The rapid pace of technological advances creates many difficulties for R&D practitioners in analyzing emerging technologies. Patent information analysis is an effective tool in this situation. Conventional patent information analysis has focused on the extraction of vacant, promising, or core technologies and the monitoring of technological trends. From a technology management perspective, the ultimate purpose of R&D is technology commercialization. The core of technology commercialization is the technology transfer phase. Although a great number of patents are filed, publicized, and registered every year, many commercially relevant patents are filtered through registration processes that examine novelty, creativity, and industrial applicability. Despite the efforts of these selection processes, the number of patents being transferred is low when compared with total annual patent registrations. To deal with this problem, this study proposes a predictive model for technology transfer using patent analysis. In the predictive model, patent analysis is conducted to reveal the quantitative relations between technology transfer and a range of variables included in the patent data.

Most companies develop technologies to improve their competitiveness in the marketplace. Typically, they then patent these technologies around the world in order to protect their intellectual property. Other companies may use patented technologies to develop new products, but must pay royalties to the patent holders or owners. Should they fail to do so, this can result in legal disputes in the form of patent infringement actions between companies. To avoid such situations, companies attempt to research and develop necessary technologies before their competitors do so. An important part of this process is analyzing existing patent documents in order to identify emerging technologies. In such analyses, extracting sustainable technology from patent data is important, because sustainable technology drives technological competition among companies and, thus, the development of new technologies. In addition, selecting sustainable technologies makes it possible to plan their R&D (research and development) efficiently. In this study, we propose a network model that can be used to select the sustainable technology from patent documents, based on the centrality and degree of a social network analysis. To verify the performance of the proposed model, we carry out a case study using actual patent data from patent databases.

Technology valuation (TV) is an important issue in management of technology (MOT). We use TV results for technology transfer, research and development (R&D) planning, and technology marketing. Diverse TV studies have been applied to MOT. Most of them were dependent on domain experts’ knowledge, so their TV results could be subjective and unstable. To solve this problem, we propose an objective TV model using quantitative patent analysis. In this article, we consider text mining, social network analysis, technology clustering, and descriptive statistics in constructing our TV model. To verify the performance of our model, we perform a case study of technology transfer in big data marketing.

Many studies have predicted the future performance of companies for the purpose of making investment decisions. Most of these are based on the qualitative judgments of experts in related industries, who consider various financial and firm performance information. With recent developments in data processing technology, studies have started to use machine learning techniques to predict corporate performance. For example, deep neural network-based prediction models are again attracting attention, and are now widely used in constructing prediction and classification models. In this study, we propose a deep neural network-based corporate performance prediction model that uses a company’s financial and patent indicators as predictors. The proposed model includes an unsupervised learning phase and a fine-tuning phase. The learning phase uses a restricted Boltzmann machine. The fine-tuning phase uses a backpropagation algorithm and a relatively up-to-date training data set that reflects the latest trends in the relationship between predictors and corporate performance.

In today’s knowledge-based society, industry-university cooperation (IUC) is recognized as an effective tool for technological innovation. Many studies have shown that selecting the right partner is essential to the success of the IUC. Although there have been a lot of studies on the criteria for selecting a suitable partner for IUC or strategic alliances, there has been a problem of making decisions depending on the qualitative judgment of experts or staff. While related works using patent analysis enabled the quantitative analysis and comparison of potential research partners, they overlooked the fact that there are several sub-technologies in one specific technology domain and that the applicant’s research concentration and competency are not the same for every sub-technology. This study suggests a systematic methodology that combines the Latent Dirichlet Allocation (LDA) topic model and the clustering algorithm in order to classify the sub-technology categories of a particular technology domain, and identifies the best college partners in each category. In addition, a similar-patent density (SPD) index was proposed and utilized for an objective comparison of potential university partners. In order to investigate the practical applicability of the proposed methodology, we conducted experiments using real patent data on the electric vehicle domain obtained from the Korean Intellectual Property Office. As a result, we identified 10 research and development sectors wherein Hyundai Motor Company (HMC) focuses using LDA and clustering. The universities with the highest values of SPD for each sector were chosen to be the most suitable partners of HMC for collaborative research.

Technology analysis (TA) is an important issue in the management of technology. Most R&D (Research & Development) policies have depended on diverse TA results. Traditional TA results have been obtained through qualitative approaches such as the Delphi expert survey, scenario analysis, or technology road mapping. Although they are representative methods for TA, they are not stable because their results are dependent on the experts’ knowledge and subjective experience. To solve this problem, recently many studies on TA have been focused on quantitative approaches, such as patent analysis. A patent document has diverse information of developed technologies, and thus, patent is one form of objective data for TA. In addition, sustainable technology has been a big issue in the TA fields, because most companies have their technological competitiveness through the sustainable technology. Sustainable technology is a technology keeping the technological superiority of a company. So a country as well as a company should consider sustainable technology for technological competition and continuous economic growth. Also it is important to manage sustainable technology in a given technology domain. In this paper, we propose a new patent analysis approach based on statistical analysis for the management of sustainable technology (MOST). Our proposed methodology for the MOST is to extract a technological structure and relationship for knowing the sustainable technology. To do this, we develop a hierarchical diagram of technology for finding the causal relationships among technological keywords of a given domain. The aim of the paper is to select the sustainable technology and to create the hierarchical technology paths to sustainable technology for the MOST. This contributes to planning R&D strategy for the sustainability of a company. To show how the methodology can be applied to real problem, we perform a case study using retrieved patent documents related to telematics technology.