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

Attention and Social Cognition in Virtual Reality:The effect of engagement mode and character eye-gaze

Technical developments in virtual humans are manifest in modern character design. Specifically, eye gaze offers a significant aspect of such design. There is need to consider the contribution of participant control of engagement. In the current study, we manipulated participants’ engagement with an interactive virtual reality narrative called Coffee without Words. Participants sat over coffee opposite a character in a virtual café, where they waited for their bus to be repaired. We manipulated character eye-contact with the participant. For half the participants in each condition, the character made no eye-contact for the duration of the story. For the other half, the character responded to participant eye-gaze by making and holding eye contact in return. To explore how participant engagement interacted with this manipulation, half the participants in each condition were instructed to appraise their experience as an artefact (i.e., drawing attention to technical features), while the other half were introduced to the fictional character, the narrative, and the setting as though they were real. This study allowed us to explore the contributions of character features (interactivity through eye-gaze) and cognition (attention/engagement) to the participants’ perception of realism, feelings of presence, time duration, and the extent to which they engaged with the character and represented their mental states (Theory of Mind). Importantly it does so using a highly controlled yet ecologically valid virtual experience