Prediction of Emerging Technologies Based on Analysis of the U.S. Patent Citation Network

The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (i) identifies actual clusters of patents: i.e. technological branches, and (ii) gives predictions about the temporal changes of the structure of the clusters. A predictor, called the {citation vector}, is defined for characterizing technological development to show how a patent cited by other patents belongs to various industrial fields. The clustering technique adopted is able to detect the new emerging recombinations, and predicts emerging new technology clusters. The predictive ability of our new method is illustrated on the example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of patents is determined based on citation data up to 1991, which shows significant overlap of the class 442 formed at the beginning of 1997. These new tools of predictive analytics could support policy decision making processes in science and technology, and help formulate recommendations for action

Recognition of Emerging Technology Trends. Class-selective study of citations in the U.S. Patent Citation Network

By adopting a citation-based recursive ranking method for patents the evolution of new fields of technology can be traced. Specifically, it is demonstrated that the laser / inkjet printer technology emerged from the recombination of two existing technologies: sequential printing and static image production. The dynamics of the citations coming from the different "precursor" classes illuminates the mechanism of the emergence of new fields and give the possibility to make predictions about future technological development. For the patent network the optimal value of the PageRank damping factor is close to 0.5; the application of d=0.85 leads to unacceptable ranking results.Comment: 8 pages, 2 tables, 1 figure , (accepted). in Scientometrics 201

Connect the Dots: Patents and Interdisciplinarity

This Article unravels a troubling paradox in the ecosystem of innovation. Interdisciplinarity is widely recognized as a source of valuable innovation and a trigger for technological breakthroughs. Yet, patent law, a principal legal tool for promoting innovation, fails to acknowledge it in an explicit, consistent manner. Moreover, although the scientific understanding of the significance of interdisciplinarity for innovation increasingly relies on big data analyses of patent databases, patent law practically ignores patent data as a source of information about interdisciplinary innovation. This Article argues that patent law should connect the dots—explicitly recognize interdisciplinarity as a positive indication when deciding whether an invention deserves patent protection and use information derived from patent databases to evaluate the interdisciplinarity of inventions. Relying on cutting edge research in economics and network-science, this Article explores nuanced manners for implementing these proposals, calling, ultimately, for the development of an algorithmic “recombination metric” that would allow courts and patent offices to identify interdisciplinary inventions in an accessible, standardized manner. The adoption of this Article’s proposals would align patent doctrine with its ultimate goal of promoting high-risk, socially valuable, innovation; inject an objective and measurable criterion into various patent doctrines famously criticized for their ambiguity and unpredictability; and allow patent law to realize some of the enormous potential of patent data—a treasure that current patent doctrine leaves untapped

NLP Driven Models for Automatically Generating Survey Articles for Scientific Topics.

This thesis presents new methods that use natural language processing (NLP) driven models for summarizing research in scientific fields. Given a topic query in the form of a text string, we present methods for finding research articles relevant to the topic as well as summarization algorithms that use lexical and discourse information present in the text of these articles to generate coherent and readable extractive summaries of past research on the topic. In addition to summarizing prior research, good survey articles should also forecast future trends. With this motivation, we present work on forecasting future impact of scientific publications using NLP driven features.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113407/1/rahuljha_1.pd