Our new report on “Exploring the Future of Patent Analytics” contributes to expanding the field of patent analytics for more effective exploitation of the largest worldwide repository of technological information. The report may further help to facilitate collaboration and coordinated action within the patent analytics community.
The report presents a domain-level technology roadmap following a three-stage technology roadmapping and problem-solving approach involving a substantial amount of key stakeholders from the patent analytics community. This research was funded by the United Kingdom Engineering Physical Science Research Council (EPSRC), through the Cambridge Big Data initiative as part of an EPSRC Institutional Sponsorship Grant 2016 – Small Partnership Awards, supported by Aistemos Ltd as the industrial partner.
The research identifies 11 priority technologies, such as artificial intelligence and neural networks, 5 additional technologies, such as technologies for linking databases, and 15 complementary technologies, such as block chain, to be adopted in the IP field. Also, 21 enablers are identified for potential breakthrough progress in the field that cluster around 4 themes: technology development cycles and methodologies; legislation and standardisation for patent data quality; continuous professional development; and cooperation between industry and academia.
The IP management module covers topics such as protection, enforcement and risk management, markets for technology and licensing, open innovation and appropriation strategies, but as well as insights into how firms organize for effective IP management and IP analytics for technology intelligence.
Over the last two days I attended the DRUID conference in New York. It has been an excellent experience. While this might be partially explained by the unbeatable location, I found the intellectual conversations and particularly the DRUID debates extremely inspiring and on a fantastic high level.
The conference features two debates, with the first one on engaging with practice (and the problem to create impact) and the second on focused on theoretical and empirical contributions in academic papers.
Particularly when I was listening to the second DRUID debate it struck me that the debate circled around a particularly view of what must be considered one particular way to understand what theory is. It seems to me that most colleagues (possibly mostly the younger ones) understand theory predominantly as an instrument to provide explanations of existing phenomenon, particularly of those which warrant explanation (i.e. are relevant). Accordingly, the type of theory to be developed and tested usually seeks to explain ‘why’, in other words to explain the causalities within and around certain phenomenon (i.e. interrelations of constructs). From my understanding, such an understanding essentially forces researchers to become concerned with existing (ex post) conceptual or empirical phenomenon and will inevitable aim at developing explanatory theories. Let me try to explain why I think this approach is linked the problem that the community faces about creating impact through research (i.e. discussed during the first debate) and how this could possibly be changed.
There is an alternative way of thinking about what theory is, which was briefly touched upon by Martin Kilduff during the second debate. One may want to consider whether to disregard the phenomenon focused approach and substitute it with a focus on economic or managerial problems (e.g. the strategic alignment problem). Focusing on a problem (instead of phenomenon) offers a different cognitive space and thus a way for the field to create the impact it deserves with all the intellectually power of extremely capable colleagues behind it. Let me try to explain how I see it.
Focusing on problems will drive researchers to theorize solutions, some of which might be sub-optimal, but through empirical testing, validation and refinement one may (eventually) arrive at a fairly optimal solution to a certain problem. Hence, when focusing on problems the type of theory to be developed will be of inherently different nature than the theorizing that takes place when aiming to explain phenomenon. Instead of taking an ex-post observatory approach researchers will be forced to develop theories, i.e. an understanding of causalities that need to be set in action for solving a problem. These kind of theories can likewise be tested empirically, even though we may need slightly different methods (e.g. simulations). The next step would then be to develop policy frameworks (which can turn out to be highly relevant policy implications) as well as tools and techniques (to be put forward as managerial implications), which in turn can be tested empirically (i.e. their performance). We may also need to develop particularly skills, if not guidelines to frame and define problems.
Taking such an approach to empirically supported theory development may contribute to changing the research conducted in the community towards a way that creates impact, while being rigorous. Research will then become a creative (problem solving) endeavour with relevance and impact.
Very nice to seeing Mingjin earlier this week in the Bay area, who was in fact my first PhD student. Great to see that she is getting on so well with her research trying to find out how complementors in an ecosystem impact the focal firm’s IP strategy.