A Semantic-based Intellectual Property Management System (SIPMS) for supporting patent analysis

Patent databases provide valuable information for technology management. However, the rapid growth of patent documents, the lengthy text and the rich of content in technical terminology, and the complicated relationships among the patents, make it taking a lot of human effort for conducting analyses. As a result, an automated system for assisting the inventors in patent analysis as well as providing support in technological innovation is in great demand. In this paper, a Semantic-based Intellectual Property Management System (SIPMS) has been developed for supporting the management of intellectual properties (IP). It incorporates semantic analysis and text mining techniques for processing and analyzing the patent documents. The method differentiates itself from the traditional technological management tools in its knowledge base. Instead of eliciting knowledge from domain experts, the proposed method adopts global patent databases as sources of knowledge. The system enables users to search for existing patent documents or relevant IP documents which are related to a potential new invention and to support invention by providing the relationships and patterns among a group of IP documents. The method has been evaluated by benchmarking with the performance against traditional text mining technique and has successfully been implemented at a selected reference site.     

Document clustering method using dimension reduction and support vector clustering to overcome sparseness

Many studies on developing technologies have been published as articles, papers, or patents. We use and analyze these documents to find scientific and technological trends. In this paper, we consider document clustering as a method of document data analysis. In general, we have trouble analyzing documents directly because document data are not suitable for statistical and machine learning methods of analysis. Therefore, we have to transform document data into structured data for analytical purposes. For this process, we use text mining techniques. The structured data are very sparse, and hence, it is difficult to analyze them. This study proposes a new method to overcome the sparsity problem of document clustering. We build a combined clustering method using dimension reduction and K-means clustering based on support vector clustering and Silhouette measure. In particular, we attempt to overcome the sparseness in patent document clustering. To verify the efficacy of our work, we first conduct an experiment using news data from the machine learning repository of the University of California at Irvine. Second, using patent documents retrieved from the United States Patent and Trademark Office, we carry out patent clustering for technology forecasting.     

Development of a patent document classification and search platform using a back-propagation network

In order to process large numbers of explicit knowledge documents such as patents in an organized manner, automatic document categorization and search are required. In this paper, we develop a document classification and search methodology based on neural network technology that helps companies manage patent documents more effectively. The classification process begins by extracting key phrases from the document set by means of automatic text processing and determining the significance of key phrases according to their frequency in text. In order to maintain a manageable number of independent key phrases, correlation analysis is applied to compute the similarities between key phrases. Phrases with higher correlations are synthesized into a smaller set of phrases. Finally, the back-propagation network model is adopted as a classifier. The target output identifies a patent document’s category based on a hierarchical classification scheme, in this case, the international patent classification (IPC) standard. The methodology is tested using patents related to the design of power hand-tools. Related patents are automatically classified using pre-trained neural network models. In the prototype system, two modules are used for patent document management. The automatic classification module helps the user classify patent documents and the search module helps users find relevant and related patent documents. The result shows an improvement in document classification and identification over previously published methods of patent document management.     

Searching in Cooperative Patent Classification: Comparison between keyword and concept-based search

International patent corpus is a gigantic source containing today about 80 million of documents. Every patent is manually analyzed by patent officers and then classified by a specific code called Patent Class (PC). Cooperative Patent Classification CPC is the new classification system introduced since January 2013 in order to standardize the classification systems of all major patent offices. Like keywords for papers, PCs point to the core of the invention, describing concisely what they contain inside. Most of patents strategies are based on PC as filter for results therefore the selection of relevant PCs is often a primary and crucial activity. This task is considered particularly challenging and only few tools have been specially developed for this purpose. The most efficient tools are provided by patent offices of EPO and WIPO.This paper analyzes their PCs search strategy (mainly based on keyword-based engines) in order to identify main limitations in terms of missing relevant PCs (recall) and non-relevant results (precision). Patents have been processed by KOM, a semantic patent search tool developed by the authors. Unlike all other PC search tools, KOM uses semantic parser and many knowledge bases for carrying out a conceptual patent search. Its functioning is described step by step through a detailed analysis pointing out the benefits of a concept-based search vis-à-vis a keyword-based search. An exemplary case is proposed dealing with CPCs describing the sterilization of contact lenses. Comparison could be likewise conducted on other PCs such as International (IPC), European (ECLA) or United States (USPC) patent classification codes.     

A patent quality analysis for innovative technology and product development

Enterprises evaluate intellectual property rights and the quality of patent documents in order to develop innovative products and discover state-of-the-art technology trends. The product technologies covered by patent claims are protected by law, and the quality of the patent insures against infringement by competitors while increasing the worth of the invention. Thus, patent quality analysis provides a means by which companies determine whether or not to customize and manufacture innovative products. Since patents provide significant financial protection for businesses, the number of patents filed is increasing at a fast pace. Companies which cannot process patent information or fail to protect their innovations by filing patents lose market competitiveness. Current patent research is needed to estimate the quality of patent documents. The purpose of this research is to improve the analysis and ranking of patent quality. The first step of the proposed methodology is to collect technology specific patents and to extract relevant patent quality performance indicators. The second step is to identify the key impact factors using principal component analysis. These factors are then used as the input parameters for a back-propagation neural network model. Patent transactions help judge patent quality and patents which are licensed or sold with intellectual property usage rights are considered high quality patents. This research collected 283 patents sold or licensed from the news of patent transactions and 116 patents which were unsold but belong to the technology specific domains of interest. After training the patent quality model, 36 historical patents are used to verify the performance of the trained model. The match between the analytical results and the actual trading status reached an 85% level of accuracy. Thus, the proposed patent quality methodology evaluates the quality of patents automatically and effectively as a preliminary screening solution. The approach saves domain experts valuable time targeting high value patents for R&D commercialization and mass customization of products.     

Intelligent compilation of patent summaries using machine learning and natural language processing techniques

Patents are a type of intellectual property with ownership and monopolistic rights that are publicly accessible published documents, often with illustrations, registered by governments and international organizations. The registration allows people familiar with the domain to understand how to re-create the new and useful invention but restricts the manufacturing unless the owner licenses or enters into a legal agreement to sell ownership of the patent. Patents reward the costly research and development efforts of inventors while spreading new knowledge and accelerating innovation. This research uses artificial intelligence natural language processing, deep learning techniques and machine learning algorithms to extract the essential knowledge of patent documents within a given domain as a means to evaluate their worth and technical advantage. Manual patent abstraction is a time consuming, labor intensive, and subjective process which becomes cost and outcome ineffective as the size of the patent knowledge domain increases. This research develops an intelligent patent summarization methodology using artificial intelligence machine learning approaches to allow patent domains of extremely large sizes to be effectively and objectively summarized, especially for cases where the cost and time requirements of manual summarization is infeasible. The system learns to automatically summarize patent documents with natural language texts for any given technical domain. The machine learning solution identifies technical key terminologies (words, phrases, and sentences) in the context of the semantic relationships among training patents and corresponding summaries as the core of the summarization system. To ensure the high performance of the proposed methodology, ROUGE metrics are used to evaluate precision, recall, accuracy, and consistency of knowledge generated by the summarization system. The Smart machinery technologies domain, under the sub-domains of control intelligence, sensor intelligence and intelligent decision-making provide the case studies for the patent summarization system training. The cases use 1708 training pairs of patents and summaries while testing uses 30 randomly selected patents. The case implementation and verification have shown the summary reports achieve 90% and 84% average precision and recall ratios respectively.     

专利电子审批系统中工作流技术的应用

介绍了工作流技术在专利电子审批系统中的应用,从行业特征的角度入手,分别对工作流定义、模型设计和流程设计进行了分析和叙述.文中使用偏序集作为理论基础,并引用专利审批中的业务过程作为示例.最后也指出了在实际应用工作流技术时所面临的一些问题.     

What is patentable?

This article gives attention to patents, which are the legal manifestations of creation. A patent is a legal document that entitles its holder the right to prevent others from making, using, or selling the invention during the term of the patent. The specific invention being protected must be clearly articulated in legal claim language.     

Analyzing a firm’s international portfolio of technological knowledge: A declarative ontology-based OWL approach for patent documents

Patent databases contain large amounts of information about the inventions and metadata of corporate patents (such as the technological domain they belong to, their applicants, and inventors). These databases are available online but since they do not provide explicit information about the relationships between different patent metadata, it is not possible for computers to automatically process such relationships. Several patent ontologies have been proposed so far in order to provide patent knowledge bases with semantics by merging information from different databases and establishing a common vocabulary. However, previous ontology literature has paid limited attention to the representation of specific relationships among metadata and the design of reasoning procedures that would allow some information not explicitly specified in the databases or ontologies to be inferred. This article proposes a methodological approach for the definition of relationships and reasoning tasks for patent analysis by using patent ontologies, and provides a real illustration of its potential in the context of international flows of research knowledge. This declarative method is based on the formal definition of key patent analysis indicators (KPAIs). The case study analysis is relevant because global competition and the importance of multinational firms in the patent process have resulted in firms not only patenting on their domestic markets but also transferring their patents to other markets and developing patents in different countries. In this context, it is important to analyze the connections between the patenting processes and the international knowledge flows of research and development. More specifically, the paper illustrates the applicability of the proposed methodology by classifying patents into the five patterns of internationalization identified by the Organization for Economic Co-operation and Development (OECD).     

A rough set based approach to patent development with the consideration of resource allocation

Patent strategy is the overriding mechanism that helps direct investment, resource allocation, expectations, and policy development within an organization. Much studies of patent, for example, patent classification, patent analysis, patent management, patent strategy planning have been made. Due to the high cost of devoting to the research and development for a new patent application, it is essential for a company to develop the patent portfolio based on analyzing related information for fitting with cost constraint and maximizing the benefit. However, only few research attempts to develop new patents with the consideration of resource allocation, for example, optimizing budget utilization. In addition, the previous studies did not derive significant technologies and induct rules for resource allocation through patent analysis. In some cases, the patent analysis may process qualitative information that is difficult to analyze by standard statistical techniques. The rough set approach, which is suitable for processing qualitative information, is required to induct decision rules to derive critical technologies of patents. In this paper, a systematic approach to analyze existing patent information based on rough set theory with the consideration of resource allocation is developed. A case study is presented to demonstrate the contribution of the proposed approach which assists on decision-making in patent reform or invention with constraint resource.     

Cover3

In recent years, edge computing has emerged as a popular mechanism to deliver dynamic Web content to clients. However, many existing edge cache networks have not been able to harness the full potential of edge computing technology. In this paper, we argue and experimentally demonstrate that cooperation among the individual edge caches coupled with scalable server-driven document consistency mechanisms can significantly enhance the capabilities and performance of edge cache networks in delivering fresh dynamic content. However, designing large-scale cooperative edge cache networks presents many research challenges. Toward addressing these challenges, this paper presents cooperative edge cache grid (cooperative EC grid, for short) - a large-scale cooperative edge cache network for efficiently delivering highly dynamic Web content with varying server update frequencies. The design of the cooperative EC grid focuses on the scalability and reliability of dynamic content delivery in addition to cache hit rates, and it incorporates several novel features. We introduce the concept of cache clouds as a generic framework of cooperation in large-scale edge cache networks. The architectural design of the cache clouds includes dynamic hashing-based document lookup and update protocols, which dynamically balance lookup and update loads among the caches in the cloud. We also present cooperative techniques for making the document lookup and update protocols resilient to the failures of individual caches. This paper reports a series of simulation-based experiments which show that the overheads of cooperation in the cooperative EC grid are very low, and our architecture and techniques enhance the performance of the cooperative edge networks     

A knowledge centric methodology for dental implant technology assessment using ontology based patent analysis and clinical meta-analysis

The medical equipment industry has been one of the fastest growing sectors of the decade with predicted global sales reaching US$ 430 billion in 2017 [22]. During the period from 1995 to 2008, the patent applications in medical technology increased rapidly worldwide (World Intellectual Property Organization, 2012). Patent analysis, although useful in forecasting technology development trends, has posed a challenging analysis task since the volume and diversity of new patent applications has surpassed the ability of regular firms and research teams to process and identify relevant information. Further, medical related technologies rely on clinical trials to validate and gain regulatory approval for patient treatment even though patents, protecting the intellectual property rights of inventors, have been granted. This research focuses on developing a knowledge centric methodology and system to analyze and assess viable medical technology innovations and trends considering both patents and clinical reports. Specifically, the design innovations of dental implant connections are used as a case study. A novel and generic methodology combining ontology based patent analysis and clinical meta-analysis is developed to analyze and identify the most effective patented techniques in the dental implant field. The research establishes and verifies a computer supported analytical approach and system for the strategic prediction of medical technology development trends.     

基于词汇相似度的IPC与CLC映射

专利作为一种具有特殊性质的文献,包含先进的技术方案,但存在管理困难、相对孤立、使用率低等弊端。针对该问题,定义分类法类目的概念模型,通过计算类目之间的概念相似度,为国际专利分类法与中国图书分类法建立类目映射。在计算类目相似度中引入与类目相关的词汇语义相似度计算,综合考虑类目的上下文环境对类目间关系的影响,降低专利数据的孤立性,实现专利数据与其他期刊数据的交互操作。实验表明,该方法能有效提高类目间相似度计算的准确率。     

A study of patent analysis of LED bicycle light by using modified DEMATEL and life span

The present study proposed a modified decision-making trial and evaluation laboratory (DEMATEL) method. This innovation method involves collecting the repeated or identically defined technical keywords of patent techniques related to light-emitting diode (LED) bicycle light to determine the ratios of the normalized numerical values of these technical keywords by using one technical domain as the primary domain and another as a variable. The values obtained are then converted to mutual influence levels on a scale of 0 to 4, replacing the conventional expert questionnaire. In this study, in accordance with the operational steps of the decision-making trial and evaluation laboratory method, a general relational influence matrix, direct and indirect relationships diagram, and values of centrality (D + R) and causality (D − R) were obtained. A causal diagram was therefore created. The causal diagram was drawn using values of (D + R) and (D − R) as the two axes and facilitated determining the levels of mutual influence between technical domains. In accordance with the proposed modified decision-making trial and evaluation laboratory method, this study collected patents related to LED bicycle light; moreover, the normalized numerical values of key technical, part/component, and function words that appeared in these patents were calculated. Furthermore, clusters of technical and part or component words were defined in accordance with the first-layer technical category. The second-layer technical categories and functional categories were subsequently defined under the first-layer technical categories to establish the technique–function matrix, thereby dividing the techniques related to LED bicycle light into seven main technical domains. This study then analyzed patent life span. Patent life span was calculated using the announcement date of related patents. Finally, this study investigated the development potential of each technical domain of LED bicycle light by conducting a combined analysis of causal diagram obtained by modified DEMATEL method, activity trend chart of techniques, and patent life spans. The proposed patent analysis method and results can serve companies and engineers as references to facilitate developing new patents.     

基于功能面的产品布局与人机工程协同设计的研究

在产品概念设计中,布局设计和人机工程设计是两个相互影响和制约的设计过程,。为了将这种布局设计和人机工程设计协同起来,将功能面作为概念设计发解的依据,并在此基础上提出了基于功能面的协同概念设计过程和方法,以及布局设施 人机工程协同设计模型和形式化描述方法,最后结合摩托车的概念设计实例给出了一个计算机支持的布局和人机工程协同设计系统的实现方案。     

单机网格应用系统开发实验方法

针对网格应用系统开发实验过程中的困难，提出了一种通过在单机环境中利用虚拟机技术构建网格原型实验系统，对网格应用软件进行实验的方法，已申请国家发明专利(申请号：200510018916.0)。与各种同类实验方法相比，该方法操作简单、成本低廉，所得到的实验结果真实可信，所构建的单机网格原型实验系统具有高度的灵活性和可靠性，易于维护和管理，并能迅速地迁移到真实应用环境中。     

Welfare analysis of alternative patent policies for software innovations

We present a duopoly model that extends existing patent policy design models in the economics literature to formalize the links among the patent policy levers set by public policy (patent height and width), the strategic decisions made by firms (R&D investments, product development, product imitation, patent decision, and product pricing), the purchasing decisions made by consumers, and the market parameters. This integrated model enables policymakers to better analyze the impact of alternative patent policies on the level of social welfare and the distribution of that welfare among innovators, imitators, and consumers in a range of industry contexts—specifically targeting issues of software patents. Critical results include (1) an increase in patent width unambiguously increases R&D spending to generate a novel idea; (2) an increase in patent height may increase or decrease R&D spending depending on the efficiency with which an innovator can transform the novel idea into a commercial product; (3) while enforced patents will improve innovator profits they may worsen imitator profits, consumer welfare, or both and may even worsen total social welfare; (4) the optimal (social welfare maximizing) policy design is characterized by a relatively high patent height and moderate patent width.     

A two-level parser for patent claim parsing

Patent claim parsing can contribute in many patent-related applications, such as patent search, information extraction, machine translation and summarization. However, patent claim parsing is difficult due to the special structure of patent claims. To overcome this difficulty, the challenges facing the patent claim parsing were first investigated and the peculiarities of claim syntax that obstruct dependency parsing were highlighted. To handle these peculiarities, this study proposes a new two-level parser, in which a conventional parser is imbedded. A patent claim is pre-processed in order to remove peculiarities before passed to the conventional parser. The process is based on a new dependency-based syntax called Independent Claim Segment Dependency Syntax (ICSDS). This two-lever parser has demonstrated promising improvement for patent claim parsing on both effectiveness and efficiency over the conventional parser.     

专利战略分析系统的设计与实现

分析了目前专利分析工作的不足，介绍了专利分析的理论基础PM，以及专利战略分析系统的系统框架、总体功能设计、数据库设计、流程设计和实现技术。     

Design patent map visualization display

Design patent map is an important strategic tool for establishing design strategies. Of types of patents, design patents occupy a unique patent field, since design patents are not as definitive as other patent fields. Therefore, the construction of design patent map is extremely difficult. Until now, in world-renowned patent periodicals, studies on design patent map have not been found; thus, this study is very innovative. In this study, design patents from between 2005 and 2007 in the United States were selected, 74 patent samples in the car industry were selected as research subjects. Ten design patent examiners who have an average of 10 years of patent evaluation experience in the Taiwan Intellectual Property Office (TIPO) and 29 industrial designers who have had an average of over 3 years of design experience are enlisted to assist in construction of design patent map. Corporations can use the concrete steps provided by this study, with the multi-dimensional scale, to establish a design patent map and use this to form patent strategies. This strategic tool will become a new tool for competition for firms facing the knowledge economy era!