A patent quality analysis for innovative technology and product development

Trappey, Amy J.C.; Trappey, Charles V.; Wu, Chang-Luen; Lin, Chi‐Wei

doi:10.1016/j.aei.2011.06.005

Cited by 170 publications

(56 citation statements)

References 22 publications

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“…Personal communication with peers, coeducation is important to them, but now the opportunity to work in a small group in separate convenient space is at the forefront requirement. Large spaces for training large number of students lose popularity faster [11][12][13]. Thus, the education in advanced countries moves towards individualization of learning in higher education, active use of scientific and educational IP-based networks, distance learning technologies allow geographically remote from one to another students work together to solve the task.…”

Section: Discussionmentioning

confidence: 99%

Enhancing the Role of Educational Services of Higher Education System in the Competitive Specialists Training for Industry

Kuznetsova

Yumaev

et al. 2017

E3S Web Conf.

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Abstract. The paper shows determination of educational services effect of higher education system to the competitive specialists training for the industry. Universities should focus their efforts on the development of innovative scientific and educational environment of the global level if they want to supply modern industry with highly qualified specialists. Despite the good results achieved in training for industry, universities should always be a step ahead in enhancing the quality of educational services. According to the findings options for professional trainings are offered; also the regional specialized educational resources launch is suggested.

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Section: Discussionmentioning

confidence: 99%

Enhancing the Role of Educational Services of Higher Education System in the Competitive Specialists Training for Industry

Kuznetsova

Yumaev

et al. 2017

E3S Web Conf.

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“…It is a necessary tool for technology management and can also be used for technology forecasting and R&D management [4,7,9,14,[19][20][21]. In the initial research on technology management using patent data, Biju et al used only quantitative information from five-year patent data from India to identify trends and innovation levels in India [5].…”

Section: Patent Analysis For Sustainable Technology Managementmentioning

confidence: 99%

Ensemble Modeling for Sustainable Technology Transfer

et al. 2018

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These days, technological advances are being made through technological conversion. Following this trend, companies need to adapt and secure their own sustainable technological strategies. Technology transfer is one such strategy. This method is especially effective in coping with recent technological developments. In addition, universities and research institutes are able to secure new research opportunities through technology transfer. The aim of our study is to provide a technology transfer prediction model for the sustainable growth of companies. In the proposed method, we first collected patent data from a Korean patent information service provider. Next, we used latent Dirichlet allocation, which is a topic modeling method used to identify the technical field of the collected patents. Quantitative indicators on the patent data were also extracted. Finally, we used the variables that we obtained to create a technology transfer prediction model using the AdaBoost algorithm. The model was found to have sufficient classification performance. It is expected that the proposed model will enable universities and research institutes to secure new technology development opportunities more efficiently. In addition, companies using this model can maintain sustainable growth in line, coping with the changing pace of society.

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“…However, there are no acceptable and common measures for this purpose. Trappey et al [23] proposed a methodology to shorten the time required to determine and rank the quality of patents with respect to their potential values in the IP rights marketplace. The methodology involves extracting relevant patent quality performance indicators, identifying the key impact factors using principal component analysis, and using a back-propagation neural network (BPN) trained model to predict the quality of patents and forecast the IP market potential.…”

Section: Post-grantmentioning

confidence: 99%