中文专利数据可视化分析系统研究与设计

现有的专利分析系统在可视化方面存在诸多不足，针对中文专利的可视化分析工具更是少之又少。结合文本挖掘技术，设计并实现“中文专利数据可视化分析系统”,具有专利态势分析、专利聚类分析、专利引证分析三种分析功能，多角度分析了中文专利数据，并将数据分析结果通过可视化模块直观地呈现出来，为专利信息使用者提供更好服务。     

基于网络实时监测获取的专利情报系统设计

调研了当前一些专利情报系统,针时这些专利情报系统存在的缺点和不足,提出一个能够从网络上实时进行专利监测和获取的系统.系统采用自然语言的问句查询,以实现不同语言和不同语言格式的检索,并通过在线翻译和WordNet本体等技术进行扩展查询,实现了一定的语义搜索功能,能对专利情报进行语义分析的监测和获取.实验对系统实现的效果进行了详细测试和分析,并验证了该系统设计方法的可靠性和可行性.     

专利资助服务平台的设计与实现

对专利资助工作进行分析,设计开发一套基于网络的服务平台,实现专利资助的在线办理。系统采用Loushang框架技术,实现审核业务流的快速定义与开发,简化了系统开发的过程。     

专利信息本体的设计及应用

讨论了本体与国际专利分类表的内在联系。在专利信息服务系统中，以国际专利分类表为基础，提出了符合专利信息特点的IPC本体；设计了采用本体检索与关键字检索有效结合的组合检索方式；增加了从企业用户信息中提取出的用户兴趣模型。把用户兴趣模型转化为符合系统检索规则的检索条件加入到原组合检索中，进一步缩小检索范围，在一定程度上提高了专利信息检索的查全率和查准率。     

专利地图在医学科研活动中的应用初探

介绍了专利地图的设计制作方法及流程,对比分析了三种类型专利地图各自的制作目标、主要变量及针对问题,尝试运用专利地图为医学科研活动提供借鉴。     

美国专利引证可视化系统的设计与实现

分析了目前专利引证分析工具现状与不足,介绍了美国专利引证可视化系统的总体结构、关键技术、主要算法以及可视化实现,指出了系统构建的可视化分析方法与以往分析方法的不同。     

基于专利的知识获取系统研究

应用现有的数据库技术、专利知识以及专家系统技术，研制了一个基于专利的知识获取系统，方便了以数据库为载体的专利知识系统的管理维护一体化。     

中国专利检索系统的设计与性能分析

介绍了J2EE的技术框架和服务，基于该技术框架提出了一个适合中国专利文献检索和管理的体系结构，设计并实现了该系统的基本功能模块，分析了专利检索系统的几种优化策略，并给出了相应的试验结果。     

专用专利数据库系统的研究

为了能充分利用现有专利信息加快企业中产品的开发速度、保护自己的知识产权并避免涉及专利侵权纠纷,提出并实现了一种基于现有专利数据光盘信息,建立专用专利数据库及查询系统的新模式。     

基于SooPat专利数据库的惠州市专利申请竞争力研究

利用国家知识产权局互联网检索数据库SooPat专利数据库,采集广东省惠州市从2004年1月1日至2013年12月31日期间的专利文献,建立广东省惠州市专利申请数据库。再选择国家知识产权局专利信息服务平台分析系统分别从专利申请总量、IPC技术类别分布、专利申请人分布和惠州市专利申请趋势等方面,对广东省惠州市专利申请进行细致、严密、综合的竞争力分析,并从专利文献中提取有用信息,为企业实现其特有的经济价值。     

A patent quality analysis and classification system using self-organizing maps with support vector machine

A plethora of patents are approved by the patent officers each year and current patent systems face a solemn quandary of evaluating these patents’ qualities. Traditional researchers and analyzers have fixated on developing sundry patent quality indicators only, but these indicators do not have further prognosticating power on incipient patent applications or publications. Therefore, the data mining (DM) approaches are employed in this article to identify and to classify the new patent's quality in time. An automatic patent quality analysis and classification system, namely SOM-KPCA-SVM, is developed according to patent quality indicators and characteristics, respectively. First, the self-organizing map (SOM) approach is used to cluster patents published before into different quality groups according to the patent quality indicators and defines group quality type instead of via experts. The kernel principal component analysis (KPCA) approach is used to transform nonlinear feature space in order to improve classification performance. Finally, the support vector machine (SVM) is used to build up the patent quality classification model. The proposed SOM-KPCA-SVM is applied to classify patent quality automatically in patent data of the thin film solar cell. Experimental results show that our proposed system can capture the analysis effectively compared with traditional manpower approach.     

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.     

Creating patents on the new technology using analogy-based patent mining

Patents on the new technology–a technology not yet commercialized and in an early stage of its life cycle–give firms many benefits. However, existing methods are inadequate because of dependencies on customers and physical prototypes. And there is lack of systems, focused on a problem identification process or an inter-technological comparison. In this research, to remedy existing limitations, analogy-based patent mining system is suggested. The system is developed based on an assumption that similar problems would occur in technologies that have similar properties or functions. So, the system is focused on identification of a Problem Solved Concept (PSC), which describes what problem is solved in the patent. At the first part of the system, the mature technology–a technology relatively matured than the new technology–is described with a property and a function; one of the property or the function should be similar to which of the new technology considered. And the system extract PSCs, construct patent map, and evaluate PSCs utilizing patents on the new and the mature technologies. As a result, the PSCs with high opportunities are revealed and patents related to the PSCs are examined. Then users of this system select some patents as resources for analogy. The system is tested by a case study of wireless charger technology. For the case study, 352 patents on wireless router technology and 227 patents on wireless charger technology are used. At the final, patents related to ‘handoff’, showed a high opportunity score and one of the patents is introduced to show the possibility of patent creation through analogy.     

Intelligent patent recommendation system for innovative design collaboration

Patents' search is increasingly critical for a company's technological advancement and sustainable marketing strategy. When most innovative designs are created collaboratively by a diverse team of researchers and technologists, patent knowledge management becomes time consuming with repeated efforts creating additional task conflicts. This research develops an intelligent recommendation methodology and system to enable timely and effective patent search prior, during, and after design collaboration to prevent potential infringement of existing intellectual property rights (IPR) and to secure new IPR for market advantage. The research develops an algorithm to dynamically search related patents in global patent databases. The system clusters users with similar patent search behaviors and, subsequently, infers new patent recommendations based on inter-cluster group member behaviors and characteristics. First, the methodology evaluates the filtered information obtained from collaborative patent searches. Second, the system clusters existing users and identifies users' neighbors based on the collaborative filtering algorithm. Using the clusters of users and their behaviors, the system recommends related patents. When collaborative design teams are planning R&D policies or searching patents and prior art claims to create new IP and prevent or settles IP legal disputes, the intelligent recommendation system identifies and recommends patents with greater efficiency and accuracy than previous systems and methods described in the literature.     

Development of a GTM-based patent map for identifying patent vacuums

The patent map has long been considered as a useful tool for mining latent technological information. Among others, the detection of patent vacuums, defined as unexplored areas of new technologies, deserves intensive research. However, previous studies for identifying patent vacuums on the patent map have been subjected to some limitations, stemming from the subjective and manual identification of patent vacuums. To address these limitations, this paper proposes a generative topographic mapping (GTM)-based patent map, which aims to automatically identify a patent vacuum. Since GTM is a probabilistic approach of mapping multidimensional data space onto a low-dimensional latent space and vice versa, it contributes to the automatic detection and interpretation of patent vacuums. The proposed approach consists of three stages. Firstly, text mining is executed in order to transform patent documents into keyword vectors as structured data. Secondly, the GTM is employed to develop the patent map, subsequently leading to the discovery of patent vacuums, which are expressed as blank areas in the map. Lastly, the meaning of each patent vacuum is interpreted by the inverse mapping of patent vacuums onto the original keyword vector. The case study is conducted with lithography technology-related patents. We believe the proposed approach not only saves time and effort for identifying patent vacuums, but also increases objectivity and reliability.     

A cooperative multi-agent platform for invention based on patent document analysis and ontology

We propose a cooperative multi-agent platform to support the invention process based on the patent document analysis. It helps industrial knowledge managers to retrieve and analyze existing patent documents and extract structure information from patents with the aid of ontology and natural language processing techniques. It allows the invention process to be carried out through the cooperation and coordination among software agents delegated by the various domain experts in the complex industrial R&D environment. Furthermore, it integrates the patent document analysis with the inventive problem solving method known as TRIZ method that can suggest invention directions based on the heuristics or principles to resolve the contradictions among design objectives and engineering parameters. We chose the patent invention for chemical mechanical polishing (CMP) as our case study. However, the platform and techniques could be extended to most cooperative invention domains.     

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.     

Lessons that Europe can learn from the US patent assertion entity phenomenon

This paper investigates the patent assertion entities (PAEs) problem in Europe. First, it argues that PAEs should be not as active in Europe as it is in the USA simply because European inventors infringe less. They infringe less because there are fewer patents to infringe. PAEs, however, can still thrive in Europe. Using the example of the UK, this paper shows that PAEs visible activities are non-negligible even if they pale compare to the level of activities in the USA. Using the example of France, this paper shows that governments have not waited for PAEs to come their countries; some have taken steps to create their own governmentally sponsored PAE. Finally, this paper argues that the USA is trying to learn from the EU and bring fee-shifting to their system; fee shifting, in and of itself cannot solve the PAE problem. This paper also argues that the EU is taken on some of the problem as the USA and responding in similar ways with regard to injunctions; injunction remains an important part of the PAE arsenal; yet, the highest courts in both jurisdictions have made clear that infringing a patent does not automatically great a patent holder a right to exclude others. The paper concludes by discussing the Unitary Patent Court system and the impact it will have on PAE activities in Europe: some PAEs may benefit but most will still find Europe a hostile environment for their activities.     

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.