As mentioned earlier, patents can be aggregated and analysed in various ways, including:
- Type of inventors, companies or groups of companies
- Applicability in one or more technological domains
- Patenting activity of a country or region
- Patterns of patenting behavior over time
Patent indicators can be developed on three levels, whereby the required level of aggregation depends on the objective of the patent study: macro (country/region), meso (industry/sector) or micro (single firm/institute).
The simplest type of patent indicator is obtained by merely counting the number of patents, based on one or more criteria (such as technological domain, application year, inventor, applicant). Comparing patent volumes between countries, industries or companies in a particular technology domain can provide insight into technological performance differences. Taking into account population size, scientific texture and technology infrastructure, analysts relate patent volumes with demographic, economic and research variables (such as GDP and R&D expenditures). Such normalizations yield patent indicators that are independent of country size, allowing for more accurate comparisons.
An often used – but sometimes criticized – indicator is the ‘propensity to patent’. This indicator represents the number of patents per dollar (or other currency), invested in R&D. It shows the extent to which R&D input is translated into patents and can hence be considered as a measure of R&D output. Since the definition, delineation, measurement and quantification of the input data are not always straightforward, the ‘propensity to patent’ indicators need to be interpreted with caution.
Specialization indices can be used to answer questions about the position of a specific country or region in various technological domains, compared to other countries or regions. The ‘Revealed Technological Advantage (RTA)’ is the most frequently used specialization index.
The information in patents can also be used to construct ‘maps of technology’. For this purpose – besides information about the innovating company and on features of the invention itself – information can be gathered about the references contained in each patent application, both to previous patents and to scientific articles. ‘Maps’ of various technological (sub-)domains can then be constructed by examining the interrelation between frequently cited patents. In addition, co-citation, co-classification or co-word analyses are possible. Within each (sub)domain, one can assess the relative position of different players, including companies, research institutions, countries/regions. Finally - and conditional on a detailed and careful identification cited scientific sources - these citations can be used to map relations between science and technology. This includes mapping knowledge flows between science and technology (at the level of countries, regions, institutions and even individuals) or the development of concordance schemes between scientific disciplines and technology domains. Increasingly also, big data analytics (text mining and machine learning algorithms) are used in the technometrics field for content-based mappings of technological architectures and networks.
ECOOM’s technometric research group has in-depth expertise and experience on the outlined technometric indicators and techniques.
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