지능형 반도체(Neuromorphic chip)는 메모리와 비메모리로 나누어지는 기존 반도체와는 달리 하나의 반도체가 저장과 연산 기능을 모두 수행하며, 인공지능 기술의 핵심인 비정형 데이터 인식과 패턴분석을 효과적으로 처리할 수 있다. 특히 사람의 뇌신경을 모방한 시냅스 구조를 통해 4차 산업 혁명시대에 요구되는 복합적인 기능을 수행할 때 기존 반도체 대비 1억분의 1 수준으로 전력 소비량을 줄일 수 있다. 이와 같은 특성은 지능형 반도체가 미래 반도체 시장의 핵심 기술이 될 것을 쉽게 예측할 수 있게 해주고 있다. 따라서 성장한계에 부딪힌 기존 반도체 시장에서의 지속·혁신 성장과 미래 반도체 시장에서의 경쟁우위를 선점하기 위해서는 지능형 반도체 분야의 기술경쟁력 확보가 필수적이다. 본 논문은 지능형 반도체 분야의 기술경영전략을 수립하기 위해 한국, 유럽, 미국, 일본의 특허를 수집하고 비지도 기계학습을 이용한 정량적인 특허분석을 수행하였다. 최적의 분석결과를 도출하기 위하여 주성분 분석, 계층적 기술군집화, 비계층적 기술군집화를 결합한 방법을 이용하여 지능형 반도체소자 기술의 세부기술을 도출하고 기술경쟁력 확보를 위한 IP-R&D 전략을 제안하였다.

양자점은 발광파장 조절이 쉽고 색 재현율과 양자 효율이 뛰어나 능동 유기 발광 다이오드(AMOLED)에 이은 다음 세대 디스플레이의 핵심 소재로 주목받아 왔다. 양자점을 이용한 디스플레이는 대면적화가 어려웠으나, 최근 전사프린팅 기술을 이용해 기술적 한계를 극복하고 상용화되기 시작하였다. 따라서, 변화하는 디스플레이 시장에서 기업의 지속적인 성장을 위해 기술혁신을 통한 경쟁우위와 신성장동력을 확보해야 한다. 특허 분석을 통해 핵심기술을 파악하고 부상 기술을 예측하는 것은 성공적인 기술혁신 결과를 얻기 위한 효과적인 방법이다. 특허 지표는 해당 특허가 시장이나 기술적인 측면에서 어떤 위치에 있는지 평가할 수 있는 정보를 정량적인 수치로 나타내준다. 자기조직화지도는 반복적인 자율학습을 통해 군집분석을 수행하고 그 결과를 2차원의 지도를 통해 시각화할 수 있어 특허 분석에 효과적으로 이용될 수 있습니다. 본 연구에서는 특허 지표와 자기조직화지도를 이용한 군집 분석을 수행하여 양자점 디스플레이 분야의 핵심 기술, 부상 기술, 공백 기술을 찾아내고 특허 대응 전략을 수립하였다.

Recently, sustainable growth and development has become an important issue for governments and corporations. However, maintaining sustainable development is very difficult. These difficulties can be attributed to sociocultural and political backgrounds that change over time [1]. Because of these changes, the technologies for sustainability also change, so governments and companies attempt to predict and manage technology using patent analyses, but it is very difficult to predict the rapidly changing technology markets. The best way to achieve insight into technology management in this rapidly changing market is to build a technology management direction and strategy that is flexible and adaptable to the volatile market environment through continuous monitoring and analysis. Quantitative patent analysis using text mining is an effective method for sustainable technology management. There have been many studies that have used text mining and word-based patent analyses to extract keywords and remove noise words. Because the extracted keywords are considered to have a significant effect on the further analysis, researchers need to carefully check out whether they are valid or not. However, most prior studies assume that the extracted keywords are appropriate, without evaluating their validity. Therefore, the criteria used to extract keywords needs to change. Until now, these criteria have focused on how well a patent can be classified according to its technical characteristics in the collected patent data set, typically using term frequency–inverse document frequency weights that are calculated by comparing the words in patents. However, this is not suitable when analyzing a single patent. Therefore, we need keyword selection criteria and an extraction method capable of representing the technical characteristics of a single patent without comparing them with other patents. In this study, we proposed a methodology to extract valid keywords from single patent documents using relevant papers and their authors’ keywords. We evaluated the validity of the proposed method and its practical performance using a statistical verification experiment. First, by comparing the document similarity between papers and patents containing the same search terms in their titles, we verified the validity of the proposed method of extracting patent keywords using authors’ keywords and the paper. We also confirmed that the proposed method improves the precision by about 17.4% over the existing method. It is expected that the outcome of this study will contribute to increasing the reliability and the validity of the research on patent analyses based on text mining and improving the quality of such studies.

선행기술조사는 지식재산의 창출 및 활용 과정에서 발명자 및 출원인, 등록 가부를 판정하는 심사관, 변리업계 종사자 등에 의해 수행되는 기술경영의 핵심적인 프로세스이다. 그동안 체계적인 선행기술 탐색방법론에 관한 학술연구가 충분히 뒷받침되지 못한 결과, 현장에서는 조사자의 주관적 판단에 의존하여 선행기술조사를 수행하는 경우가 많다. 시맨틱 기반으로 선행기술을 탐색하는 기존 연구들 또한 동일한 기술사상이 다양한 용어로 표현되는 특허문서의 특성상 주요 선행기술의 유사성을 저평가할 위험이 있다. 본 연구는 특허의 인용정보를 활용한 계층적 인용관계분석을 기반으로 하는 효과적인 선행기술탐색 방법론을 제안한다. 제안하는 방법론은 특허성을 검토하고자 하는 특허를 중심으로 인용관계에 있는 특허들 중 상대적인 중요도에 따라 가중치를 산정함으로써 핵심 선행기술을 선별하는 명확한 기준을 제시한다. 제안 방법론의 실제 적용가능성을 검증하기 위해 디스플레이 분야의 특허 1건에 대한 핵심 선행기술을 탐색하는 사례연구를 수행한 결과 206건의 선행 특허 중 10건의 핵심 선행기술 후보군을 선별 가능하였다. Prior art search is a core process of technology management performed by inventors and applicants, patent examiners, and employees in the patent industry. As a result of insufficient academic research on a systematic prior art search methodology, the process has been often carried out depending on the subjective judgment of researchers. Previous studies on exploring prior arts based on semantics have also have the risk of underestimating the similarity of major prior arts due to the nature of patent documents where the same technical ideas are expressed in various terms. In this study, we propose an effective prior art search methodology based on hierarchical citation analysis, which provides a clear criterion for selecting core prior arts by calculating weights according to the relative importance of the collected patents. In order to verify the feasibility of the proposed methodology, a case study was conducted to explore the core prior art of one patent in the display field. As a result, 10 core prior art candidates were selected out of the 206 precedent patents.

A humanoid, which refers to a robot that resembles a human body, imitates a human’s intelligence, behavior, sense, and interaction in order to provide various types of services to human beings. Humanoids have been studied and developed constantly in order to improve their performance. Humanoids were previously developed for simple repetitive or hard work that required significant human power. However, intelligent service robots have been developed actively these days to provide necessary information and enjoyment; these include robots manufactured for home, entertainment, and personal use. It has become generally known that artificial intelligence humanoid technology will significantly benefit civilization. On the other hand, Successful Research and Development (R & D) on humanoids is possible only if they are developed in a proper direction in accordance with changes in markets and society. Therefore, it is necessary to analyze changes in technology markets and society for developing sustainable Management of Technology (MOT) strategies. In this study, patent data related to humanoids are analyzed by various data mining techniques, including topic modeling, cross-impact analysis, association rule mining, and social network analysis, to suggest sustainable strategies and methodologies for MOT.

3D TV의 보급으로 인해 무안경식 3차원 디스플레이 기술에 대한 중요성이 강조되고 있고 현재 무안경식 3차원 디스플레이 기술은 특정한 기술이 주도하고 있지 않기 때문에 차별화된 고유한 기술을 개발하고 확보함으로써 경쟁력을 확보할 수 있다. 본 논문에서는 무안경식 3차원 디스플레이 기술에 대한 기술경영전략, 즉 R&D 방향을 제시하기 위해 특허 분석을 실시하였다. 특허 출원 동향과 특허지수를 이용한 기업-기술 매트릭스 분석을 통해 효과적인 R&D를 위한 유망 기술 분야를 도출하고 유망 기술 분야에서 중요하고 구체적인 R&D 방향을 제시해 줄 수 있는 핵심 특허를 도출한다. 마지막으로 핵심 특허를 인용한 특허들을 시간의 흐름에 따라 나열한 기술 발전도를 통해 최종적으로 R&D 방향을 제시한다. By occasion of propagation of 3D TV, technology of glassless 3D display is increasingly important. Currently, there was no lead technology In this technology. Thus it is important to develop differentiated technology for secure competitiveness. In this paper, We analyze patents for R&D strategy about glassless 3D display. Through Company-Technology matrix analysis and Patent trend analysis, We extract promising technology field and core technology. Lastly We suggest R&D strategy by using patent road map.

자율주행자동차는 자동차 스스로가 도로 위의 상황을 분석하고 판단하여 움직이는 인공지능과 자동차가 결합된 형태이다. 자율주행자동차에 대한 연구결과가 최근 언론을 통해 공개가 되고 있으며, 선두기업으로 구글이 평가받고 있다. 기술경영에서 기업의 연구개발방향 파악 및 개발전략수립을 위해 다양한 정보를 포함하고 있는 특허정보의 활용은 좋은 대안으로 평가받고 있다. 본 논문에서는 구글의 자율주행자동차에 대한 집중연구방향 파악 및 기술개발전략수립을 위해 구글의 자율주행자동차 관련 특허문서를 대상으로 문헌의 질적 측면을 평가할 수 있는 인용정보를 이용하여 사회네트워크분석 기반의 연구집중도 분석을 수행한다. 분석결과, 구글에서는 하드웨어 분야에 대한 기술이 미흡하여 최근까지 하드웨어 제어부분에 대한 기술개발에 집중한 것을 확인할 수 있으며, 현재 이 기술에 대하여 상당한 성과를 이룬 것으로 파악된다. 후발 기업에서는 향후 표준화를 대비하여 구글과의 공동연구를 진행하는 것이 필요할 것으로 예상된다. An autonomous vehicle is a convergence of artificial intelligence and a vehicle which can drive itself while analyzing the real-time situation on a road without a driver. A lot of research achievements have been revealed through the media and Google is considered to be the best leading company in this field. The use of patent information which contains various information such as bibliographic data and information about technologies is a good way to find out the R&D direction of a company and develop a reasonable strategy. This study is aimed at investigating the direction to which Google focuses its R&D capabilities and establishing strategies for technology development. Google's patents about autonomous vehicles were collected and the degree of research bias was analyzed using Social Network Analysis based on citations indicating the quality of a patent. Based on the results, the strategies for technology development was eventually proposed. As a result, it was revealed that Google focused its R&D capabilities on the part of hardware control to make up for its lack of hardware-oriented technologies. As of now, Google obtained remarkable achievements, so it seems reasonable that last-movers consider cooperative research with Google.

The number of patents with critical information related to various technologies is increasing by the day. This trend has led corporations and countries to consider patent analysis as an important element in their analysis methodology for research and development. The present study seeks to determine and forecast vacant technology with considerable development potential through an analysis of patents. In order to identify a vacant technology cluster, the unstructured patent documents need to be structured into groups of similar technologies by using k-means clustering. Furthermore, silhouette width, Davies-Bouldin Index (DBI), and Pseudo F are used for enhancing reliability of determining the optimal number of clusters. From each technology cluster, a generative topic model, latent Dirichlet allocation (LDA), is adopted to extract latent topics specifically for examination of technologies. Renewable energy patents from the United States Patent and Trademark Office (USPTO) are analyzed for the case study, which verifies the proposed methodology.

Over recent years, it is more becoming important to intellectual property all over the world, many scholars study on application methods of intellectual property. Especially, most of them study actively on fore- casting of future promising technology using patent. This study analyze various analysis techniques to technology forecasting using patent.

This study aims at analyzing the convergence of Internet-of-Things and wearables technologies using cooperative patent classification(CPC). CPC, introduced to an increasing number of technological fields of Korean patents, is expected to be widely used in Patent Informatics because the classification codes in CPC are more specific than those of IPC, which reflect the characteristics of technologies in detail with accuracy. CPC has seldom been used up to date and most of the previous researches on technological convergence used IPC. As a pre-analysis step for analyzing the trend of technological convergence of IoT and wearables, CPC and IPC codes assigned to each patent were compared. By applying association rule mining to the analysis of CPC codes, we identified the technological fields where convergence frequently takes place and examined the trend of technological convergence over time.

As technology has been advanced these days, many companies try technology innovation for differentiation and competitive advantages over rival companies. Especially, new product development is integral to the survival of business. However, it has been largely dependent on qualitative methods, such as QFD method, for developing new products. This paper researches on the quantitative methods based on intellectual property to establish new product development strategy.

최근 사회는 아날로그 시대를 거쳐 디지털, 스마트 시대로 접어들었고, 모든 분야의 기술은 끊임없는 변화와 매우 빠른 발전을 하고 있다. 이러한 경쟁사회에서 지식재산, 특히 특허분석을 통한 R&D 전략 수립은 기술경쟁력 향상에 많은 도움이 될 수 있다. 특허문서는 명칭, 요약, 상세한 설명, 청구항, 기술분류정보 등 서지정보, 기술문헌과 권리문헌으로 이루어져 있어 대중은 이를 통해 해당 기술에 대한 많은 정보를 수집할 수 있다. 특허문서의 특징을 정량적으로 활용하고 기술 분석을 실시함으로써 분석대상 기술의 동향을 파악하는 것뿐만 아니라, 해당 기술 분야의 핵심기술과 특허를 탐색하여 경쟁력을 향상시키는 것이 가능하다. 본 논문은 특허 데이터에 대한 정량적인 방법을 기반으로 한 핵심 기술과 핵심 특허의 도출 방법을 제안한다. 특허문서에 포함되어 있는 기술분류정보, IPC 코드에 통계분석과 사회네트워크분석을 적용하여 연구개발이 활발한 분야와 중심성이 높은 기술을 탐색한다. 그 후 특허의 인용정보와 패밀리정보 분석을 통해 핵심 기술 분야에서 중요성이 높은 특허를 추출하여, 최종적으로 기술경영 및 특허경영 전략 수립 방법을 제안한다. Society has been developed through analogue, digital, and smart era. Every technology is going through consistent changes and rapid developments. In this competitive society, R&D strategy establishment is significantly useful and helpful for improving technology competitiveness. A patent document includes technical and legal rights information such as title, abstract, description, claim, and patent classification code. From the patent document, a lot of people can understand and collect legal and technical information. This unique feature of patent can be quantitatively applied for technology analysis. This research paper proposes a methodology for extracting core technology and patents based on quantitative methods. Statistical analysis and social network analysis are applied to IPC codes in order to extract core technologies with active R&D and high centralities. Then, core patents are also extracted by analyzing citation and family information.

Technological trend analyses using patents are increasingly developed for effective R&D strategies recently. Especially, a patent mat analysis including core patents is widely used. However, extraction of core patents from the mat requires experts and consumes enormous time and cost. We propose a quantitative methodology of patent analysis using text mining techniques to solve this problem. This study analyzed the trend of technology of thermal insulation materials by building a patent map based on patent keywords.

The preceding researches on functional analysis of patents using patent classification codes or keywords as function classifiers are lack of consideration in the fact that functional elements vary from patent to patent. This study proposes the methodology for systematically identifying functional elements of patents using patent-function matrix, allowing us to better match and compare them with the actual functions in commercialized products. By applying the new methodology to the case study of Korean patents regarding smart watch, we could easily find out the distribution of patents amongst functional elements and identify the functional range of each patent. Patent documents deal with technological inventions for solving a certain problem and they describe functions, physical structures and the way they are combined very specifically. Engineers can determine specific R&D directions by conducting a functional analysis of patents in the technical field of concern. Many of the preceding researches regarding a functional analysis of patents regard patent-classification codes or abstract keywords extracted by text mining techniques as functions of the patents (4, 5). Though it is efficient in terms of time and cost spent for the analysis, there are several limitations of that approach. First, it is difficult to identify specific functional elements of patents in detail by the approach. The functions defined by classification codes or keywords are too comprehensive to be matched with the actual functions of the related products commercialized in the market. Thus, commercialization of the patents is difficult to be determined in this analytical method In addition, it is difficult to differentiate specific functions of each patent with the current method. In general, a patent has one or more functional elements and the elements vary from patent to patent. Even if some patents are assigned to the same classification code and have the same keywords, the functional elements of a patent are not necessarily the same with the other patents in the same group. Some of functional elements in a patent are unique and different from the elements in other patents, while the other elements commonly appear in the other patents of the same field. Thus, the functional range or coverage that varies from patent to patent and the distribution of patents amongst functional elements cannot be exactly evaluated by the current approach.

Forecasting of emerging technology plays important roles in business strategy and R&D investment. There are various ways for technology forecasting including patent analysis. Qualitative analysis methods through experts’ evaluations and opinions have been mainly used for technology forecasting using patents. However qualitative methods do not assure objectivity of analysis results and requires high cost and long time. To make up for the weaknesses, we are able to analyze patent data quantitatively and statistically by using text mining technique. In this paper, we suggest a new method of technology forecasting using text mining and ARIMA analysis.

These days, governments and enterprises are analyzing trends in technology as a part of their investment strategy and R&D planning. Qualitative methods by experts are mainly used in technology trend analyses. However, such methods are inefficient in terms of cost and time for large amounts of data. In this study, we quantitatively analyzed patent data using text mining with TF-IDF used as weights. Keywords and noises were also classified using TF-IDF weighting. In addition, we propose new criteria for removing noises more effectively, and visualize the resulting keywords derived from patent data using social network analysis (SNA).