Study On Patent Information Service Model For University Libraries Driven By Data Science

In recent years,with the far-reaching impact of information technology represented by big data and artificial intelligence on various industries,the role of data for patent information services has become more and more prominent.However,in the face of the VUCA era,which combines Volatility,Uncertainty,Complexity and Ambiguity,the lack of resilience of patent information services in university libraries has been exposed.On the one hand,the solidification of traditional service concepts and the backward performance of technical tools have led to the slow response of university libraries to users’ needs and the low efficiency of processing patent information.On the other hand,the large quantity,complicated format,fuzzy boundary and dynamic changes of patent data have led to the lack of ability of university libraries to provide deep services and the lack of ways to present analysis results.Data science,as an emerging discipline that allows “data” to speak,i.e.“using scientific methods to study data”,can not only transform from data to information,knowledge and wisdom,but also eliminate systemic uncertainties and help organisations to implement resilient It can also help organisations implement resilient services by removing system uncertainties.Therefore,how to use data science to build a flexible and resilient patent information service model that can adapt to changes in this internal and external environment has become an urgent problem to be studied and solved for university libraries.Based on data science theory,information ecology theory and resilience theory,this study adopts literature survey method,network survey method,patent information measurement method and case study method to construct and apply a data science-driven patent information service model for university libraries.Firstly,based on the discussion of relevant concepts and theoretical foundations,the development history,model evolution,constituent elements and existing problems of patent information services in university libraries in China are summarised,taking into account the development rules of patent policies and the research results of existing literature.Then,it analyses the power sources of data science-driven patent information service in university libraries from four aspects: internal research paradigm and theoretical system,external policy system and user needs,reveals the tenacity characteristics of data science-driven patent information service in university libraries from three dimensions: theoretical methods,technical tools and results presentation,and constructs a data science-driven patent information service model by combining service concept,service content and service process.The model of data science-driven patent information service in university libraries is also constructed by combining the service concept,service content and service process.Finally,two different subjects,universities and enterprises,are selected to apply this model to the patent information services for university decision-making support and enterprise technological innovation respectively,and relevant development strategies are proposed with the empirical revelation of the service process.From the perspective of data science,this study explores the model design and process optimization of patent information service in university libraries by taking the acquisition,processing,storage and analysis of patent information as a clue,and verifies the feasibility of the data science-driven patent information service model in university libraries with practical case studies.The study shows that the mode which driven by data science is useful in guiding the patent information service of university libraries from the traditional short-term concept of “treating the headache and treating the foot”to the modern long-term thinking of “scientific adaptation and active change”.It is of great significance to improve the service level of patent information creation,application,protection and management in university libraries.