A Hybrid Method of Analyzing Patents for Sustainable Technology Management in Humanoid Robot Industry

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.

Topic modelling and social network analysis of publications and patents in humanoid robot technology

This article presents analysis of data from scientific articles and patents to identify the evolving trends and underlying topics in research on humanoid robots. We used topic modelling based on latent Dirichlet allocation analysis to identify underlying topics in sub-areas in the field. We also used social network analysis to measure the centrality indices of publication keywords to detect important and influential sub-areas and used co-occurrence analysis of keywords to visualise relationships among subfields. The research result is useful to identify evolving topics and areas of current focus in the field of humanoid technology. The results contribute to identify valuable research patterns from publications and to increase understanding of the hidden knowledge themes that are revealed by patents.

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

In today’s knowledge-based society, industry-university cooperation (IUC) is recognized as an effective tool for technological innovation. Many studies have shown that selecting the right partner is essential to the success of the IUC. Although there have been a lot of studies on the criteria for selecting a suitable partner for IUC or strategic alliances, there has been a problem of making decisions depending on the qualitative judgment of experts or staff. While related works using patent analysis enabled the quantitative analysis and comparison of potential research partners, they overlooked the fact that there are several sub-technologies in one specific technology domain and that the applicant’s research concentration and competency are not the same for every sub-technology. This study suggests a systematic methodology that combines the Latent Dirichlet Allocation (LDA) topic model and the clustering algorithm in order to classify the sub-technology categories of a particular technology domain, and identifies the best college partners in each category. In addition, a similar-patent density (SPD) index was proposed and utilized for an objective comparison of potential university partners. In order to investigate the practical applicability of the proposed methodology, we conducted experiments using real patent data on the electric vehicle domain obtained from the Korean Intellectual Property Office. As a result, we identified 10 research and development sectors wherein Hyundai Motor Company (HMC) focuses using LDA and clustering. The universities with the highest values of SPD for each sector were chosen to be the most suitable partners of HMC for collaborative research.