Facilitating Technology Transfer by Patent Knowledge Graph

Technologies are one of the most important driving forces of our societal development and realizing the value of technologies heavily depends on the transfer of technologies. Given the importance of technologies and technology transfer, an increasingly large amount of money has been invested to encourage technological innovation and technology transfer worldwide. However, while numerous innovative technologies are invented, most of them remain latent and un-transferred. The comprehension of technical documents and the identification of appropriate technologies for given needs are challenging problems in technology transfer due to information asymmetry and information overload problems. There is a lack of common knowledge base that can reveal the technical details of technical documents and assist with the identification of suitable technologies. To bridge this gap, this research proposes to construct knowledge graph for facilitating technology transfer. A case study is conducted to show the construction of a patent knowledge graph and to illustrate its benefit to finding relevant patents, the most common and important form of technologies

Technological taxonomies for hypernym and hyponym retrieval in patent texts

This paper presents an automatic approach to creating taxonomies of technical terms based on the Cooperative Patent Classification (CPC). The resulting taxonomy contains about 170k nodes in 9 separate technological branches and is freely available. We also show that a Text-to-Text Transfer Transformer (T5) model can be fine-tuned to generate hypernyms and hyponyms with relatively high precision, confirming the manually assessed quality of the resource. The T5 model opens the taxonomy to any new technological terms for which a hypernym can be generated, thus making the resource updateable with new terms, an essential feature for the constantly evolving field of technological terminology.Comment: ToTh 2022 - Terminology & Ontology: Theories and applications, Jun 2022, Chamb{\'e}ry, Franc

ARIZ85 and patent-driven knowledge support

AbstractThe growing complexity of technical solutions, which encompass knowledge from different scientific fields, makes necessary, also for multi-disciplinary working teams, the consultation of information sources. Indeed, tacit knowledge is essential, but often not sufficient to achieve a proficient problem solving process. Besides, the most comprehensive tool of the TRIZ body of knowledge, i.e. ARIZ, requires, more or less explicitly, the retrieval of new knowledge in order to entirely exploit its potential to drive towards valuable solutions.A multitude of contributions from the literature support various common tasks encountered when using TRIZ and requiring additional information; most of them hold the objective of speeding up the generation of inventive solutions thanks to the capabilities of text mining techniques. Nevertheless, no global study has been conducted to fully disclose the effective knowledge requirements of ARIZ. With respect to this deficiency, the present paper illustrates an analysis of the algorithm with the specific objective of identifying the different types of information needs that can be satisfied by patents. The results of the investigation lay bare the most significant gaps of the research in the field. Further on, an initial proposal is advanced to structure the retrieval of relevant information from patent sources currently not supported by existing methodologies and software applications, so as to exploit the vast amount of technical knowledge contained in there. An illustrative experiment sheds light on the relevance of control parameters as input terms for the definition of search queries aimed at retrieving patents sharing the same physical contradiction of the problem to be treated

Using natural language processing techniques to inform research on nanotechnology

Literature in the field of nanotechnology is exponentially increasing with more and more engineered nanomaterials being created, characterized, and tested for performance and safety. With the deluge of published data, there is a need for natural language processing approaches to semi-automate the cataloguing of engineered nanomaterials and their associated physico-chemical properties, performance, exposure scenarios, and biological effects. In this paper, we review the different informatics methods that have been applied to patent mining, nanomaterial/device characterization, nanomedicine, and environmental risk assessment. Nine natural language processing (NLP)-based tools were identified: NanoPort, NanoMapper, TechPerceptor, a Text Mining Framework, a Nanodevice Analyzer, a Clinical Trial Document Classifier, Nanotoxicity Searcher, NanoSifter, and NEIMiner. We conclude with recommendations for sharing NLP-related tools through online repositories to broaden participation in nanoinformatics

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

Patent Data for Engineering Design: A Critical Review and Future Directions

Patent data have long been used for engineering design research because of its large and expanding size, and widely varying massive amount of design information contained in patents. Recent advances in artificial intelligence and data science present unprecedented opportunities to develop data-driven design methods and tools, as well as advance design science, using the patent database. Herein, we survey and categorize the patent-for-design literature based on its contributions to design theories, methods, tools, and strategies, as well as the types of patent data and data-driven methods used in respective studies. Our review highlights promising future research directions in patent data-driven design research and practice.Comment: Accepted by JCIS