Community Detection and Growth Potential Prediction from Patent Citation Networks

The scoring of patents is useful for technology management analysis. Therefore, a necessity of developing citation network clustering and prediction of future citations for practical patent scoring arises. In this paper, we propose a community detection method using the Node2vec. And in order to analyze growth potential we compare three ''time series analysis methods'', the Long Short-Term Memory (LSTM), ARIMA model, and Hawkes Process. The results of our experiments, we could find common technical points from those clusters by Node2vec. Furthermore, we found that the prediction accuracy of the ARIMA model was higher than that of other models.Comment: arXiv admin note: text overlap with arXiv:1607.00653 by other author

Science linkages in technologies patented in Japan

We constructed an original database concerning science linkages based on text of Japanese Patent Gazette published since 1994. We discovered that Japanese inventers cite many academic papers in the texts of the patent applications in the Japanese Patent System. Based on this finding, we constructed science citation index by data mining the texts of Japanese patent system for the first time. First, more than 880,000 patent data classified into about 600 categories. Then, we extracted non-patent references from all the granted patents and counted the number of them. This number shows the strength of the linkage between science and technology and therefore is called "science linkage index." The science linkage indexes among different patent classifications differ significantly from each other. The technologies related to bio -technology were by far the closest to science. It suggests that the process of creating new technology differs from technology to technology.

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

Aerospace medicine and Biology: A continuing bibliography with indexes, supplement 177

This bibliography lists 112 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1978

Toward higher effectiveness for recall-oriented information retrieval: A patent retrieval case study

Research in information retrieval (IR) has largely been directed towards tasks requiring high precision. Recently, other IR applications which can be described as recall-oriented IR tasks have received increased attention in the IR research domain. Prominent among these IR applications are patent search and legal search, where users are typically ready to check hundreds or possibly thousands of documents in order to find any possible relevant document. The main concerns in this kind of application are very different from those in standard precision-oriented IR tasks, where users tend to be focused on finding an answer to their information need that can typically be addressed by one or two relevant documents. For precision-oriented tasks, mean average precision continues to be used as the primary evaluation metric for almost all IR applications. For recall-oriented IR applications the nature of the search task, including objectives, users, queries, and document collections, is different from that of standard precision-oriented search tasks. In this research study, two dimensions in IR are explored for the recall-oriented patent search task. The study includes IR system evaluation and multilingual IR for patent search. In each of these dimensions, current IR techniques are studied and novel techniques developed especially for this kind of recall-oriented IR application are proposed and investigated experimentally in the context of patent retrieval. The techniques developed in this thesis provide a significant contribution toward evaluating the effectiveness of recall-oriented IR in general and particularly patent search, and improving the efficiency of multilingual search for this kind of task

Study on Scientific outputs of Scholars in the Field of Digital Libraries Using Altmetrics Indicators

The current study aims to calculate the relationship between Altmetric scores obtained from the observation and dissemination of digital library resources in the Dimensions database and the number of citations received in the Scopus database. Also, in another part of the research, the predictive power of the number of Scopus citations by Altmetric scores is examined. The present research is applied in terms of purpose and survey-descriptive in terms of type, which is done by the scientometric method and with an Altmetric approach. The statistical population of the study includes all articles in the field of digital libraries (24183 records) that are indexed in the Scopus citation database during 1960-2020. Dimensions database has been used to evaluate the Altmetric scores obtained from these articles on social networks. Due to the limited access to the required data in the Scopus database, 2000 highly cited articles in the field of digital libraries in this Scopus database were studied through the Dimensions database. The data collection tools are Scopus Citation Database and Dimensions Database. The required data is collected through the Scopus database. In this study, the studied indicators from the Dimensions database appear as the independent variable of the research. The dependent variables in this study are the number of citations to articles in the Scopus database. Correlation tests and multiple regression between the studied indices are used to examine the relationships between variables and perform statistical tests. The software used is Excel and SPSS version 23. The present study results show that the social networks Patent, Facebook, Wikipedia, and Twitter have the highest correlation with the number of citations in the Dimensions database. The social networks Blog, Google User, and Q&amp;A do not significantly relate to the number of citations received in Dimensions. Patent social networks, Wikipedia, and Twitter have the highest correlation with the number of Scopus citations. In this case, the social networks of Blog, Google User, Pulse Source and Q&amp;A do not significantly correlate with the number of citations received. Among the citation databases studied, Mendeley has the highest correlation between the numbers of citations. Other results indicate that the publication and viewing of documents on social networks cannot predict the number of citations in the Dimensions and Scopus databases.https://dorl.net/dor/20.1001.1.20088302.2022.20.4.10.

Patent citation analysis with Google

This is an accepted manuscript of an article published by Wiley-Blackwell in Journal of the Association for Information Science and Technology on 23/09/2015, available online: https://doi.org/10.1002/asi.23608 The accepted version of the publication may differ from the final published version.Citations from patents to scientific publications provide useful evidence about the commercial impact of academic research, but automatically searchable databases are needed to exploit this connection for large-scale patent citation evaluations. Google covers multiple different international patent office databases but does not index patent citations or allow automatic searches. In response, this article introduces a semiautomatic indirect method via Bing to extract and filter patent citations from Google to academic papers with an overall precision of 98%. The method was evaluated with 322,192 science and engineering Scopus articles from every second year for the period 1996–2012. Although manual Google Patent searches give more results, especially for articles with many patent citations, the difference is not large enough to be a major problem. Within Biomedical Engineering, Biotechnology, and Pharmacology & Pharmaceutics, 7% to 10% of Scopus articles had at least one patent citation but other fields had far fewer, so patent citation analysis is only relevant for a minority of publications. Low but positive correlations between Google Patent citations and Scopus citations across all fields suggest that traditional citation counts cannot substitute for patent citations when evaluating research

Evaluating Information Retrieval and Access Tasks

This open access book summarizes the first two decades of the NII Testbeds and Community for Information access Research (NTCIR). NTCIR is a series of evaluation forums run by a global team of researchers and hosted by the National Institute of Informatics (NII), Japan. The book is unique in that it discusses not just what was done at NTCIR, but also how it was done and the impact it has achieved. For example, in some chapters the reader sees the early seeds of what eventually grew to be the search engines that provide access to content on the World Wide Web, today’s smartphones that can tailor what they show to the needs of their owners, and the smart speakers that enrich our lives at home and on the move. We also get glimpses into how new search engines can be built for mathematical formulae, or for the digital record of a lived human life. Key to the success of the NTCIR endeavor was early recognition that information access research is an empirical discipline and that evaluation therefore lay at the core of the enterprise. Evaluation is thus at the heart of each chapter in this book. They show, for example, how the recognition that some documents are more important than others has shaped thinking about evaluation design. The thirty-three contributors to this volume speak for the many hundreds of researchers from dozens of countries around the world who together shaped NTCIR as organizers and participants. This book is suitable for researchers, practitioners, and students—anyone who wants to learn about past and present evaluation efforts in information retrieval, information access, and natural language processing, as well as those who want to participate in an evaluation task or even to design and organize one

The state-of-the-art on Intellectual Property Analytics (IPA): A literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data

Big data is increasingly available in all areas of manufacturing and operations, which presents an opportunity for better decision making and discovery of the next generation of innovative technologies. Recently, there have been substantial developments in the field of patent analytics, which describes the science of analysing large amounts of patent information to discover trends. We define Intellectual Property Analytics (IPA) as the data science of analysing large amount of IP information, to discover relationships, trends and patterns for decision making. In this paper, we contribute to the ongoing discussion on the use of intellectual property analytics methods, i.e artificial intelligence methods, machine learning and deep learning approaches, to analyse intellectual property data. This literature review follows a narrative approach with search strategy, where we present the state-of-the-art in intellectual property analytics by reviewing 57 recent articles. The bibliographic information of the articles are analysed, followed by a discussion of the articles divided in four main categories: knowledge management, technology management, economic value, and extraction and effective management of information. We hope research scholars and industrial users, may find this review helpful when searching for the latest research efforts pertaining to intellectual property analytics.The authors would like to acknowledge support of the Engineering and Physical Sciences Research Council (EPSRC)