Integrating 5-Hydroxymethylcytosine into the Epigenomic Landscape of Human Embryonic Stem Cells

Covalent modification of DNA distinguishes cellular identities and is crucial for regulating the pluripotency and differentiation of embryonic stem (ES) cells. The recent demonstration that 5-methylcytosine (5-mC) may be further modified to 5-hydroxymethylcytosine (5-hmC) in ES cells has revealed a novel regulatory paradigm to modulate the epigenetic landscape of pluripotency. To understand the role of 5-hmC in the epigenomic landscape of pluripotent cells, here we profile the genome-wide 5-hmC distribution and correlate it with the genomic profiles of 11 diverse histone modifications and six transcription factors in human ES cells. By integrating genomic 5-hmC signals with maps of histone enrichment, we link particular pluripotency-associated chromatin contexts with 5-hmC. Intriguingly, through additional correlations with defined chromatin signatures at promoter and enhancer subtypes, we show distinct enrichment of 5-hmC at enhancers marked with H3K4me1 and H3K27ac. These results suggest potential role(s) for 5-hmC in the regulation of specific promoters and enhancers. In addition, our results provide a detailed epigenomic map of 5-hmC from which to pursue future functional studies on the diverse regulatory roles associated with 5-hmC

Technology Commercialization Activation Model Using Imagification of Variables

Various institutions such as universities and corporations strive to commercialize technologies produced through R&D investment. The ideal way to commercialize technology is to transfer it, recognizing the value of the developed technology. Technology transfer is the transfer of technology from R&D entities, such as universities, research institutes, and companies, to others, with the advantage of spreading research results and maximizing cost efficiency. In other words, if enough technology is transferred, it can be commercialized. Although many institutions have various support measures to assist in transferring technology, there is no substitution for quantitative, objective methods. To solve this problem, this paper proposes a technology transfer prediction model based on the information found in patents. However, it is not realistic to include the information from all patents in the quantitative, objective method, so patterns related to technology transfer must be identified to select the appropriate patents that can be used in the predictive model. In addition, a method is needed to address the insufficient training data for the model. Training data are limited because some technology transfer information is not disclosed, and there is little technology transferred in new technology fields. The technology transfer prediction model proposed in this paper searches for hidden patterns related to technology transfer by imaging the patent information, which can also be applied to image analysis models. Furthermore, augmenting the data can solve the problem of the lack of learning data for technology transfer. To examine whether the proposed model can be used in real industries, we collected patents related to artificial intelligence technology registered in the United States and conducted experiments. The experimental results show that the models trained by imaging patent information performed excellently. Moreover, it was shown that the data augmentation technique can be used when there are insufficient data for technology transfer

Technology Commercialization Activation Model Using Imagification of Variables

Various institutions such as universities and corporations strive to commercialize technologies produced through R&D investment. The ideal way to commercialize technology is to transfer it, recognizing the value of the developed technology. Technology transfer is the transfer of technology from R&D entities, such as universities, research institutes, and companies, to others, with the advantage of spreading research results and maximizing cost efficiency. In other words, if enough technology is transferred, it can be commercialized. Although many institutions have various support measures to assist in transferring technology, there is no substitution for quantitative, objective methods. To solve this problem, this paper proposes a technology transfer prediction model based on the information found in patents. However, it is not realistic to include the information from all patents in the quantitative, objective method, so patterns related to technology transfer must be identified to select the appropriate patents that can be used in the predictive model. In addition, a method is needed to address the insufficient training data for the model. Training data are limited because some technology transfer information is not disclosed, and there is little technology transferred in new technology fields. The technology transfer prediction model proposed in this paper searches for hidden patterns related to technology transfer by imaging the patent information, which can also be applied to image analysis models. Furthermore, augmenting the data can solve the problem of the lack of learning data for technology transfer. To examine whether the proposed model can be used in real industries, we collected patents related to artificial intelligence technology registered in the United States and conducted experiments. The experimental results show that the models trained by imaging patent information performed excellently. Moreover, it was shown that the data augmentation technique can be used when there are insufficient data for technology transfer

Real Options Analysis for Acquisition of New Technology: A Case Study of Korea K2 Tank’s Powerpack

For sustainable defense management, it is essential to acquire weapons systems that can adapt to future uncertain threats and, at the same time, to invest efficiently with limited budgets. Economic analysis is used to examine the costs, benefits and uncertainties of alternatives. In particular, the use of the real options valuation, which is one of the methodologies of economic analysis, is expanding. The real options valuation has shown effectiveness across various industries to evaluate investment strategies. In this paper, we apply the real options valuation to the weapon systems development case and confirm its usefulness. Unlike previous studies, the real option valuation methodology is applied retroactively to the finished project, compared to existing research mainly applying real options to value research and development (R&D) without knowing how the project completed. We use the following procedure. (1) Define the uncertainties of the three acquisition alternatives (development, technology adoption, and purchase). (2) Calculate the benefits of the three acquisition alternatives with expected and actual data without uncertainties. (3) Model the decision tree without options and with options. (4) Analyze and compare results with benefit and benefit cost ratio. We analyzed the Korea K2 tank powerpack development case by applying real options. We could see that the real options could have reduced the risk of losses when the development risk is high and market uncertainty exists. From the case study of the development of the powerpack, we learned the following three lessons. First, we reaffirmed the importance of objective value analysis in project decision making. Second, we need to analyze the project value continuously and revise the acquisition strategy accordingly. Third, the effectiveness of the real options valuation was confirmed for sustainable defense management. In addition, the real option analysis data acquired from similar finished projects can be useful for establishing a new product acquisition strategy and, at every decision-making phase, the real option evaluation should be continuously performed with updated information. In this paper, we first perform real option valuation of finished weapon systems in the Korean defense field. This paper is valuable in establishing a rational methodology for applying economic analysis to weapon system acquisition projects

A Novel Forecasting Methodology for Sustainable Management of Defense Technology

A dynamic methodology for sustainable management of defense technology is proposed to overcome the limitations of the static methodology, which involves comparative analysis based on the criterion of the highest technology level and has limitations for time series analysis, because the country with the highest level undergoes technical changes over time. To address these limitations, this study applies a technology growth model for a dynamic analysis of the Delphi result. An effective method using patents is also proposed to verify and adjust the analysis results. First, technology levels of the present and future are examined by the Delphi technique, and the growth curve is extracted based on the technology growth model. Second, the technology growth curve based on patents is extracted using the annual number of unexamined and registered patents related to the technology. Lastly, the statistical significance of the two growth curves is examined using regression analysis. Then the growth curves are adjusted by the rate of increase in patents. This methodology could provide dynamic technology level data to facilitate sustainable management of defense technology. The results could be useful to research institutions, as they establish strategies for securing technologies in defense or private domains

Technology Clusters Exploration for Patent Portfolio through Patent Abstract Analysis

This study explores technology clusters through patent analysis. The aim of exploring technology clusters is to grasp competitors’ levels of sustainable research and development (R&D) and establish a sustainable strategy for entering an industry. To achieve this, we first grouped the patent documents with similar technologies by applying affinity propagation (AP) clustering, which is effective while grouping large amounts of data. Next, in order to define the technology clusters, we adopted the term frequency-inverse document frequency (TF-IDF) weight, which lists the terms in order of importance. We collected the patent data of Korean electric car companies from the United States Patent and Trademark Office (USPTO) to verify our proposed methodology. As a result, our proposed methodology presents more detailed information on the Korean electric car industry than previous studies

Patent Keyword Extraction for Sustainable Technology Management

Recently, sustainable growth and development has become an important issue for governments and corporations. However, maintaining sustainable development is very difficult. These difficulties can be attributed to sociocultural and political backgrounds that change over time [1]. Because of these changes, the technologies for sustainability also change, so governments and companies attempt to predict and manage technology using patent analyses, but it is very difficult to predict the rapidly changing technology markets. The best way to achieve insight into technology management in this rapidly changing market is to build a technology management direction and strategy that is flexible and adaptable to the volatile market environment through continuous monitoring and analysis. Quantitative patent analysis using text mining is an effective method for sustainable technology management. There have been many studies that have used text mining and word-based patent analyses to extract keywords and remove noise words. Because the extracted keywords are considered to have a significant effect on the further analysis, researchers need to carefully check out whether they are valid or not. However, most prior studies assume that the extracted keywords are appropriate, without evaluating their validity. Therefore, the criteria used to extract keywords needs to change. Until now, these criteria have focused on how well a patent can be classified according to its technical characteristics in the collected patent data set, typically using term frequency–inverse document frequency weights that are calculated by comparing the words in patents. However, this is not suitable when analyzing a single patent. Therefore, we need keyword selection criteria and an extraction method capable of representing the technical characteristics of a single patent without comparing them with other patents. In this study, we proposed a methodology to extract valid keywords from single patent documents using relevant papers and their authors’ keywords. We evaluated the validity of the proposed method and its practical performance using a statistical verification experiment. First, by comparing the document similarity between papers and patents containing the same search terms in their titles, we verified the validity of the proposed method of extracting patent keywords using authors’ keywords and the paper. We also confirmed that the proposed method improves the precision by about 17.4% over the existing method. It is expected that the outcome of this study will contribute to increasing the reliability and the validity of the research on patent analyses based on text mining and improving the quality of such studies

Sustainable Technology Analysis of Artificial Intelligence Using Bayesian and Social Network Models

Recent developments in artificial intelligence (AI) have led to a significant increase in the use of AI technologies. Many experts are researching and developing AI technologies in their respective fields, often submitting papers and patent applications as a result. In particular, owing to the characteristics of the patent system that is used to protect the exclusive rights to registered technology, patent documents contain detailed information on the developed technology. Therefore, in this study, we propose a statistical method for analyzing patent data on AI technology to improve our understanding of sustainable technology in the field of AI. We collect patent documents that are related to AI technology, and then analyze the patent data to identify sustainable AI technology. In our analysis, we develop a statistical method that combines social network analysis and Bayesian modeling. Based on the results of the proposed method, we provide a technological structure that can be applied to understand the sustainability of AI technology. To show how the proposed method can be applied to a practical problem, we apply the technological structure to a case study in order to analyze sustainable AI technology

Hybrid Corporate Performance Prediction Model Considering Technical Capability

Many studies have tried to predict corporate performance and stock prices to enhance investment profitability using qualitative approaches such as the Delphi method. However, developments in data processing technology and machine-learning algorithms have resulted in efforts to develop quantitative prediction models in various managerial subject areas. We propose a quantitative corporate performance prediction model that applies the support vector regression (SVR) algorithm to solve the problem of the overfitting of training data and can be applied to regression problems. The proposed model optimizes the SVR training parameters based on the training data, using the genetic algorithm to achieve sustainable predictability in changeable markets and managerial environments. Technology-intensive companies represent an increasing share of the total economy. The performance and stock prices of these companies are affected by their financial standing and their technological capabilities. Therefore, we apply both financial indicators and technical indicators to establish the proposed prediction model. Here, we use time series data, including financial, patent, and corporate performance information of 44 electronic and IT companies. Then, we predict the performance of these companies as an empirical verification of the prediction performance of the proposed model

Integrated Survival Model for Predicting Patent Litigation Hazard

Patent litigation occurs when a company’s product or service violates the scope of another company’s patent rights. When they occur, companies suffer a disruption to the sales of their products and services, thus hindering the sustainability of their business activities. For this reason, companies have established and analyzed wide-ranging strategies to prevent patent litigation. Of those, statistical and machine learning-based quantitative methods using patent big data have several advantages, such as a reduced cost and objective results. Existing quantitative methods analyze patent information and litigation based on the time of data collection. However, the values of patents and their litigation hazards change over time. In addition, the existing methods do not take into account censored data; that is, patents that may result in litigation after the data is collected. In this paper, to solve this problem we propose an integrated survival model that considers censored data and predicts patent litigation hazards over time. The proposed model is a non-parametric survival analysis method based on a random survival forest. It uses pre-trained word2vec and clustering to effectively reflect the technology fields as well as the quantitative information of the patent. The word2vec is a technique for natural language processing and enables the use of patent text information. In order to examine the practicality of the integrated survival model, an experiment is conducted with patent big data related to sensor semiconductors based on AI technology applicable to robotics. In the experiment, it was found that the litigation hazard occurred 150 months after the patent application and increase rapidly from 200 months. Furthermore, the proposed model showed better predictive performance than other survival analysis models. The proposed model could be used by potential defendants to protect their patents