The worldwide count of priority patents: A new indicator of inventive activity

This paper describes a new patent-based indicator of inventive activity. The indicator is based on counting all the priority patent applications filed by a country's inventors, regardless of the patent office in which the application is filed, and can therefore be considered as a complete 'matrix' of all patent counts. The method has the advantage of covering more inventions than the selective Patent Cooperation Treaty (PCT) or triadic families counts, while at the same time limiting the home-country bias of single-country-based indicators (inventors from a particular country tend to file in their own country). The indicator is particularly useful to identify emerging technologies and to assess the innovation performance of developing economies. Â© 2012 Elsevier B.V.

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Gaétan Jean A de Rassenfosse
2013
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https://infoscience.epfl.ch/record/205784?ln=fr

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Selection bias in innovation studies: A simple test

Gaétan Jean A de Rassenfosse

The study of the innovative output of organizations often relies on a count of patents filed at one single office of reference such as the European Patent Office (EPO). Yet, not all organizations file their patents at the EPO, raising the specter of a selection bias. Using novel datasets of the whole population of patents by Belgian firms and German universities, we show that the single-office count results in a selection bias that affects econometric estimates of invention production functions. We propose an easy-to-implement methodology to evaluate whether estimates that rely on the single-office count are affected by a selection bias. Â© 2013 Elsevier Inc.

2014

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Technology Protectionism and the Patent System: Evidence from China

Gaétan Jean A de Rassenfosse, Emilio Raiteri

Governments have strong incentives to allow their inventors to free ride on foreign technologies. They can achieve this result by discriminating against foreigners in the patent system-by refusing to grant foreigners a patent for their inventions. International patent law treaties forbid this practice, which may lower the global innovation incentives and may hurt international trade. Using data on half a million inventions submitted to the Chinese patent office, we find robust evidence of anti-foreign bias in the issuance of patents in 'strategic' technology areas. Foreigners are about fifty per cent more likely to be refused a strategic patent than locals.

WILEY2022

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Natural Language Processing (NLP) driven categorisation and detection of discourse in historical US patents

Jérôme Baudry, Nicolas Christophe Chachereau, Prakhar Gupta, Bhargav Srinivasa Desikan

Patents have traditionally been used in the history of technology as an indication of the thinking process of the inventors, of the challenges or “reverse salients” they faced, or of the social groups influencing the construction of technology. More recently, historians of science and technology also read them to interpret the way people described technology and how the specific inscriptions of inventions mattered for the justification and operation of the patent system. The digitization of historical patents opens up unique opportunities to assess the feasibility of unsupervised machine learning and natural language methods for such explorations. In this project, we analyze over a million US historical patents from 1830-1930 using a variety of text-based methods, with two major aims: 1) categorizing patents into coherent technical categories, 2) identifying discourses of safety, reflexivity, and environmental concern in technological innovation. We use both frequency-based and context-based methods, and find that a bag of words-based methods such as TDF-IDF and topic modeling do not perform well on semantic categorization due to the linguistic peculiarities of patent specifications. This suggests that a successful approach to categorizing patents would require contextual semantic representations such as Transformers-based methods (e.g. BERT), or static embedding based methods (e.g Word2Vec, Doc2Vec) which have relatively low computational costs but less expressive in some scenarios. We run early experiments using these methods and find that word embedding models are effective in learning semantics from the descriptions of the patents. In this poster, we will describe our early results, as well as exploratory data analysis on this massive historical patents dataset.

2022

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