Chao-Fu Hong

Extracting the significant-rare keywords for patent analysis

Brainstorming for keywords is used in retrieving patent documents, but even experienced engineers are irresolute in dealing with this critical issue. The quality of a patent report is usually already determined by the keywords they used in the first step. In order to improve the stumbling stone, this paper demonstrates a new method of how to find the significant-rare in a patent database. The results show that a systematic patent search (snowball-rolling-procedure) could consider the heterogeneous terms used by assignees, attorneys, and inventors that include some hidden information. Overall, the reliability of patent data can be ameliorated further.

Visualization of Financial Trends Using Chance Discovery Methods

Due to the concern of business performance and the consideration of possible investment, stakeholders inside and outside the business need to understand the financial status of a company. Usually, reading and interpreting the public offering financial statements is a common way to obtain an overall financial situation of a company. Apart from reading them directly, some visualization methods were employed so as to assist the less-professional stakeholders to understand the financial status more intuitively and painlessly. Chance discovery is one of the visualization methods which may be suitable to be applied in the financial trend detection area. In this study, a financial data visualization framework has been proposed to produce the serial KeyGraph for explaining the financial situations of two companies. Subsequently, an integrated map of financial trend was generated for depicting the overview of the accumulative five-year scenario. Finally, the visualization of financial trend was attained using serial KeyGraph and integrated map.

Applying Chance Discovery with Dummy Event in Technology Monitoring of Solar Cell

One of the green energy, solar cell, is growing rapidly; the monitoring of its technological situation becomes critical for the stakeholders nowadays. Meanwhile, the patent data contains plentiful technological information from which is worthwhile for exploring further knowledge. Therefore, a graph-based approach, chance discovery, is employed so as to analyze the patent data, to form the technological scenarios, and to explain the overview of solar cell technology. Finally, the relationships between technology and companies, between application and companies have been observed, and the strategic suggestions have been proposed accordingly.

Trend detection on thin-film solar cell technology using cluster analysis and modified data crystallization

Thin-film solar cell, one of green energies, is growing at a fast pace with its long-lasting and non-polluting natures. To detect the potential trends of this technology is essential for companies and relevant industries so that the competitive advantages of companies can be retained and the developing directions of industries can be perceived. Therefore, a research framework for trend detection has been formed where cluster analysis is employed to perform the similarity measurement, and data crystallization is adopted to conduct the association analysis. Consequently, the relation patterns were identified from the relations among companies, issue years, and techniques. Finally, according to the relation patterns, the potential trends of thin-film solar cell were detected for companies and industries.

Social chance discovery

Information technology such as data mining or text mining, can extract useful data structure from data. But extracted data structure was hardly used in explaining the meaning of data itself. In reality, a laborer can hardly catch any meaningful job information from members of his work group (strong tie) when it comes to changing jobs. However, some of his friends (weak tie), although not so close to him, associated to other work groups, can bring him information about new jobs. This information is very important in job hunting. This kind of personal central base social networks can be used to realize a persons motivation and enrich his social capital, extends his resources to the resources of his linked friends. This process is called social chance discovery. Base on this concept, in this study, we build model of Qualitative Chance Discovery and apply it on DVD news. Experiment results show this model can be used as an interactive tool to help company understands the way of breaking entry barrier to compete with existing companies in the harsh environment of DVD industry.

Using text mining and chance discovery for exploring technological directions via patent data

To work out the situation and directions of a technology is essential for an industry, companies, and stakeholders so as to facilitate the decision makers to bring up rational judgments for their decisions. Solar cell, one of renewable energies, is growing at a fast pace with its long-lasting and non-polluting natures. In order to explore the situation and directions of technology, two approaches, namely text mining and chance discovery are employed to conduct the cluster analysis and association analysis on the patent data. Consequently, the technical topics have been found after similarity measurement; the subtopics and relations have been recognized after KeyGraph generation and data crystallization. Finally, according to the relation patterns, the situation and directions of thin-film solar cell have been identified and stated.

Chance discovery with data crystallization for scenario formation in solar cell technology

As the solar cell, one of renewable energies, is growing at a fast pace, the recognition of its technological situation becomes necessary for a company and stakeholders nowadays. Meanwhile, the patent data contains plentiful technological information from which is worth exploring to extract further knowledge. Therefore, a graph-based approach, chance discovery, is employed so as to analyze the patent data, to form the technological scenarios, and to explain the tendency of solar cell technology. Finally, several topics of solar cell technology have been identified, the directions of each topic have been depicted, and the relations between topics have been also observed.

An Experiment Model Of Chance Discovery For Visualizing The Financial Situation And Trend

In order to visualize the financial situation and trend of a company, an experiment model of chance discovery has been proposed, which enables the stakeholders inside and outside the business to recognize the performance of management and realize the possibility of investment. Usually, the statistical analysis and prediction models are common ways to understand the overall financial trend. Apart from analyzing the public offering financial statements directly, some visualization methods are potential approaches for stakeholders to understand the financial status intuitively. Chance discovery is one of the visualization methods which may be suitable for being applied in the exploration of financial status and trend. Using the experiment model, the annual and serial KeyGraphs as well as the trend diagram and integrated map were generated to depict the overview of the successive five-year scenario. Finally, the visualization of financial trend was then attained in this study.

The framework of competitive advantage based chance discovery

To analyze the competitive advantage, the analyst has to collect and analyze useful news. Therefore, the text mining technology is necessary for extracting information from the collected news. Both data mining and text mining are, as we know, frequency based methods; the low frequency data are usually deleted to discover the associative rules. As a result, some rare but actually important events for future are also deleted. Chance discovery, a discipline that focuses on revealing rare but important events, can solve the problem mentioned above to some extent. However, the canonical scenarios, which are used to assist in identifying rare events within the chance discovery process, are manually determined by participators. The work would be very complicated while data are numerous. A framework that fuses Porters five forces and the strategy of competitive advantage has thus been proposed in this paper. Both concepts fused are utilized to automatically define scenarios. Furthermore, the scenarios determined by the proposed framework could be zoomed in / out under the direction of participators. In other words, the amount of data viewed by participators at a time is under control. Cases concerning the optical disk industry in Taiwan have been studied by using our framework. The experimental results indicated that CMC was as good as the Ritek and Prodisk in both CD and DVD techniques. Subsequently, both CMC and Ritek were better than Prodisk in the double layer products. Finally, CMC became the best in developing Blue-Ray products. These analyses verified the company whose competitive advantages are superior to others will have a great chance to success in the market.

Visualization of the user's preference using multi-values analysis

In our research, we proposed a method of text analysis; it combines the concept that grounded theory and chance discovery. In the interactive process of user and documents, analyst can get some information faster and accurately which he want to know. Through the visualization of documents, analyst can read and understand easier. This way can avoid the trouble that a large number of documents are difficult to expl.ain, and not easy to find out the relation between the words. Our research utilizes the Web news of WII to explain the story of documents. To prove the analysis procedure which proposed from our research is feasible. In the research, we can prove that check the documents with different viewpoint, can obtain different results of problem. To recombine values and analysis result repeatedly, can great to help in distinguishing the problem. And discuss Porters five forces analysis finally; it can fill the deficiency of making the competitive strategy that the procedure proposed from our research. This is a very important discovery in our research.