Patent Pools: Intellectual Property Rights and Competition

Patent pools do not correct all problems associated with patent thickets. In this respect, patent pools might not stop the outsider problem from striking pools. Moreover, patent pools can be expensive to negotiate, can exclude patent holders with smaller numbers of patents or enable a group of major players to form a cartel that excludes new competitors. For all the above reasons, patent pools are subject to regulatory clearance because they could result in a monopoly. The aim of this article is to present the relationship between patents and competition in a broad context

Intellectual Property, Open Science and Research Biobanks

In biomedical research and translational medicine, the ancient war between exclusivity (private control over information) and access to information is proposing again on a new battlefield: research biobanks. The latter are becoming increasingly important (one of the ten ideas changing the world, according to Time magazine) since they allow to collect, store and distribute in a secure and professional way a critical mass of human biological samples for research purposes. Tissues and related data are fundamental for the development of the biomedical research and the emerging field of translational medicine: they represent the “raw material” for every kind of biomedical study. For this reason, it is crucial to understand the boundaries of Intellectual Property (IP) in this prickly context. In fact, both data sharing and collaborative research have become an imperative in contemporary open science, whose development depends inextricably on: the opportunities to access and use data, the possibility of sharing practices between communities, the cross-checking of information and results and, chiefly, interactions with experts in different fields of knowledge. Data sharing allows both to spread the costs of analytical results that researchers cannot achieve working individually and, if properly managed, to avoid the duplication of research. These advantages are crucial: access to a common pool of pre-competitive data and the possibility to endorse follow-on research projects are fundamental for the progress of biomedicine. This is why the "open movement" is also spreading in the biobank's field. After an overview of the complex interactions among the different stakeholders involved in the process of information and data production, as well as of the main obstacles to the promotion of data sharing (i.e., the appropriability of biological samples and information, the privacy of participants, the lack of interoperability), we will firstly clarify some blurring in language, in particular concerning concepts often mixed up, such as “open source” and “open access”. The aim is to understand whether and to what extent we can apply these concepts to the biomedical field. Afterwards, adopting a comparative perspective, we will analyze the main features of the open models – in particular, the Open Research Data model – which have been proposed in literature for the promotion of data sharing in the field of research biobanks. After such an analysis, we will suggest some recommendations in order to rebalance the clash between exclusivity - the paradigm characterizing the evolution of intellectual property over the last three centuries - and the actual needs for access to knowledge. We argue that the key factor in this balance may come from the right interaction between IP, social norms and contracts. In particular, we need to combine the incentives and the reward mechanisms characterizing scientific communities with data sharing imperative

Explicitly searching for useful inventions: dynamic relatedness and the costs of connecting versus synthesizing

Inventions combine technological features. When features are barely related, burdensomely broad knowledge is required to identify the situations that they share. When features are overly related, burdensomely broad knowledge is required to identify the situations that distinguish them. Thus, according to my first hypothesis, when features are moderately related, the costs of connecting and costs of synthesizing are cumulatively minimized, and the most useful inventions emerge. I also hypothesize that continued experimentation with a specific set of features is likely to lead to the discovery of decreasingly useful inventions; the earlier-identified connections reflect the more common consumer situations. Covering data from all industries, the empirical analysis provides broad support for the first hypothesis. Regressions to test the second hypothesis are inconclusive when examining industry types individually. Yet, this study represents an exploratory investigation, and future research should test refined hypotheses with more sophisticated data, such as that found in literature-based discovery research

The Bayh-Dole Act of 1980 and University–Industry Technology Transfer: A Model for Other OECD Governments?

Recent initiatives by a number of OECD governments suggest considerable interest in emulating the Bayh-Dole Act of 1980, a piece of legislation that is widely credited with stimulating significant growth in university--industry technology transfer and research collaboration in theUS. We examine the effects of Bayh-Dole on university--industry collaboration and technology transfer in the US, emphasizing the lengthy history of both activities prior to 1980 and noting the extent to which these activities are rooted in the incentives created by the unusual scale and structure (by comparison with Western Europe or Japan) of the US higher education system. Efforts at “emulation” of the Bayh-Dole policy elsewhere in the OECD are likely to have modest success at best without greater attention to the underlying structural differences among the higher education systems of these nations.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43108/1/10961_2004_Article_5384361.pd