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.     

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.     

Using function-based patent analysis to identify potential application areas of technology for technology transfer

Under the open innovation paradigm, identification of application areas of the internally developed technologies is important to maximize the profits from them. However, many companies have failed to identify opportunity for additional applications. The fundamental reason is that companies have insufficient understanding of the potential applications of their technologies, because different industries use far different technologies and technological terminologies. However, technologies can be linked with outside industries by analyzing them from the functional perspective, because functions used in different industries are generally similar. Therefore, this article proposes a function-based patent analysis to identify the potential application areas of a technology. In general, technologies in a patent are invented for use in a specific industry, and thus a patent can be categorized into a specific industry. By identifying patents which contain a specific function, industries that use the function can be identified. Industries in which the importance of the function is relatively high can be considered as areas in which technologies performing the function have potential to be applied, and the proposed indexes evaluate the relative importance of the function within each industry. Finally, the practical usefulness of the proposed method was verified by identifying the potential areas in which vortex reduction technology can be applied.     

Consumer driven product technology function deployment using social media and patent mining

The capability of identifying real-time customer needs is critical for manufacturers that provide short life cycle consumer products such as smart phones. Companies need to form research and development (R&D) strategies to improve key functional features for short lifespan products to reflect the adoption of innovative technologies and changing customer expectations. With the pervasive use of the Internet, this research crawls and analyzes the online voice of customers (VoC), overcoming the time lag of offline surveys, to identify and prioritize product functions for deployment using extended quality function deployment (eQFD) models. In this research, the novel analytics of the manufacturer’s patent portfolio is added as an additional eQFD dimension to map ranked functional improvements to a manufacturer’s R&D capabilities. Thus, a computer supported eQFD system is developed to perform the unique mappings and gap analyses between the VoC, the prioritized product functions, and the manufacturer’s patent portfolio. The newly developed eQFD methodology and its novel discoveries are demonstrated in detail using a case study of three smart phones launched during the same time frame. The products include the Samsung Galaxy S7, the Huawei Honor 5X, and the ASUS Zenfone 3. The newly developed methodology is generally applicable to support VoC-centric product function deployment and R&D strategic planning in other domains.     

A repartition method improving visual quality for PCA image coding

Image coding using principal component analysis (PCA), a type of image compression technique, projects image blocks to a subspace that can preserve most of the original information. However, the blocks in the image exhibit various inhomogeneous properties, such as smooth region, texture, and edge, which give rise to difficulties in PCA image coding. This paper proposes a repartition clustering method to partition the data into groups, such that individuals of the same group are homogeneous, and vice versa. The PCA method is applied separately for each group. In the clustering method, the genetic algorithm acts as a framework consisting of three phases, including the proposed repartition clustering. Based on this mechanism, the proposed method can effectively increase image quality and provide an enhanced visual effect.     

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.     

Assessment of water quality variation of a monitoring network using exploratory factor analysis and empirical orthogonal function

This study suggests a systematic assessment method that jointly uses the exploratory factor analysis (EFA) and empirical orthogonal function (EOF-patterns) of Principal Component Analysis (PCA) to assess the water quality variation of the monitoring network of Nakdong River, Korea, in which 28 stations measuring 15 water quality parameters are located. The EFA results showed the monitoring stations to be distinguished by two main factors. The representative stations of which the variance was almost explained by the specific factor were selected. We applied PCA to the monitoring data of representative stations, and then analyzed the EOF-patterns that indicate the characteristics of water-quality variation for each factor. With the interpretation of main factors and EOF-patterns causing dominant water quality variations, the monitoring network of Nakdong River could be spatially and seasonally evaluated according to the contribution of each factor.     

A general theory of a class of linear neural nets for principal and minor component analysis

This paper presents a unified theory of a class of learning neural nets for principal component analysis (PCA) and minor component analysis (MCA). First, some fundamental properties are addressed which all neural nets in the class have in common. Second, a subclass called the generalized asymmetric learning algorithm is investigated, and the kind of asymmetric structure which is required in general to obtain the individual eigenvectors of the correlation matrix of a data sequence is clarified. Third, focusing on a single-neuron model, a systematic way of deriving both PCA and MCA learning algorithms is shown, through which a relation between the normalization in PCA algorithms and that in MCA algorithms is revealed. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 1998     

基于SOC网络的商业银行信用风险识别模型

针对自组织竞争(SOC)神经网络在解决模式分类问题上的优势,结合主成分分析法来构建商业银行信用风险识别模型.首先构造一套用于描述贷款企业信用状况的指标体系,然后使用主成分分析法提取特征指标,再采用SOC神经网络进行非监督分类.通过选取陕西省2007年度在沪、深两市交易的26家上市公司作为样本进行实证分析,实证结果表明:模型对信用风险具有较强的识别能力,同时对商业银行还有较好的预测功能.     

TrendPerceptor: A property-function based technology intelligence system for identifying technology trends from patents

Technology intelligence systems are vital components for planning of technology development and formulation of technology strategies. Although such systems provide computation supports for technology analysis, much effort and intervention of experts, who may be expensive or unavailable, is required in gathering processes of information for analysis. As a remedy, this paper proposes TrendPerceptor, a system that uses a property-function based approach. The proposed system assists experts (1) to identify trends in invention concepts from patents, and (2) to perform evolution trend analysis of patents for technology forecasting. For this purpose, a module of the system uses grammatical analysis of textual information to automatically extract properties and functions, which show innovation directions in a given technology. Using the identified properties and functions, a module for invention concept analysis based on network analysis and a module for evolution trend analysis based on TRIZ (Russian acronym of the Theory of Inventive Problem Solving) trends are suggested. This paper describes the architecture of a system composed of these three modules, and illustrates two case studies using the system.     

A product network analysis for extending the market basket analysis

In this study, we propose a product network analysis, a network-based analysis to analyze a network-leveled relation among all products. Compared to market basket analysis, which focuses on the transaction-leveled relation between products, the suggested product network analysis focuses on extended network-leveled point of view of the relation between all products. For such a purpose, we suggest two kinds of product networks, market basket networks and co-purchased product networks. Two networks are comparatively evaluated to analyze the topological characteristics and the structure of those networks. The extended use of market basket analysis, network-leveled analysis are expected to be more effectively and efficiently used in personalized services, such as cross selling, up selling, and personalized product display utilizing the deep relation between products.     

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.     

A direct method for cluster analysis

An algorithm based on a least-mean-square (LMS) criterion is presented. This algorithm partitions a multi-dimensional data set directly into a desired number of clusters. The result is compared favorably to existing methods in both performance and computational efficiency. An efficient method for determining a reasonable set of distributed initial cluster centers based on principal component analysis is also presented. This clustering algorithm is shown to converge to a unique minimum based on the LMS criterion and is demonstrated by digital computer simulation and applied to the analysis of vectorcardiograms.     

Accounting for dynamics in attribute-importance and for competitor performance to enhance reliability of BPNN-based importance-performance analysis

Importance-performance analysis (IPA) is a decision-support tool used in prioritizing quality improvements of products/services. Recently, back-propagation neural network (BPNN)-based approaches have been proposed to deal with the problem of asymmetric effects in customer satisfaction formation. Though reliability of IPA is increased by the integration of BPNN, shortcomings of the analytical framework remain that (a) it does not provide insight into forms and degrees of these asymmetric effects, (b) it does not account for differences between the relevance and determinance of quality attributes, and (c) it neglects the competitor dimension in attribute-prioritization. Since all these issues have important managerial implications, the authors of this study propose an extended BPNN-based IPA that uses a multidimensional operationalization of attribute-importance, and that considers competitive performance levels. Using data from an airline satisfaction survey, an empirical test reveals that the proposed approach significantly outperforms conventional BPNN-based IPA. In particular, conventional BPNN-IPA would mislead managerial action with regard to 3 out of 8 quality components (37.5%).     

An improved PCA scheme for sensor FDI: Application to an air quality monitoring network

In this paper a sensor fault detection and isolation procedure based on principal component analysis (PCA) is proposed to monitor an air quality monitoring network. The PCA model of the network is optimal with respect to a reconstruction error criterion. The sensor fault detection is carried out in various residual subspaces using a new detection index. For our application, this index improves the performance compared to classical detection index SPE. The reconstruction approach allows, on one hand, to isolate the faulty sensors and, on the other hand, to estimate the fault amplitudes.     

一种全局收敛的PCA神经网络学习算法

主元分析(PCA)也称为K-L变换是进行特征提取的一种重要方法。近年来,为了处理海量数据,许多基于Hebbian学习算法的PCA神经网络被提出来。传统的算法,通常不能保证其收敛性或者收敛速度较慢。基于CRLS神经网络,本文提出了一种新的确保权向量收敛的学习算法,本算法无须在计算中规格化权向量。同时也证明了该学习算法使得权向量收敛到最大特征值所对应的特征向量。实验表明,与传统的CRLS神经网络比较,本文算法准确性得到极大提高。     

How to identify promising chances for technological innovation: Keygraph-based patent analysis

Few would argue that technology has driven numerous innovations. Therefore, the body of literature has been almost unanimous in employing patent information to identify opportunities. Previous studies using patent database employed a frequency-based approach, which assumes that frequent keywords play a key role in innovation. However, innovations can occur not only from frequent trends, but also from infrequent trends. Some previous research, albeit infrequent, has attempted keygraph approach in order to reflect infrequent trends to the new innovation development. Despite the effort, however, little is known about effective interpretation of keygraph results to identify new opportunities. Therefore, we suggested a keygraph-based approach combined with the index-based validation approach. We conducted a case study on healthcare services to illustrate the effectiveness of the proposed approach.     

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.     

A comparison of three methods for principal component analysis of fuzzy interval data

Vertices Principal Component Analysis (V-PCA), and Centers Principal Component Analysis (C-PCA) generalize Principal Component Analysis (PCA) in order to summarize interval valued data. Neural Network Principal Component Analysis (NN-PCA) represents an extension of PCA for fuzzy interval data. However, also the first two methods can be used for analyzing fuzzy interval data, but they then ignore the spread information. In the literature, the V-PCA method is usually considered computationally cumbersome because it requires the transformation of the interval valued data matrix into a single valued data matrix the number of rows of which depends exponentially on the number of variables and linearly on the number of observation units. However, it has been shown that this problem can be overcome by considering the cross-products matrix which is easy to compute. A review of C-PCA and V-PCA (which hence also includes the computational short-cut to V-PCA) and NN-PCA is provided. Furthermore, a comparison is given of the three methods by means of a simulation study and by an application to an empirical data set. In the simulation study, fuzzy interval data are generated according to various models, and it is reported in which conditions each method performs best.     

基于PCA和IFOA-BP神经网络的股价预测模型

针对基于BP神经网络的股票价格预测模型在价格预测时存在较大误差的问题,在BP神经网络方法的基础上引入了主成分分析方法(PCA)和改进的果蝇算法(IFOA),提出一种基于PCA-IFOA-BP神经网络的股票价格预测模型。通过PCA对股票历史数据进行降维,减少冗余信息;采用改进的果蝇算法优化BP神经网络的初始权值和阈值;建立基于PCA和IFOA-BP神经网络的股票价格预测模型。对上证指数股票价格数据进行仿真验证,仿真结果表明:在股票价格预测中,该模型比BP神经网络、PCA-BP和PCA-FOA-BP的预测精度更高,是一种有效可行的预测方法。