliometrics as a cousin to traditional bibliometrics, the latter being concerned with academic publications, and the former with patent data. The literature review is rich, highlighting how patent analysis has been used since the 1960s to investigate innovation dynamics (e.g. Schmookler, 1966) and how it continues to inform the study of emerging technologies such as nanotechnology.
Notably, the paper includes a helpful comparison between scientific literature and patent publications, pointing out differences in accessibility, motivation for publication, and content characteristics.
The Method:
The authors propose a structured approach for using patent bibliometrics in technology watch. They first distinguish between single field analysis (e.g., top applicants by patent count) and multiple field analysis (e.g., patent co-authorship networks). Then, they classify patent indicators into four detailed categories:
- Performance: measuring output from applicants and inventors.
- Technology: assessing the technological landscape via patent classifications.
- Patent value: identifying economic significance via factors like familiy size and citations.
- Collaboration: mapping co-invention and organisational partnerships.
To illustrate their framework, the authors provide a case study focused on nanotechnology patents in Spain between 2004 and 2014 (3.400 documents). They use data from Espacenet and visualisation tools like Matheo Patent to explore patent patterns geographically, institutionally, and technologically.
Key Points:
- Patent bibliometrics is robust and well-structured thanks to standardised documentation.
- Indicators can reveal hidden strategic trends, as they shed light on the evolution of research within a company.
- Data visualisation (with network maps, scatter graphs, and choropleth maps) is useful for representating the information effectively.
- Patents serve as proxies for innovation, making them particularily powerful sources of insight in fast-moving fields.
Limitations & What’s left to explore:
The paper also discuss some of the limitations of patent analysis:
- Patents are published 18 months after filling, so this delay needs to be taken into account.
- Not every innovation is patented, as the cost, lack of interest, or patentability issues can play a role.
- There is a sector variation as some industries rely more heavily on patents than others, making cross-sector comparisons tricky.
- While patent counts are useful, they do not necessarily reflect the true impact of an innovation.
Conclusions:
The paper conclude that, despite some inherent limitations, patent bibliometrics is a powerful tool for competitive and technology intelligence. Especially when paired with effective visualisation techniques, patent data can assist organisations and policymakers in tracking technological developments and making informed strategic decisions. This work offers not just a compelling theoretical framework but also a practical toolkit.
See you on the next paper =)