A New Visualization Method for Patent Map: Application to Ubiquitous ...

A New Visualization Method for Patent Map: Application to Ubiquitous Computing Technology Jong Hwan Suh1 and Sang Chan Park1,2 1 Department

of Industrial Engineering and 2 Graduate School of Culture Technology Korea Advanced Institute of Science and Technology (KAIST), Guseong-dong, Yuseong-gu, Daejeon, Republic of Korea {SuhJongHwan, sangchanpark}@major.kaist.ac.kr

Abstract. As technologies develop in faster and more complicated ways, it is getting more important to expect the direction of technological progresses. So many methods are being proposed all around world and one of them is to use patent information. Moreover, with efforts of governments in many countries, many patent analysis methods have been exploited and suggested usually on the basis of patent documents. However, current patent analysis methods have some limitations. In this paper, we suggest a new visualization method for a patent map, which represents patent analysis results with considering both structured and unstructured items of each patent document. And by the adoption of the k-means clustering algorithm and semantic networks, we suggest concrete steps to make a patent map which gives a clear and instinctive insight on the targeted technology. In application, we built up a patent map for the ubiquitous computing technology and discussed an overall view of its progresses.

1 Introduction With technologies developing in more complicated ways, it’s becoming more important to understand how technologies are going on. Analyzing patent information is one of methods to recognize those progresses. And the patent map is the visualized expression of total patent analysis results to understand complex and various patents’ information easily and effectively [1]. To build up the patent map, we usually utilize patent documents which contain dozens of items for analysis. In patent documents, structured items mean they are uniform in semantics and in format across patents such as patent number, filing date, or investors. On the other hand, the unstructured ones represent free texts that are quite different in length and content for each patent such as claims, abstracts, or descriptions of the invention. The visualized analysis results of the former items are called patent graphs and those of the later are called patent maps, although loosely patent maps may refer to both cases [2]. Patent documents are often lengthy and rich in technical and legal terminology and are thus hard to read and analyze for non-specialists [3]. Therefore visualization methods to analyze patent information and represent analysis results are more attractive. However, visualization methods for patent maps are limited on their representation. They do not summarize X. Li, O.R. Zaiane, and Z. Li (Eds.): ADMA 2006, LNAI 4093, pp. 566 – 573, 2006. © Springer-Verlag Berlin Heidelberg 2006

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overall information effectively and consider only one aspect between structured and unstructured items of each patent document. Hence more integrated and balanced visualization approaches are required. In this paper, we suggest a new visualization method for a patent map, which represents patent analysis results with considering both structured and unstructured items of each patent document. Thereby we can keep the balance of analysis features by using a filing date as a structured item, and a keyword as an unstructured item. The rest of the paper is structured as follows. Section 2 begins by introducing related works and Section 3 gives an overview of our approach. And in Section 4 we apply this visualization method to develop a patent map for the ubiquitous computing technology, and discuss implications of the patent map. In Section 5 finally we conclude the paper with a discussion of patent map’s implications in the ubiquitous computing technology.

2 Related Work Current technological development necessitates conducting searches of patent information to avoid unnecessary investment as well as gaining the seeds for technological development and the applicable fields contained in the parent information. The Japan Patent Office has been producing and providing more than 50 types of expressions and more than 200 maps for several technology fields since 1997 [4]. Many other countries such as Korea and United States also provide patent maps [5, 6]. Researches on intelligent patent analysis have been made as well. The neural methods for mapping scientific and technical information (articles, patents) and for assisting a user in carrying out the complex process of analyzing large quantities of such information are concerned [7]. Machine learning technology is applied to text classification on United States patents to automatically differentiate between patents relating the biotech industry and those unrelated [6].

3 Visualization Method for Patent Map Steps to implement a new visualization method for a patent map which we propose in this paper are as follows. Firstly, we target a domain technology interested in. And we select keywords to search related patent documents. After that, we redefine a list of keywords for further analysis. And now with a set of patent documents and a list of keywords, we check existence of each keyword within texts of each patent document. To record this step’s result, we need to form a matrix with a column index of keywords (1...n) and a row index of patent documents (1...m). So, if the jth keyword exists within texts of the ith patent document, then an element of (i, j) is filled with ‘1’. But if it does not, then the element of (i, j) is filled with ‘0’ (see Figure 1). The next step is to cluster patent documents by the k-means clustering algorithm using the completed matrix. Here each keyword’s value between ‘0’ or ‘1’ plays as a feature’s value for

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each patent document. So the keywords’ values are used to classify patent documents into ‘k’ groups (see Figure 2). Now with clustered patent documents, we investigate what keyword each group has. For example, let’s assume that patent documents ‘A’ and ‘B’ belong to the group 1. According to the matrix in Figure 1, patent document ‘A’ has keywords of ‘a’ and ‘c’ and patent document ‘B’ has keywords of ‘b’ and ‘c’. Then, the group 1 consists of three keywords of ‘a’, ‘b’, and ‘c’. Like this way, we investigate keywords for each group (see Figure 3).

Fig. 1. A matrix of which elements are filled with ‘0’ or ‘1’ according to whether a keyword exists within texts of a patent document

Fig. 2. Clustering patent documents by the k-means clustering algorithm using the completed matrix

Fig. 3. Investigating keywords for each group after clustering patent documents

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And using the list of keywords for each group, we make a semantic network. In the figure, group 1 has keywords of ‘a’, ‘b’, and ‘c’. On the other hand group 2 has keywords of ‘c’ and‘d’. Then two groups share ‘b’ and therefore relationship between two groups can be represented by three nodes: (a, b), (c), and (d). Here the shared node is higher than the others, so arrows are drawn from (c) to (a, b) and (d). Like this way, we make a semantic network which consists of nodes with a keyword or more than two keywords (see Figure 4).

Fig. 4. Forming semantic networks using lists of keywords for each group

Actually, the semantic network is based on the previous steps such as ‘clustering patent documents with the k-means clustering algorithm’ and ‘investigating key words for clustered patent documents’. Therefore, the semantic network is dependent on the number of groups which is set temporarily by the k-means clustering algorithms, so there are many semantic networks. There are many executable programs which can perform the k-means clustering algorithm. Using any of them, easily we can repeat the clustering with increasing the number of groups. And for each time based on the clustering result, we repeat both steps of ‘investigation of keywords for each group’ and ‘formation of a semantic network’. Finally, we get ‘n’ semantic networks after ‘n’ repetitions but here we do not consider the case when the number of groups is just one. And then we have to choose one of many semantic networks. Usually we select one which explains the most of the relations of keywords. Actually, this is a manual operation. But usually as the number of groups in the chosen semantic network increases, it gets better to explain the relations of keywords by the semantic network. However, too big number makes it worse to form a semantic network therefore we have to find a point of comprise. Since now, we have explained how to form a semantic network of keywords, unstructured items, from patent documents related to the target technology. From now on, we explain how to make use of structured items to complete a patent map on the basis of semantic networks. Let’s assume that finally we reached the semantic network as shown in Figure 5. Firstly, we have to investigate a filing date of each node in

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the semantic network. The filing date of each node is the earliest filing date among patent documents which have keywords of the node. For example, in the figure, node 2 consists of keywords of ‘a’ and ‘b’. And if ‘a’ belongs to patent documents of ‘A’, and ‘b’ belongs to patent documents ‘B’, then the filing date which node 1 has is the earliest filing date among document ‘A’ and ‘B’. Therefore, the filing date of node 2 is ‘1997-11-27’. Similarly, the filing date of node 3 is ‘2000-08-18’. Like this way, we accomplish a semantic network of keywords of which each node has its filing date. Now the semantic network has both aspects of structured and unstructured items within patent documents. Finally, we move on to the stage for building up a patent map using the accomplished semantic network. Nodes of the semantic network are rearranged according to their filing dates. Figure 6 shows an example of the proposed patent map.

Fig. 5. Forming a semantic network with filing date information

Fig. 6. Forming a patent map on the basis of a semantic network with filing date information

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4 Application to Ubiquitous Computing Technology In the long term, Ubiquitous Computing is expected to take on great economic significance. So a lot of patents related to ubiquitous computing technology are being invented all around world. According to these circumstances, it’s been important to analyze those patents. Therefore in this paper, we targeted the ubiquitous computing technology for the application of our visualization method. With steps in Section 3, we ended up with a patent map for ubiquitous computing technology. Those steps for the application are described as follows. Firstly we searched keywords related to the ubiquitous computing technology. And then we searched patent documents related to the ubiquitous computing technology using those keywords. Totally 96 patent documents were searched and we used them for patent information analysis. We rebuilt up the list of keywords based on the searched patent document and the final list of keywords is as shown in table 1. On the basis of keywords in table 1, we clustered 96 patent documents using Clementine™. And then we made semantic networks with increasing the number of groups, and selected a semantic network with 5 groups. The list of keywords for each group is shown in table 2. Table 1. The list of keywords redefined from searched patent documents, and the earliest filing dates of patent documents each keyword belongs to

Number

Keyword

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

RFID Universal PnP Trigger HAVI HTML↔VXML interchangeability Fabrication Shop floor Magnetic memory device Logistics Automatic identification PDA, mobile, handheld device Intelligent Remote Control System GPS Ubiquitous computing Sensor network Smart Identification Manufacturing Distribution Lifecycle Healthcare Blue tooth Tracking Context awareness Inventory

Earliest Filing Date 1987-04-07 2002-10-01 2002-08-22 2002-08-22 2003-04-02 2002-08-22 1987-08-18 2003-04-30 198704-07 1987-04-07 2002-05-21 2002-12-27 2002-10-02 2002-05-21 2000-06-02 2001-01-31 2001-03-15 1987-04-07 1997-08-22 2000-06-02 2002-08-22 1987-08-18 2002-08-22 1991-12-24 2002-07-29 1987-04-07

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Table 2. The list of keywords of each group of clustered patent documents with 5 groups

Group 1 2 3 4 5

Keyword 1, 2, 3, 9, 10, 11, 13, 14, 15, 16, 18, 19, 20, 21, 24, 25, 26 1, 3, 4, 6, 9, 15, 17, 18, 20, 21, 23, 25, 26 2, 4, 5, 8, 10, 11, 12, 13, 14, 15, 17, 19, 20, 23, 24, 25 1, 4, 7, 9, 10, 11, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26 1, 11, 12, 13, 15, 16, 19, 20, 23, 24, 26

Using the result of clustering, we completed the final semantic network as shown in Figure 7. And then based on the semantic network, the patent map for the ubiquitous computing technology was made as shown in Figure 8.

Fig. 7. A semantic network with nodes of keywords from clustered patent documents and filing information

Fig. 8. A patent map based on a semantic network of Figure 7

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5 Conclusion In this paper, we proposed a new visualization method for a patent map and applied it to the ubiquitous computing technology. Comparing to the other methods in the literature of Section 2, our research considered both sides of structured items and the unstructured items of patent documents. Thereby it provided a balanced approach to analyze patent information. Moreover, we suggested concrete steps to form semantic networks by the k-means clustering algorithm with keywords and filing date, and finally a patent mp as described concretely in Section 3. By doing so, we expect a patent map will turn out a more intelligent and sophisticated patent map. Also non expert also can make a patent map with more understandings because this paper explains how to make a patent map in clear and instinctive ways. In addition, using the suggested framework of a visualization method for a patent map, we suggested a semantic network and a patent map for the ubiquitous computing technology. From the patent map, we can find what kinds of patents on the ubiquitous computing technology have appeared and how those patents are merged and divided as time passes. Figure 8 shows patents on the ubiquitous computing technology have progressed towards HTML VXML interchangeability and magnetic memory devices in 2003 since the patents related to automatic identification, inventory, RFID, and logistics appeared in 1987. Like this, the proposed patent map gives a complete view of the development of patents which are related to the target technology. Also it helps the person concern on the target technology to have an insight to the next patents, thereby to avoid unnecessary investments and find the seeds for the next patent.

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Acknowledgement. This research is supported by the KAIST Graduate School of Culture Technology.

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