یک سیستم هوش ثبت اختراع برای برنامه ریزی استراتژیک فناوری

Patent intelligence—the transformation of content found in multiple patents into technical, business, and legal insight—is considered a key factor in gaining a competitive advantage in technologically competitive business environments. Although keyword-based patent intelligence tools are widely used due to their simplicity and ease of use, they are limited in that they cannot represent key technological concepts and inventive knowledge by relying only on the frequency of occurrence of defined keywords. As a remedy, this paper proposes a Subject–Action–Object (SAO)-based patent intelligence system. SAO structures that can be extracted from textual patent information are known as the expertise and inventive findings of the relevant patent. On the basis of semantic analysis of patent SAO structures, our proposed intelligence system constructs patent maps and patent networks. Building on the maps and networks, the system provides specific functionalities including identification of technology trends and significant patents, detection of novel technologies, and identification of potential infringement. This paper describes the architecture of our proposed patent intelligence system in detail, and illustrates the system’s functionalities using case studies. We anticipate that our proposed system will be incorporated into the technology planning process to assist experts in the formulation of technology strategies.

In today’s competitive business environments, patent intelligence—the transformation of content found in patents into technical, business, and legal insight—is getting much attention as a tool to aid in efforts to secure competitive advantages. Analyzing patents that are representative industrial property provides information about specific conditions in relation to technological or market-related development, which aids decision makers in the tracking of competitors’ activities and new innovation trends (Cantrell, 1996). Recently, companies have increasingly been making patent applications in order to protect their inventive knowledge. This increase in the number of patents makes patent intelligence a vital tool for formulating strategic technology planning (Yoon & Kim, 2012b). Patent intelligence tools incorporate a variety of functionalities including technology monitoring, technology assessment, and technology forecasting (Lichtenthaler, 2004). They provide several advantages over qualitative patent analysis done by human experts. These include (1) ability to analyze large amounts of patent data that cannot be analyzed by humans alone, (2) ability to generate much useful information, such as visual relationships between technology and companies (characteristic of statistical analysis technology), which humans cannot produce, (3) ability to support decision making processes by providing relevant information including technology assessment and technology forecasting (Yoon, 2008 and Yoon and Kim, 2012b). Many patent intelligence tools have been developed to support decision-making in technology planning. Overall, they can be classified into two approaches—the bibliographic approach and the content-based approach. The bibliographic approach uses bibliographic patent information including citations, applicants, inventors, and international patent classification (IPC) codes. Although the bibliographic approach is widely used to identify meso- and macro-trends in technologies, companies, and inventors (No & Park, 2010), it cannot identify detailed technological features and significant insights because it mainly relies on bibliographic information, which is considered to be superficial data (Yoon & Park, 2004). In this regard, the content-based approach emphasizes technologically significant patterns, trends, and opportunities by extracting useful information such as abstracts, detailed description of invention and claims from patent text (Tseng et al., 2007 and Yoon et al., 2011). Content-based patent intelligence tools are increasingly being proposed by researchers. One representative content-based approach method is keyword-based analysis (KWA). Many researchers have developed keyword-based patent intelligence tools to identify trends in high-technology (Yoon & Park, 2004), to discover new technological opportunities from patent vacuums (Lee, Yoon, & Park, 2009), to forecast new technological concepts (Yoon & Park, 2005), and to develop technology roadmaps (Lee et al., 2008 and Yoon et al., 2008). The keyword-based patent intelligence approach uses occurrence information including frequencies of defined keywords and co-occurrences among the keywords. In general, by exploiting the vector space model (Salton, Wong, & Yang, 1975) used in information retrieval, KWA transforms each patent into a keyword vector, identifies similarities among patents using similarity measures including Cosine similarity and Euclidean distance, and then constructs patent maps and patent networks. Despite its simplicity and ease of use, KWA is limited in that it cannot incorporate key technological concepts such as objectives, uses, and structures of the relevant patent (Cascini et al., 2004, Cascini and Zini, 2008 and Yoon et al., 2011). Furthermore, several studies have indicated that frequencies and co-occurrences of keywords in patents cannot represent the inventive knowledge and method of relevant patents (Park et al., 2013 and Wanner et al., 2008). As a remedy, this paper proposes an SAO-based patent intelligence system. SAO structures are the grammatically sequenced sentence of a subject, a verb, and an object that can be extracted by exploiting natural language processing (NLP) of textual patent information (Cascini et al., 2004). Specifically, SAO structures that are obtainable from a patent are considered as being able to provide the expertise, know-how, and significant findings of the patent (Bergmann et al., 2008, Moehrle et al., 2005 and Sternitzke and Bergmann, 2009). Building on the semantic similarities of patents’ SAO structures, the system constructs patent maps and patent networks that provide several functionalities for patent intelligence. Using the patent maps and patent networks, the system creates significant information to support decision-making for technology planning. In this paper, we describe the architecture of the proposed intelligence system and illustrate the system’s functionalities using several practical case studies. We anticipate that the proposed patent intelligence system will be incorporated into the technology planning process to assist experts in the formulation of technology strategies. The rest of this paper is organized as follows. Section 2 overviews related work while Section 3 describes the architecture and functionalities of the proposed system. In Section 4, the functionalities of the system are illustrated using case studies, and finally, Section 5 concludes the paper by outlining future research topics.

This paper proposed an SAO-based patent intelligence system to support the decision-making process of experts in strategic technology planning. Although many researchers developing patent intelligence tools adopted KWA due to its simplicity and easy application, KWA cannot incorporate key technological concepts and inventive knowledge in patents in that the keyword-based approach only uses occurrence information including frequencies of defined keywords and co-occurrences among the keywords. As a remedy, we adopted SAO-based patent analysis that can consider the expertise, know-how, and significant findings of the patents. Building on the semantic similarities of SAO structures from patents, the proposed system constructs patent maps and patent networks to provide decisive technological information to support experts. Specifically, patent map-based analyzers identify changing technology trends, new technology opportunities, emerging or outlier technologies, and patent infringement, while patent network-based analyzers identify overall and specific technology trends, such as dominant and verified, promising, significant, emerging, and widely-used technologies or technology functions. By using the proposed patent intelligence system, experts can obtain decisive technological information to aid decision-making for developing technology strategies. However, the present study has several limitations that need to be resolved in the future. First, in the study, several individual tools, such as Knowledgist 2.5 to extract SAO structures from patent documents and NetMiner 3 to generate the patent maps and patent networks, were utilized. However, these tools are not integrated in one complete system. Users have to convert unstructured data to a structured form that fits the input-data format of these tools. This process is inconvenient and might be difficult tasks for users, especially for IT unfriendly users. To overcome this, these tools need to be integrated in one complete system to enhance the usability and practicality of the proposed system. Second, although WordNet as an upper ontology for general concepts was exploited to measure semantic similarities between terms extracted from patents, certain unusual domain-specific terms and concepts may be absent from its collections. We defined unusual technological terms used in several technology fields analyzed in case studies, but it is also not enough to cover various technology domains. Thus, further work should extend the local concept hierarchy for domain-specific terms to improve the credibility of the proposed system.