Exploring potential R&D collaboration partners through patent analysis based on bibliographic coupling and latent semantic analysis

The aim of the present research is to provide a new systematic methodology to explore potential R&D collaboration partners using patent information. The potential R&D collaboration partners are visualized as a patent assignee level-map based on technological similarity between patents by using network analysis. The proposed framework utilises two analytic methods to measure technological similarity. The first method, bibliographic coupling analysis, measures technological similarity based on the citation relationship using patent bibliographic information. Second, latent semantic analysis is utilized based on semantic similarity using patent textual information. The fuel cell membrane electrode assembly (MEA) technology field is selected and applied to illustrate the proposed methodology. The proposed approach allows firms, universities, research institutes, governments to identify potential R&D collaborators as a systematic decision-making support tool.This is an Accepted Manuscript of an article published by Taylor & Francis in Technology Analysis and Strategic Management on the 22nd of October 2014, available online: http://wwww.tandfonline.com/10.1080/09537325.2014.971004. This version will be under embargo until the 22nd of April 2016

A hybrid similarity measure method for patent portfolio analysis

© 2016 Elsevier Ltd Similarity measures are fundamental tools for identifying relationships within or across patent portfolios. Many bibliometric indicators are used to determine similarity measures; for example, bibliographic coupling, citation and co-citation, and co-word distribution. This paper aims to construct a hybrid similarity measure method based on multiple indicators to analyze patent portfolios. Two models are proposed: categorical similarity and semantic similarity. The categorical similarity model emphasizes international patent classifications (IPCs), while the semantic similarity model emphasizes textual elements. We introduce fuzzy set routines to translate the rough technical (sub-) categories of IPCs into defined numeric values, and we calculate the categorical similarities between patent portfolios using membership grade vectors. In parallel, we identify and highlight core terms in a 3-level tree structure and compute the semantic similarities by comparing the tree-based structures. A weighting model is designed to consider: 1) the bias that exists between the categorical and semantic similarities, and 2) the weighting or integrating strategy for a hybrid method. A case study to measure the technological similarities between selected firms in China's medical device industry is used to demonstrate the reliability our method, and the results indicate the practical meaning of our method in a broad range of informetric applications

Patent analysis as an input to strategy: case of electric vehicle industry

This research work examines technological developments of an emerging field from the perspective of patented innovations and major industry players. The selected domain of the study is the electric vehicle (EV) industry, which represents an emerging technological field driven by innovations. Patent-to-patent citation information and entities associated with each patent (i.e. patent holder, technology field, country) were utilized for the visualization of the relationship between patents in the form of a network. Bibliographic coupling (BC) is the methodology used to establish these relationships. From the viewpoint of companies, this relationship indicates similarities in the technological development direction and areas of R&D activities, suggesting for possible competition or cooperation. From the perspective of patented technology, the association indicates that the technologies or their applications are closely related. The focus of the study is placed on the possibilities and limitations provided by patent analysis based on BC, so to facilitate further exploration and application of this methodology as a valuable tool for the support of managers' assessment of technological environment in real time and planning of the R&D projects within an emerging field. Managers can use patent maps as an additional source of information and communication support in the strategic decision-making process. Using the bibliographic information of patents, the technological landscape and recent developments in EV sector during the recent six years were analyzed based on the statistical examination and the graph theory provided by social network analysis. Citation networks were divided into clusters, the patent assignee in each cluster were tracked, and citation networks with characteristic technology field for each cluster were analyzed. Overall structural changes of the EV industry were explored by categorizing patent assignees into four main groups, i.e. automotive OEMs, suppliers, infrastructure providers and other players, and exploring the changes in patenting activities between these groups. Analysis of patent network dynamics reveals the changes in the structure of innovation landscape within an emerging field of EVs. Expert opinion of a Finnish automaker was included in the analysis of this study. Limitations of the methodology and suggestions for further research directions are discussed

Deriving Technology Intelligence from Patents: Preposition-based Semantic Analysis

Patents are one of the most reliable sources of technology intelligence, and the true value of patent analysis stems from its capability of describing the content of technology based on the relationships between keywords. To date a number of techniques for analyzing the information contained in patent documents that focus on the relationships between keywords have been suggested. However, a drawback of the existing keyword approaches is that they cannot yet determine the types of relationships between the keywords. This study proposes a novel approach based on preposition semantic analysis network which overcomes the limitations of the existing keywords-based network analysis and demonstrates its potential through an application. A preposition is a word that defines the relationship between two neighboring words, and, in the case of patents, prepositions aid in revealing the relationships between keywords related to technologies. To demonstrate the approach, patents regarding an electric vehicle were employed. 13 prepositions were identified which could be used to define 5 relationships between neighboring technological terms: “inclusion (utilization),” “objective (purpose),” “effect,” “process,” and “likeness.” The proposed approach is expected to improve the usability of keyword-based patent analyses and support more elaborate studies on patent documents

Estudio comparativo sobre la visualización de redes de co-words a través de los descriptores del Science Citation Index y de Medline

Cantos Mateos, Gisela; Zulueta, María Ángeles; Vargas-Quesada, Benjamín; Chinchilla-Rodríguez, Zaida. Estudio comparativo sobre la visualización de redes de co-words a través de los descriptores del Science Citation Index y de Medline. Atas de I Congresso ISKO Espanha e Portugal / XI Congresso ISKO Espanha, Oporto (Portugal), 7 a 9 de novembro 201, p. 173-189Objetivos: El presente estudio se centra en la investigación desarrollada en España sobre células madre comprendida entre los años 1997 y 2010. El objetivo fundamental consiste en la comparación de las líneas de investigación que ofrece el análisis de distintos tipos de descriptores, según su naturaleza documental, a partir de su aparición conjunta en los documentos. Material y Métodos: Las fuentes utilizadas han sido las bases de datos del Science Citation Index Expanded (SCI-E) y Medline, empleando para el estudio el mismo conjunto documental. El análisis aplicado ha consistido en la representación y visualización de las relaciones que se establecen entre los términos de indización. De un lado, se han empleado los descriptores utilizados por el SCI para indizar sus documentos: KeywordsPlus (KW+) y Keywords Author (KWA) y de otro, los descriptores MeSH utilizados por Medline. Las herramientas utilizadas para la visualización han sido el software Pajek, en combinación con el algoritmo PathfinderNetwork (PfNET) para la simplificación de las relaciones y el software VOSviewer.Resultados y Discusión: se han recuperado 3.078 documentos. A partir de ellos, en función del tipo de descriptor seleccionado, se han obtenido distintas imágenes sobre la investigación española en células madre entre 1997 y 2010. La visualización más clara y completa es la que ofrecen los descriptores KW+, permitiendo detectar hasta un total de seis líneas de investigación. La visualización de los KWA, por su parte, ofrece una imagen más diluida de las líneas de investigación, reflejando, sobre todo, la investigación de carácter más básico. Finalmente, la representación de las relaciones de los descriptores MeSH también se aproxima, sobre todo, a los estudios de carácter más básico. Conclusión: la comparación de las visualizaciones ha permitido determinar que los descriptores KW+ son la unidad de análisis más adecuada a la hora de realizar un análisis temático sobre un domino científico.Peer reviewe

Study on open science: The general state of the play in Open Science principles and practices at European life sciences institutes

Nowadays, open science is a hot topic on all levels and also is one of the priorities of the European Research Area. Components that are commonly associated with open science are open access, open data, open methodology, open source, open peer review, open science policies and citizen science. Open science may a great potential to connect and influence the practices of researchers, funding institutions and the public. In this paper, we evaluate the level of openness based on public surveys at four European life sciences institute

Investigación española en células madre (1997-2010). Localización y evolución de las principales líneas de investigación a través de los KeyWords Plus

•Objective: To render and analyze the evolution and dynamics of the main research lines in the Spanish scientific output on stem cells between 1997 and 2010. •Material and Methods: We retrieved the documents from the Science Citation Index (SCI). The units of analysis were the descriptors KeyWords Plus (KW +). The tools used for visualization have been software Pajek combined with Pathfinder Network (PFnet), and VOSviewer software. This study analyzes the period includes between 1997-2007, and the evolution of the research into three sub-periods: 1997-2001, 2002-2006 and 2007-2010. •Results and Discussion: The results of the full period locate up to six main research lines. In the first period, there is a strong presence of the descriptors that represent documents related to hematology and oncology. However, the other research lines do not begin to be clearly detected until the second period. Here,we can locate clusters of descriptors related to hematopoietic stem cell research, others related with the generation, proliferation and differentiation of stem cells and finally, alternate associated to an emerging cluster of neural progenitor cells. The results show that the weight of these latter groups make them more evident in the last period. •Conclusion: The visualization of the relationships between the KW + has yielded two complementary images of the situation and evolution of Spanish research on stem cells. The methodology has identified areas of research both consolidated and emerging, intuiting the development of a thematic domain over time. The visualization software complement each other quite well, matching in the identification of the main research lines and in the location of the most influential descriptors

Development of Spanish research on stem cells. Visualization and identification of the main research fronts

Using visualization techniques based on social networks, this study aims to analyze stem cell research in Spain, as reflected in the Science Citation Index (SCI) database between 1997 and 2010 , divided into three sub-periods. The selected unit of analysis was the KeyWords Plus descriptors (KW+), the unit of measurement was their co-occurrence , and the Pajek and VOSviewer tools were used to generate and display the social networks. The results show two complementary images of research: the static structure, distinguishing between clinical and basic research, and the evolutionary dynamic, analysing both the most established and the emerging lines. The main contribution of this work is to present a methodology for the visualization and detection of the main r esearch lines over time, demonstrating its applicability and its predictability in scientific and geographic domains