Patent Classification Research Articles

협력적 특허분류의 동시분류 네트워크 분석과 키워드 동시출현 분석을 통한 전자상거래 핵심 기술 탐색에 관한 연구

Concerning the trend of online shopping for domestic and global markets, a curve of rapid sales growth every year can be found. The offline distribution market has recently been on a downward curve, and the proportion of online sales in the distribution industry is expected to continue to expand. Accordingly, e-commerce companies are implementing various strategies to expand the market and meet customer needs. For instance, Coupang’s core strategies are its signature delivery system called “Rocket Delivery” and expanding its infrastructure of logistics centers. To counteract Coupang’s market dominance, Naver has launched the “Naver Fulfillment Alliance” by partnering with key industry players. However, these market dynamics are Individual company strategies through research data and published reviews, and it is difficult to grasp the core technology strategy in the e-commerce field. Therefore, this study intends to derive and analyze core technologies using patent information, which is an important indicator of technological innovation in the domestic e-commerce field. To this end, data from 659 patents filed between 2001 and 2021 were sampled to conduct simultaneous network classification analysis using an advanced patent classification (CPC) system. In addition, the summary of the patent was analyzed using the text mining technique to analyze the simultaneous keyword appearance network. Through this study, seven technology group groups were analyzed in the e-commerce field, and product recommendation and shopping interface technology were analyzed as core technologies. As a result of this study, it is expected to help establish strategies and policies for the development of core technologies in the field of e-commerce.

The growing role of intellectual property in Japan's national economic security: legislative provision

Keywords: intellectual property, innovation, national security, economic security,critical technology, critical infrastructure, inventor, non-disclosure guidelines, securityexpertise, secret patent
 The article examines the role ofintellectual property, in particular, the Institute of Secret Inventions in the developmentof the Japanese economy, ensuring the national economic security of the state.An analysis of the new Law on Strengthening National Security through ComprehensiveEconomic Measures, adopted on May 11, 2022, is given. The organizational andeconomic mechanism for ensuring measures aimed at strengthening Japan's national economic security is considered: a system that ensures a stable supply of critical materials,a system for supporting the development of critical technologies, and a secretpatent system. Japan's Economic Security Act gives the Japanese government theright to determine economic threats and risks associated with foreign economic activity.Focusing on four areas of economic security — supply chains, basic infrastructure,advanced technology and the publication of patents on sensitive technologies — thelaw allows the national government to intervene in Japanese companies' deals withforeign companies. It is concluded that the economic and legal institution of secret inventions,which directly affects national security, will gain more and more importance.And certain provisions of the Law can be used in law-making and law-enforcementactivities in Ukraine: a system for supporting the development of critical technologies,designated patent classifications (Designated Patent Classifications) in thefields of technology that can affect national security, guidelines for non-disclosure, securityexpertise, a system of preliminary determination secrecy, government compensationfor restrictions in the form of payment of «ordinary damages», sanctions forleaking undisclosed patent information.

What is your next invention? — A framework of mining technological development rules and assisting in designing new technologies based on BERT as well as patent citations

In the past 20 years, with the rapid development of technology, the number of granted patents has also been increasing over years. How to utilize these data effectively is very important for making R&D policies and assisting in designing new technologies. In this paper, we map patents into a low-dimensional vector space, which is constructed by International Patent Classification (IPC) codes, through a deep learning model, i.e., Bidirectional Encoder Representations from Transformers (BERT). Then, this research makes the following contributions: first, we find that the generated vectors can describe the new technologies’ invention perspectives of patents accurately according to their texts; second, these vectors are combined with the physical meaning of patent citations (technological application) for the first time to solve some issues in designing new technologies from a different view; third, the citation relations and vectors of patents are adopted to explore the development rules of technology in terms of new technology’s invention perspective; fourth, an approach is raised to assist inventors in designing new technologies through the forward citations of patents, whose vectors have great similarities to the initial ones’; finally, the patents granted by USPTO in the past 20 years are used to verify the effectiveness of our framework.

기술 기반의 서비스 기회 분석 방법: 특허-상표 연계 데이터와 링크 예측 활용

As the importance of technology in services increases, studies that identify technology-driven service opportunities have been conducted. Although patent-based studies have been conducted, they have limitations in obtaining service information due to the dependence on technical data only. Therefore, this study suggests a method to identify technology-driven new service opportunities using patent-trademark linking data and link prediction. First, a technology element network is constructed based on the co-occurrences of patent classification codes for all patents. Second, a set of existing technology elements used for a target service area are identified using the patent-trademark linking database. Third, the method identifies technology elements, which can be newly introduced for the target service, via link prediction. Finally, technology-driven service opportunities are discovered by evaluating the new technology elements with novelty and influence indexes. A case study on educational services is carried out to show the applicability of the proposed method. This method could be used to identify new service opportunities based on technological advances and support establishing policies for service industries.

Cross-Border Dynamics of IP Modularity: International Patenting in LEDs and Lithium-Ion Secondary Battery Technology

To profit from their inventions, multinational enterprises rely on various appropriation and internationalization strategies. Intellectual property modularity serves as a reliable option to master the tradeoff between owning the “right” (i.e., valuable) patents in a technology and sharing other “less valuable” patents to spur innovation and foster technology dissemination. Through an inductive, multi-case approach looking at light-emitting diodes and lithium-ion secondary battery technology, we expand prior intellectual property modularity to incorporate internationalization effects across borders. Relying on patent classifications, we trace the development of these two technologies and key multinational enterprises in various countries longitudinally from 1990 to 2018. We introduce the Dynamic IP Modularity Application Matrix and demonstrate that integrating the firm and country levels yields insights into dynamic internationalization developments, particularly when considering the drawbacks to intellectual property modularity. Herein, decision-makers need to secure not only currently valuable but also potentially valuable intellectual property to successfully apply an international intellectual property modularity value capture strategy.

Asymmetric effects of heterogeneous environmental standards on green technology innovation: Evidence from China

Exploring the asymmetric effects of heterogeneous environmental standards on green technology innovation can enrich the Porter hypothesis and play a vital role in transforming industrial production modes and promoting sustainable development. Based on the Organization for Economic Co-operation and Development Technology Concordance, this study transforms the International Patent Classification (IPC)-based green patent data into provincial-industrial level green patent data during 2006–2015. Treating the implementation of cleaner production standards and pollution emission standards as a quasi-natural experiment, we employ the difference-in-differences (DID) and propensity score matching DID (PSM-DID) models to investigate the asymmetric effects of heterogeneous environmental standards on green technology innovation and examine the heterogeneous effects between different regions and industries. In addition, we use an interaction regression model to identify three influence mechanisms: market competition, foreign direct investment (FDI), and research and development (R&D) inputs. The results show that the green technology innovation effects of heterogeneous environmental standards are asymmetric. Cleaner production standards can significantly promote green technology innovation. However, pollution emission standards produce a significant inhibition effect on green technology innovation. After a series of robustness checks, such asymmetric effects are still evident. Moreover, the green technology innovation effects of cleaner production standards exhibit no heterogeneity at the regional and industrial levels. Nevertheless, the inhibitory effect of pollution emission standards on green technology innovation is confirmed in non-energy-intensive industries rather than in energy-intensive industries, indicating a significant heterogeneity at the industrial level. Concerning the influence mechanism, increasing market competition has advantages and disadvantages for the green technology innovation effects of cleaner production standards in eastern China and midwestern China, respectively. The pollution halo effect of FDI triggered by cleaner production standards exists in eastern China and non-energy-intensive industries. Cleaner production standards can stimulate green technology innovation by increasing R&D inputs. However, under the regulation of pollution emission standards in non-energy-intensive industries, R&D inputs are not conducive to green technology innovation.

Reliable Multi-View Deep Patent Classification

Patent classification has long been regarded as a crucial task in patent information management and patent knowledge mining. In recent years, studies combining deep learning automatic patent classification methods with deep neural networks have significantly increased. Although great efforts have been made in the patent deep classification task, they mainly focus on information extraction from a single view (e.g., title or abstract view), but few studies concern multi-view deep patent classification, which aims to improve patent classification performance by integrating information from different views. To that end, we propose a reliable multi-view deep patent classification method. Within this method, we fuse multi-view patent information at the evidence level from the perspective of evidence theory, which not only effectively improves classification performance but also provides a reliable uncertainty estimation to solve the unreliability of classification results caused by property differences and inconsistencies in the different patent information sources. In addition, we theoretically prove that our approach can reduce the uncertainty of classification results through the fusion of multiple patent views, thus facilitating the performance and reliability of the classification results. The experimental results on 759,809 real-world multi-view patent data in Shanghai, China, demonstrate the effectiveness, reliability, and robustness of our approach.

PEMT: a patent enrichment tool for drug discovery.

Drug discovery practitioners in industry and academia use semantic tools to extract information from online scientific literature to generate new insights into targets, therapeutics and diseases. However, due to complexities in access and analysis, patent-based literature is often overlooked as a source of information. As drug discovery is a highly competitive field, naturally, tools that tap into patent literature can provide any actor in the field an advantage in terms of better informed decision-making. Hence, we aim to facilitate access to patent literature through the creation of an automatic tool for extracting information from patents described in existing public resources. Here, we present PEMT, a novel patent enrichment tool, that takes advantage of public databases like ChEMBL and SureChEMBL to extract relevant patent information linked to chemical structures and/or gene names described through FAIR principles and metadata annotations. PEMT aims at supporting drug discovery and research by establishing a patent landscape around genes of interest. The pharmaceutical focus of the tool is mainly due to the subselection of International Patent Classification codes, but in principle, it can be used for other patent fields, provided that a link between a concept and chemical structure is investigated. Finally, we demonstrate a use-case in rare diseases by generating a gene-patent list based on the epidemiological prevalence of these diseases and exploring their underlying patent landscapes. PEMT is an open-source Python tool and its source code and PyPi package are available at https://github.com/Fraunhofer-ITMP/PEMT and https://pypi.org/project/PEMT/, respectively. Supplementary data are available at Bioinformatics online.

Identifying patent classification codes associated with specific search keywords using machine learning

The purpose of this research is to retrieve relevant patent documents and identify classification codes and search keywords that best characterize a given technological domain found in patent literature. The World Intellectual Property Organization (WIPO) recorded a rising number of patent applications filed under the Patent Cooperation Treaty (PCT) which is becoming the norm for filing patents in multiple jurisdictions. As such, PCT documents are a valuable source of information related to innovation activities with some degree of entrepreneurial intention. However, searching for relevant patent documents can be a daunting and uncertain process. We constructed a high-dimensional matrix consisting of two data types: classification codes and search keywords known as the code-keyword matrix. In turn, two machine learning algorithms called principal components analysis (PCA) and k-means clustering were used to derive insights from the high-dimensional dataset. Consequently, a two-dimensional PCA biplot and clustering on an optimized PCA dataset called Eigen-PCA were obtained using our combined machine learning method. Using such algorithms, we were able to identify correlation relationships found between the two data types. We also clustered the classification codes by least-relevance, medium-relevance, and high-relevance for the domain of anti-corrosion technologies, an impactful area for steel infrastructure in maritime environments. Such patent data analytics can be adapted to other areas such as medical technologies, green energy transition towards Net Zero and conservation of biological diversity.

Pharmacological Activities of the Genus Passiflora (Passifloraceae): A Patent Review.

Passiflora L. is a genus belonging to the Passifloraceae family, with many species widely used in folk medicine and several pharmacological activities described in the scientific literature, being a major target for the development of new therapeutic products. Studies have identified several bioactive compounds in their composition as responsible for these activities, mainly C-glycoside flavonoids. The aim of this study was to carry out a review of patents related to the genus and its application in several pharmacological activities, important for the development of new drugs and formulations. The search was carried out in 5 specialized databases, INPI, EPO, WIPO, Latipat and Derwent, using the term 'Passiflora' combined with 'A61K and A61P', subclasses of section A of the International Patent Classification (IPC), which are destined to medical, dental or hygienic purposes, and therapeutic activity of chemical compounds or medicinal preparation, respectively. 1,198 patents citing the genus in the title or abstract have been found, 508 being duplicates. After exclusion and inclusion criteria, 23 patents written in English, Portuguese and Spanish were selected, which demonstrated biological assays in vivo with species of Passiflora as the only active constituent or incorporated in formulations with other compounds. The findings of this search showed growing interest in research and industrial areas in the pharmaceutical development with species of Passiflora, suggesting that the different bioactive compounds present in the genus can be considered as an important tool for the development of new effective and safe products with pharmacological potential.

Technological prospection of sewage treatment device directed to single-family use

The sewage collection and treatment system are one of the biggest problems of contemporary civilization, likewise, Brazil has a deficit in the proportion of production/collection and treatment, moreover, this senile contributes to the pollution of the environment and the proliferation of diseases, since waste is released in nature in the streets and river beds in regions where there is no sewage network. Based on this problem, this work aims to prospectively survey patents related to devices that present sewage treatment technology. For this purpose, the European Patent Office (ESPACENET) database was used. The prospecting was based on keywords and international classification of patents and resulted in 10796 records, of which 75% refer to IPC C02F9, which encompasses technologies covering the multi-stage treatment of water, waste water or sewage.

Identifying firm-specific technology opportunities in a supply chain: Link prediction analysis in multilayer networks

Although technology opportunity discovery (TOD) for a target firm has considered the firm’s internal characteristics such as technological capabilities and R&D directions, the firm’s external environment, especially the supply chains it is in, is generally ignored. This paper proposes a novel approach to identifying firm-specific technology opportunities in each echelon of a multi-echelon supply chain, with the interactions among the technologies of different echelons considered. The proposed approach comprises four steps: 1) dividing the supply chain echelons and collecting patents for each echelon; 2) constructing a firm-centered International Patent Classification (IPC) multilayer network of the multi-echelon supply chain; 3) recommending IPC codes in each echelon of the supply chain for the target firm using a link prediction algorithm; and 4) evaluating technology opportunities to support the firm’s R&D decisions with the indicators of technology importance, technology maturity, and technology impact. Using the General Motors Company (GM) in the fuel cell vehicle (FCV) supply chain as a case study, it is verified that the method can identify technology opportunities for GM efficiently and effectively. This work proposes an innovative method that identifies firm-specific technology opportunities from a supply chain perspective, and pioneers to apply multilayer networks to TOD.

Identifying Trends and Quantifying Growth for Technological Innovation in Knee Arthroplasty: An Analysis of a Patent Database (1990 to 2020).

Technological innovation is the key for surgical progress in knee arthroplasty and improvement in patient outcomes. Exploring patented technologies can help elucidate trends and growth for numerous innovative technologies. However, patent databases, which contain millions of patents, remain underused in arthroplasty research. Therefore, the present study aimed to: (1) quantify patent activity; (2) group patents related to similar technologies into well-defined clusters; and (3) compare growth between technologies in the field of knee arthroplasty over a 30-year period. An open-source international patent database was queried from January 1990 to January 2020 for all patents related to knee arthroplasty A search strategy identified 70,154 patents, of which 24,425 were unique and included analysis. Patents were grouped into 14 independent technology clusters using Cooperative Patent Classification (CPC) codes. Patent activity was normalized via a validated formula adjusting for exponential growth. Compound annual growth rates (CAGR) were calculated (5-year, 10-year, and 30-year CAGR) and compared for each cluster. Overall yearly patent activity increased by 2,023%, from 104 patents in 1990 to 2,208 patents in 2020. The largest technology clusters were "drugs" (n = 5,347; 23.8%), "components" (n = 4,343; 19.0%), "instruments" (n = 3,130; 13.7%), and "materials" (n = 2,378; 10.4%). The fastest growing technologies with their 5-year CAGR were: "user interfaces for surgical systems" (58.1%); "robotics" (28.6%); "modularity" (21.1%); "navigation" (15.7%); and "computer modeling" (12.5%). Since 1990, overall patent growth rate has been greatest for "computer modeling" (8.4%), "robotics" (8.0%), "navigation" (7.9%), and "patient-specific instrumentation" (6.4%). Most patents in knee arthroplasty for the last 30 years have focused on drugs, components, instruments, and materials. Recent exponential growth was mainly observed for user interfaces for surgical systems, robotics, modularity, navigation, and computer-assisted technologies. Innovation theory would suggest that these rapidly growing technologies are experiencing high innovation output, increased resource investments, growing adoption by providers, and significant clinical impact. Periodic monitoring of technological innovation via patent databases can be useful to establish trends and future directions in the field of knee arthroplasty.

An Analysis of Aconiti Ciliare Radix Patents

Objectives: The purpose of this study is to investigate and analyze Korean domestic patents on aconiti ciliare radixand to identify the trend of aconitum tuber technology.Methods: To analyze the patent, a combinations of words such as “aconitum” or “korean aconite root” were usedin search engine Kipris(www.kirpis.or.kr). The patents of aconiti ciliare radix were analyzed in three ways: year trendanalysis, internatonal patent classification (IPC) code analysis related to content classification, and patent registrationstatus analysis.Results: Among the patents found in the search results, 17 patents with significant contents were analyzed. Resultsshowed that, first, patents were steadily registered until 2018, but recently there has been no new patent registration.Second, there were many patents related to efficacy verification and decoction method, and the number of IPC codesrelated to them was also high. Third, there are five patents maintaining the registration status, and they are patentsrelated to the aconiti ciliare radix extraction method, toxicity removal, and combination method.Conclusions: In this study, the domestic patents of aconiti ciliare radix were analyzed. The analysis results of thisstudy are expected to be exploited as basic data for the development of Korean medicine analgesics with fewer sideeffects by suppressing tuber toxicity and the creation of new medical technologies.

See You in Your Backyard: Multipoint Contact, Firm’s Capacity and Capability, and Technological Expansion

Past studies of multipoint competition have mainly focused on the effect of multipoint contact in product markets on firm competitive behaviors in those markets only. Our study advances this literature by examining how multipoint contact in product markets affects a firm’s expansion into a rival’s technological areas. We further investigate the factors that moderate the proposed effect. Our analyses suggest that the degree of multipoint contact between a firm and a rival in product markets has an inverted U-shaped relationship with the degree of the firm’s expansion into the rival’s technological areas (patent classes). Furthermore, the firm’s capacity (market share) and capability (status in terms of technological development) relative to the rival’s have different moderating effects on the relationship between multipoint contact in product markets and the firm’s expansion into the rival’s technological areas. Our findings contribute to the literatures on multipoint contact, competitive dynamics, and firm technology strategies.

Cellulose-Based Hydrogels: Patent Analysis

Background: Among biopolymers, cellulose and its derivatives are the most commonly used for hydrogel formulations. The innovation and improvement of cellulose-based hydrogels concerned the raw materials, synthesis and methods of preparation, formulations and fabrication processes, as well as applications.
 Objectives: This study, in the form of patent analysis, presents the state by introducing what has been innovated and patented concerning cellulose-based hydrogels.
 Methods: Three databases have been used in this study: the Patentscope, the Espacenet, and the Lens patent data set. A detailed analysis has been provided regarding publication dates, patent families, jurisdictions, inventors, applicants, owners, and patent classifications.
 Results: A total of 8053 patent documents related to cellulose-based hydrogels have been published between 1965 and 2021. The United States leads the patent race in this sector, and the Massachusetts Institute of Technology is one of the top academic applicants.
 Conclusion: Based on patent classifications, most patent documents are related to medicinal preparations characterized by special physical forms. More specifically, the classification concerns materials for prostheses or coating prostheses, including cellulose derivatives characterized by their function or physical properties, such as macromolecular gels, hydrogels, or hydrocolloids.

Japanese industrial technological structure based on the number of patent applications by technical field

PurposeThis research outlines the technological structure of the entire Japanese manufacturing and service industry using the patent information from research and development (R&D) activities to set R&D goals.Design/methodology/approachBy analyzing the technological development capability of individual companies, the direction of the companies' R&D activities and current state of technological fusion between them can be understood. A group of companies participating in a particular product/service market must have the same technological development capabilities. As a result, the ratio of patent applications by a company to the total number of applications in a technical field will be similar across companies. This study uses the inter-company correlation coefficient of the ratio of patent applications by technical field as an index of technological development capability. A total of 167 major companies covering the major industries of Japan were analyzed. The analysis period was 15 years from 2004 to 2018, and the technical fields were rearranged to 42 fields with reference to the International Patent Classification (IPC)-Technology Concordance used by the World Intellectual Property Organization (WIPO). Considering the fluctuation in patent application opportunities, the number of patent applications was collected for at least three years for the analysis of patent applications by technical field, company and industry.FindingsExamining the entire Japanese industry, the research found that chemicals, ceramics, non-ferrous metals and electrical/electronic equipment act as intermediaries between the respective groups and are linked to the transportation equipment, electrical/electronic equipment and information and communication services industries that are currently driving the Japanese economy. However, the technical connections between these groups are relatively loose. Over the last 15 years, the propagation structure of technical knowledge information has not changed. The progress of technological fusion remains within the scope of commerce and is conditioned by commerce.Originality/valueStudies focusing on the technological development capability between companies and the technological structure of the Japanese manufacturing and service industries are almost non-existent since 2000 when Japan's economic growth slowed. The analytical methods presented in this research can be applied to individual companies to gain an understanding of technical positions of companies and can be useful for planning a technical environment, business or R&D strategy.

Two-stage technology opportunity discovery for firm-level decision making: GCN-based link-prediction approach

In this study, we propose a graph convolution network (GCN)-based patent-link prediction to predict technology convergence. We address the limitations of previous works, which neglect both the global information of a convergence network and the node features. We employ three features: GCN node features to represent global information, node features to characterize what kinds of information they have and how they are similar, and edge similarity to represent how frequently the two nodes are connected. Considering three categories of information, we conduct link prediction using machine learning (ML) to identify potential opportunities. To identify areas of technology convergence, we also support firm-level decision making using portfolio analysis. This study consists of two main stages: opportunity discovery which employs both GCN-based link prediction and ML, and opportunity validation which evaluates whether the identified technology opportunities are suitable from the firm's perspective. A case study is conducted for the mobile payment industry. A total of 17,540 patent documents with 36,871 positive links are used for GCN link prediction and ML. As a result of firm-level opportunity validation, a total of 395 cooperative patent classifications (CPC) were predicted to be possibly linked with 32 current CPCs of the target firm. The contributions come from two main aspects. From a theoretical perspective, this study employs GCN and node features to reflect the global graph structure for technology convergence. From a practical perspective, this study suggests how to validate the identified opportunities for firm-level applications.

A Use Case of Patent Classification Using Deep Learning with Transfer Learning

Abstract Purpose Patent classification is one of the areas in Intellectual Property Analytics (IPA), and a growing use case since the number of patent applications has been increasing worldwide. We propose using machine learning algorithms to classify Portuguese patents and evaluate the performance of transfer learning methodologies to solve this task. Design/methodology/approach We applied three different approaches in this paper. First, we used a dataset available by INPI to explore traditional machine learning algorithms and ensemble methods. After preprocessing data by applying TF-IDF, FastText and Doc2Vec, the models were evaluated by cross-validation in 5 folds. In a second approach, we used two different Neural Networks architectures, a Convolutional Neural Network (CNN) and a bi-directional Long Short-Term Memory (BiLSTM). Finally, we used pre-trained BERT, DistilBERT, and ULMFiT models in the third approach. Findings BERTTimbau, a BERT architecture model pre-trained on a large Portuguese corpus, presented the best results for the task, even though with a performance of only 4% superior to a LinearSVC model using TF-IDF feature engineering. Research limitations The dataset was highly imbalanced, as usual in patent applications, so the classes with the lowest samples were expected to present the worst performance. That result happened in some cases, especially in classes with less than 60 training samples. Practical implications Patent classification is challenging because of the hierarchical classification system, the context overlap, and the underrepresentation of the classes. However, the final model presented an acceptable performance given the size of the dataset and the task complexity. This model can support the decision and improve the time by proposing a category in the second level of ICP, which is one of the critical phases of the grant patent process. Originality/value To our knowledge, the proposed models were never implemented for Portuguese patent classification.

Investigating the Structure of the Internet of Things Patent Network Using Social Network Analysis

The Internet of Things (IoT) makes the world around us more intelligent and more responsive by developing cyber–physical systems. The IoT is about to change how we live and how we work. It is essential to have a perspective on this technology’s innovation path to creating more novel applications. Due to increasing Internet connectivity through smartphones, IoT is expected to be an all-pervasive technology, and lately, its patents have grown significantly. Patents contain valuable information, especially including the path of utilizing IoT in different fields. Patent analysis provides valuable information about innovation in IoT technology. This article performs a patent analysis based on the international patent classification (IPC) co-occurrence network in different patents. This network is analyzed by social network analysis (SNA) algorithms. In addition, the most critical nodes are identified by SNA measures and the TOPSIS method. The findings illustrate the structure of the innovation path of IoT, and they will help create more applications and combinations of IoT with other fields. These results provide a guideline that shows how steps for innovation should start.