A Methodology of Partner Selection for Sustainable Industry-University Cooperation Based on LDA Topic Model

A Multi-Class Classification Model for Technology Evaluation

This paper proposes a multi-class classification model for technology evaluation (TE) using patent documents. TE is defined as converting technology quality to its present value; it supports efficient research and development using intellectual property rights–research & development (IP–R&D) and decision-making by companies. Through IP–R&D, companies create their patent portfolios and develop technology management strategies. They protect core patents and use those patents to cooperate with other companies. In modern society, as conversion technology has been rapidly developed, previous TE methods became difficult to apply to technology. This is because they relied on expert-based qualitative methods. Qualitative results are difficult to use to guarantee objectivity. Many previous studies have proposed models for evaluating technology based on patent data to address these limitations. However, those models can lose contextual information during the preprocessing of bibliographic information and require a lexical analyzer suitable for processing terminology in patents. This study uses a lexical analyzer produced using a deep learning structure to overcome this limitation. Furthermore, the proposed method uses quantitative information and bibliographic information of patents as explanatory variables and classifies the technology into multiple classes. The multi-class classification is conducted by sequentially evaluating the value of a technology. This method returns multiple classes in order, enabling class comparison. Moreover, it is model-agnostic, enabling diverse algorithms to be used. We conducted experiments using actual patent data to examine the practical applicability of the proposed methodology. Based on the experiment results, the proposed method was able to classify actual patents into an ordered multi-class. In addition, it was possible to guarantee the objectivity of the results. This is because our model used the information in the patent specification. Furthermore, the model using both quantitative and bibliographic information exhibited higher classification performance than the model using only quantitative information. Therefore, the proposed model can contribute to the sustainable growth of companies by classifying the value of technology into more detailed categories.

Communication Barrier, Spillover Effect and Industrial-Technological Innovation

Industrial-technological innovation (ITI) has far-reaching significance for China’s sustainable development. In this study, we construct an ITI model to analyze the impact of communication barrier and spillover effects in ITI system from the perspective of system by means of system dynamics. Statistical data of smartphone industry were collected to verify the authenticity of the model, which show that the model has higher goodness of fit with the real-world. Simulation experiments were carried out and the results showed that: (1) Spillover effects have obvious effects on ITI in both positive and negative radiation directions, and deeper exchanges and cooperation should be encouraged to carry out among firm. (2) Communication barrier play an important role in regulating ITI. Under the condition of non-barrier and complete-barrier, the completion time of the whole process has increased by 76.5%, it is urgent to reduce communication carrier. The research puts forward suggestions to help improve ITI from institutional, market, and government.