## 3 Methodology
### Basic Concepts and Overall Process  
This research proposed a new methodology for generating an MA-based TRM to look for new business opportunities according to a combination of technology and product morphology in a morphology matrix and an extension process incorporating TRIZ. Thus, we assumed that a company that possesses new technology that has not been promoted in markets or applied to products yet as well as transformed as structured documents explaining the technology and that it seeks new business opportunities through roadmapping. Taking an MA perspective, the process of TRM design starts by detecting a set of shapes from keywords that represent a technology with potential or concept of product. The suggested TRM is established based on existing technology and products by text mining and MA, by revealing the current situation of corporations. After roadmapping based on the current status, new chances for technologies and products are found through the inventive principles of TRIZ and experts' knowledge. The participation of TRIZ influences the process of technology foresight based on existing technologies and products.  
This study was conducted to test the rationality of the framework of the proposed MA-based TRM, which focuses on two layers, the technology and the product. MA is usually applied to structure a problem by decomposing a system into some subsystems, identifying the morphology of the existing products and technologies, which contributes to roadmaps evolvement by the suggested new opportunities. Therefore, methods based on MA can be positive to the development of roadmaps by utilizing large volumes of product and technology data that might otherwise be inaccessible. The creative feature of TRIZ enables the process of technology foresight in TRMs to be less random and subjective. Furthermore, the framework of the proposed TRM allows quantitative and cyber-analytic tools to be combined to decrease the dependency on experts' knowledge.  
The whole process in this paper consisted of five phases, as shown in Figure 2. First, proper data sources were identified to provide the MA-based technology roadmap for each layer. Patent documents and product manuals were collected from two different datasets for the TRM. The second phase was to find out the keyword list for each layer, denoting the technology and product shapes. Text mining tools were applied in keyword extraction and relative keyword vectors were generated consequently. Third, morphological matrix was constructed in this phase. K-means algorithm, the topic cluster algorithm and cosine correlation coefficient were applied to analyze the interconnections among the collected keywords. Following the identification of the relations among keywords, the overall morphology matrix of the target research field was constructed for the definition of possible configurations of products and technology, providing opportunities for novel products and technology to be developed. Fourth, based on the morphology matrix, the existing TRM was accomplished by evolving the technological and product part of the roadmap based on the similarity between each pair of nodes, representing the multidimensional morphological relevance in this study. Finally, the SGTM-based map was established to identify sparse areas based on the extracted keywords, which contain the potential to future development. The MA-based TRM was extended through the SGTM-based maps during the technology foresight process and improved TRIZ inventive principles were used in concretizing the target field future development path. The additional possibilities of new technologies and products were analyzed by experts.