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Identification of promising patents for technology transfers using TRIZ evolution trends
Expert Systems with Applications 40 (2013) 736–743
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Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
Identification of promising patents for technology transfers using TRIZ evolution trends Hyunseok Park a, Jason Jihoon Ree b, Kwangsoo Kim b,⇑ a b
Department of Technology and Innovation Management, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang 790-784, Republic of Korea Department of Industrial and Management Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang 790-784, Republic of Korea
a r t i c l e
i n f o
Keywords: Open innovation Technology transaction Patent evaluation Technology evaluation Patent mining Patent analysis Text mining Subject–Action–Object SAO
a b s t r a c t Technology transfer is one of the most important mechanisms for acquiring knowledge from external sources to secure innovative and advanced technologies in high-tech industries. For successful technology transfer, identification of high-value technologies is a fundamental task. In particular, identifying future promising patents is important, because most technology transfer transactions are aimed at acquiring technologies for future uses. This paper proposes a new approach to identification of promising patents for technology transfer. We adopted TRIZ evolution trends as criteria to evaluate technologies in patents, and Subject–Action–Object (SAO)-based text-mining technique to deal with big patent data and analyze them automatically. The applicability of the proposed method was verified by applying it to technologies related to floating wind turbines. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction Companies should continuously secure innovative and advanced technologies to respond to market trends and customer needs. The fundamental activity for obtaining technological capabilities is internal research and development. Conventionally, most companies focused primarily on achieving a competitive advantage by internally developing exclusive technologies (Elmquist & Le Masson, 2009; Ganuza, Llobet, & Domınguez, 2009). However, because of today’s fast-changing market trends and customer needs, a single company can have difficulties responding to such changes only by using its internal technological capabilities (Cassiman & Veugelers, 2006; Du & Ai, 2008). To overcome these difficulties, companies have tried to use technology transfer to remedy their technological limitations or to acquire necessary technologies from external sources. Recently, the increasing number and volume of deals has made technology transfer one of the most important mechanisms to develop a company’s technological capabilities. For acquisition of external technologies to be financially beneficial to the acquiring company, it must target high-value technologies (Gibson & Williams, 1990; Rogers, Takegami, & Yin, 2001). Especially, considering (1) that technologies acquired by technology transfer are mainly aimed at achieving future-oriented goals rather than present-oriented goals, and (2) that development of future competitive advantages is a significant task in any technology
⇑ Corresponding author. Tel.: +82 54 279 8235; fax: +82 54 279 5998. E-mail addresses: [email protected] (H. Park), [email protected] (J.J. Ree), [email protected] (K. Kim). 0957-4174/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.eswa.2012.08.008
domain, identifying and acquiring future promising patents is becoming increasingly important to gain such advantages. Despite the importance of identifying promising technologies or patents for technology transfer, little research has been conducted to provide ways of doing so. In an attempt to provide a practical guide, we propose a new approach to identification of promising patents for technology transfer; this approach adopts TRIZ evolution trends and an SAO-based text mining technique. ‘TRIZ evolution trends’ is a TRIZ tool that presents specific sequences of technological transitions or trends, which indicate how a system or technology evolves over time (Petrov, 2002). Thus, TRIZ trends have been recognized as a useful tool for technology analysis from the future perspective and have actively been employed in various research fields, such as predicting future improvements of technologies (Verhaegen, D’hondt, Vertommen, Dewulf, & Duflou, 2009), forecasting the design of eco-products (Yang & Chen, 2011), identifying technology trends (Wang, Chang, & Kao, 2010; Yoon & Kim, 2011d) and supporting decision-making in product development (Zhang, Xu, & Hu, 2004). In general, several TRIZ trends can apply to a given technology domain, and the technological importance of those TRIZ trends in the technology domain are not equal. Specifically, according to a current lifecycle of a specific technology, some TRIZ trends should be considered more than others. For instance, technologies related to improving performance of products or systems are important during the stage in which the technology is becoming increasingly pervasive, whereas technologies related to increasing the reliability or convenience of products or systems are important during the stage in which the technology is widespread. Although prior studies using TRIZ trends equally
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weighted each related TRIZ trend without regarding the lifecycle of a technology domain (Mann, 2003; Verhaegen, D’hondt, Vertommen, Dewulf, & Duflou, 2009; Yoon & Kim, 2011a), this paper considers the changes in the importance of each TRIZ trend according to the technology’s current lifecycle stage. Although TRIZ is recognized as a useful tool for technology analysis, practical application of TRIZ without the aid of experts is not easy. However, an expert’s analysis of a large set of patent data requires considerable labor or may be an impossible task. To overcome the limitations, we propose a text-mining technique. In particular, we suggest an SAO-based text mining approach instead of a conventional keyword-based text-mining approach. SAO structures can reflect the technological key concept in a patent document (Cascini & Zini, 2008; Moehrle, Walter, Geritz, & Müller, 2005) and can be directly compared with the rule base of ‘reasons for jumps’ (RFJ) of TRIZ trends that are expressed in Action–Object (AO) structure. Thus, use of SAO structures is a more appropriate text mining approach than a keyword-based approach for this method of identifying promising patents. For verification, the method was applied to technologies related to floating wind turbines. The remainder of this paper is structured as follows. Section 2 provides a brief overview of theoretical background. Section 3 describes the proposed methodology. Section 4 illustrates the approach. Section 5 provides conclusions and future directions. 2. Theoretical background 2.1. TRIZ evolution trends TRIZ was originally developed by the Soviet inventor Genrich Al’tshuller and his associates by analyzing a vast number of patents across many different fields (Al’tshuller, 1984; Salamatov, Souchkov, Strogaia, & Yakovlev, 1999). TRIZ is a powerful tool to provide systematic and innovative ideas for problem-solving and technology analysis (Mann, 2001). The TRIZ evolution trends, is a TRIZ tool that reveals the patterns of evolution of business and technology systems, and is useful to evaluate the status of the system today and how it will evolve in the future. Classical TRIZ discovered eight patterns of evolution of technical systems: Completeness of parts of the system, Energy conductivity of a system, Harmonizing the rhythm of the system’s parts, Increasing ideality, Uneven development of the system’s parts, Transition to a super-system, Transition from macro- to micro-level, and Increasing the s-field development. Recently, Mann (2002) suggested updated TRIZ trends (Fig. 1). In particular, what makes TRIZ evolution trends a useful tool for technology evaluation and forecasting is that almost every TRIZ trend follows the basic principle of the TRIZ philosophy, Increasing Ideality, which means that technology systems evolve to increasing benefits while reducing harm, and that most technologies and systems evolved only in this direction (left-to-right direction in the TRIZ trends). For instance, the ‘Dynamization’ evolution trend entails evolution from an immobile system to a jointed system, to a flexible system, and ultimately to a field system (Fig. 2). 2.2. Technology lifecycle Besides the TRIZ trends, every technology and system follows a typical development cycle (Ayres, 1994; Christensen, 1993; Foster, 1986): From an innovation stage, through a growth stage, to a maturity stage. During the innovation stage, various innovative designs and systems emerge and compete with each other to be selected as a dominant design in the technology industry. During the growth stage, technologies for improving product performance and technological productivity are emphasized. During the maturity stage, customer demands on products shift from highperformance to reliability, convenience, low price and emotional
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aspects (Christensen, 2003). A general lifecycle pattern of technology or system is visualized as an S-curve graph (Fig. 3). When technologies reach the maturity stage, further increasing the technology’s performance becomes increasingly difficult or impossible (Schilling & Esmundo, 2009). To break through this technological limitation, discontinuous innovation which creates a technology paradigm shift is necessary: this entails a shift from the original S-curve to a new one (Fig. 3). A new S-curve is usually generated by a new system or design, which offers new value to customers or displaces previous systems and technologies. This new S-curve also follows the lifecycle pattern of innovation, growth, and maturity stages. 2.3. SAO-based text mining To automatically analyze unstructured technological information in patents, it should be transformed into an abstracted form which includes the technological key concepts and structural relations among components. To this end, Natural Language Processing (NLP)-based text mining techniques are required. Text mining approaches can be broadly classified into two types: keyword-based, and SAO-based. Although much previous research has employed a keyword-based approach due to its simplicity and ease of use, it is insufficient to reflect the specific technological key concepts and structural relationships among components, because the keyword vector abstracted from the patent by keyword-based text mining, is composed only of frequency of keyword occurrence. In contrast, an SAO-based approach can reflect specific technological key concepts and relations (Yoon & Kim, 2011b). S represents ‘solution’ and AO represents ‘problem’ in the technological sentence, and thus an SAO structure reflects specific key findings in the patent (Cascini & Zini, 2008). In addition, S and O represent the components and A denotes the effect or relations between them; thus an SAO structure describes the structural relationships among components in a patent (Moehrle et al., 2005). In particular, an SAO-based approach is appropriate for this study because the rule base of RFJ of TRIZ evolution trends is expressed in AO structure, so functional semantic similarity between the rule base and AO structures from extracted SAOs can be measured directly. Recent studies which adopted text mining based patent analysis for technology management have started to exploit an SAO-based approach instead of a keyword-based approach. Examples include analyzing patent risk (Bergmann et al., 2008; Park, Yoon, & Kim, 2012), profiling inventors (Moehrle et al., 2005), monitoring technology (Gerken & Moehrle, 2012), constructing technology trees (Choi, Park, Kang, Lee, & Kim, 2012), analyzing technological trends (Choi, Yoon, Kim, Lee, & Kim, 2011; Yoon & Kim, 2011c) and detecting signals of new technological opportunities (Yoon & Kim, 2012). 3. Research design The overall procedure for identification of future promising patents using TRIZ evolution trends involves collecting patents, identifying the stage that the selected technology occupies in the technological lifecycle, extracting SAO structures from the patent documents, identifying TRIZ evolution trends, and evaluating the patents (Fig. 4). These processes will be explained in detail in the following sections. 3.1. Patent collection As the first step, a technology domain should be selected and the patents within the domain should be collected by using International Patent Classifications (IPC) or keyword retrieval from free patent databases such as the United States Patent and Trademark
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Fig. 1. TRIZ evolution trends, redrawn from Mann (2002).
patents that pertain to that technology. The number of patent filings generally increases over time in an S-shaped pattern (Fig. 5) which is similar to the technology lifecycle (Daim, Rueda, Martin, & Gerdsri, 2006; Martino, 2003). In the innovation stage, the number of filed patents is small and increasing slowly. During the fastgrowing period of the growth stage, the number of filed patents rapidly increases, and then reaches a plateau, the maturity stage. 3.3. Extraction of SAO structures
Fig. 2. Example of TRIZ evolution trend: ‘Dynamization’.
Office (USPTO) or the European Patent Office (EPO), or from commercial patent databases, such as LexisNexis TotalPatent™, Thomson Innovation or WIPS (www.wips.co.kr). To maximize the credibility of the analysis, collecting all or almost all patents in the selected technology domain is recommended. 3.2. Identification of technology lifecycle stage A current technology lifecycle stage of the selected technology domain is identified by analyzing the cumulative number of filed
As a pre-processing procedure to identify the TRIZ trends and trend phases, every patent should be transformed into an abstracted form. To this end, we adopted an SAO-based text mining approach. NLP, which is a text mining technique for syntactic analysis of natural language, is used to extract SAO structures (Table 1) from the patents. NLP tools such as Stanford parser, Minipar and Knowledgist™ can be adopted for this purpose. 3.4. Identification of TRIZ evolution trends and phases TRIZ trends and trend phases of patents can be identified by comparing semantic functional similarity between the AO structure of RFJ and the extracted SAO structure (Fig. 6). However, only using RFJ as criteria to determine TRIZ trends and trend phases of a patent can cause two limitations. First, technologies which are in
Fig. 3. General pattern of technology lifecycle.
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Fig. 4. Overall procedure of the method for identification of future promising patents.
Fig. 5. Example of technology lifecycle pattern: organic light-emitting diode technology in Korea from 1994 to 2007, redrawn from KIPO (2009).
Table 1 Example of extracted SAO structure from a patent (US 7612,462). Subject
Action
Object
Buoyancy chambers Large clearance
Provide Facilitate
Pumps Rotatable position retention device Support tower and stability arm Tower structure Wind turbine rotor
Adjust Facilitate
Variable buoyancy Use of articulated rotor hubs Buoyancy Deep water installations
Structure Include Allow
Balance tension At least one stability arm Wind turbine
the first phase in a TRIZ trend cannot be identified. RFJ is a set of functions that cause a technology to jump or evolve to the next phase in the trend, and thus the lowest phase that can be identified by RFJ is the second phase. Second, in principle, an AO structure from a patent should be related to one TRIZ trend and trend phase. However, some AO structures from patents may be related to more than one RFJ, because some RFJ rules are expressed in a similar AO structure without any important context or additional technological information. For example, a function ‘increase surface area’ is defined as the RFJ in two ways: (1) as the cause of a transition from ‘smooth surface phase’ to ‘surface with rib protrusions phase’ of
the surface segmentation trend, and (2) as the cause of a transition from ‘fluid phase’ to ‘segmented fluid phase’ of the object segmentation trend. To circumvent these limitations, the proposed method considered the subject (‘S’) in an SAO structure that represents a solution, material or method of the technology. Thus, the TRIZ trend and trend phase are determined by the overlap between the identified results from S and AO (Fig. 6).
3.4.1. Definition of rule bases The rule bases as criteria to identify TRIZ trends related to each patent are defined by using the updated TRIZ trends (Mann, 2002). The proposed method requires two rule bases: one for TRIZ trend hints and one for RFJ of each TRIZ trend. ‘TRIZ trend hints’ is a set of Nouns or Noun phrases that are related to each TRIZ trend. For example, terms such as ‘gel-filled material’, ‘self-disassembly polymers’, ‘shape-memory alloy’ and ‘switchable glass’ are related to the Smart materials trend, and thus they can be a hint to identify patents related to this trend. Although the updated TRIZ trends already provided many of the terms, these cannot include all domain-specific terms. Thus, some domain-specific terms which are important in a technology domain but are not included in updated TRIZ trends should be added to a rule base to increase the credibility of the analysis.
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Fig. 6. Identification of TRIZ evolution trends and trend phases.
‘RFJ of TRIZ trend’ is a set of Verb–Noun or Verb–Noun phrase combinations that are key functions which should be achieved to jump (increasing benefits while reducing harm) to the next phase in each TRIZ trend. The generic RFJ are already defined in the Mann’s updated TRIZ trends (Mann, 2002). An example of RFJ in the Smart material trend is listed on (Table 2).
where c3 is the maximally specific superclass of both c1 and c2, d(c3) is the depth which is distance from the root of the taxonomy, and d(c1) and d(c2) are the depths of c1 and c2 on the path through c3. Sim ranges from 0 (completely different) to 1 (identical) (details in (Park, Yoon, & Kim, 2012)).
3.4.2. Measurement of semantic similarities To measure functional proximities between AOs from the extracted SAO structure and rule base of RFJ, we measure semantic sentence similarity Simpson & Dao (2010):
3.5. Identification of promising patents
MatAv rðA; BÞ ¼
2 MatchðA; BÞ ; jAj þ jBj
ð1Þ
where Match(A, B) is the number of words shared by AO structures A and B, and |A| and |B| mean the number of word tokens in AO structures A and B, respectively. When MatAvr between an AO from a patent and an AO in RFJ rule base exceeds a threshold value, the two AOs can be judged to represent a similar or identical function, and thus the TRIZ trend and the trend phase of the patent can be determined. For example, if MatAvr between the AO from Patent X and the AO in RFJ that causes a technological jump from the first phase to the second phase of TRIZ trend Y is larger than the defined threshold value, the TRIZ trend and trend phase of Patent X can be determined as the TRIZ evolution trend Y and the second evolution phase of it. To identify the identicalness of matching words (1) between the AO in the SAO structure from a patent and RFJ rule base and (2) between the S in the SAO structure from a patent and a term in the rule base of TRIZ trend hints, similarity between each pair of words is calculated. The semantic identicalness between words (Sim(c1, c2)) is automatically judged by using hierarchical ontology such as WordNet, Cyc or ConceptNet (Wu & Palmer, 1994):
Simðc1 ; c2 Þ ¼
2 dðc3 Þ dðc1 Þ þ dðc2 Þ
ð2Þ
In this step, each patent is evaluated to identify promising patents. A patent is evaluated as a high future-value patent if it is related to future-important TRIZ trends in the selected technology domain and is in a trend phase which is equal to or higher than the average trend phase of the domain. The future-important TRIZ trends in the technology domain can be judged by its current lifecycle stage. Basically, all types of technological changes or innovations are generated somewhere on the technology lifecycle, and each stage of this lifecycle has distinct innovation characteristics: technologies for new systems and designs occur mainly in the innovation stage; technologies for improving product performances or technological productivity occur mainly in the growth stage; and technologies related to reliability, convenience and emotional aspects occur mainly in the maturity stage (Table 3). Thus, every TRIZ trend can be classified based on the technology lifecycle stage (Park, Ree, & Kim, 2012). By using the classified TRIZ trends and the current lifecycle of the selected technology domain, important TRIZ evolution trends in the future can be identified. For example, if a technology domain is in the middle of the growth stage, TRIZ trends related to the maturity stage can be accepted as future-important TRIZ trends. To identify future promising patents and rank them, we set a simple scoring rule for patent evaluation: a patent gains points under two conditions: (1) when a patent is related to a future-important TRIZ trend regardless of its phase (one point per trend) and (2) when the trend phase of a patent is one level higher than the trend phase of the domain average (one point per level or phase above
Table 2 Example of ‘reason for jumps’ rule base of the Smart materials trend. Trend phase
Functional achievement for phase jump
Passive to one-way
Solve a physical contradiction, create two different states (create big and small states, create viscous and non-viscous states), reduce complexity of systems, make simple measurement, change to self-organizing systems (change to self-serving systems) Increase operational flexibility (increase control flexibility), enable three-way switching (enable multi-way switching) Increase user adaptations, increase usability, satisfy user requirements, improve mass customization, offer continuous variability, sophisticate measurement indicators
One-way to two-way Two-way to fully adaptive
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H. Park et al. / Expert Systems with Applications 40 (2013) 736–743 Table 3 Classified TRIZ evolution trends. Lifecycle stage
TRIZ evolution trends related to the stage
Innovation stage Growth stage
Smart material, design point (optimization), object segmentation, dynamization, rhythm co-ordination, (matching to external) non-linearity, degree of freedom, reducing energy conversions Space segmentation, surface segmentation, nano scale, webs and fibers, decreasing density, boundary breakdown, geometric evolution (linear), geometric evolution (volumetric), action co-ordination, increasing asymmetry (to match external asymmetries), mono-bi-poly (similar) – interface, mono-bi-poly (similar) – time, mono-bi-poly (various) – interface, mono-bi-poly (various) – time Human involvement, design methodology, mono-bi-poly (increasing differences) – interface, mono-bi-poly (increasing differences) – time, reduced damping (control), trimming, color interaction, increasing use of senses, transparency, controllability
Maturity stage
Table 4 Patent retrieval query for floating wind power technology. Retrieval query
The number of collected patents
((windpower⁄ or (wind adj (force⁄ or power⁄ or energy⁄ or turbine⁄))).TI. and (ocean⁄ or offshore⁄ or marinefloat⁄ or buoyan⁄).KEY.) AND @AD>=19990101<=20110630
92 (after eliminating overlapping and irrelevant patents)
domain average phase). The patents which receive high scores can be determined as future promising patents. 4. Case study This empirical study applied the proposed method to patents of related to floating wind power turbine technologies. Wind power is a clean and renewable energy source; floating wind turbines can generate more electricity than onshore wind turbines with reducing visual pollution and providing better accommodation for fishing and shipping lanes (Musial, Butterfield, & Boone, 2004). Many corporations are developing or acquiring floating ocean wind power technologies to obtain competitive advantages in the renewable energy sector, and thus analysis of floating wind turbine technology is appropriate to verify the usefulness of the proposed method. 4.1. Data We collected 92 patents related to floating wind power technologies from the WIPS database by using patent retrieval query (Table 4). All were filed in between January 1, 1999 and June 30, 2011; US patent application numbers ranged from 1999-357130 to 2011-168943. 4.2. Identifying current lifecycle stage To identify the lifecycle stage, we plotted a cumulative frequency graph by using the collected patents (Fig. 7). Considering that the total number of filed patents is small (92) and that the number of applied patents slowly increased during the whole period, the current lifecycle stage of the floating wind power can be determined as the innovation stage. Thus, TRIZ trends related to the growth stage are considered as future-important TRIZ trends in this technology. 4.3. Extracting SAO structures NLP is used to transform technological information in patents into SAO structures. Patents include several sections (e.g., title, abstract, description and claims) that contain technological information; we extracted SAO structures from patent abstracts because they contain key technological concepts and the essence of the invention (Chen, Tokuda, & Adachi, 2003). To extract SAO structures, we used a commercial NLP software package, Knowledgist™ 2.5.
Fig. 7. Patent application trend in floating wind turbine technology.
4.4. Identifying promising patents In this step, TRIZ trends and trend phase of patents are identified by semantic similarity measurement, and then future promising patents in the floating turbine power technology are identified. To facilitate the analysis, we developed a .NET-based application (Fig. 8). Analysis revealed that floating wind turbine technology was connected with nine TRIZ trends related to the growth stage, and that their corresponding trend phases were mostly in the first or second phase, possibly because the this domain is currently in the innovation stage of the lifecycle. The trends and their phases are as follows: space segmentation, phase 1; surface segmentation, phase 1; webs and fibers, phase 2; geometric evolution (linear), phase 3; geometric evolution (volumetric), phase 2; action co-ordination, phase 1; increasing asymmetry, phase 2; mono-by-poly (similar), phase 1; mono-by-poly (various), phase 1. By evaluating each patent compared with the domain average, we identified 17 promising patents in the domain. The scores ranged from 1 to 15 points. The highest-scoring patent was US 2009-988121 filed by Principle Power; this patent gained six points because it is connected with six TRIZ trends which are related to the growth stage and gained nine points because it made nine phase jumps in the six TRIZ trends (Fig. 8). The technologies in this patent involve a new system for floating wind turbines that can improve the power generation of the turbine platform. In detail, it has (1) hollowed columns that allow the structure to be relatively lightweight, (2) 2D-meshed stringer strakes that improve its buoyancy, (3) an aerodynamic structure that improves its floating balance and power generation efficiency, (4) an asymmetric mooring system and active ballast system facilitate production of a structure and
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Fig. 8. Screen shot of the system developed to identify future promising patents.
allow relatively light weight and (5) multiple chain lines to secure the mooring system that prevent high tension spikes from being transmitted from the platform to each individual anchor. Although the patent has not yet received much attention because it was filed relatively recently and has not been granted, its technologies are more relevant for the growth stage (next stage) than for the innovation stage (current stage). We expect that the technologies in this patent will receive significant attention in the future.
5. Conclusions and future directions Technology transfer is one of the most important mechanisms for acquiring knowledge from external sources to secure innova-
tive and advanced technologies. Although most technology transfer transactions are aimed at future-oriented purposes, research into ways of identifying future promising technologies or patents is insufficient. In an attempt to provide a practical guide, this paper proposes a new approach that uses TRIZ evolution trends and textmining to identify future promising patents for technology transfer. TRIZ trends, which present evolving patterns of technologies or systems, were adopted as criteria to evaluate the future value of a technology in a patent. Although the TRIZ trends in a specific technology domain are of differing importance, previous research adopting TRIZ trends did not consider these differences when analyzing a specific technology domain. However, the proposed method reflected these differences by considering the technological characteristics in each technology cycle stage.
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To deal with large patent datasets and analyze them automatically, we adopted SAO-based text mining which can reflect the technological key concepts in a patent and can be directly compared with the defined rule bases. Furthermore, to integrate individual modules and facilitate the analysis, we developed a .NET-based application for measuring semantic similarity and identifying promising patents. As a last step, this paper verified the proposed method by applying it to technologies related to floating wind turbines and identifying promising patents in the domain. However, some limitations remain. First, because the classified TRIZ trends consist of a generic categorization of TRIZ trends by considering characteristic of each lifecycle stage, it may not always be applicable to every technology domain. To eliminate this limitation, some revisions of classification for each technology domain by domain experts with TRIZ knowledge may be required. Second, this paper set simple scoring rules to simplify the process of conducting a case study aimed at verifying the method. However, when the patent set is (very) large, many patents may receive the same or similar evaluation scores, and thus identifying promising patents may be difficult. Thus, more appropriate evaluation rules which can generate reasonable variation to clearly determine promising patents may be required. Third, we only considered TRIZ trends related to the next lifecycle stage as future-important TRIZ trends, because moving to the next lifecycle stage generally takes a long time. However, the cycle of technology evolution in fast-moving industries such as IT and semiconductors may be very short. Thus, when applying the proposed method to these industries, the scope of the lifecycle stage of future-important TRIZ trends can be adjusted to increase the method’s flexibility. For example, considering TRIZ trends related to the next two lifecycle stages as future-important TRIZ trends can be a practicable option. Finally, in this paper, the related TRIZ trends and trend phase of a patent were identified by semantic similarities between rule bases and extracted SAO structures from a patent. Although we defined the rule base of ‘reasons for jumps’ and ‘TRIZ trend hints’ based on Mann’s updated TRIZ trends reference (Mann, 2002) and adopted WordNet as a hierarchical ontology, they cannot cover all terms and functions, particularly domain-specific terms and rarelyoccurring functions. Thus, to increase the accuracy of the similarity measurement, rule bases should be continuously updated by experts, local ontologies should be constructed. Acknowledgements This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MEST) (No. 2009-0088379). References Al’tshuller, G. S. (1984). Creativity as an exact science. The theory of the solution of inventive problems (Vol. 5). CRC. Ayres, R. U. (1994). Toward a non-linear dynamics of technological progress. Journal of Economic Behavior & Organization, 24, 35–69. Bergmann, I., Butzke, D., Walter, L., Fuerste, J. P., Moehrle, M. G., & Erdmann, V. A. (2008). Evaluating the risk of patent infringement by means of semantic patent analysis: The case of DNA chips. R&D Management, 38, 550–562. Cascini, G., & Zini, M. (2008). Measuring patent similarity by comparing inventions functional trees. Computer-Aided Innovation (CAI), 31–42. Cassiman, B., & Veugelers, R. (2006). In search of complementarity in innovation strategy: Internal R&D and external knowledge acquisition. Management Science, 52, 68. Chen, L., Tokuda, N., & Adachi, H. (2003). A patent document retrieval system addressing both semantic and syntactic properties. In Proceedings of the ACL2003 workshop on Patent corpus processing (pp. 1-6). Morristown, NJ, USA: Association for Computational Linguistics.
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Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
Identification of promising patents for technology transfers using TRIZ evolution trends Hyunseok Park a, Jason Jihoon Ree b, Kwangsoo Kim b,⇑ a b
Department of Technology and Innovation Management, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang 790-784, Republic of Korea Department of Industrial and Management Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang 790-784, Republic of Korea
a r t i c l e
i n f o
Keywords: Open innovation Technology transaction Patent evaluation Technology evaluation Patent mining Patent analysis Text mining Subject–Action–Object SAO
a b s t r a c t Technology transfer is one of the most important mechanisms for acquiring knowledge from external sources to secure innovative and advanced technologies in high-tech industries. For successful technology transfer, identification of high-value technologies is a fundamental task. In particular, identifying future promising patents is important, because most technology transfer transactions are aimed at acquiring technologies for future uses. This paper proposes a new approach to identification of promising patents for technology transfer. We adopted TRIZ evolution trends as criteria to evaluate technologies in patents, and Subject–Action–Object (SAO)-based text-mining technique to deal with big patent data and analyze them automatically. The applicability of the proposed method was verified by applying it to technologies related to floating wind turbines. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction Companies should continuously secure innovative and advanced technologies to respond to market trends and customer needs. The fundamental activity for obtaining technological capabilities is internal research and development. Conventionally, most companies focused primarily on achieving a competitive advantage by internally developing exclusive technologies (Elmquist & Le Masson, 2009; Ganuza, Llobet, & Domınguez, 2009). However, because of today’s fast-changing market trends and customer needs, a single company can have difficulties responding to such changes only by using its internal technological capabilities (Cassiman & Veugelers, 2006; Du & Ai, 2008). To overcome these difficulties, companies have tried to use technology transfer to remedy their technological limitations or to acquire necessary technologies from external sources. Recently, the increasing number and volume of deals has made technology transfer one of the most important mechanisms to develop a company’s technological capabilities. For acquisition of external technologies to be financially beneficial to the acquiring company, it must target high-value technologies (Gibson & Williams, 1990; Rogers, Takegami, & Yin, 2001). Especially, considering (1) that technologies acquired by technology transfer are mainly aimed at achieving future-oriented goals rather than present-oriented goals, and (2) that development of future competitive advantages is a significant task in any technology
⇑ Corresponding author. Tel.: +82 54 279 8235; fax: +82 54 279 5998. E-mail addresses: [email protected] (H. Park), [email protected] (J.J. Ree), [email protected] (K. Kim). 0957-4174/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.eswa.2012.08.008
domain, identifying and acquiring future promising patents is becoming increasingly important to gain such advantages. Despite the importance of identifying promising technologies or patents for technology transfer, little research has been conducted to provide ways of doing so. In an attempt to provide a practical guide, we propose a new approach to identification of promising patents for technology transfer; this approach adopts TRIZ evolution trends and an SAO-based text mining technique. ‘TRIZ evolution trends’ is a TRIZ tool that presents specific sequences of technological transitions or trends, which indicate how a system or technology evolves over time (Petrov, 2002). Thus, TRIZ trends have been recognized as a useful tool for technology analysis from the future perspective and have actively been employed in various research fields, such as predicting future improvements of technologies (Verhaegen, D’hondt, Vertommen, Dewulf, & Duflou, 2009), forecasting the design of eco-products (Yang & Chen, 2011), identifying technology trends (Wang, Chang, & Kao, 2010; Yoon & Kim, 2011d) and supporting decision-making in product development (Zhang, Xu, & Hu, 2004). In general, several TRIZ trends can apply to a given technology domain, and the technological importance of those TRIZ trends in the technology domain are not equal. Specifically, according to a current lifecycle of a specific technology, some TRIZ trends should be considered more than others. For instance, technologies related to improving performance of products or systems are important during the stage in which the technology is becoming increasingly pervasive, whereas technologies related to increasing the reliability or convenience of products or systems are important during the stage in which the technology is widespread. Although prior studies using TRIZ trends equally
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weighted each related TRIZ trend without regarding the lifecycle of a technology domain (Mann, 2003; Verhaegen, D’hondt, Vertommen, Dewulf, & Duflou, 2009; Yoon & Kim, 2011a), this paper considers the changes in the importance of each TRIZ trend according to the technology’s current lifecycle stage. Although TRIZ is recognized as a useful tool for technology analysis, practical application of TRIZ without the aid of experts is not easy. However, an expert’s analysis of a large set of patent data requires considerable labor or may be an impossible task. To overcome the limitations, we propose a text-mining technique. In particular, we suggest an SAO-based text mining approach instead of a conventional keyword-based text-mining approach. SAO structures can reflect the technological key concept in a patent document (Cascini & Zini, 2008; Moehrle, Walter, Geritz, & Müller, 2005) and can be directly compared with the rule base of ‘reasons for jumps’ (RFJ) of TRIZ trends that are expressed in Action–Object (AO) structure. Thus, use of SAO structures is a more appropriate text mining approach than a keyword-based approach for this method of identifying promising patents. For verification, the method was applied to technologies related to floating wind turbines. The remainder of this paper is structured as follows. Section 2 provides a brief overview of theoretical background. Section 3 describes the proposed methodology. Section 4 illustrates the approach. Section 5 provides conclusions and future directions. 2. Theoretical background 2.1. TRIZ evolution trends TRIZ was originally developed by the Soviet inventor Genrich Al’tshuller and his associates by analyzing a vast number of patents across many different fields (Al’tshuller, 1984; Salamatov, Souchkov, Strogaia, & Yakovlev, 1999). TRIZ is a powerful tool to provide systematic and innovative ideas for problem-solving and technology analysis (Mann, 2001). The TRIZ evolution trends, is a TRIZ tool that reveals the patterns of evolution of business and technology systems, and is useful to evaluate the status of the system today and how it will evolve in the future. Classical TRIZ discovered eight patterns of evolution of technical systems: Completeness of parts of the system, Energy conductivity of a system, Harmonizing the rhythm of the system’s parts, Increasing ideality, Uneven development of the system’s parts, Transition to a super-system, Transition from macro- to micro-level, and Increasing the s-field development. Recently, Mann (2002) suggested updated TRIZ trends (Fig. 1). In particular, what makes TRIZ evolution trends a useful tool for technology evaluation and forecasting is that almost every TRIZ trend follows the basic principle of the TRIZ philosophy, Increasing Ideality, which means that technology systems evolve to increasing benefits while reducing harm, and that most technologies and systems evolved only in this direction (left-to-right direction in the TRIZ trends). For instance, the ‘Dynamization’ evolution trend entails evolution from an immobile system to a jointed system, to a flexible system, and ultimately to a field system (Fig. 2). 2.2. Technology lifecycle Besides the TRIZ trends, every technology and system follows a typical development cycle (Ayres, 1994; Christensen, 1993; Foster, 1986): From an innovation stage, through a growth stage, to a maturity stage. During the innovation stage, various innovative designs and systems emerge and compete with each other to be selected as a dominant design in the technology industry. During the growth stage, technologies for improving product performance and technological productivity are emphasized. During the maturity stage, customer demands on products shift from highperformance to reliability, convenience, low price and emotional
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aspects (Christensen, 2003). A general lifecycle pattern of technology or system is visualized as an S-curve graph (Fig. 3). When technologies reach the maturity stage, further increasing the technology’s performance becomes increasingly difficult or impossible (Schilling & Esmundo, 2009). To break through this technological limitation, discontinuous innovation which creates a technology paradigm shift is necessary: this entails a shift from the original S-curve to a new one (Fig. 3). A new S-curve is usually generated by a new system or design, which offers new value to customers or displaces previous systems and technologies. This new S-curve also follows the lifecycle pattern of innovation, growth, and maturity stages. 2.3. SAO-based text mining To automatically analyze unstructured technological information in patents, it should be transformed into an abstracted form which includes the technological key concepts and structural relations among components. To this end, Natural Language Processing (NLP)-based text mining techniques are required. Text mining approaches can be broadly classified into two types: keyword-based, and SAO-based. Although much previous research has employed a keyword-based approach due to its simplicity and ease of use, it is insufficient to reflect the specific technological key concepts and structural relationships among components, because the keyword vector abstracted from the patent by keyword-based text mining, is composed only of frequency of keyword occurrence. In contrast, an SAO-based approach can reflect specific technological key concepts and relations (Yoon & Kim, 2011b). S represents ‘solution’ and AO represents ‘problem’ in the technological sentence, and thus an SAO structure reflects specific key findings in the patent (Cascini & Zini, 2008). In addition, S and O represent the components and A denotes the effect or relations between them; thus an SAO structure describes the structural relationships among components in a patent (Moehrle et al., 2005). In particular, an SAO-based approach is appropriate for this study because the rule base of RFJ of TRIZ evolution trends is expressed in AO structure, so functional semantic similarity between the rule base and AO structures from extracted SAOs can be measured directly. Recent studies which adopted text mining based patent analysis for technology management have started to exploit an SAO-based approach instead of a keyword-based approach. Examples include analyzing patent risk (Bergmann et al., 2008; Park, Yoon, & Kim, 2012), profiling inventors (Moehrle et al., 2005), monitoring technology (Gerken & Moehrle, 2012), constructing technology trees (Choi, Park, Kang, Lee, & Kim, 2012), analyzing technological trends (Choi, Yoon, Kim, Lee, & Kim, 2011; Yoon & Kim, 2011c) and detecting signals of new technological opportunities (Yoon & Kim, 2012). 3. Research design The overall procedure for identification of future promising patents using TRIZ evolution trends involves collecting patents, identifying the stage that the selected technology occupies in the technological lifecycle, extracting SAO structures from the patent documents, identifying TRIZ evolution trends, and evaluating the patents (Fig. 4). These processes will be explained in detail in the following sections. 3.1. Patent collection As the first step, a technology domain should be selected and the patents within the domain should be collected by using International Patent Classifications (IPC) or keyword retrieval from free patent databases such as the United States Patent and Trademark
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Fig. 1. TRIZ evolution trends, redrawn from Mann (2002).
patents that pertain to that technology. The number of patent filings generally increases over time in an S-shaped pattern (Fig. 5) which is similar to the technology lifecycle (Daim, Rueda, Martin, & Gerdsri, 2006; Martino, 2003). In the innovation stage, the number of filed patents is small and increasing slowly. During the fastgrowing period of the growth stage, the number of filed patents rapidly increases, and then reaches a plateau, the maturity stage. 3.3. Extraction of SAO structures
Fig. 2. Example of TRIZ evolution trend: ‘Dynamization’.
Office (USPTO) or the European Patent Office (EPO), or from commercial patent databases, such as LexisNexis TotalPatent™, Thomson Innovation or WIPS (www.wips.co.kr). To maximize the credibility of the analysis, collecting all or almost all patents in the selected technology domain is recommended. 3.2. Identification of technology lifecycle stage A current technology lifecycle stage of the selected technology domain is identified by analyzing the cumulative number of filed
As a pre-processing procedure to identify the TRIZ trends and trend phases, every patent should be transformed into an abstracted form. To this end, we adopted an SAO-based text mining approach. NLP, which is a text mining technique for syntactic analysis of natural language, is used to extract SAO structures (Table 1) from the patents. NLP tools such as Stanford parser, Minipar and Knowledgist™ can be adopted for this purpose. 3.4. Identification of TRIZ evolution trends and phases TRIZ trends and trend phases of patents can be identified by comparing semantic functional similarity between the AO structure of RFJ and the extracted SAO structure (Fig. 6). However, only using RFJ as criteria to determine TRIZ trends and trend phases of a patent can cause two limitations. First, technologies which are in
Fig. 3. General pattern of technology lifecycle.
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Fig. 4. Overall procedure of the method for identification of future promising patents.
Fig. 5. Example of technology lifecycle pattern: organic light-emitting diode technology in Korea from 1994 to 2007, redrawn from KIPO (2009).
Table 1 Example of extracted SAO structure from a patent (US 7612,462). Subject
Action
Object
Buoyancy chambers Large clearance
Provide Facilitate
Pumps Rotatable position retention device Support tower and stability arm Tower structure Wind turbine rotor
Adjust Facilitate
Variable buoyancy Use of articulated rotor hubs Buoyancy Deep water installations
Structure Include Allow
Balance tension At least one stability arm Wind turbine
the first phase in a TRIZ trend cannot be identified. RFJ is a set of functions that cause a technology to jump or evolve to the next phase in the trend, and thus the lowest phase that can be identified by RFJ is the second phase. Second, in principle, an AO structure from a patent should be related to one TRIZ trend and trend phase. However, some AO structures from patents may be related to more than one RFJ, because some RFJ rules are expressed in a similar AO structure without any important context or additional technological information. For example, a function ‘increase surface area’ is defined as the RFJ in two ways: (1) as the cause of a transition from ‘smooth surface phase’ to ‘surface with rib protrusions phase’ of
the surface segmentation trend, and (2) as the cause of a transition from ‘fluid phase’ to ‘segmented fluid phase’ of the object segmentation trend. To circumvent these limitations, the proposed method considered the subject (‘S’) in an SAO structure that represents a solution, material or method of the technology. Thus, the TRIZ trend and trend phase are determined by the overlap between the identified results from S and AO (Fig. 6).
3.4.1. Definition of rule bases The rule bases as criteria to identify TRIZ trends related to each patent are defined by using the updated TRIZ trends (Mann, 2002). The proposed method requires two rule bases: one for TRIZ trend hints and one for RFJ of each TRIZ trend. ‘TRIZ trend hints’ is a set of Nouns or Noun phrases that are related to each TRIZ trend. For example, terms such as ‘gel-filled material’, ‘self-disassembly polymers’, ‘shape-memory alloy’ and ‘switchable glass’ are related to the Smart materials trend, and thus they can be a hint to identify patents related to this trend. Although the updated TRIZ trends already provided many of the terms, these cannot include all domain-specific terms. Thus, some domain-specific terms which are important in a technology domain but are not included in updated TRIZ trends should be added to a rule base to increase the credibility of the analysis.
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Fig. 6. Identification of TRIZ evolution trends and trend phases.
‘RFJ of TRIZ trend’ is a set of Verb–Noun or Verb–Noun phrase combinations that are key functions which should be achieved to jump (increasing benefits while reducing harm) to the next phase in each TRIZ trend. The generic RFJ are already defined in the Mann’s updated TRIZ trends (Mann, 2002). An example of RFJ in the Smart material trend is listed on (Table 2).
where c3 is the maximally specific superclass of both c1 and c2, d(c3) is the depth which is distance from the root of the taxonomy, and d(c1) and d(c2) are the depths of c1 and c2 on the path through c3. Sim ranges from 0 (completely different) to 1 (identical) (details in (Park, Yoon, & Kim, 2012)).
3.4.2. Measurement of semantic similarities To measure functional proximities between AOs from the extracted SAO structure and rule base of RFJ, we measure semantic sentence similarity Simpson & Dao (2010):
3.5. Identification of promising patents
MatAv rðA; BÞ ¼
2 MatchðA; BÞ ; jAj þ jBj
ð1Þ
where Match(A, B) is the number of words shared by AO structures A and B, and |A| and |B| mean the number of word tokens in AO structures A and B, respectively. When MatAvr between an AO from a patent and an AO in RFJ rule base exceeds a threshold value, the two AOs can be judged to represent a similar or identical function, and thus the TRIZ trend and the trend phase of the patent can be determined. For example, if MatAvr between the AO from Patent X and the AO in RFJ that causes a technological jump from the first phase to the second phase of TRIZ trend Y is larger than the defined threshold value, the TRIZ trend and trend phase of Patent X can be determined as the TRIZ evolution trend Y and the second evolution phase of it. To identify the identicalness of matching words (1) between the AO in the SAO structure from a patent and RFJ rule base and (2) between the S in the SAO structure from a patent and a term in the rule base of TRIZ trend hints, similarity between each pair of words is calculated. The semantic identicalness between words (Sim(c1, c2)) is automatically judged by using hierarchical ontology such as WordNet, Cyc or ConceptNet (Wu & Palmer, 1994):
Simðc1 ; c2 Þ ¼
2 dðc3 Þ dðc1 Þ þ dðc2 Þ
ð2Þ
In this step, each patent is evaluated to identify promising patents. A patent is evaluated as a high future-value patent if it is related to future-important TRIZ trends in the selected technology domain and is in a trend phase which is equal to or higher than the average trend phase of the domain. The future-important TRIZ trends in the technology domain can be judged by its current lifecycle stage. Basically, all types of technological changes or innovations are generated somewhere on the technology lifecycle, and each stage of this lifecycle has distinct innovation characteristics: technologies for new systems and designs occur mainly in the innovation stage; technologies for improving product performances or technological productivity occur mainly in the growth stage; and technologies related to reliability, convenience and emotional aspects occur mainly in the maturity stage (Table 3). Thus, every TRIZ trend can be classified based on the technology lifecycle stage (Park, Ree, & Kim, 2012). By using the classified TRIZ trends and the current lifecycle of the selected technology domain, important TRIZ evolution trends in the future can be identified. For example, if a technology domain is in the middle of the growth stage, TRIZ trends related to the maturity stage can be accepted as future-important TRIZ trends. To identify future promising patents and rank them, we set a simple scoring rule for patent evaluation: a patent gains points under two conditions: (1) when a patent is related to a future-important TRIZ trend regardless of its phase (one point per trend) and (2) when the trend phase of a patent is one level higher than the trend phase of the domain average (one point per level or phase above
Table 2 Example of ‘reason for jumps’ rule base of the Smart materials trend. Trend phase
Functional achievement for phase jump
Passive to one-way
Solve a physical contradiction, create two different states (create big and small states, create viscous and non-viscous states), reduce complexity of systems, make simple measurement, change to self-organizing systems (change to self-serving systems) Increase operational flexibility (increase control flexibility), enable three-way switching (enable multi-way switching) Increase user adaptations, increase usability, satisfy user requirements, improve mass customization, offer continuous variability, sophisticate measurement indicators
One-way to two-way Two-way to fully adaptive
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H. Park et al. / Expert Systems with Applications 40 (2013) 736–743 Table 3 Classified TRIZ evolution trends. Lifecycle stage
TRIZ evolution trends related to the stage
Innovation stage Growth stage
Smart material, design point (optimization), object segmentation, dynamization, rhythm co-ordination, (matching to external) non-linearity, degree of freedom, reducing energy conversions Space segmentation, surface segmentation, nano scale, webs and fibers, decreasing density, boundary breakdown, geometric evolution (linear), geometric evolution (volumetric), action co-ordination, increasing asymmetry (to match external asymmetries), mono-bi-poly (similar) – interface, mono-bi-poly (similar) – time, mono-bi-poly (various) – interface, mono-bi-poly (various) – time Human involvement, design methodology, mono-bi-poly (increasing differences) – interface, mono-bi-poly (increasing differences) – time, reduced damping (control), trimming, color interaction, increasing use of senses, transparency, controllability
Maturity stage
Table 4 Patent retrieval query for floating wind power technology. Retrieval query
The number of collected patents
((windpower⁄ or (wind adj (force⁄ or power⁄ or energy⁄ or turbine⁄))).TI. and (ocean⁄ or offshore⁄ or marinefloat⁄ or buoyan⁄).KEY.) AND @AD>=19990101<=20110630
92 (after eliminating overlapping and irrelevant patents)
domain average phase). The patents which receive high scores can be determined as future promising patents. 4. Case study This empirical study applied the proposed method to patents of related to floating wind power turbine technologies. Wind power is a clean and renewable energy source; floating wind turbines can generate more electricity than onshore wind turbines with reducing visual pollution and providing better accommodation for fishing and shipping lanes (Musial, Butterfield, & Boone, 2004). Many corporations are developing or acquiring floating ocean wind power technologies to obtain competitive advantages in the renewable energy sector, and thus analysis of floating wind turbine technology is appropriate to verify the usefulness of the proposed method. 4.1. Data We collected 92 patents related to floating wind power technologies from the WIPS database by using patent retrieval query (Table 4). All were filed in between January 1, 1999 and June 30, 2011; US patent application numbers ranged from 1999-357130 to 2011-168943. 4.2. Identifying current lifecycle stage To identify the lifecycle stage, we plotted a cumulative frequency graph by using the collected patents (Fig. 7). Considering that the total number of filed patents is small (92) and that the number of applied patents slowly increased during the whole period, the current lifecycle stage of the floating wind power can be determined as the innovation stage. Thus, TRIZ trends related to the growth stage are considered as future-important TRIZ trends in this technology. 4.3. Extracting SAO structures NLP is used to transform technological information in patents into SAO structures. Patents include several sections (e.g., title, abstract, description and claims) that contain technological information; we extracted SAO structures from patent abstracts because they contain key technological concepts and the essence of the invention (Chen, Tokuda, & Adachi, 2003). To extract SAO structures, we used a commercial NLP software package, Knowledgist™ 2.5.
Fig. 7. Patent application trend in floating wind turbine technology.
4.4. Identifying promising patents In this step, TRIZ trends and trend phase of patents are identified by semantic similarity measurement, and then future promising patents in the floating turbine power technology are identified. To facilitate the analysis, we developed a .NET-based application (Fig. 8). Analysis revealed that floating wind turbine technology was connected with nine TRIZ trends related to the growth stage, and that their corresponding trend phases were mostly in the first or second phase, possibly because the this domain is currently in the innovation stage of the lifecycle. The trends and their phases are as follows: space segmentation, phase 1; surface segmentation, phase 1; webs and fibers, phase 2; geometric evolution (linear), phase 3; geometric evolution (volumetric), phase 2; action co-ordination, phase 1; increasing asymmetry, phase 2; mono-by-poly (similar), phase 1; mono-by-poly (various), phase 1. By evaluating each patent compared with the domain average, we identified 17 promising patents in the domain. The scores ranged from 1 to 15 points. The highest-scoring patent was US 2009-988121 filed by Principle Power; this patent gained six points because it is connected with six TRIZ trends which are related to the growth stage and gained nine points because it made nine phase jumps in the six TRIZ trends (Fig. 8). The technologies in this patent involve a new system for floating wind turbines that can improve the power generation of the turbine platform. In detail, it has (1) hollowed columns that allow the structure to be relatively lightweight, (2) 2D-meshed stringer strakes that improve its buoyancy, (3) an aerodynamic structure that improves its floating balance and power generation efficiency, (4) an asymmetric mooring system and active ballast system facilitate production of a structure and
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Fig. 8. Screen shot of the system developed to identify future promising patents.
allow relatively light weight and (5) multiple chain lines to secure the mooring system that prevent high tension spikes from being transmitted from the platform to each individual anchor. Although the patent has not yet received much attention because it was filed relatively recently and has not been granted, its technologies are more relevant for the growth stage (next stage) than for the innovation stage (current stage). We expect that the technologies in this patent will receive significant attention in the future.
5. Conclusions and future directions Technology transfer is one of the most important mechanisms for acquiring knowledge from external sources to secure innova-
tive and advanced technologies. Although most technology transfer transactions are aimed at future-oriented purposes, research into ways of identifying future promising technologies or patents is insufficient. In an attempt to provide a practical guide, this paper proposes a new approach that uses TRIZ evolution trends and textmining to identify future promising patents for technology transfer. TRIZ trends, which present evolving patterns of technologies or systems, were adopted as criteria to evaluate the future value of a technology in a patent. Although the TRIZ trends in a specific technology domain are of differing importance, previous research adopting TRIZ trends did not consider these differences when analyzing a specific technology domain. However, the proposed method reflected these differences by considering the technological characteristics in each technology cycle stage.
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To deal with large patent datasets and analyze them automatically, we adopted SAO-based text mining which can reflect the technological key concepts in a patent and can be directly compared with the defined rule bases. Furthermore, to integrate individual modules and facilitate the analysis, we developed a .NET-based application for measuring semantic similarity and identifying promising patents. As a last step, this paper verified the proposed method by applying it to technologies related to floating wind turbines and identifying promising patents in the domain. However, some limitations remain. First, because the classified TRIZ trends consist of a generic categorization of TRIZ trends by considering characteristic of each lifecycle stage, it may not always be applicable to every technology domain. To eliminate this limitation, some revisions of classification for each technology domain by domain experts with TRIZ knowledge may be required. Second, this paper set simple scoring rules to simplify the process of conducting a case study aimed at verifying the method. However, when the patent set is (very) large, many patents may receive the same or similar evaluation scores, and thus identifying promising patents may be difficult. Thus, more appropriate evaluation rules which can generate reasonable variation to clearly determine promising patents may be required. Third, we only considered TRIZ trends related to the next lifecycle stage as future-important TRIZ trends, because moving to the next lifecycle stage generally takes a long time. However, the cycle of technology evolution in fast-moving industries such as IT and semiconductors may be very short. Thus, when applying the proposed method to these industries, the scope of the lifecycle stage of future-important TRIZ trends can be adjusted to increase the method’s flexibility. For example, considering TRIZ trends related to the next two lifecycle stages as future-important TRIZ trends can be a practicable option. Finally, in this paper, the related TRIZ trends and trend phase of a patent were identified by semantic similarities between rule bases and extracted SAO structures from a patent. Although we defined the rule base of ‘reasons for jumps’ and ‘TRIZ trend hints’ based on Mann’s updated TRIZ trends reference (Mann, 2002) and adopted WordNet as a hierarchical ontology, they cannot cover all terms and functions, particularly domain-specific terms and rarelyoccurring functions. Thus, to increase the accuracy of the similarity measurement, rule bases should be continuously updated by experts, local ontologies should be constructed. Acknowledgements This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MEST) (No. 2009-0088379). References Al’tshuller, G. S. (1984). Creativity as an exact science. The theory of the solution of inventive problems (Vol. 5). CRC. Ayres, R. U. (1994). Toward a non-linear dynamics of technological progress. Journal of Economic Behavior & Organization, 24, 35–69. Bergmann, I., Butzke, D., Walter, L., Fuerste, J. P., Moehrle, M. G., & Erdmann, V. A. (2008). Evaluating the risk of patent infringement by means of semantic patent analysis: The case of DNA chips. R&D Management, 38, 550–562. Cascini, G., & Zini, M. (2008). Measuring patent similarity by comparing inventions functional trees. Computer-Aided Innovation (CAI), 31–42. Cassiman, B., & Veugelers, R. (2006). In search of complementarity in innovation strategy: Internal R&D and external knowledge acquisition. Management Science, 52, 68. Chen, L., Tokuda, N., & Adachi, H. (2003). A patent document retrieval system addressing both semantic and syntactic properties. In Proceedings of the ACL2003 workshop on Patent corpus processing (pp. 1-6). Morristown, NJ, USA: Association for Computational Linguistics.
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