Visualization of patent analysis for emerging technology

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1 Available online at Expert Systems with Appliations Expert Systems with Appliations 34 (28) Visualization of patent analysis for emerging tehnology Young Gil Kim, Jong Hwan Suh, Sang Chan Park * Department of Industrial Engineering, Korea Advaned Institute of Siene and Tehnology, Guseong-dong, Yuseong-gu, Daejeon 35-7, South Korea Abstrat Many methods have been developed to reognize those progresses of tehnologies, and one of them is to analyze patent information. And visualization methods are onsidered to be proper for representing patent information and its analysis results. However, urrent visualization methods for patent analysis patent maps have some drawbaks. Therefore, we propose an alternative visualization method in this paper. With olleted keywords from patent douments of a target tehnology field, we luster patent douments by the k-means algorithm. With the lustering results, we form a semanti network of keywords without respet of filing dates. And then we build up a patent map by rearranging eah keyword node of the semanti network aording to its earliest filing date and frequeny in patent douments. Our approah ontributes to establishing a patent map whih onsiders both strutured and unstrutured items of a patent doument. Besides, differently from previous visualization methods for patent analysis, ours is based on forming a semanti network of keywords from patent douments. And thereby it visualizes a lear overview of patent information in a more omprehensible way. And as a result of those ontributions, it enables us to understand advanes of emerging tehnologies and foreast its trend in the future. Ó 27 Elsevier Ltd. All rights reserved. Keywords: Visualization; Patent analysis; k-means lustering; Semanti network; Ubiquitous omputing tehnology. Introdution It has been a ritial issue to understand tehnologial trends not only to avoid unneessary investment but also to gain the seeds for tehnologial development. So, many methods have been developed to reognize those progresses of tehnologies, and one of them is to analyze patent information. However, it is hard for non-speialists to analyze patent information beause patent information is enormous and rih in tehnial and legal terminology. Therefore, patent information needs to be transformed into something simpler and easier to understand. And visualization methods are onsidered to be proper for representing patent information and its analysis results. Generally, visualization methods are known as one of the best data mining ways to understand beause graphial display methods often offer superior result ompared to other * Corresponding author. Tel.: ; fax: address: sanghanpark@kaist.a.kr (S.C. Park). onventional tehniques (Westphal & Blaxton, 998). Espeially to top managers who deide diretions of tehnology investments, visualization methods are more useful than onventional ways suh as textual, tabular, and list for quik and easy knowledge disovery (Ganapathy, Ranganathan, & Sankaranarayanan, 24). Those visualization methods for patent analysis are alled a patent map at large. A patent map is the visualized expression of total patent analysis results to understand omplex patent information easily and effetively. And it is produed by gathering related patent douments of a target tehnology field, proessing, and analyzing them (WIPO, 23). In general, a patent doument ontains dozens of items for analysis whih are lassified into strutured and unstrutured item groups. Strutured items are uniform in semantis and in format aross a patent doument suh as patent number, filing date, or investors. On the other hand, the unstrutured items are free texts and quite different in length and ontent for a patent doument like laims, abstrats, or desriptions of the invention. The visualized analysis results of the former items are alled /$ - see front matter Ó 27 Elsevier Ltd. All rights reserved. doi:.6/j.eswa

2 Y.G. Kim et al. / Expert Systems with Appliations 34 (28) patent graphs, and those of the latter are alled patent maps, although loosely a patent map may refer to both ases (Liu, 23) Likewise, urrent visualization methods for patent analysis are based on mapping patent information and its analysis results. However, urrent patent maps have some drawbaks. First, most of them are time based, ranking based or matrix maps whih onsider only one aspet between strutured items or unstrutured items of eah patent doument. More integrated and balaned visualization approah is required to provide the overall struture of patent information effetively. Seond, they are ompliated networks of patent douments though they use different methods. As a result, they make patent analysis results inomprehensible and unlear to analyzer. Consequently, those defiient patent maps fail to provide an intuitive insight into the onerned tehnology field. And this is the third drawbak we d like to make mention of. Espeially for an emerging tehnology like ubiquitous omputing and bio informatis, it is essential to reognize its advane and make an estimate of the hereafter. Hene, we propose an alternative visualization method for patent analysis to overome drawbaks of urrent patent maps. Our approah ontributes to establishing a patent map whih onsiders both strutured and unstrutured items of a patent doument. We expet to keep the balane of analysis features by using filing dates as strutured items and keywords and its frequeny as unstrutured items. Besides, differently from previous visualization methods for patent analysis, ours is based on forming a semanti network of keywords from patent douments. And thereby it visualizes a lear overview of patent information in a more omprehensible way. And as a result of those ontributions, it enables us to understand advanes of emerging tehnologies and foreast its trend in the future. The rest of the paper is strutured as follows. In Setion 2, we introdue related works with visualization methods for patent information and its analysis results. In Setion 3, we explain an overview of our approah. In Setion 4, we apply our visualization method to develop a patent map of the ubiquitous omputing tehnology as an emerging tehnology field. And in Setion 5, finally we onlude the paper with a disussion of the proposed patent map s impliations in the ubiquitous omputing tehnology. 2. Literature review Related to researhes using patent information, there are two mainstreams. One of them is to study visualization methods for patent information and its analysis results. And in this paper, we are interested in the former researh area. This researh area has attrated attention of the persons onerned. That s beause urrent tehnologial development neessitates it to avoid unneessary investment as well as gaining the seeds for tehnologial development and the appliable fields. Also, the attention is inreased to promote the effiient use of patent information by deepening related institutions understanding of patent information. On the basis of these awareness, the Japan Patent Offie has been produing and providing more than 5 types of expressions and more than 2 maps for several tehnology fields sine 997 (JIII, 2). In addition, many other ountries suh as Korea (Ryoo & Kim, 25), Italy (Camus & Branaleon, 23; Fattori, Pedrazzi, & Turra, 23) and the USA (Morris, DeYong, Wu, Salman, & Yemenu, 22) also provide many kinds of patent maps. Currently, most patent analyses use patent itations to represent the meaningful relationship in patent information. But it is known that patent itation analysis has some serious drawbaks. Aording to Yoon and Park (24), there are four drawbaks of patent itation analysis desribed. First, it is diffiult to grasp the overall relationship among patent douments. Seond, related to the first problem, the sope of analysis and the rihness of potential information are limited. Third, itation has no apability of onsidering internal relationship between patent douments. Finally, itation analysis is a time-onsuming task beause it needs only an exhaustive searh. So, Yoon and Park (24) proposed a network-based patent analysis as an alternative method. But the network patent analysis still has some limitations. Researhes on the intelligent methods for patent analysis have been made as well. The neural methods for mapping sientifi and tehnial information (artiles, patents) and for assisting a user in arrying out the omplex proess of analyzing large quantities of suh information are onerned by Lamirel, Shehabi, Hoffmann, and Franois (22). Based on text mining tehniques, Tseng, Wang, Juang, and Lin (25) reated a real world patent map for an important tehnology domain: arbon nano-tube experimentally. And the other mainstream is onerned about patent lassifiation. By Blak and Ciolo (24), mahine learning tehnology is applied to text lassifiation on United States patent information to automatially differentiate between patents relating the bioteh industry and those unrelated. Fall, Törsvári, Fiévet, and Karetka (24) reported the results of applying a variety of mahine learning algorithms for training expert systems in Germanlanguage patent lassifiation tasks. And Trappey, Hsu, Trappey, and Lin (26) developed a platform for patent doument lassifiation and searh using a bak-propagation network. 3. Methodology Our visualization method steps are summed up as follows. With olleted keywords from patent douments of a target tehnology field, we luster patent douments by the k-means algorithm. With the lustering results, we form a semanti network of keywords without respet of

3 86 Y.G. Kim et al. / Expert Systems with Appliations 34 (28) Extrating keywords from patent douments related to a target tehnology field Clustering patent douments with keywords using k-means algorithm Forming a semanti network of keywords Forming Patent Map Fig.. Framework of visualization method for patent analysis Clustering patent douments with keywords To luster patent douments with merged keywords, several steps are required (see Fig. 3). First, we hek existene of eah keyword within texts of a patent doument. So we form a keyword existene matrix with a olumn index of keywords (,...,j,...,n) and a row index of patent douments (,...,i,...,m). If j-keyword exists within texts of i-patent doument, then an element of (i, j) is filled with the number of. But if it does not, then the element of (i, j)is filled with the number of. As a result, we make a keyword existene matrix of whih elements are filled with or. Next step is to luster patent douments by k-means algorithm using the keyword existene matrix. Here eah keyword s value between or plays as a feature s value for a patent doument. So the keywords values are used to lassify patent douments into k groups (k P 2) Forming semanti networks of keywords filing dates. And then we build up a patent map by rearranging eah keyword node of the semanti network aording to its earliest filing date and frequeny in patent douments. An overview of our approah is desribed in Fig Colleting keywords Our approah begins by targeting a domain tehnology whih analyzers are interested in. And then initial keywords needs to be olleted from experts to searh related patent douments. After searhing patent douments, we ollet keywords from patent douments. And then, they are merged with the initial keywords. As a result, the list of merged keywords are ompleted to be used for next steps (see Fig. 2). Now with lustered patent douments, we investigate what keyword eah group has. For example, let s assume that patent douments A and B belong to the group. Aording to the matrix in Fig. 4, patent doument A has keywords of a and. And patent doument B has keywords of b and. Then, the group onsists of three keywords of a, b, and. Like this way, we investigate keywords for eah group (see Fig. 4). And using the list of keywords for eah group, we make a semanti network. How to form a semanti network is desribed in Fig. 5. Aording to it, group has keywords of a, b, and. On the other hand, group 2 has keywords of and d. Then two groups share b, and therefore relationship between two groups an be represented by three nodes: (a, b), (), and (d). Here the shared node is higher than the others, so arrows are drawn from () to (a, b) Target a onerned field of tehnology Inquire reommendable keywords from experts Searh patent douments using the reommended keywords Extrat keywords predefined by inventors from the patent douments Patent doument databases Merge the reommended and predefined keywords Fig. 2. Extrating keywords from patent douments.

4 Y.G. Kim et al. / Expert Systems with Appliations 34 (28) Chek existene of keywords ourring in a patent doument Form a keyword existene matrix k-means algorithm Fig. 3. Clustering patent douments using k-means algorithm. a b d A B C D A keyword existene matrix a, b,, d Group A, B C, D Investigation of keywords for eah group Group Clustered patent douments: (A, B), (C, D) Fig. 4. Investigating keywords belonging to lustered groups. a, b,, d a, b,, d Group a, b d Investigation of keywords for eah group keyword Group a, b, Level, d List of keywords for eah group a, b d Level Formation of a semanti network using keywords Fig. 5. Forming a semanti network of keywords. and (d). Like this way, we make a semanti network whih onsists of nodes with a keyword or more than two keywords. The semanti network desribed in this setion is based on the previous steps suh as lustering patent douments with the k-means algorithm and investigating key words

5 88 Y.G. Kim et al. / Expert Systems with Appliations 34 (28) for lustered patent douments. Therefore, the semanti network is dependent on the number of groups whih is set temporarily by the k-means algorithm, and there an be so many semanti networks. There are many exeutable programs whih an perform the k-means algorithm. Using any of them, easily we an repeat the lustering with inreasing the number of groups. And for eah time based on the lustering result, we repeat both steps of investigation of keywords for eah group and formation of a semanti network. As a result of n-repetitions, we get n-semanti networks. Of ourse, the number of groups must be more than. Now we have to hoose one of the n-semanti networks. Usually we selet one whih explains the most of the relations of keywords. And this is a manual operation. But usually as the number of groups in the hosen semanti network inreases, it gets better to explain the relations of keywords by the semanti network. However, too big number makes it worse to form a semanti network therefore we have to find a point of omprise. Atually, there an be a number of ways to find the proper number of groups for the semanti network. However, in this paper, we do not inlude it as our onern and leave it as a further work. For the semanti network obtained, we have to investigate eah node s frequeny in the lustered groups of patent douments. For eah node, it is defined by ounting the number of keywords existenes in the lustered groups of patent douments. For example, in Fig. 6, of node appears in group and 2. So the frequeny of node is 2. The frequeny of is beause both a and b appear in group. And the s is beause d belongs (a, b) only to group. Like this way, we add a frequeny of eah node in the semanti network of keywords Forming a patent map node () (d) Fig. 6. The ompleted semanti network of keywords. Sine now, we have explained how to form a semanti network of keywords and their frequenies as unstrutured items from patent douments related to the target tehnology. From now on, we explain how to make use of strutured items in patent douments to omplete a patent map on the basis of semanti networks. Let s assume that finally we reahed the semanti network as shown in Fig. 7. We have to investigate a filing date of eah node in the semanti network. The filing date of eah node is the earliest filing date among patent douments whih have keywords of the node. For example, in Fig. 8, onsists of keywords of a and b. And if a belongs to patent douments of A, and b belongs to patent douments B, then the filing date whih node has is the earliest filing date among doument A and B. Therefore, the filing date of is Similarly, the filing a b d A B C D a, b d Filing date A node () B C D (a, b) (d) A semanti network with filing date information Fig. 7. Forming a semanti network with filing date information.

6 Y.G. Kim et al. / Expert Systems with Appliations 34 (28) keyword Group a, b,, d a, b d Node Node 2 keyword a, b node 2 Node 3 d A semanti network with frequeny information Fig. 8. Forming a semanti network with frequeny information. date of is Like this way, we add a filing date of eah node in the semanti network of keywords. By these two steps, the semanti network has both aspets of strutured items filing dates, and unstrutured items keywords and their frequenies in patent douments. And now we move on to the next stage for building up a patent map using the aomplished semanti network. The patent map is ompleted by arranging eah node of the semanti network aording to its filing date in an x-diretion and their frequenies in a y-diretion respetively. Fig. 9 shows an example of the proposed patent map. 4. Appliation to ubiquitous omputing tehnology as an emerging tehnology The term, ubiquitous omputing, was oined more than years ago by Mark Weiser who, at that time, was the hief sientist at the Xerox Palo Alto Researh Center. Weiser defined ubiquitous omputing as the method of enhaning omputer use by making many omputers available throughout the physial environments, but making them effetively invisible to the user (Weiser, 99). frequeny node () 2 2 node () (a, b) (d) A semanti network with filing date information (a, b) (d) filing date Fig. 9. Forming a patent map using a semanti network. The proposed patent map

7 8 Y.G. Kim et al. / Expert Systems with Appliations 34 (28) In the long term, ubiquitous omputing is expeted to take on great eonomi signifiane (Fleish, 25). Numerous appliations in the business environment will beome possible as physial and informational world ontinue to merge. And thus, additional information on Table The list of merged keywords for patent analysis Number Keyword Earliest filing date RFID Universal PnP Trigger HAVI HTML M VXML interhangeability Fabriation Shop floor Magneti memory devie Logistis Automati identifiation PDA, mobile, handheld devie Intelligent Remote Control System GPS Ubiquitous omputing Sensor network Smart Identifiation Manufaturing Distribution Lifeyle Healthare Blue tooth Traking Context awareness Inventory objets, proesses, and individuals may be gathered, exhanged and proposed in a ost effiient way (Müller & Zimmermann, 22). Ubiquitous omputing triggers suh funtions as inreased transpareny, differential priing and disintermediation of the value hain (Clemons & Hitt, 2). Aording to these irumstanes, a lot of patents are being invented all around world as a result of the advent of ubiquitous omputing tehnology. To survive as a ompetitive leader in the market of ubiquitous omputing tehnology, it is important to analyze patent information related to ubiquitous omputing tehnology. Hene, in this paper we targeted ubiquitous omputing tehnology as an emerging tehnology. And we applied our visualization method for building its patent map. Afterwards, we performed steps in Setion 3 to apply our method and to build up a patent map. Their results for those steps are as follows. Related to Setion 3., first we olleted keywords reommended from experts related to ubiquitous omputing tehnology. And then we searhed patent douments related to ubiquitous omputing tehnology using those Table 2 The list of keywords in eah group of lustered patent douments Group Keyword, 2, 3, 9,,, 3, 4, 5, 6, 8, 9, 2, 2, 24, 25, 26 2, 3, 4, 6, 9, 5, 7, 8, 2, 2, 23, 25, , 4, 5, 8,,, 2, 3, 4, 5, 7, 9, 2, 23, 24, 25 4, 4, 7, 9,,, 5, 6, 7, 8, 9, 2, 22, 23, 24, 25, 26 5,, 2, 3, 5, 6, 9, 2, 23, 24, 26 node Ubiquitous Computing Distribution node 4 node 5 node 6 node 7 Context awareness RFID Inventory Blue Tooth PDA Manufaturing Mobile devie Traking node 8 node 9 node node Logistis Identifiation HAVI Smart Sensor Network Automati Identifiation Remote Control System Trigger Lifeyle node 4 node 5 Universal PnP Intelligent GPS node 6 node 7 node 8 Fabriation Shop floor HTML VXML interhangeability Healthare Magneti memory devie Fig.. A semanti network of nodes ontaining keywords.

8 Y.G. Kim et al. / Expert Systems with Appliations 34 (28) reommended keywords. As a result, totally 96 patent douments were searhed. And then, we investigated predefined keywords from the 96 patent douments. And then, we merged reommended and predefined keywords. The final list of merged keywords for ubiquitous omputing tehnology is as shown in Table. As desribed in Setion 3.2, we heked eah keyword s existene in the searhed patent douments. Based on the result, we lustered 96 patent douments with the k-means algorithm of Clementine TM. And then, we made semanti networks with inreasing the number of groups, and seleted a semanti network with five groups. The list of keywords for eah group is shown in Table 2. Related to Setion 3.3, we ompleted the final semanti network of keywords using the result of lustering (see Fig. ). And then based on the semanti network, we investigated earliest filing date and frequeny of eah node in the semanti network (see Table 3). Aording to Setion 3.4, with filing date and frequeny of eah node, the semanti network is transformed into the patent map for the ubiquitous omputing tehnology (see Fig. ). From the patent map, we see tehnologies related to ubiquitous omputing tehnology have progressed towards HTML! VXML interhangeability and magneti memory devies in 23 sine the patents related to Table 3 Filing date and frequeny for eah node of the semanti network in Fig. Node Filing date Frequeny Keyword (number) Ubiquitous omputing (5) Distribution (2) Context awareness (25) RFID () Inventory (26) Blue tooth (23) PDA, mobile, handheld devie () Manufaturing (9) Traking (24) Logistis (9) Identifiation (8) HAVI (4) Smart (7) Sensor network (6) Automati identifiation () Remote Control System (3) Trigger (3) Lifeyle (2) Universal PnP (2) GPS (4) Intelligent (2) Fabriation (6) Shop floor (7) Healthare (22) HTML M VXML interhangeability (5) Magneti memory devie (8) frequeny Inventory RFID Automati Identifiation Logistis Identifiation Manufaturing Traking Ubiquitous Computing Distribution 2--3 Sensor Network HAVI Smart PDA Mobile devie Blue Tooth Context awareness Fabriation Universal PnP GPS Remote Control System Intelligent Trigger Lifeyle Shop floor Healthare HTML VXML interhangeability Magneti memory devie year Fig.. A patent map based on a semanti network of Fig..

9 82 Y.G. Kim et al. / Expert Systems with Appliations 34 (28) automati identifiation, inventory, RFID, and logistis appeared in Conlusions and further works In this paper, we proposed a new visualization method for patent analysis to overome drawbaks of urrent patent maps. Comparing to the other methods in the literature of Setion 2, our approah onsidered both sides of strutured items and the unstrutured items of patent douments. Thereby it provided an integrated and balaned approah to analyze patent information. Moreover, we suggested building up a patent map based on a semanti network of keywords from patent douments with the k- Means algorithm. So it ould visualize a lear overview of patent information in a more omprehensible way. Finally, using the suggested framework of a visualization method for a patent map, we ompleted a patent map for ubiquitous omputing tehnology as an emerging tehnology. From the patent map, we an find what kinds of patents on the ubiquitous omputing tehnology have appeared and how those patents are merged and divided as time passes. Likewise, the proposed patent map gives a omplete view of emerging tehnology s advane. Also it helps us to have an insight to the tehnology field, thereby to avoid unneessary investments and find the seeds for the next patent. As a further work, we had like to modify our approah into more sophistiated one with up-to-date data mining tehniques. Espeially, to make our visualization method more onrete, we plan to investigate methods whih an determine the number of lustering groups in Setion 3.3. Moreover, we plan to apply our approah to other emerging tehnologies in addition to ubiquitous omputing tehnologies. Referenes Blak, D., & Ciolo, P. (24) Mahine learning for patent lassifiation. Camus, C., & Branaleon, R. (23). Intelletual assets management: From patents to knowledge. World Patent Information, 25(2), Clemons, E. K., & Hitt, L. M. (2). The Internet and the future of finanial servies: Transpareny, differential priing, disintermediation. Disussion Draft, University of Pennsylvania, The Warton Shool. Fall, C. J., Törsvári, A., Fiévet, P., & Karetka, G. (24). Automated ategorization of German-language patent douments. Expert Systems with Appliations, 26(2), Fattori, M., Pedrazzi, G., & Turra, R. (23). Text mining applied to patent mapping: A pratial business ase. World Patent Information, 25(4), Fleish, E. (25). Ubiquitous network soieties: Their impat on the teleommuniation industry. Bakground Paper in ITU Workshop. Ganapathy, S., Ranganathan, C., & Sankaranarayanan, B. (24). Visualization strategies and tools for enhaning ustomer relationship management. Communiation of ACM, 47(), Japan Institute of Invention and Innovation. (2). Guide book for pratial use of patent map for eah tehnology field. Lamirel, J. C., Shehabi, S. A., Hoffmann, M., & Franois, C. (22). Intelligent patent analysis through the use of a neural network: Experiment of multi-view point analysis with the MultiSom model. In Proeedings of the ACL-23 workshop on patent orpus proessing. Liu, S. J. (23). A route to a strategi intelligene of industrial ompetitiveness. The first Asia-Paifi Conferene on Patent Maps, 2 3. Morris, S., DeYong, C., Wu, Z., Salman, S., & Yemenu, D. (22). DIVA: A visualization system for exploring douments databases for tehnology foreasting. Computers & Industrial Engineering, 43(4), Müller, C. D., & Zimmermann, H. D. (22). Beyond mobile: Researh topis for upoming tehnologies in the insurane industry. In Proeedings of the 36th Hawaii international onferene on system sienes. Ryoo, J. H., & Kim, I. G. (25). Workshop H What patent analysis an tell about ompanies in Korea, Far East Meets West in Vienna. Trappey, A. J. C., Hsu, F. C., Trappey, C. V., & Lin, C. I. (26). Development of a patent doument lassifiation and searh platform using a bak-propagation network. Expert Systems with Appliations, 3(4), Tseng, Y. H., Wang, Y. M., Juang, D. W., & Lin, C. J. (25). Text mining for patent map analysis. In IACIS Paifi 25 onferene proeedings (pp. 9 6). Weiser, M. (99). The omputer of the twenty-first entury. Sientifi Ameria, 94. Westphal, C., & Blaxton, T. (998). Data mining solution. New York: Wiley. WIPO. (23). Patent map with exerises (related). WIPO-MOST intermediate training ourse on pratial intelletual property issues in business, Theme 6. Yoon, B., & Park, Y. (24). A text-mining-based patent network: Analyti tool for high-tehnology trend. The Journal of High Tehnology Management Researh, 5(), 37 5.