Patent Analytics Based on Feature Vector Space Model: A Case of IoT

The number of approved patents worldwide increases rapidly each year, which requires new patent analytics to efficiently mine the valuable information attached to these patents. Vector space model (VSM) represents documents as high-dimensional vectors, where each dimension corresponds to a unique term. While originally proposed for information retrieval systems, VSM has also seen wide applications in patent analytics, and used as a fundamental tool to map patent documents to structured data. However, VSM method suffers from several limitations when applied to patent analysis tasks, such as loss of sentence-level semantics and curse-of-dimensionality problems. In order to address the above limitations, we propose a patent analytics based on feature vector space model (FVSM), where the FVSM is constructed by mapping patent documents to feature vectors extracted by convolutional neural networks (CNN). The applications of FVSM for three typical patent analysis tasks, i.e., patents similarity comparison, patent clustering, and patent map generation are discussed. A case study using patents related to Internet of Things (IoT) technology is illustrated to demonstrate the performance and effectiveness of FVSM. The proposed FVSM can be adopted by other patent analysis studies to replace VSM, based on which various big data learning tasks can be performed

?????? ???????????? ????????? ?????? ??????????????? ?????? : ????????? ?????? ?????? ??????????????? ????????????

Department of Management EngineeringFirms participating in printer industries have invested their constrained resources into technology development in order to sustain their competitiveness in the industry. Considering the fast-changing market circumstances, each firm???s own investment decisions on technology portfolio may directly affect their performance. In this study, we analyzed patent data, namely number of forward citations and technological classification data (CPC). Using this data, the technological portfolio of a specific firm can be identified, which can further help our understanding on firms??? R&D investment strategies. Number of studies mainly focused on patent class combinations of individual technology level, but portfolios of patent class at a firm level have been understudied. In this study, we tracked the change of class composition within each firms??? technological patents??? portfolio and attempted to identify practical and theoretical implications to portfolio management. We utilized Entropy Index, Co-occurrence and cosine similarities measurements for each indicating diversification, patent scope and portfolio similarities within each patents??? classification subclasses. Additionally, performance evaluation of each portfolio is conducted using forward citation data. This paper shows that in-depth patent data analysis can allow us to explore deeper insights at various levels, individual technology, products and product lines, and firms sufficing different stories.ope

Tracing The Spatial Patterns Of Innovation Determinants In Regional Economic Performance

This paper investigates factors of innovation and their role in regional economic performance for a sample of 261 EU NUTS 2 regions over 2009–2012. In our study, we identify regions with spillover as well as drain effects of innovation factors on economic performance. The spatial analysis indicates that both regional innovativeness and regional development, are strongly determined by the region’s location and neighbourhood, with severe consequences for the Eastern and Central Europe. We assessed the impact of innovation factors and their spatial counterparts on economic performance by spatial Durbin panel model. The model is designed to test the existence and strength of country-effect of innovativeness on the level of regional economic status. This allows for controlling the country-specific socio-economic factors, without reducing the number of degrees of freedom. Our model shows that regions benefit economically from their locational spillovers in terms of social capital. However, the decomposition of R&D expenditures revealed competition effect between internal R&D and external technology acquisition favouring in-house research over the outsourced ones

Mapping Patent Classifications: Portfolio and Statistical Analysis, and the Comparison of Strengths and Weaknesses

The Cooperative Patent Classifications (CPC) jointly developed by the European and US Patent Offices provide a new basis for mapping and portfolio analysis. This update provides an occasion for rethinking the parameter choices. The new maps are significantly different from previous ones, although this may not always be obvious on visual inspection. Since these maps are statistical constructs based on index terms, their quality--as different from utility--can only be controlled discursively. We provide nested maps online and a routine for portfolio overlays and further statistical analysis. We add a new tool for "difference maps" which is illustrated by comparing the portfolios of patents granted to Novartis and MSD in 2016.Comment: Scientometrics 112(3) (2017) 1573-1591; http://link.springer.com/article/10.1007/s11192-017-2449-

Knowledge Spillovers in Europe and its Consequences for Systems of Innovation

knowledge spillovers; innovation systems

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

Market Demand, Technological Opportunity and Research Spillovers on R&D Intensity and Productivity Growth

This paper uses sales and patent distribution data to establish the market and technological "positions" of firms. A notion of technological proximity of firms is developed in order to quantify potential R&D spillovers. The importance of the position variables and the potential spilover pool in explaining R&D intensity, patent productivity and TFP growth is explored.I find that both technological and market positions are signifi-cant in explaining R&D intensity, and that the technological effects are significant in explaining patent productivity. I cannot distinguish between the two effects in explaining TFP growth. Spillovers are important in all three contexts. Firms in an area where there is a high level of research by other firms do more R&D themselves, they produce more patents per R&D dollar, and their productivity grows faster, even controlling for the increased R&D and patents. These effects are present controlling for both industry and technological position effects.

Agglomeration economies, knowledge spillovers, technological diversity and spatial clustering of innovations.

This paper explores the spatial patterns of innovative activities from an empirical perspective and with reference to the Italian case. Using patent and other economic data at the NUTS 3 level (provinces), it borrows methodology and techniques from spatial statistics in order to analyse the way innovative and economic activities are arranged in space. Results show that innovative activities are considerably more spatially concentrated than production, but that there are also large differences across sectors in the spatial patterns of innovation. In mechanical engineering, industrial equipment and instruments sectors innovative activities tend to cluster around local systems of contiguous provinces, while in most chemical and electronic sectors innovative activities tend to concentrate in few metropolitan provinces surrounded by other non-innovative provinces. Regression analysis is also carried out to evaluate the impact of agglomeration economies, knowledge spillovers and technological diversity on the innovative performance of provinces.