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ISSN: 2686-6706 (Print)
ISSN: 2686-6714 (Online)
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Dear authors and readers! Starting from the issue 2, 2019 the name of the scientific journal has been changed. The new name of the journal is "Science Governance and Scientometrics". From 2020 Science Governance and Scientometrics has been included in the VAK (Higher Attestation Commission List.
ISSN: 2686-6706 (Print)
ISSN: 2686-6714 (Online)
Subscription index: 65055
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Dear authors and readers! Starting from the issue 2, 2019 the name of the scientific journal has been changed. The new name of the journal is "Science Governance and Scientometrics". From 2020 Science Governance and Scientometrics has been included in the VAK (Higher Attestation Commission List.

Volume 15, issue 2/2020

Focus and scope: Scientometrics

Leverage Patent Analytics to Achieve Business-Oriented Objectives: A Pragmatic Approach

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Abstract

Introduction. Because patent publications are at the forefront of emerging technologies and are related to technologies with commercial potential, many companies consume patent landscapes or analytics to get more information, more data on competitors and cleverly construct incremental improvement. Nevertheless, it is not enough to anticipate technological changes and a true structuration of the information is essential to identify business markers and trends. Methods. We have used a big data and data mining approach to process patent information and determine weak signals and market shifts. The following process has been followed: mapping, categorization according to a taxonomy, business markers and trends identifi cation. The domain of AI in medical devices has been studied to illustrate the method. Maps are used for simplifying the data analysis by leveraging keywords identifi ed by semantic algorithm. Considering the volume of the topics (macro, meso, micro) the analysis will be adapted to get certain insights. To pass from maps to categorization analysis we have set up a taxonomy, based on the knowledge of experts and previous data mining work, which allows us to search for non-obvious solutions and and objectively focus our attention on all the segments. Supervised machine learning methods help to distribute documents according to taxonomies. Then, maturity and aggressiveness can be qualifi ed based on IP events such as litigations, licensing actions, growth rate or number of applicants. The last step is related to the essence of a landscape, interpreting any weak signals to anticipate the future success. Results and Discussion. We have focused on recent patents, deserted areas on the map and the taxonomy and on analyzing “unusual” patent proceedings to determine new R&D directions and innovation pathways for the use of AI in medical devices. Conclusion. We have found it particularly relevant to use taxonomies and IP events landscape of patents to anticipate technological trends and market directions and we are convinced that the sophistication of AI-based solutions will push the predictions of the markets further.

Keywords

Patent analytics, scientometrics, intellectual property, patinformatics, patent mapping, categorization, technological trends, clustering techniques, data mining

Information about the authors

Benoit Chevalier, Engineer Electronics and Telecoms, Senior Сonsultant at Questel (1 boulevard de la Madeleine, Paris 75001, France), co-founder of Avenium Consulting (now Questel Consulting), Member of LES France (Licensing Executive Society), ORCID: https://orcid.org/0000-0001-8357-1631.

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