• information retrieval
• natural language processing
• patent analysis
• tech mining
• technology analysis
• text mining

Innovation Management is a key process for mapping a technological domain and managing technological capabilities, either for companies, research institutions and policy makers. It allows managers and decision makers to anticipate trends for accurate forecast and effective foresight.
A technological innovation starts with the ideation phase, in which researchers, inventors, and engineers (innovation actors) design and develop a novel technology. The main discipline that studies and attempts to standardize the technology design and development processes is Engineering Design (ED). The innovation actors operate in these phases using ED practices and concepts, such as technology functions, users, technical problems and so on. Moreover, they are pushed by industrial and academic system, to produce patents, scientific publications, and company technical documents to describe their inventive steps. These documents are an unavailable source of knowledge that reflects the cognitive process of innovation actors and encapsulate the main technical concepts of ED.
Among these sources, patents are the widest technical open access database used in literature and in practice for studying technological phenomena. Nowadays, Text mining and Natural Language Processing (NLP) provides new methods for the analysis of patent texts. NLP is a branch of text mining for the automatic processing of the human language (natural language in jargon) in written form. The application of NLP for the analysis of technological information is called Tech Mining. However, most Tech Mining methods used in literature do not consider the meaning of the textual information and the ED expert knowledge generated during the process of research, design and develop.
My Ph.D research is focused on demonstrating that the identification and mapping of the ED knowledge from the text of patents may enhance the innovation management process. First, I review the literature to provide a clear picture of state-of-the-art in Tech Mining focused on ED concepts. Then, I present a Tech Mining system for identifying ED concepts (i.e., technological terms, technical problems, solution to the problems and advantageous effects) from patents. Finally, I prose an approach to study the trends of technological evolution using the ED concepts.
My study delineates valid Tech Mining tools that can be integrated in any text analysis pipeline to support academics and companies in investigating a technological domain. This tool allows organizations to focus on value-added activities of technological forecasting and management process.