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

Coolest Student Papers at Finland Futures Research Centre 2017–2018 : Tulevaisuuden tutkimuskeskuksen valittuja opiskelijatöitä 2017–2018.

This eBook contains the inspired and inspiring picks from the student essays written by students and student groups in the courses organised by Finland Futures Research Centre (FFRC). This year’s selection shows that brilliant new students arrive our courses. The topics range from sustainability transitions to corporate foresight, from ethics to methodology, from data business to geoengineering. Independent, constructively critical open deliberation of how futures studies should be carried out is one of the core goals of our education and a key to further development of the courses and the whole field of futures studies

Semantic compared cross impact analysis

The aim of cross impact analysis (CIA) is to predict the impact of a first event on a second. For organizations strategic planning, it is helpful to identify the impacts among organizations internal events and to compare these impacts to the corresponding impacts of external events from organizations competitors. For this, literature has introduced compared cross impact analysis (CCIA) that depicts advantages and disadvantages of the relationships between organizations events to the relationships between competitors' events. However, CCIA is restricted to the use of patent data as representative for competitors events and it applies a knowledge structure based text mining approach that does not allow considering semantic aspects from highly unstructured textual information . In contrast to related work, we propose an internet based environmental scanning procedure to identify textual patterns represent competitors events. To enable processing of this highly unstructured textual information, the proposed methodology uses latent semantic indexing (LSI) to calculate the compared cross impacts (CCI) for an organization. A latent semantic subspace is built that consists of semantic textual patterns. These patterns are selected that represent organizations events. A web mining approach is used for crawling textual information from the internet based on keywords extracted from each selected pattern. This textual information is projected into the same latent semantic subspace. Based on the relationships between the semantic textual patterns in the su bspace, CCI is calculated for different events of an organization. A case study shows that the proposedapproach successfully calculates the CCI for technologies processed by a governmental organization. This enables decision makers to direct their investments more targeted