A Predictive Model of Technology Transfer Using Patent Analysis

The Study of Evaluation Index of Growth Evaluation of Science and Technological Innovation Micro-Enterprises

Micro-enterprises are critical to the national economy in many countries, although they have many limitations. Therefore, governments have proposed many policies and programs to generate and enhance micro-enterprises’ growth to assure national economies’ growth and sustainability. This study evolves an index to evaluate growth in science and technological innovation among micro-enterprises that participated in collaborative innovation through a counselling program. The results not only define growth indicators through a literature review but also examine evidence of growth among 10 micro-enterprises through interviewing subjects located in various regions in Taiwan and related to different industries. This study uses multiple case study methods and data statistics to verify growth indicators; this reveals that micro-enterprises focusing on collaborative technological innovation demonstrated an increasing trend toward this study’s chosen growth indicators in a majority of cases. According to the survey results, nine indicators lead to positive growth. Finally, after establishing growth assessment indicators, the data collected from an empirical investigation proves the indicators’ credibility. The study then discusses the chosen cases’ experiences with growth and presents managerial implications.

Exploring Technology Influencers from Patent Data Using Association Rule Mining and Social Network Analysis

A patent is an important document issued by the government to protect inventions or product design. Inventions consist of mechanical structures, production processes, quality improvements of products, and so on. Generally, goods or appliances in everyday life are a result of an invention or product design that has been published in patent documents. A new invention contributes to the standard of living, improves productivity and quality, reduces production costs for industry, or delivers products with higher added value. Patent documents are considered to be excellent sources of knowledge in a particular field of technology, leading to inventions. Technology trend forecasting from patent documents depends on the subjective experience of experts. However, accumulated patent documents consist of a huge amount of text data, making it more difficult for those experts to gain knowledge precisely and promptly. Therefore, technology trend forecasting using objective methods is more feasible. There are many statistical methods applied to patent analysis, for example, technology overview, investment volume, and the technology life cycle. There are also data mining methods by which patent documents can be classified, such as by technical characteristics, to support business decision-making. The main contribution of this study is to apply data mining methods and social network analysis to gain knowledge in emerging technologies and find informative technology trends from patent data. We experimented with our techniques on data retrieved from the European Patent Office (EPO) website. The technique includes K-means clustering, text mining, and association rule mining methods. The patent data analyzed include the International Patent Classification (IPC) code and patent titles. Association rule mining was applied to find associative relationships among patent data, then combined with social network analysis (SNA) to further analyze technology trends. SNA provided metric measurements to explore the most influential technology as well as visualize data in various network layouts. The results showed emerging technology clusters, their meaningful patterns, and a network structure, and suggested information for the development of technologies and inventions.

Patent Data Analysis of Artificial Intelligence Using Bayesian Interval Estimation

Technology analysis is one of the important tasks in technology and industrial management. Much information about technology is contained in the patent documents. So, patent data analysis is required for technology analysis. The existing patent analyses relied on the quantitative analysis of the collected patent documents. However, in the technology analysis, expert prior knowledge should also be considered. In this paper, we study the patent analysis method using Bayesian inference which considers prior experience of experts and likelihood function of patent data at the same time. For keyword data analysis, we use Bayesian predictive interval estimation with count data distributions such as Poisson. Using the proposed models, we forecast the future trends of technological keywords of artificial intelligence (AI) in order to know the future technology of AI. We perform a case study to provide how the proposed method can be applied to real areas. In this paper, we retrieve the patent documents related to AI technology, and analyze them to find the technological trend of AI. From the results of AI technology case study, we can find which technological keywords are more important or critical in the entire structure of AI industry. The existing methods for patent keyword analysis were depended on the collected patent documents at present. But, in technology analysis, the prior knowledge by domain experts is as important as the collected patent documents. So, we propose a method based on Bayesian inference for technology analysis using the patent documents. Our method considers the patent data analysis with the prior knowledge from domain experts.

Impact of Construction IT Technology Convergence Innovation on Business Performance

As the era of the fourth Industrial revolution begins, there is high demand for new information technology (IT) innovation to address the challenges of a dramatically changing industrial landscape. The construction industry, which is one the most important traditional industries in Korea, has adopted new IT innovation in their products to meet customer demand, regulation from government, energy efficiency, security, and so on. The aim of our study is to empirically clarify the impact of technology innovation on the business performance for Korean domestic construction conglomerates. The study performs an empirical analysis with time series analysis on business performance from the perspective of client satisfaction with the services the target companies provide and the production process improvement, as well as from a financial perspective. As analysis data, statistical data from the Ministry of Science, ICT, and Future Planning; Brand Stock; Bank of Korea; and each company are utilized, and the target companies for our study are limited to 19 construction companies with an apartment brand. Multiple regression analysis is used as a fundamental analysis methodology of our study. For Time Series Analysis, the Box Jenkins Model, namely, ARIMA is utilized. According to our results, it is found that any improvement of IT convergence innovation competence such as business efficiency IT index, collaboration IT index, and strategy management IT index has a positive impact on the production process, financial performance, and customer satisfaction with the services the companies provide.