Advanced Patent Analytics: Patent Indicators in Strategic and Intellectual Property Management

Language
en
Document Type
Doctoral Thesis
Issue Date
2023-08-07
Issue Year
2023
Authors
Guderian, Carsten Christian
Editor
Abstract

Patents constitute one of the most comprehensively available and frequently used information sources to im-prove strategic and intellectual property management decisions, amongst other in innovation management, research and development management, technology management, competitive and technology intelligence, and patent/technology valuation. Unlike other innovation/technology and related performance indicators such as research and development expenditures, which are only available for a limited number of firms that have to publish their efforts due to publication and reporting requirements, patents yield information on research and development outputs and are publicly available for all firms that choose to patent-protect their proprietary knowledge – irrespective of their sizes, locations, or publication and reporting requirements. This provides in-sights into the technology base, strategy, and development of various firms, from startups to small- and medi-um-sized firms and international conglomerates. However, issues inhibiting the usefulness particularly of pub-licly available patent data and patent analytics exist. Some of these issues pertain to correct patent ownership assignment, the incorporation of legal status information, and incorporation of quality differences in patents and the technologies which they protect. Consequently, various patent indicators have been developed in prior research.

This dissertation focuses on advanced patent analytics by providing an overview of the status quo and devel-oping novel insights to support the strategic and intellectual property management. To answer the overarching research question, i.e., what is the status quo in patent analytics and how can advancements support the stra-tegic and intellectual property management, this dissertation comprises five distinct yet related research pro-jects. Herein, the development of patent indicators used in patent analytics and typical empirical results are traced, and patent indicators are linked to the concept of sustainability, yielding ample future research opportu-nities and insights for practitioners in the first research project. Further, it is identified that additional insights can be derived when complementing conventional patent indicators with smart patent indicators and longitudi-nal data in technology landscaping analyses in the second research project. Furthermore, potential relations between open innovation collaborations and intellectual property are detected by drawing on multiple stages beyond the modus operandi and multiple levels of analysis in the third research project. Moreover, the innova-tion performance of open innovation collaborations is evaluated based on patent measures by incorporating ownership information in the fourth research project. For this purpose, on prior research from other application areas is drawn upon to assess the presence of open innovation collaborations more precisely. In addition, prior research on integrated intellectual property strategies based on patents and trademarks is revisited in the fifth research project.

For this dissertation and the individual research projects, extant literature particularly from the strategic man-agement and intellectual property management realm is drawn upon. The covered prior research topics com-prise – but are not limited to – competitive and technology intelligence, technology landscaping, firms’ financial and innovation performances, intellectual property like patents and trademarks and intellectual property integra-tion, patent analytics and patent valuation, patent indicators and metrics, in particular (smart) patent indicators, sustainability, and open innovation, open innovation modes, and (patent-based) open innovation measures.

For the individual research projects, a variety of methods, levels of analysis, and unique data sets are relied upon. The first research project constitutes a literature review of 123 publications on patent indicators and is focused on the patent/patent portfolio level. The second research project constitutes a technology landscaping case study on 24,264 patent families from the smart houses technology and is focused on the technology field (patent families) level. The third research project constitutes a mixed-methods approach by combining (1) a conceptual paper on the relationship between intellectual property and open innovation and (2) a case study on the development of Lyrica and Pregabalin based on publications, interviews, and patents. This case study is focused on the invention (patents) level. The fourth research project constitutes a quantitative-empirical anal-yses (ordinary least squares regressions) of 21,898 patent families stemming from open and closed innovation collaborations by 44 German small- and medium-sized firms with 9,902 entities in their corporate trees. This quantitative-empirical analysis is focused on the invention (patent families) level. The fifth research project con-stitutes a multi-methods approach based on quantitative-empirical analyses (ordinary least squares regres-sions) of (1) 17,045 European firms and (2) a subsample of 182 European firms. This research project is fo-cused on the firm (patent and trademark portfolios) level.

In answering the overarching research question, the joint findings and implications of this dissertation suggest that patents yield valuable insights for strategic and intellectual property management, particularly when ac-counting for common issues such as the harmonization or correct assignment of ownership, incorporation of legal status information and associated enforceability, as well as aspects affecting patent indicators like patent citations. Patent-based information, when properly and effectively used, provides valuable insights for a variety of firms and a plethora of use cases. However, the results also echo the famous colloquial saying of “garbage in, garbage out”: if patent analytics are based on incomplete data and deficient patent indicators, biased results are inevitable, leading to suboptimal strategic decisions, and ultimately an inefficient use of firm resources. Moreover, decision-makers should make use of options to combine patent-based data with other internal and external data sources, including performance data. It is also beneficial to turn to experts in preparing, conduct-ing, and interpreting patent analytics’ results and to pertain to the limitations of these analyses. In addition, harmonized patent data that accounts for patents’ legal status and ownership structures as well as smart and other novel patent indicators may also be applied in economic, legal, and other managerial perspectives on patents, including litigation, standard setting, or the development of dominant designs.

DOI
Zugehörige ORCIDs