Advertisement

Soft Computing

, Volume 23, Issue 2, pp 537–556 | Cite as

Ontology-based GFML agent for patent technology requirement evaluation and recommendation

  • Chang-Shing LeeEmail author
  • Mei-Hui Wang
  • Yung-Chang Hsiao
  • Bing-Heng Tsai
Methodologies and Application
  • 165 Downloads

Abstract

Patent technology requirement evaluation and recommendation are critical for patent strategy, patent management, and patent usage in an organization. This paper proposes a patent technology evaluation and recommendation agent based on a soft-computing approach to enhance patent expansibility and technology transfer. First, we investigate the relationship between patent technology and patent owners, such as academic institutes or organizations, and integrate the collected patent data with the characteristics of organizations to establish a popular patent ontology for general academic institutes or organizations. Then, the patent’s suitability for a specific organization is determined based on concepts extracted using Chinese Knowledge Information Processing. Next, we refer to the Japan Patent Office evaluation index and intellectual property quotient to describe the knowledge base and rule base of the patent quality evaluation agent by using fuzzy markup language (FML). A comprehensive patent quality evaluation mechanism is implemented, and the genetic algorithm is adopted to improve the performance of the proposed agent. Additionally, the patent requirement level evaluation mechanism infers the patent requirement level according to the basic information of an organization. Finally, we present a novel FML-based patent requirement recommendation agent to recommend a patent for an organization by considering the suitability of the patent technology for such an organization, the results of the comprehensive patent quality evaluation process, and the results of the evaluation of the demander’s patent requirements. According to the results, the proposed agent is feasible for patent recommendation. In the future, we will combine an intelligent robot with the GFML agent to assist humans or organizations in recommending an appropriate patent.

Keywords

Ontology Genetic fuzzy markup language Patent evaluation Patent recommendation Intelligent agent 

Notes

Acknowledgements

The authors would like to thank the financially support sponsored by the Ministry of Science Technology (MOST) of Taiwan under the grant MOST 106-3114-E-024-001, MOST 106-2221-E-024-019, and MOST 105-2622-E-024-003-CC2. Additionally, the authors also would like to express their great appreciation to the members of R&D office of National University of Tainan (NUTN), Taiwan, for their valuable comments and help on patent’s application.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human participants

This article does not contain any studies with human participants or animals participants performed by any of the authors.

References

  1. Abbas A, Zhang L, Khan SU (2014) A literature review on the state-of-the-art in patent analysis. World Patent Inf. 37:3–13CrossRefGoogle Scholar
  2. Acampora G, Loia V (2005) Fuzzy control interoperability and scalability for adaptive domotic framework. IEEE Trans Ind Inf 1(2):97–111CrossRefGoogle Scholar
  3. Acampora G, Loia V (2008) A proposal of ubiquitous fuzzy computing for ambient intelligence. Inf Sci 178(3):631–646CrossRefGoogle Scholar
  4. Acampora G, Loia V, Lee CS, Wang MH (2013) On the Power of fuzzy markup language. Springer, BerlinCrossRefGoogle Scholar
  5. Acampora G, Vitiello A (2013) Interoperable neuro-fuzzy services for emotion-aware ambient intelligence. Neurocomputing 122:3–12CrossRefGoogle Scholar
  6. Acampora G, Stefano BD, Vitiello A (2016) IEEE 1855TM: the first IEEE standard sponsored by IEEE computational intelligence society. IEEE Comput Intell Mag 11(4):4–6CrossRefGoogle Scholar
  7. Agrawal A, Henderson RM (2002) Putting patents in context: exploring knowledge transfer from MIT. Manag Sci 48(1):44CrossRefGoogle Scholar
  8. Anderson TR, Daim TU, Lavoie FF (2007) Measuring the efficiency of university technology transfer. Technovation 27(5):306–318CrossRefGoogle Scholar
  9. Branstetter L, Ogura Y (2005) Is academic science driving a surge in industrial innovation? Evidence from patent citations. NBER Working Paper 11561. National Bureau of Economic Research, Cambridge, MAGoogle Scholar
  10. Berman EM (1990) The economic impact of industry-funded university R&D. Res Policy 19:340–355CrossRefGoogle Scholar
  11. Barney JA (2001) Comparative quality analysis: a statistical approach for rating and valuing patent assets. NACVA Valuation Examiner White PaperGoogle Scholar
  12. Berman B (2009) From assets to profits: competing for IP value and return. Wiley, HobokenGoogle Scholar
  13. Bessen J (2008) The value of U.S. patents by owner and patent characteristics. Res Policy 37(5):932–945CrossRefGoogle Scholar
  14. Cohen W, Nelson R, Walsh J (2000) Protecting their intellectual assets: appropriability conditions and why U.S. manufacturing firms patent (or not). National Bureau of Economic Research, Inc. http://ideas.repec.org/p/nbr/nberwo/7552.html. Accessed 01 Mar 2010
  15. Cantrell RL (2009) Out pacing the competition: patent-based business strategy. Wiley, HobokenGoogle Scholar
  16. Gallini NT (2002) The economics of patents: lessons from recent U.S. patent reform. J Econ Perspect 16(2):131–154CrossRefGoogle Scholar
  17. Geuna A, Nesta L (2006) University patenting and its effects on academic research: the emerging European evidence. Res Policy 35(6):790–807CrossRefGoogle Scholar
  18. Gittelman M (2007) Does geography matter for science-based firms? Epistemic communities and the geography of research and patenting in biotechnology. Organ Sci 18:724–741CrossRefGoogle Scholar
  19. Giuri P, Brusoni M, Crespi G, Francoz D, Gambardella A, Garcia-Fontes W, Geuna A, Gonzales R, Harhoff D, Hoisl K, Bas CL, Maggazzini L, Nesta L, Nomaler O, Palomeras N, Patel P, Romanelli M, Verspagen B (2007) Inventors and invention processes in Europe: results from the PatVal-EU survey. Res Policy 36(8):1107–1127CrossRefGoogle Scholar
  20. Godin B, Dore C (2006) Measuring the impacts of science: beyond the economic dimension. Working paper, mimeoGoogle Scholar
  21. Goldenberg DH, Linton JD (2012) The patent paradox–new insights through decision support using compound options. Technol Forecast Soc Change 79(1):180–185CrossRefGoogle Scholar
  22. Gronqvist C (2009) The private value of patents by patent characteristics: evidence from Finland. J Technol Transf 34(2):159–168CrossRefGoogle Scholar
  23. Hanel P (2006) Intellectual property rights business management practices: a survey of the literature. Technovation 26(8):895–931CrossRefGoogle Scholar
  24. Harrison SS, Sullivan P (2006) Einstein in the boardroom: moving beyond intellectual capital to i-stuff. Wiley, HobokenGoogle Scholar
  25. IEEE Standards Association (2016) 1855-2016-IEEE Standard for fuzzy markup language. http://ieeexplore.ieee.org/document/7479441/?arnumber=7479441&filter=AND(p_Publication_Number:7479439)
  26. Lee CS, Jian ZW, Huang LK (2005) A fuzzy ontology and its application to news summarization. IEEE Trans Syst Man Cybern B Cybern 35(5):859–880CrossRefGoogle Scholar
  27. Lee CS, Wang MH, Acampora G, Hsu CY, Hagas H (2010) Diet assessment based on type-2 fuzzy ontology and fuzzy markup language. Int J Intell Syst 25(12):1187–1216CrossRefGoogle Scholar
  28. Lee CS, Wang MH, Hagas H, Chen ZW, Lan ST, Kuo SE, Kuo HC, Cheng HH (2012) A novel genetic fuzzy markup language and its application to healthy diet assessment. Int J Uncertain Fuzziness Knowl Based Syst 20(2):247–278CrossRefGoogle Scholar
  29. Lee CS, Wang MH, Wu MJ, Nakagawa Y, Tsuji H, Yamazaki Y, Hirota K (2013) Soft-computing-based emotional expression mechanism for game of Computer Go. Soft Comput 17(7):1263–1282CrossRefGoogle Scholar
  30. Lee CS, Wang MH, Lan ST (2015a) Adaptive personalized diet linguistic recommendation mechanism based on type-2 fuzzy sets and genetic fuzzy markup language. IEEE Trans Fuzzy Syst 23(5):1777–1802CrossRefGoogle Scholar
  31. Lee CS, Wang MH, Wu MJ, Teytaud O, Yen SJ (2015b) T2FS-based adaptive linguistic assessment system for semantic analysis and human performance evaluation on game of Go. IEEE Trans Fuzzy Syst 23(2):400–420CrossRefGoogle Scholar
  32. Lee CS, Wang MH, Yen SJ, Wei TH, Wu IC, Chou PC, Chou CH, Wang MW, Yang TH (2016a) Human versus computer Go: review and prospect. IEEE Comput Intell Mag 11(3):67–72CrossRefGoogle Scholar
  33. Lee CS, Wang MH, Yang SC, Hung PH, Lin SW, Shuo N, Kubota N, Chou CH, Chou PC, Kao CH (2016b) FML-based dynamic assessment agent for human–machine cooperative system on game of Go. Int J Uncertain Fuzziness Knowl Based Syst 25(5):677–705MathSciNetCrossRefGoogle Scholar
  34. Lee CS, Wang MH, Kao CH, Yang SC, Nojima Y, Saga R, Shuo N, Kubota N (2017) FML-based prediction agent and its application to game of Go. In: Joint 17th World congress of international fuzzy systems association and 9th international conference on soft computing and intelligent systems (IFSA-SCIS 2017), Otsu, JapanGoogle Scholar
  35. Lee H, Park Y, Choi H (2009) Comparative evaluation of performance of national R&D programs with heterogeneous objectives: a DEA approach. Eur J Oper Res 196(3):847–855Google Scholar
  36. Marini F, Walczak B (2015) Particle swarm optimization (PSO). A tutorial. Chemom Intell Lab Syst 149:153–165CrossRefGoogle Scholar
  37. Mowery DC, Sampat BN (2006) Universities in national innovation systems. In: Fagerberg J, Mowery DC (eds) The Oxford handbook of innovation. Oxford University, New YorkGoogle Scholar
  38. Muscio A, Quaglione D, Ramaciotti L (2016) The effects of university rules on spinoff creation: the case of academia in Italy. Res Policy 45(7):1386–1396CrossRefGoogle Scholar
  39. Ndonzuau FN, Pirnay F, Surlemont B (2002) A stage model of academic spin-off creation. Technovation 22:281–289CrossRefGoogle Scholar
  40. Nightingale P (1998) A cognitive model of innovation. Res Policy 27(7):689–709CrossRefGoogle Scholar
  41. Nightingale P (2004) Technological capabilities, invisible infrastructure and the un-social construction of predictability: the overlooked fixed costs of useful research. Res Policy 33(9):1259–1284CrossRefGoogle Scholar
  42. Ocean TOMO (2014b). http://www.oceantomo.com/home
  43. Perkmann M, Neely A, Walsh K (2011) How should firms evaluate success in university-industry alliances? A performance measurement system. R&D Manag 44:202–216CrossRefGoogle Scholar
  44. Science & Technology Policy Research and Information Center, National Applied Research Laboratories (NARLabs), Taiwan (2014) Market report for various industries. http://iknow.stpi.narl.org.tw/Post/Default.aspx?CateID=3 (in Chinese)
  45. Small and Medium Enterprise Administration, Ministry of Economic Affairs, Taiwan (2016). http://www.moeasmea.gov.tw/mp.asp?mp=2
  46. Sneed KA, Johnson DKN (2009) Selling ideas: the determinants of patent value in an auction environment. R&D Manag 39(1):87–94CrossRefGoogle Scholar
  47. Taiwan Patent Search, Taiwan Intellectual Property Office (TIPO), MOEA, Taiwan (2014). http://twpat2.tipo.gov.tw/
  48. Trappey ACJ, Trappey CV, Wu CY, Fan CY, Lin YL (2013) Intelligent patent recommendation system for innovative design collaboration. J Netw Comput Appl 36(6):1441–1450CrossRefGoogle Scholar
  49. Trune DR, Goslin LN (1998) University technology transfer programs: a profit/loss analysis—a preliminary model to measure the economic impact of university licensing. Technol Forecast Soc Change 57(3):197–204CrossRefGoogle Scholar
  50. Wang MH, Hsiao YC, Tsai BH, Lee CS, Lin TT (2015) Fuzzy markup language with genetic learning mechanism for invention patent quality evaluation. In: Proceeding of 2015 IEEE congress on evolutionary computation (IEEE CEC 2015), Sendai, Japan, pp 251–258Google Scholar
  51. Wang MH, Kurozumi K, Kawaguchi M, Lee CS, Tsuji H, Tsumoto S (2016) Healthy diet assessment mechanism based on fuzzy markup language for Japanese food. Soft Comput 20(1):359–3762CrossRefGoogle Scholar
  52. Wu JL, Chang PC, Tsao CC, Fan CY (2016) A patent quality analysis and classification system using self-organizing maps with support vector machine. Appl Soft Comput 41:305–316CrossRefGoogle Scholar
  53. Wu Y, Welch EW, Huang WL (2015) Commercialization of university inventions: individual and institutional factors affecting licensing of university patents. Technovation 36–37:12–25CrossRefGoogle Scholar
  54. Yu WD, Lo SS (2009) Patent analysis-based fuzzy inference system for technological strategy planning. Autom Constr 18(6):770–776CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Computer Science and Information EngineeringNational University of TainanTainanTaiwan
  2. 2.Department of Business and ManagementNational University of TainanTainanTaiwan

Personalised recommendations