International Patent Classification Research Articles

Laser-based disassembly of end-of-life automotive traction batteries: A systematic patent analysis

Battery recycling is a vital component in the decarbonization of the transportation sector. The disassembly step represents a pivotal element within the process chain for battery recycling, as it paves the way for subsequent steps. Semi-destructive disassembly technologies will be key to developing efficient disassembly processes for end-of-life automotive traction batteries. Laser-based separation technologies have emerged as a promising emerging technological approach. Following a systematic search in multiple databases, the obtained records underwent a comprehensive descriptive analysis in terms of both regional and historical context, while key inventors and assignees were identified. Furthermore, citation networks have been identified to provide an overview of the patent landscape and the interrelations within this field. Moreover, an analysis concerning the International Patent Classification and the Cooperative Patent Classification was conducted. An in-depth analysis of relevant patents provides insights into the specific techno-strategic elements currently pursued. The study yields an implication and recommendation framework for research, industry, and policy makers to drive the industrialization of laser-based disassembly technologies as an avenue for efficient end-of-life automotive traction battery disassembly of the future. This framework enables relevant stakeholders to drive their techno-strategic developments in the field of laser-based disassembly, resulting in enhanced laser-based semi-destructive disassembly technologies.

Assessing Scientific Text Similarity: A Novel Approach Utilizing Non-Negative Matrix Factorization and Bidirectional Encoder Representations from Transformer

The patent serves as a vital component of scientific text, and over time, escalating competition has generated a substantial demand for patent analysis encompassing areas such as company strategy and legal services, necessitating fast, accurate, and easily applicable similarity estimators. At present, conducting natural language processing(NLP) on patent content, including titles, abstracts, etc., can serve as an effective method for estimating similarity. However, the traditional NLP approach has some disadvantages, such as the requirement for a huge amount of labeled data and poor explanation of deep-learning-based model internals, exacerbated by the high compression of patent content. On the other hand, most knowledge-based deep learning models require a vast amount of additional analysis results as training variables in similarity estimation, which are limited due to human participation in the analysis part. Thus, in this research, addressing these challenges, we introduce a novel estimator to enhance the transparency of similarity estimation. This approach integrates a patent’s content with international patent classification (IPC), leveraging bidirectional encoder representations from transformers (BERT), and non-negative matrix factorization (NMF). By integrating these techniques, we aim to improve knowledge discovery transparency in NLP across various IPC dimensions and incorporate more background knowledge into context similarity estimation. The experimental results demonstrate that our model is reliable, explainable, highly accurate, and practically usable.

Adaptive Taxonomy Learning and Historical Patterns Modeling for Patent Classification

Patent classification aims to assign multiple International Patent Classification (IPC) codes to a given patent. Existing methods for automated patent classification primarily focus on analyzing the text descriptions of patents. However, apart from the textual information, each patent is also associated with some assignees, and the knowledge of their previously applied patents can often be valuable for accurate classification. Furthermore, the hierarchical taxonomy defined by the IPC system provides crucial contextual information and enables models to leverage the correlations between IPC codes for improved classification accuracy. However, existing methods fail to incorporate the above aspects and lead to reduced performance. To address these limitations, we propose an integrated framework that comprehensively considers patent-related information for patent classification. To be specific, we first present an IPC codes correlations learning module to capture both horizontal and vertical information within the IPC codes. This module effectively captures the correlations by adaptively exchanging and aggregating messages among IPC codes at the same level (horizontal information) and from both parent and children codes (vertical information), which allows for a comprehensive integration of knowledge and relationships within the IPC hierarchical taxonomy. Additionally, we design a historical application patterns learning component to incorporate previous patents of the corresponding assignee by aggregating high-order temporal information via a dual-channel graph neural network. Finally, our approach combines the contextual information from patent texts, which encompasses the semantics of IPC codes, with assignees’ sequential preferences to make predictions. Experimental evaluations on real-world datasets demonstrate the superiority of our proposed approach over existing methods. Moreover, we present the model’s ability to capture the temporal patterns of assignees and the semantic dependencies among IPC codes.

Cross-National Comparison of Technology Innovation Capabilities in Automation and Robotics

In today’s dynamic and global economic environment, technological innovations are a key determinant of international competitiveness. In the field of automation and robotics, the development of modern technologies is of particular significance, influencing not only industrial progress but also social aspects. Automation and robotics play a crucial role in the concept of modern industry, contributing to the creation of more sustainable, flexible, and human-centric production systems. The article aims to conduct an in-depth analysis of the patent activity in automation and robotics in Europe. Patent documentation serves as a source of knowledge about the directions of research, inventive activities, and consequently, the innovative and competitive potential of the economy. The number of patents over time reflects the dynamics of a country’s technological development. The article discusses the use of patent databases to evaluate a country’s innovation in automation and robotics. The conducted analysis of data on European countries enabled the identification of trends based on the International Patent Classification and specialisation from a geographical perspective using revealed comparative technological advantage. The European countries were classified using cluster analysis, demonstrating the diversity, and identifying leaders in each group.

A transfer learning based patent transfer prediction model: Discussion about the condition of China and U.S.

In the same patent classification, there are significant differences in the patent transfer quantity for different countries. For the countries with small patent transfer quantities, the performance of the patent transfer prediction is worse than that of high-transfer countries. To improve the performance of the patent prediction model learned for the low-transfer countries, we propose a transfer learning based patent transfer prediction model, for which the high-transfer countries are the source domain and the low-transfer countries are the target domain. In the model, the textual feature of each patent text is embedded using BERT-CNN with the parameters fine-tuned on the target domain from the source domain. The textual features are optimized to minimize the domain loss of Maximum Mean Discrepancy between source and target domain. What is more, we extract graph feature vectors by embedding the patent node in a three-layer heterogeneous network which consists of patent nodes, International Patent Classification nodes, consumer nodes, consumer location nodes and their relations. Finally, the patent text vectors, structural feature vectors, and graph feature vectors are spliced together to predict target domain patents transfer or not. The performance of the model is verified by some comparative experiments. The experimental results show that the proposed model outperforms significantly the baseline models in the values of Precision, Recall, and F1 of patent data improve by 6.47%, 6.54%, and 6.58% on average, respectively.

Exploring Electric Vehicle Patent Trends through Technology Life Cycle and Social Network Analysis

In response to environmental and energy challenges, electric vehicles (EVs) have re-emerged as a viable alternative to internal combustion engines. However, existing research lacks a comprehensive analysis of the technology life cycle of EVs in both global and South Korean contexts and offers limited strategic guidance. This study introduces a novel approach to address these gaps by integrating the S-curve model with social network analysis (SNA), time series analysis, and core applicant layouts. The study specifically utilizes the logistic curve to model technology growth. It applies SNA methods, including International Patent Classification (IPC) co-occurrence analysis and the betweenness centrality metric, to identify the stages of technological development and sustainable research directions for EVs. By analyzing patent data from 2004 to 2023, the study reveals that EV technologies have reached the saturation phase globally and in South Korea, with South Korea maintaining a two-year technological advantage. The research identifies sustainable research directions, including fast charging technology and charging infrastructure, battery monitoring and management, and artificial intelligence (AI) applications. Additionally, the study also determined the sustainability of these research directions by examining the sustainability challenges faced by EVs. These insights offer a clear view of EV technology trends and future directions, guiding stakeholders.

Mapping the Nanotechnology Patent Landscape in the Field of Cancer.

Currently, cancer is still a significant disease that seriously endangers human health. Therefore, advanced diagnostic technology and treatment protocols are urgently needed. The rapid development of nanotechnology is expected to provide new ideas for cancer diagnosis and treatment. The research aims to comprehensively demonstrate the hotspots of nanotechnology applications in cancer. In this study, an International Patent Classification codes co-occurrence network is constructed to visualize the technology landscape by simultaneously locating and ranking technologies that play an integral role in nanotechnology diffusion and bridging in the field of cancer. In addition, community identification and topic modeling highlight the latent topics in patent documents. The visualization results of the patent network yield five main clusters: Cluster 0 is a nanoparticle composition delivery system with liposomes as the primary carrier. Cluster 1 is mainly represented by nano-immunotherapy with immune checkpoint inhibitors. Cluster 2 is nano phototherapy based on photodynamic therapy and photothermal therapy. Cluster 3 is diagnostic imaging involving nanotechnology. Cluster 4 is a drug delivery system with nanovesicles and albumin nanoparticles as carriers. It was found that carriers represented by liposomes, vesicles, and albumin nanoparticles are essential nanomaterials in the current anticancer drug delivery systems. Integrating next-generation immunosuppressants and nanotechnology will become an important development direction for future immunotherapy. Organic/inorganic nanomaterials are pivotal in cancer imaging diagnosis and phototherapy.

Patent status of biodegradable polymers and identification of new application areas by IPC network analysis

Biodegradable polymers (BDPs), due to their degradability, have great merits for applications in the environmental fields compared to conventional polymers. Furthermore, they are recently receiving renewed attention with the rise of globally issued environmental problems caused by plastic wastes. However, BDPs are still produced and utilized with limited amounts and applications in comparison to conventional plastics. To help expand the application area of BDPs, we mainly focus on the emerging and exotic technologies of BDPs by analyzing International Patent Classification (IPC) co-classification networks constructed from 2,862 patents on BDPs filed with the US Patent and Trademark Office from 2006 to 2022. As a result, by detecting both recently appearing and isolated IPCs, new and relatively unknown application areas of BDPs such as animal trap, polishing composition, rope lubricant, underground structure, ammunition, digital data carrier are identified. Our IPC analysis methodology studied in the field of BDPs would be also useful for researchers and entrepreneurs searching new and emerging applications in various technology areas.

Examining the Global Patent Landscape of Artificial Intelligence-Driven Solutions for COVID-19

Artificial Intelligence (AI) technologies have been widely applied to tackle Coronavirus Disease 2019 (COVID-19) challenges, from diagnosis to prevention. Patents are a valuable source for understanding the AI technologies used in the COVID-19 context, allowing the identification of the current technological scenario, fields of application, and research, development, and innovation trends. This study aimed to analyze the global patent landscape of AI applications related to COVID-19. To do so, we analyzed AI-related COVID-19 patent metadata collected in the Derwent Innovations Index using systematic review, bibliometrics, and network analysis., Our results show diagnosis as the most frequent application field, followed by prevention. Deep Learning algorithms, such as Convolutional Neural Network (CNN), were predominantly used for diagnosis, while Machine Learning algorithms, such as Support Vector Machine (SVM), were mainly used for prevention. The most frequent International Patent Classification Codes were related to computing arrangements based on specific computational models, information, and communication technology for detecting, monitoring, or modeling epidemics or pandemics, and methods or arrangements for pattern recognition using electronic means. The most central algorithms of the two-mode network were CNN, SVM, and Random Forest (RF), while the most central application fields were diagnosis, prevention, and forecast. The most significant connection between algorithms and application fields occurred between CNN and diagnosis. Our findings contribute to a better understanding of the technological landscape involving AI and COVID-19, and we hope they can inform future research and development’s decision making and planning.

СРАВНИТЕЛЬНЫЙ ОБЗОР РЕЗУЛЬТАТОВ ИНТЕЛЛЕКТУАЛЬНОЙ ДЕЯТЕЛЬНОСТИ РОССИЙСКИХ УЧЕНЫХ ПО ВЫЯВЛЕНИЮ ИНСАЙДЕРОВ В ОРГАНИЗАЦИЯХ

The work is devoted to the task of detecting insiders in an organization, thereby increasing the information security of its resources. To do this, a review of the results of intellectual activity (certificates of state registration of programs and databases, patents for utility models) of Russian scientists in the Russian science citation indexwas carried out using the keywords «insider (as noun)» (7 publications) and «insider (as adjective)» (10 publications). All solutions found are systematized in tabular form according to criteria such as year of publication, type, programming language, international patent classification and scope. A number of the following fundamental conclusions have been made: demand for practical solutions, a slight increasing trend in the relevance of the problem, the preference for using the programming languages «C++» and «C#», patenting mainly solutions for theft alarms, the main application in the field of assessing the personal and behavioral characteristics of employees.

Reviewing twenty years of patents on ultrasonic-assisted pectin extraction from food and food waste

A growing number of alternative agro-industrial waste sources are being considered as possible sources of pectin because of the growing market demand. Pectin's multifunctionality stems from the nature of its molecule, which varies in its chemical structures, physicochemical characteristics, and possible uses based on the extraction methods and sources used. Green methods utilizing microwaves and ultrasound are becoming more popular, in addition to the traditional extraction of pectin, which relies on the use of acids and/or chelators. As a result, this review focuses on patents related to the extraction of pectin from food and food waste using ultrasound assisted technologies. The following patent databases were searched using International Patent Classification (IPC) codes and relevant keywords: PatFT-AppFT, Espace Patent Search, Information Retrieval System, Eurasian Patent Office Search, and Patentscope. Based on the analysis findings, which cover the years 2003 to 2023, 54 pertinent documents—the majority of which were issued in China—were chosen and categorized into multiple groups based on their objectives, methods for extraction intensification, and raw materials. According to the data, 2017 had the greatest number of patent registrations in the industry.

Emerging Industrial Internet of Things Open-Source Platforms and Applications in Diverse Sectors

Revolutionary advances in technology have been seen in many industries, with the IIoT being a prime example. The IIoT creates a network of interconnected devices, allowing smooth communication and interoperability in industrial settings. This not only boosts efficiency, productivity, and safety but also provides transformative solutions for various sectors. This research looks into open-source IIoT and edge platforms that are applicable to a range of applications with the aim of finding and developing high-potential solutions. It highlights the effect of open-source IIoT and edge computing platforms on traditional IIoT applications, showing how these platforms make development and deployment processes easier. Popular open-source IIoT platforms include DeviceHive and Thingsboard, while EdgeX Foundry is a key platform for edge computing, allowing IIoT applications to be deployed closer to data sources, thus reducing latency and conserving bandwidth. This study seeks to identify potential future domains for the implementation of IIoT solutions using these open-source platforms. Additionally, each sector is evaluated based on various criteria, such as development requirement analyses, market demand projections, the examination of leading companies and emerging startups in each domain, and the application of the International Patent Classification (IPC) scheme for in-depth sector analysis.

Analysis on patent layout of silicon carbide ceramic membrane technology in global

Silicon carbide ceramic membranes are known as a new generation of membrane products due to their excellent chemical stability and high flux advantages. This article, based on the PatSnap patent analysis, analyzes the global patents of silicon carbide ceramic membranes, preparation methods, devices and equipment, application-related patents, and applicants from perspectives of patent applications, patent layouts, and international patent classification technical categories. It concludes that the technological hotspots in the field of silicon carbide ceramic membranes are concentrated on improving separation efficiency through separation applications, providing a reference for the technical improvement, application, and patent layout of silicon carbide ceramic membranes.

A data set of Japanese corporates’ green transformation technologies patents

Abstract* In 2020, the Government of Japan declared 2050 “carbon neutral” and launched a long-term strategy to create a “virtuous cycle of economy and environment.” Japanese corporations possess many technologies that contribute to decarbonization, which is important for expanding investments in green transformation technology inventory (GXTI) development. Patent data are the most reliable measure of business performance for applied research and development activities when investigating knowledge domains or technological evolution. Our paper describes a Japanese patent dataset of Japanese corporations’ green transformation (GX) patent applications on the Japan Platform for Patent Information, using a search method of bombinating International Patent Classification (IPC) codes and keywords. The dataset contains 37,476 GX patent applications from 298 corporations during the period 1999–2022.

Unveiling predictors influencing patent licensing: Analyzing patent scope in robotics and automation

Ensuring the sustainability of technology transfer offices depends on effective patent licensing strategies. This study investigates novel predictors for patent licensing prediction. It emphasizes the importance of judiciously selecting suitable patent-scope metrics to enhance the likelihood of successful patent licensing agreements. This work focuses on a critical aspect of patent scope, specifically examining the number of independent claims, the length of the first claim, the depth of the claim, the Cooperative Patent classification count, the non-US family count, and the family independent count. Additionally, we consider conventional metrics previously investigated in prior research, such as claim count, the count within the International Patent Classification, and Simple Family Application. Our empirical analysis harnesses a dataset comprising patents from university technology transfers within the robotics and automation domain. We analyze the relationship between patent scope measures and licensing outcomes using data visualization and statistical techniques, including the point-biserial correlation coefficient and the t-test. Comparative analysis of the statistical results is performed to identify the most impactful predictor. Our study reveals a correlation between the number of independent claims and the success of patent licensing. In contrast, the rest of the investigated measures do not impact the success of patent licensing.

Plant-based compositions for the treatment of local and topical bacterial infections: a patent review.

Aim: To examine recent patents related to plants with antimicrobial effects. Methods: Ourinvestigation was carried out using theEspacenet database, searching patents related to International Patent Classification (IPC) codes A61P31/04, and A61K36/00 between2008and2023. Results: The study began with 360 patents, of which 15 were found to be duplicates; 144 were not related to the research topicand six were not available. Then, 48 patentswere related to other administration routes. The final selection was 47 patents related to local administration compositions. The potential for these herbal compositions to be marketed as over-the-counter medicines was evident. Conclusion: Developing new plant-origin anti-infective agents could provide promising solutions for combating resistant infections and offer accessible treatment options.

Exploring the potentials of artificial intelligence towards carbon neutrality: Technological convergence forecasting through link prediction and community detection

To explore future opportunities for artificial intelligence (AI) in achieving carbon neutrality goals, this study introduces a forecasting framework that combines link prediction and community detection. The proposed framework begins by utilizing patent information and the International Patent Classification to construct a co-occurrence network. Subsequently, it is computed and validated using two emerging technology datasets: one related to AI towards carbon neutrality and the other to Virtual Reality. A feature selection method and classification model are employed in this process. Finally, predicted convergence links are clustered using network modularity detection to unveil future trends. Through a comprehensive comparison of various similarity indicators in link prediction, this study identifies local indices (Resource Allocation, Adam-Adar) and global indices (Rooted Pagerank, SimRank) as the most featured prediction indicators. These research findings address a gap in the existing literature by providing insights into the rationale behind the selection of link similarity indicators, thereby resolving the issue of randomness in indicator selection. This study also finds that among all single and ensemble models, tree-based models, particularly Random Forest, emerge as the best-performing classifiers. The predicted results suggest that AI technologies towards carbon neutrality can be categorized into two future trends: theoretical methodological foundations and application sectors. This enables the identification and recommendation of future technological potentials on both micro and macro levels. Specifically, it facilitates the discovery of the most promising subdivision technology fields in the future, operating at the microlevel of patent main grouping, often characterized by significant cross-disciplinary links, and at the macrolevel, by visualizing potential technological convergence networks and clustering the trends. The analytical processes and quantitative outcomes of this study can support strategic decision-making for AI, offering valuable insights for both policy-makers and practitioners in their pursuit of green innovation solutions towards carbon neutrality.

LINKAGE BETWEEN UNIVERSITY SCIENTIFIC AREAS AND ECONOMIC ACTIVITIES

Purpose: This paper aims to establish a methodology that indicates economic activities able to absorb patents related to the main scientific areas of the Federal University of Rio Grande do Sul (UFRGS).
 
 Methodology: Two steps were important to development this methodology. The first one was to range the UFRGS patent according their degree of importance amongst the knowledge area. For this, the International Patent Classification (IPC) was the reference to organizer the patents. The second one was indicated the industries which have more potential to absorb the UFRGS patents. For this, it was used the Algorithmic Links with Probabilities approach, developed by Lybbert and Zolas (2014).
 
 Findings: It was found that 59% of UFRGS’ patents were allocated in just ten IPC subclasses, and at least 63% of these patents refer to technological inventions in the chemistry field. The technological core of UFRGS’ patents, therefore, lies in chemical science. This demonstrates that UFRGS’ patents focus on specific and specialist areas, and that they have significant potential for being absorbed by those economic activities whose products are drugs, therapeutic treatment, beverages, vaccines and insecticides.
 
 Practical Implications: This study may help the universities select both patents and priority industries in order to transfer their technology.
 
 Originality: It is constructed an original methodology which contribute to find a market to the university innovation. Furthermore, the developed methodology can be replicated in any other TICs which have patents.

The spatial and temporal situation of China's digital technology innovation and its influencing factors.

Digital technology innovation is the core driving force for the high-quality development of the digital economy, and in-depth exploration of the regional distribution pattern and formation mechanism of digital technology innovation in China is conducive to the rational layout and coordinated development of the inter-provincial digital economy. Based on the Reference Relationship Table of the Classification of Core Industries of Digital Economy and the International Patent Classification (2023), the patent authorization data of digital technology from 2012 to 2022 were obtained, and the spatiotemporal situation of China's digital technology innovation was analyzed by using ArcGIS software, Dagum's Gini coefficient, and Moran's I index, and the spatial Dubin panel model was used to explore the influencing factors of digital technology innovation. It is found that: (1) the scale and vitality of China's digital technology innovation have increased significantly, and there are obvious spatial differentiation characteristics, and the innovation level of "eastern coastal-central and western interior" is decreasing, forming a cluster distribution pattern in the Yangtze River Delta region, Beijing, Guangdong, and other places, and the degree of agglomeration is decreasing. (2) The overall regional differences in China's digital technology innovation are large, the differences between the East and the West dominate the interregional differences, and the net differences between regions are the main factors leading to regional differences. (3) There is a significant positive spatial correlation between the scale and vitality of digital technology innovation, which has a significant spatial spillover effect. (4) The results confirm that the level of economic development, digital access, financial scientific and technological support, technology market development level, and R&D intensity have a significant positive impact on the scale and vitality of digital technology innovation; The investment in scientific and technological talents has a significant positive impact on the scale of digital technology innovation, but has no significant impact on the vitality of digital technology innovation.

An Approach to Construct Technological Convergence Networks Across Different IPC Hierarchies and Identify Key Technology Fields

The technological convergence network (TCN) is an effective method to identify the advancement of technology convergence. However, the previous TCN investigations are limited to a single level of International Patent Classification (IPC) rather than different IPC hierarchies, which can only provide decision support for policy makers with one dimension instead of various ones. In this article, we propose a new approach to construct TCNs across different IPC hierarchies based on technology coclassification analysis, and further identify key technology fields by employing the indicator of betweenness centrality in the TCNs from any IPC hierarchy. This article makes two important contributions. First, theoretically, our article is to contribute to understanding the advancement of technological convergence from various IPC hierarchies, rather than a single IPC level. Second, methodologically, the new approach we propose can benefit decision makers serving at various levels of technology management agencies. We conclude possible implications and future directions.