• Journal of the Korean Society for information Management
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• v.27 no.4
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• pp.239-258
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• 2010

The purpose of this study is to develop a prediction model of patent citation counts based on major factors which affect patent citation. To this end, we performed multiple regression analysis between the patent citation counts and five explanatory variables such as the number of pages, the number of claims, the reference-average-citation rate, the strength of bibliographic coupling, and the document similarity proved as having 5% or more standardized variances($r^2$) with patent citation counts, with a test dataset of U.S. patents in five subject fields. As a result, our prediction models showed 58.3% to 89.6% predictability depending on subject fields and revealed the document similarity has the highest impact on citation counts among the five predictive variables in all the subject fields. The result of comparison between the predicted citation counts and the actual ones confirmed the usefulness of the citation prediction models built for each subject field.

• Journal of the Korean Society for information Management
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• v.27 no.1
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• pp.103-118
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• 2010

Recently, the valuation of patented technology has been greatly emphasized, and patent citation has been accepted as a very useful index of this technology. In this study, we performed correlation analyses between the patent citation counts and 17 explanatory variables of morphological, technological, and conceptual factors with a test dataset of U.S. patents in five subject fields. Seven variables having 5% or more standardized variances($r^2$) with patent citation counts were identified; number of pages, number of claims, reference-average-citation rate, patent increase/decrease rate, strength of bibliographic coupling, co-citation counts and document similarity. The result of the ANOVA test shows that the mean values of these variables vary among most subject fields.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.77-88
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• 2017

Recently, with the advent of knowledge based society where information and knowledge make values, patents which are the representative form of intellectual property have become important, and the number of the patents follows growing trends. Thus, it needs to classify the patents depending on the technological topic of the invention appropriately in order to use a vast amount of the patent information effectively. IPC (International Patent Classification) is widely used for this situation. Researches about IPC automatic classification have been studied using data mining and machine learning algorithms to improve current IPC classification task which categorizes patent documents by hand. However, most of the previous researches have focused on applying various existing machine learning methods to the patent documents rather than considering on the characteristics of the data or the structure of patent documents. In this paper, therefore, we propose to use two structural fields, technical field and background, considered as having impacts on the patent classification, where the two field are selected by applying of the characteristics of patent documents and the role of the structural fields. We also construct multi-label classification model to reflect what a patent document could have multiple IPCs. Furthermore, we propose a method to classify patent documents at the IPC subclass level comprised of 630 categories so that we investigate the possibility of applying the IPC multi-label classification model into the real field. The effect of structural fields of patent documents are examined using 564,793 registered patents in Korea, and 87.2% precision is obtained in the case of using title, abstract, claims, technical field and background. From this sequence, we verify that the technical field and background have an important role in improving the precision of IPC multi-label classification in IPC subclass level.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.4
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• pp.145-152
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• 2020

MRC (Machine reading comprehension) is the AI NLP task that predict the answer for user's query by understanding of the relevant document and which can be used in automated consult services such as chatbots. Recently, the BERT (Pre-training of Deep Bidirectional Transformers for Language Understanding) model, which shows high performance in various fields of natural language processing, have two phases. First phase is Pre-training the big data of each domain. And second phase is fine-tuning the model for solving each NLP tasks as a prediction. In this paper, we have made the Patent MRC dataset and shown that how to build the patent consultation training data for MRC task. And we propose the method to improve the performance of the MRC task using the Pre-trained Patent-BERT model by the patent consultation corpus and the language processing algorithm suitable for the machine learning of the patent counseling data. As a result of experiment, we show that the performance of the method proposed in this paper is improved to answer the patent counseling query.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.53-77
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• 2015

Korea has witnessed an increasing number of domestic patent applications, but a majority of them are not utilized to their maximum potential but end up becoming obsolete. According to the 2012 National Congress' Inspection of Administration, about 73% of patents possessed by universities and public-funded research institutions failed to lead to creating social values, but remain latent. One of the main problem of this issue is that patent creators such as individual researcher, university, or research institution lack abilities to commercialize their patents into viable businesses with those enterprises that are in need of them. Also, for enterprises side, it is hard to find the appropriate patents by searching keywords on all such occasions. This system proposes a patent recommendation system that can identify and recommend intellectual rights appropriate to users' interested fields among a rapidly accumulating number of patent assets in a more easy and efficient manner. The proposed system extracts core contents and technology sectors from the existing pool of patents, and combines it with secondary social knowledge, which derives from tags information created by users, in order to find the best patents recommended for users. That is to say, in an early stage where there is no accumulated tag information, the recommendation is done by utilizing content characteristics, which are identified through an analysis of key words contained in such parameters as 'Title of Invention' and 'Claim' among the various patent attributes. In order to do this, the suggested system extracts only nouns from patents and assigns a weight to each noun according to the importance of it in all patents by performing TF-IDF analysis. After that, it finds patents which have similar weights with preferred patents by a user. In this paper, this similarity is called a "Domain Similarity". Next, the suggested system extract technology sector's characteristics from patent document by analyzing the international technology classification code (International Patent Classification, IPC). Every patents have more than one IPC, and each user can attach more than one tag to the patents they like. Thus, each user has a set of IPC codes included in tagged patents. The suggested system manages this IPC set to analyze technology preference of each user and find the well-fitted patents for them. In order to do this, the suggeted system calcuates a 'Technology_Similarity' between a set of IPC codes and IPC codes contained in all other patents. After that, when the tag information of multiple users are accumulated, the system expands the recommendations in consideration of other users' social tag information relating to the patent that is tagged by a concerned user. The similarity between tag information of perferred 'patents by user and other patents are called a 'Social Simialrity' in this paper. Lastly, a 'Total Similarity' are calculated by adding these three differenent similarites and patents having the highest 'Total Similarity' are recommended to each user. The suggested system are applied to a total of 1,638 korean patents obtained from the Korea Industrial Property Rights Information Service (KIPRIS) run by the Korea Intellectual Property Office. However, since this original dataset does not include tag information, we create virtual tag information and utilized this to construct the semi-virtual dataset. The proposed recommendation algorithm was implemented with JAVA, a computer programming language, and a prototype graphic user interface was also designed for this study. As the proposed system did not have dependent variables and uses virtual data, it is impossible to verify the recommendation system with a statistical method. Therefore, the study uses a scenario test method to verify the operational feasibility and recommendation effectiveness of the system. The results of this study are expected to improve the possibility of matching promising patents with the best suitable businesses. It is assumed that users' experiential knowledge can be accumulated, managed, and utilized in the As-Is patent system, which currently only manages standardized patent information.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.101-124
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• 2018

Recently, most of the technologies have been developed in various forms through the advancement of single technology or interaction with other technologies. Particularly, these technologies have the characteristic of the convergence caused by the interaction between two or more techniques. In addition, efforts in responding to technological changes by advance are continuously increasing through forecasting promising convergence technologies that will emerge in the near future. According to this phenomenon, many researchers are attempting to perform various analyses about forecasting promising convergence technologies. A convergence technology has characteristics of various technologies according to the principle of generation. Therefore, forecasting promising convergence technologies is much more difficult than forecasting general technologies with high growth potential. Nevertheless, some achievements have been confirmed in an attempt to forecasting promising technologies using big data analysis and social network analysis. Studies of convergence technology through data analysis are actively conducted with the theme of discovering new convergence technologies and analyzing their trends. According that, information about new convergence technologies is being provided more abundantly than in the past. However, existing methods in analyzing convergence technology have some limitations. Firstly, most studies deal with convergence technology analyze data through predefined technology classifications. The technologies appearing recently tend to have characteristics of convergence and thus consist of technologies from various fields. In other words, the new convergence technologies may not belong to the defined classification. Therefore, the existing method does not properly reflect the dynamic change of the convergence phenomenon. Secondly, in order to forecast the promising convergence technologies, most of the existing analysis method use the general purpose indicators in process. This method does not fully utilize the specificity of convergence phenomenon. The new convergence technology is highly dependent on the existing technology, which is the origin of that technology. Based on that, it can grow into the independent field or disappear rapidly, according to the change of the dependent technology. In the existing analysis, the potential growth of convergence technology is judged through the traditional indicators designed from the general purpose. However, these indicators do not reflect the principle of convergence. In other words, these indicators do not reflect the characteristics of convergence technology, which brings the meaning of new technologies emerge through two or more mature technologies and grown technologies affect the creation of another technology. Thirdly, previous studies do not provide objective methods for evaluating the accuracy of models in forecasting promising convergence technologies. In the studies of convergence technology, the subject of forecasting promising technologies was relatively insufficient due to the complexity of the field. Therefore, it is difficult to find a method to evaluate the accuracy of the model that forecasting promising convergence technologies. In order to activate the field of forecasting promising convergence technology, it is important to establish a method for objectively verifying and evaluating the accuracy of the model proposed by each study. To overcome these limitations, we propose a new method for analysis of convergence technologies. First of all, through topic modeling, we derive a new technology classification in terms of text content. It reflects the dynamic change of the actual technology market, not the existing fixed classification standard. In addition, we identify the influence relationships between technologies through the topic correspondence weights of each document, and structuralize them into a network. In addition, we devise a centrality indicator (PGC, potential growth centrality) to forecast the future growth of technology by utilizing the centrality information of each technology. It reflects the convergence characteristics of each technology, according to technology maturity and interdependence between technologies. Along with this, we propose a method to evaluate the accuracy of forecasting model by measuring the growth rate of promising technology. It is based on the variation of potential growth centrality by period. In this paper, we conduct experiments with 13,477 patent documents dealing with technical contents to evaluate the performance and practical applicability of the proposed method. As a result, it is confirmed that the forecast model based on a centrality indicator of the proposed method has a maximum forecast accuracy of about 2.88 times higher than the accuracy of the forecast model based on the currently used network indicators.