Opportunity Identification for New Product Planning: Ontological Semantic Patent Classification

Intelligence tools have been developed and applied widely in many different areas in engineering, business and management. Many commercialized tools for business intelligence are available in the market. However, no practically useful tools for technology intelligence are available at this time, and very little academic research in technology intelligence methods has been conducted to date. Patent databases are the most important data source for technology intelligence tools, but patents inherently contain unstructured data. Consequently, extracting text data from patent databases, converting that data to meaningful information and generating useful knowledge from this information become complex tasks. These tasks are currently being performed very ineffectively, inefficiently and unreliably by human experts. This deficiency is particularly vexing in product planning, where awareness of market needs and technological capabilities is critical for identifying opportunities for new products and services. Total nescience of the text of patents, as well as inadequate, unreliable and untimely knowledge derived from these patents, may consequently result in missed opportunities that could lead to severe competitive disadvantage and potentially catastrophic loss of revenue. The research performed in this dissertation tries to correct the abovementioned deficiency with an approach called patent mining. The research is conducted at Finex, an iron casting company that produces traditional kitchen skillets. To \u27mine\u27 pertinent patents, experts in new product development at Finex modeled one ontology for the required product features and another for the attributes of requisite metallurgical enabling technologies from which new product opportunities for skillets are identified by applying natural language processing, information retrieval, and machine learning (classification) to the text of patents in the USPTO database. Three main scenarios are examined in my research. Regular classification (RC) relies on keywords that are extracted directly from a group of USPTO patents. Ontological classification (OC) relies on keywords that result from an ontology developed by Finex experts, which is evaluated and improved by a panel of external experts. Ontological semantic classification (OSC) uses these ontological keywords and their synonyms, which are extracted from the WordNet database. For each scenario, I evaluate the performance of three classifiers: k-Nearest Neighbor (k-NN), random forest, and Support Vector Machine (SVM). My research shows that OSC is the best scenario and SVM is the best classifier for identifying product planning opportunities, because this combination yields the highest score in metrics that are generally used to measure classification performance in machine learning (e.g., ROC-AUC and F-score). My method also significantly outperforms current practice, because I demonstrate in an experiment that neither the experts at Finex nor the panel of external experts are able to search for and judge relevant patents with any degree of effectiveness, efficiency or reliability. This dissertation provides the rudiments of a theoretical foundation for patent mining, which has yielded a machine learning method that is deployed successfully in a new product planning setting (Finex). Further development of this method could make a significant contribution to management practice by identifying opportunities for new product development that have been missed by the approaches that have been deployed to date

Engineering Tech Mining: mixing Engineering Design and Tech Mining for Innovation Management

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