Patent litigation occurs when a company’s product or service violates the scope of another company’s patent rights. When they occur, companies suffer a disruption to the sales of their products and services, thus hindering the sustainability of their business activities. For this reason, companies have established and analyzed wide-ranging strategies to prevent patent litigation. Of those, statistical and machine learning-based quantitative methods using patent big data have several advantages, such as a reduced cost and objective results. Existing quantitative methods analyze patent information and litigation based on the time of data collection. However, the values of patents and their litigation hazards change over time. In addition, the existing methods do not take into account censored data; that is, patents that may result in litigation after the data is collected. In this paper, to solve this problem we propose an integrated survival model that considers censored data and predicts patent litigation hazards over time. The proposed model is a non-parametric survival analysis method based on a random survival forest. It uses pre-trained word2vec and clustering to effectively reflect the technology fields as well as the quantitative information of the patent. The word2vec is a technique for natural language processing and enables the use of patent text information. In order to examine the practicality of the integrated survival model, an experiment is conducted with patent big data related to sensor semiconductors based on AI technology applicable to robotics. In the experiment, it was found that the litigation hazard occurred 150 months after the patent application and increase rapidly from 200 months. Furthermore, the proposed model showed better predictive performance than other survival analysis models. The proposed model could be used by potential defendants to protect their patents.

Various institutions such as universities and corporations strive to commercialize technologies produced through R&D investment. The ideal way to commercialize technology is to transfer it, recognizing the value of the developed technology. Technology transfer is the transfer of technology from R&D entities, such as universities, research institutes, and companies, to others, with the advantage of spreading research results and maximizing cost efficiency. In other words, if enough technology is transferred, it can be commercialized. Although many institutions have various support measures to assist in transferring technology, there is no substitution for quantitative, objective methods. To solve this problem, this paper proposes a technology transfer prediction model based on the information found in patents. However, it is not realistic to include the information from all patents in the quantitative, objective method, so patterns related to technology transfer must be identified to select the appropriate patents that can be used in the predictive model. In addition, a method is needed to address the insufficient training data for the model. Training data are limited because some technology transfer information is not disclosed, and there is little technology transferred in new technology fields. The technology transfer prediction model proposed in this paper searches for hidden patterns related to technology transfer by imaging the patent information, which can also be applied to image analysis models. Furthermore, augmenting the data can solve the problem of the lack of learning data for technology transfer. To examine whether the proposed model can be used in real industries, we collected patents related to artificial intelligence technology registered in the United States and conducted experiments. The experimental results show that the models trained by imaging patent information performed excellently. Moreover, it was shown that the data augmentation technique can be used when there are insufficient data for technology transfer.

특허 빅데이터 분석결과 전달을 위해 다양한 시각화 기법이 사용된다. 기존에 특허 빅데이터 시각화 기법들은 데이터 간의 상대적 비교에 유용하다. 그러나 비교 대상 데이터의 증가 또는 기술 흐름 파악에는 한계점이 존재한다. 이를 보완하기 위해 일부 연구들은 생키 다이어그램을 사용하였으나, 세부기술의 식별과 기술 관계를 고려하지 못한다. 본 연구에서는 특허 빅데이터에 생키 다이어그램과 그래프 모형을 사용하여 세부기술을 식별, 기술개발 흐름 및 기술 관계를 시각화한다. 실험 데이터는 헬스케어 기술 관련 특허로 한국, 미국, 일본, 유럽에서 출원된 건이다. 실험결과로는 운동 부하 모니터링에서 스마트폰 연동 생체계측 기술로 개발이 진행되고 있다. 또한, 통신 관련 기술의 영향력이 큰 것으로 도출되었다.