Use of prediction methods for patent and trademark applications in Spain

Nuchera, Antonio Hidalgo; Gabaly, Samuel

doi:10.1016/j.wpi.2011.09.001

Cited by 20 publications

(5 citation statements)

References 18 publications

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“…We focus on years, during which Spain has experienced a steady economic growth, investment expansion and catching up to the advanced nations, which came to a halt with the arrival of the 2008 global financial crisis (Prados de la Escosura, 2017). This trend has reversed after 2008, a structural break particularly visible in patenting (Belda et al, 2014; Hidalgo & Gabaly, 2012). Hence, relating pre‐2008 collaboration patterns to post‐2008 patenting may not be comparable with the pre‐2008 analysis.…”

Section: Methodsmentioning

confidence: 99%

Collaboration networks, geography and innovation: Local and national embeddedness

Gálaso

Kovářík

2021

Papers in Regional Science

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The relationship between collaboration, geography and innovation has been analysed in economic geography. However, little is known from a social‐network perspective about whether different geographical levels of embeddedness may determine the way networks affect innovation. To address this issue, we compare the effects of regional vs. country‐level Spanish networks on future patenting. If we consider the country‐wide network, our statistical analysis reproduces the findings of the previous literature. However, negative effects prevail at the regional level, while the influence seems to be rather positive at the country level. We thus conclude that the embeddedness at different geographical scales exerts differing influence on innovation.

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Section: Methodsmentioning

confidence: 99%

Collaboration networks, geography and innovation: Local and national embeddedness

Gálaso

Kovářík

2021

Papers in Regional Science

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“…The years where there is a lower numbers of grants are relatively many more when compared to the years with higher numbers of patents. Several studies have shown that it is possible to model the series of patents with ARIMA [41][42][43][44]. Some satisfactory model adjustments can be required to decrease the model residuals.…”

Section: Methods and The Case Studymentioning

confidence: 99%

Effects of Clean Air Act on Patenting Activities in Chemical Industry: Learning from Past Experiences

Durmuşoğlu

2017

Sustainability

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Abstract:The chemical industry provides essential goods we use in our daily lives and key ingredients for many diverse industries. On the other hand, their production and use require serious attention while they may be seriously harmful to local air quality. The Clean Air Act (CAA) and its subsequent amendments regulate the emissions of hazardous air pollutants to protect public health and welfare in the U.S.A. since 1970. This study aimed to assess the impact of CAA on the rate of patenting in the chemical industry. With this in mind, basic chemical utility patents were analyzed to detect the effects of CAA on the patenting activities. Subsequent to the fitting of the ARIMA (Autoregressive Integrated Moving Average) model (producing the least square of errors), a significant outlier was detected with the Dixon's r22 Ratio Test. This outlier was the number of patents granted in 1972. Contextual queries in the text of those patent documents have shown that there was a considerable increase in the patents of chemistry which takes the sustainability relevant terms (air, emission, pollution, etc.) into consideration. It was concluded that companies in the chemistry industry adapted themselves very rapidly to the changes and CAA was an important incentive to create novel technologies.

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“…First, through "(A) Time Series Analysis (TSA)", the frequency of patent applications by technology was extracted as time series data to predict promising future technology [21,24,[28][29][30][31]34,[38][39][40][41][42][43][44]. Then, through "(B) Social Network Analysis (SNA)", the relationship between nodes as a quantitative indicator through centrality indices was extracted, and promising detailed descriptive areas based on the extracted indicators were predicted [22,[24][25][26][27][28][29][30][31][32]34,39,40,43,[45][46][47][48][49][50]. In addition to the quantitative analysis methods, the qualitative analysis method of "(C) Technology Mapping Analysis (TM)" was also used in previous studies.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In addition to the quantitative analysis methods, the qualitative analysis method of "(C) Technology Mapping Analysis (TM)" was also used in previous studies. Through "(C) Technology Mapping Analysis (TM)", the importance of the level of technology for each area was obtained, and priorities for technology development were identified [21,24,[29][30][31]38,40,[45][46][47][48][49][50][51]. In addition to predicting promising technology, previous studies have identified vacant technology by analyzing patent information of analysis target nodes through "(D) Generative Topographic Mapping Analysis (GTM)", identifying empty technology [24,27,28,40,52].…”

Section: Literature Reviewmentioning

confidence: 99%

Patent Data Analytics for Technology Forecasting of the Railway Main Transformer

et al. 2022

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The railway main transformer is considered one of the most important electrical equipment for trains. Companies and research institutes around the world are striving to develop high-performance railway main transformers. In order to be the first mover for railway main transformer technology, companies and research institutes should predict vacant technology based on the analysis of promising detailed technology areas. Therefore, in this study, a patent analysis to predict vacant technologies based on identified promising IPC technology areas is provided. In order to identify promising detailed IPC technology areas, the technology mapping analysis, the time series analysis, and the social network analysis are conducted based on the patent-IPC matrix, extracted from the data information of 707 patents from the patent database of Korea, China, Japan, United States, Canada, and Europe. Then, through the GTM analysis based on promising detailed IPC technology areas, one vacant technology node and three analysis target nodes surrounding the vacant technology node are obtained to predict vacant technologies. From the analysis, we predict the following three groups of vacant technologies: (1) blowerless technology, (2) oil-free technology, and (3) solid-state technology. This study provides insights on the technology trend in railway main transformers, as well as the analysis framework for the development of R&D strategies based on the patent data.

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Use of prediction methods for patent and trademark applications in Spain

Cited by 20 publications

References 18 publications

Collaboration networks, geography and innovation: Local and national embeddedness

Collaboration networks, geography and innovation: Local and national embeddedness

Effects of Clean Air Act on Patenting Activities in Chemical Industry: Learning from Past Experiences

Patent Data Analytics for Technology Forecasting of the Railway Main Transformer

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