Statistical Technology Analysis for Competitive Sustainability of Three Dimensional Printing

The technology of three-dimensional (3D) printing was commercialized in the late 1980s. Since then, the development of this technology has been dramatically increasing. Moreover, 3D printing technology has been used in many different fields, such as electronics and medical appliances, because 3D printing is a technological convergence based on precision instruments, chemical materials, and electrical equipment. The technological impact of 3D printing is so powerful that we need to analyze 3D printing technology to understand the 3D printing industry. In addition, we want more analytical results for understanding the sustainability of 3D printing technology. Thus, we compare the technologies between 3D printing competitors to find their technological innovations and evolution from a technological sustainability. To analyze the 3D printing technology, we propose a new methodology of statistical technology analysis combing social network analysis with time series clustering. In our case study, we make a comparison between “3D Systems” and “Stratasys”, two major 3D printing companies, because they have been leading the sustainable technologies of 3D printing in the market. We illustrate how the proposed methodology can be applied to practical problems from the case study. This paper contributes to the sustainable technology management, and our research can expand to other competitors with diverse technological fields as well as 3D printing

Bayesian Structure Learning and Visualization for Technology Analysis

To perform technology analysis, we usually search patent documents related to target technology. In technology analysis using statistics and machine learning algorithms, we have to transform the patent documents into structured data that is a matrix of patents and keywords. In general, this matrix is very sparse because its most elements are zero values. The data is not satisfied with data normality assumption. However, most statistical methods require the assumption for data analysis. To overcome this problem, we propose a patent analysis method using Bayesian structure learning and visualization. In addition, we apply the proposed method to technology analysis of extended reality (XR). XR technology is integrated technology of virtual and real worlds that includes all of virtual, augmented and mixed realities. This technology is affecting most of our society such as education, healthcare, manufacture, disaster prevention, etc. Therefore, we need to have correct understanding of this technology. Lastly, we carry out XR technology analysis using Bayesian structure learning and visualization

Sustainable Technology Analysis of Blockchain Using Generalized Additive Modeling

Blockchain is a secure distributed management technology for data. Until now, blockchain technology has been intensively developed in financial fields such as Bitcoin. As the blockchain technology develops, the application fields of blockchain are expected to further expand. We proposed a technology analysis method for sustainability of blockchain technology. We analyzed the patent documents related to blockchain for sustainable technology analysis. To carry out the technology analysis, we preprocessed the patent documents and built a structure data, document-term matrix. In general, most elements of this matrix are zeros, so it is very skewed. Due to the skewness, technology analysis by traditional methods of statistics has analytical difficulty. To overcome this problem, we proposed a technology analysis method based on generalized additive modeling. To show how our proposed method can be applied to practical fields, we collected and analyzed the patent documents of blockchain technology

Zero-Inflated Patent Data Analysis Using Compound Poisson Models

A large part of big data consists of text documents such as papers, patents or articles. To analyze text data, we have to preprocess the text documents and build a structured data based on a document-word matrix using various text mining techniques. This is because statistics and machine learning algorithms used in text analysis require structured train data. The row and column of the matrix are document and word, respectively. The element of the matrix represents the frequency value of the word occurring in each document. In general, because the number of words is much larger than the number of documents, most elements have zero values. Due to the sparsity problem caused by inflated zeros, the performance of the predictive model has decreased. In this paper, we propose a method to solve the sparsity problem and improve the model performance in text data analysis. We perform compound Poisson linear modeling to make the proposed method. To show the performance of our proposed method, we collect and analyze the patent documents from patent databases. In our experimental results, we compared the value of the Akaike information criterion (AIC) of the proposed model with traditional models, such as linear model, generalized linear model and zero-inflated Poisson model. Additionally, we illustrated that the AIC value of our proposed model is smaller than others. Therefore, we verify the validity of this paper

Patent Analysis Using Bayesian Data Analysis and Network Modeling

Patent analysis is to analyze patent data to understand target technology. Patent data contains various detailed information about the developed technology. Therefore, many studies concerning patent analysis have been carried out in the technology analysis fields. Most traditional methods for technology analysis were based on qualitative approaches such as Delphi survey. However, the patent analysis methods based on statistics and machine learning have been introduced recently. In this paper, we proposed a statistical method for quantitative patent analysis. Moreover, we selected drone technology as the target technology for patent analysis. To understand drone technology, we analyzed the patents on drone technology. We searched the patent documents related to drone technology and transformed them to structured data using text mining techniques. First, we visualized the patent keywords to identify the technological structure of a drone. Next, using Bayesian additive regression trees, we analyzed the structured patent data to construct technology scenarios for drones. To illustrate the performance and validity of our proposed research, we presented the experimental results of patent analysis using patent documents related to drone technology

A Study on the Calibrated Confidence of Text Classification Using a Variational Bayes

Recently, predictions based on big data have become more successful. In fact, research using images or text can make a long-imagined future come true. However, the data often contain a lot of noise, or the model does not account for the data, which increases uncertainty. Moreover, the gap between accuracy and likelihood is widening in modern predictive models. This gap may increase the uncertainty of predictions. In particular, applications such as self-driving cars and healthcare have problems that can be directly threatened by these uncertainties. Previous studies have proposed methods for reducing uncertainty in applications using images or signals. However, although studies that use natural language processing are being actively conducted, there remains insufficient discussion about uncertainty in text classification. Therefore, we propose a method that uses Variational Bayes to reduce the difference between accuracy and likelihood in text classification. This paper conducts an experiment using patent data in the field of technology management to confirm the proposed method’s practical applicability. As a result of the experiment, the calibrated confidence in the model was very small, from a minimum of 0.02 to a maximum of 0.04. Furthermore, through statistical tests, we proved that the proposed method within the significance level of 0.05 was more effective at calibrating the confidence than before

Patent Keyword Analysis of Disaster Artificial Intelligence Using Bayesian Network Modeling and Factor Analysis

At present, artificial intelligence (AI) contributes to most technological fields. AI has also been introduced in the disaster area to replace humans and contribute to the prevention of disasters and the minimization of damages. So, it is necessary to analyze disaster AI in order to effectively make use of it. In this paper, we analyze the patent documents related to disaster AI technology. We propose Bayesian network modeling and factor analysis for the technology analysis of disaster AI. This is based on probability distribution and graph theory. It is also a statistical model that depends on multivariate data analysis. In order to show how the proposed model can be applied to a real problem, we carried out a case study to collect and analyze the patent data related to disaster AI

Cognitive Artificial Intelligence Using Bayesian Computing Based on Hybrid Monte Carlo Algorithm

Cognitive artificial intelligence (CAI) is an intelligent machine that thinks and behaves similar to humans. CAI also has an ability to mimic human emotions. With the development of AI in various fields, the interest and demand for CAI are continuously increasing. Most of the current AI research focuses on the realization of intelligence that can make optimal decisions. Existing AI studies have not conducted in-depth research on human emotions and cognitive perspectives. However, in the future, the demand for the use of AI that can imitate human emotions in various fields, such as healthcare and education, will continue. Therefore, we propose a method to build CAI in this paper. We also use Bayesian inference and computing based on the hybrid Monte Carlo algorithm for CAI development. To show how the proposed method for CAI can be applied to practical problems, we create an experiment using simulation data

Technology Analysis of Global Smart Light Emitting Diode (LED) Development Using Patent Data

Technological developments related to smart light emitting diode (LED) systems have progressed rapidly in recent years. In this paper, patent documents related to smart LED technology are collected and analyzed to understand the technology development of smart LED systems. Most previous studies of the technology were dependent on the knowledge and experience of domain experts, using techniques such as Delphi surveys or technology road-mapping. These approaches may be subjective and lack robustness, because the results can vary according to the selected expert groups. We therefore propose a new technology analysis methodology based on statistical modeling to obtain objective and relatively stable results. The proposed method consists of visualization based on Bayesian networks and a linear count model to analyze patent documents related to smart LED technology. Combining these results, a global hierarchical technology structure is created that can enhance the sustainability in smart LED system technology. In order to show how this methodology could be applied to real-world problems, we carry out a case study on the technology analysis of smart LED systems

Patent Analysis Using Bayesian Data Analysis and Network Modeling

Patent analysis is to analyze patent data to understand target technology. Patent data contains various detailed information about the developed technology. Therefore, many studies concerning patent analysis have been carried out in the technology analysis fields. Most traditional methods for technology analysis were based on qualitative approaches such as Delphi survey. However, the patent analysis methods based on statistics and machine learning have been introduced recently. In this paper, we proposed a statistical method for quantitative patent analysis. Moreover, we selected drone technology as the target technology for patent analysis. To understand drone technology, we analyzed the patents on drone technology. We searched the patent documents related to drone technology and transformed them to structured data using text mining techniques. First, we visualized the patent keywords to identify the technological structure of a drone. Next, using Bayesian additive regression trees, we analyzed the structured patent data to construct technology scenarios for drones. To illustrate the performance and validity of our proposed research, we presented the experimental results of patent analysis using patent documents related to drone technology