Scientific names of organisms : attribution, rights, and licensing

© The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in BMC Research Notes 7 (2014): 79, doi:10.1186/1756-0500-7-79.As biological disciplines extend into the ‘big data’ world, they will need a names-based infrastructure to index and interconnect distributed data. The infrastructure must have access to all names of all organisms if it is to manage all information. Those who compile lists of species hold different views as to the intellectual property rights that apply to the lists. This creates uncertainty that impedes the development of a much-needed infrastructure for sharing biological data in the digital world. The laws in the United States of America and European Union are consistent with the position that scientific names of organisms and their compilation in checklists, classifications or taxonomic revisions are not subject to copyright. Compilations of names, such as classifications or checklists, are not creative in the sense of copyright law. Many content providers desire credit for their efforts. A ‘blue list’ identifies elements of checklists, classifications and monographs to which intellectual property rights do not apply. To promote sharing, authors of taxonomic content, compilers, intermediaries, and aggregators should receive citable recognition for their contributions, with the greatest recognition being given to the originating authors. Mechanisms for achieving this are discussed

Inventor mobility index : a method to disambiguate inventor careers

Usually patent data does not contain any unique identifiers for the patenting assignees or the inventors, as the main tasks of patent authorities is the examination of applications and the administration of the patent documents as public contracts and not the support of the empirical analysis of their data. An inventor in a patent document is identified by his or her name. Depending on the patent authority the full address or parts of it may be included to further identify this inventor. The goal is to define an inventor mobility index that traces the career of an inventor as an individual with all the job switches and relocations approximated by the patents as potential milestones. The inventor name is the main criteria for this identifier. The inventor address information on the other hand is only of limited use for the definition of a mobility index. The name alone can work for exotic name variants, but for more common names the problem of namesakes gets in the way of identifying individuals. The solution discussed here consists in the construction of a relationship network between inventors with the same name. This network will be created by using all the other information available in the patent data. These could be simple connections like the same applicant or just the same home address, up to more complex connections that are created by the overlapping of colleagues and co-inventors, similar technology fields or shared citations. Traversal of these heuristically weighted networks by using methods of the graph theory leads to clusters representing a person. The applied methodology will give uncommon names a higher degree of freedom regarding the heuristic limitations than the more common names will get

Query refinement for patent prior art search

A patent is a contract between the inventor and the state, granting a limited time period to the inventor to exploit his invention. In exchange, the inventor must put a detailed description of his invention in the public domain. Patents can encourage innovation and economic growth but at the time of economic crisis patents can hamper such growth. The long duration of the application process is a big obstacle that needs to be addressed to maximize the benefit of patents on innovation and economy. This time can be significantly improved by changing the way we search the patent and non-patent literature.Despite the recent advancement of general information retrieval and the revolution of Web Search engines, there is still a huge gap between the emerging technologies from the research labs and adapted by major Internet search engines, and the systems which are in use by the patent search communities.In this thesis we investigate the problem of patent prior art search in patent retrieval with the goal of finding documents which describe the idea of a query patent. A query patent is a full patent application composed of hundreds of terms which does not represent a single focused information need. Other relevance evidences (e.g. classification tags, and bibliographical data) provide additional details about the underlying information need of the query patent. The first goal of this thesis is to estimate a uni-gram query model from the textual fields of a query patent. We then improve the initial query representation using noun phrases extracted from the query patent. We show that expansion in a query-dependent manner is useful.The second contribution of this thesis is to address the term mismatch problem from a query formulation point of view by integrating multiple relevance evidences associated with the query patent. To do this, we enhance the initial representation of the query with the term distribution of the community of inventors related to the topic of the query patent. We then build a lexicon using classification tags and show that query expansion using this lexicon and considering proximity information (between query and expansion terms) can improve the retrieval performance. We perform an empirical evaluation of our proposed models on two patent datasets. The experimental results show that our proposed models can achieve significantly better results than the baseline and other enhanced models