Modeling the Duration of Patent Examination at the European Patent Office

We analyze the duration of the patent examination process at the European Patent Office (EPO). Our data contain information related to the patent’s economic and technical relevance, EPO capacity and workload as well as novel citation measures which are derived from the EPO’s search reports. In our multivariate analysis we estimate competing risk specifications in order to characterize differences in the processes leading to a withdrawal of the application by the applicant, a refusal of the patent grant by the examiner or an actual patent grant. Highly cited applications are approved faster by the EPO than less important ones, but they are also withdrawn less quickly by the applicant. The process duration increases for all outcomes with the application’s complexity, originality, number of references (backward citations) in the search report and with the EPO’s workload at the filing date. Endogenous applicant behavior becomes apparent in other results: more controversial claims lead to slower grants, but faster withdrawals, while relatively well-documented applications (identified by a high share of applicant references appearing in the search report) are approved faster and take longer to be withdrawn

The Open Research Web: A Preview of the Optimal and the Inevitable

The multiple online research impact metrics we are developing will allow the rich new database , the Research Web, to be navigated, analyzed, mined and evaluated in powerful new ways that were not even conceivable in the paper era – nor even in the online era, until the database and the tools became openly accessible for online use by all: by researchers, research institutions, research funders, teachers, students, and even by the general public that funds the research and for whose benefit it is being conducted: Which research is being used most? By whom? Which research is growing most quickly? In what direction? under whose influence? Which research is showing immediate short-term usefulness, which shows delayed, longer term usefulness, and which has sustained long-lasting impact? Which research and researchers are the most authoritative? Whose research is most using this authoritative research, and whose research is the authoritative research using? Which are the best pointers (“hubs”) to the authoritative research? Is there any way to predict what research will have later citation impact (based on its earlier download impact), so junior researchers can be given resources before their work has had a chance to make itself felt through citations? Can research trends and directions be predicted from the online database? Can text content be used to find and compare related research, for influence, overlap, direction? Can a layman, unfamiliar with the specialized content of a field, be guided to the most relevant and important work? These are just a sample of the new online-age questions that the Open Research Web will begin to answer

Science Quality and the Value of Inventions

Despite decades of research, the relationship between the quality of science and the value of inventions has remained unclear. We present the result of a large-scale matching exercise between 4.8 million patent families and 43 million publication records. We find a strong positive relationship between quality of scientific contributions referenced in patents and the value of the respective inventions. We rank patents by the quality of the science they are linked to. Strikingly, high-rank patents are twice as valuable as low-rank patents, which in turn are about as valuable as patents without direct science link. We show this core result for various science quality and patent value measures. The effect of science quality on patent value remains relevant even when science is linked indirectly through other patents. Our findings imply that what is considered "excellent" within the science sector also leads to outstanding outcomes in the technological or commercial realm.Comment: 44 page

Genesis of Altmetrics or Article-level Metrics for Measuring Efficacy of Scholarly Communications: Current Perspectives

The article-level metrics (ALMs) or altmetrics becomes a new trendsetter in recent times for measuring the impact of scientific publications and their social outreach to intended audiences. The popular social networks such as Facebook, Twitter, and Linkedin and social bookmarks such as Mendeley and CiteULike are nowadays widely used for communicating research to larger transnational audiences. In 2012, the San Francisco Declaration on Research Assessment got signed by the scientific and researchers communities across the world. This declaration has given preference to the ALM or altmetrics over traditional but faulty journal impact factor (JIF)-based assessment of career scientists. JIF does not consider impact or influence beyond citations count as this count reflected only through Thomson Reuters' Web of Science database. Furthermore, JIF provides indicator related to the journal, but not related to a published paper. Thus, altmetrics now becomes an alternative metrics for performance assessment of individual scientists and their contributed scholarly publications. This paper provides a glimpse of genesis of altmetrics in measuring efficacy of scholarly communications and highlights available altmetric tools and social platforms linking altmetric tools, which are widely used in deriving altmetric scores of scholarly publications. The paper thus argues for institutions and policy makers to pay more attention to altmetrics based indicators for evaluation purpose but cautions that proper safeguards and validations are needed before their adoption

On the framing of patent citations and academic paper citations in refl ecting knowledge linkage: A discussion of the discrepancy of their divergent value-orientations

It has been widely recognized that academic paper citations will reflect scientific knowledge linkage. Patent citations are similar to academic paper citations in many aspects: Citation frequency distribution is often skewed; citation frequency varies from one subject field to another and authors&rsquo;/inventors&rsquo;preference for citing relevant literature is usually confined to their own native language. However, regardless of these seemingly similarities, the patent citation is unique and special. It is constructed by incorporating information providers from multiple sources, such as from examiners, inventors, attorneys and/or the public. It is driven by a value-orientation for the monopolization of market production under regulations of Patent Laws. It is also practiced under the sway of an industrial culture embedded with a notion of &ldquo;creative destruction&rdquo;. In view of the contextual complexities of patent citations, simply applying the data criteria and citation behavior analysis of academic paper citations to that of patentbibliometrics for the purpose of reflecting knowledge linkage is both conceptually and technically illogical and unreasonable. This paper attempts to delve into the issue of the currently misconceived assertions and practice about &quot;transplanting&rdquo; the methodology of academic paper citations en masse indiscriminately into the practice of patent citations. It is hoped that such a study would yield improved result stemming from the practice of patent citations for reflecting knowledge linkage in the future.</p

Search for Evergreens in Science: A Functional Data Analysis

Evergreens in science are papers that display a continual rise in annual citations without decline, at least within a sufficiently long time period. Aiming to better understand evergreens in particular and patterns of citation trajectory in general, this paper develops a functional data analysis method to cluster citation trajectories of a sample of 1699 research papers published in 1980 in the American Physical Society (APS) journals. We propose a functional Poisson regression model for individual papers' citation trajectories, and fit the model to the observed 30-year citations of individual papers by functional principal component analysis and maximum likelihood estimation. Based on the estimated paper-specific coefficients, we apply the K-means clustering algorithm to cluster papers into different groups, for uncovering general types of citation trajectories. The result demonstrates the existence of an evergreen cluster of papers that do not exhibit any decline in annual citations over 30 years.Comment: 40 pages, 9 figure

Recognizing cited facts and principles in legal judgements

In common law jurisdictions, legal professionals cite facts and legal principles from precedent cases to support their arguments before the court for their intended outcome in a current case. This practice stems from the doctrine of stare decisis, where cases that have similar facts should receive similar decisions with respect to the principles. It is essential for legal professionals to identify such facts and principles in precedent cases, though this is a highly time intensive task. In this paper, we present studies that demonstrate that human annotators can achieve reasonable agreement on which sentences in legal judgements contain cited facts and principles (respectively, κ=0.65 and κ=0.95 for inter- and intra-annotator agreement). We further demonstrate that it is feasible to automatically annotate sentences containing such legal facts and principles in a supervised machine learning framework based on linguistic features, reporting per category precision and recall figures of between 0.79 and 0.89 for classifying sentences in legal judgements as cited facts, principles or neither using a Bayesian classifier, with an overall κ of 0.72 with the human-annotated gold standard

Modeling the Duration of Patent Examination at the European Patent Office

We analyze the duration of the patent examination process at the European Patent Office (EPO). Our data contain information related to the patent’s economic and technical relevance, EPO capacity and workload as well as novel citation measures which are derived from the EPO’s search reports. In our multivariate analysis we estimate competing risk specifications in order to characterize differences in the processes leading to a withdrawal of the application by the applicant, a refusal of the patent grant by the examiner or an actual patent grant. Highly cited applications are approved faster by the EPO than less important ones, but they are also withdrawn less quickly by the applicant. The process duration increases for all outcomes with the application’s complexity, originality, number of references (backward citations) in the search report and with the EPO’s workload at the filing date. Endogenous applicant behavior becomes apparent in other results: more controversial claims lead to slower grants, but faster withdrawals, while relatively well-documented applications (identified by a high share of applicant references appearing in the search report) are approved faster and take longer to be withdrawn.patents; patent examination; survival analysis; patent citations; European Patent Office