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Published in: Neural Computing and Applications 2/2021

15-05-2020 | S.I. : DPTA Conference 2019

Discovering the realistic paths towards the realization of patent valuation from technical perspectives: defense, implementation or transfer

Authors: Weidong Liu, Wenbo Qiao, Xin Liu

Published in: Neural Computing and Applications | Issue 2/2021

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Abstract

With the intense competition of global intellectual property, the number of authorized patents is increasing. However, the patent conversion rate is low and the patent valuation is hard. The realization of patent valuation faces some basic challenges including: (1) how to develop a patent valuation model in consideration of technical factors; (2) how to train/test the patent valuation model with the insufficient standard value data. To solve the above issues, we assume that the realization of patent valuation begins with selecting the realistic value-paths: defense, implementation or transfer. We explore a Bayesian neural network-based model to predict the paths toward the realization of patent valuation. In the model, a function-effect-based patent representation is proposed, from which some technical features are extracted. Given the patent features, we use Bayesian neural network to predict the value-paths toward the realization of patent valuation. The model is evaluated by precision, recall, F-measure. The results show our method can improve evaluation measurements significantly after the addition of technical features.

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Literature
1.
go back to reference Agrawal R, Srikant R et al (1994) Fast algorithms for mining association rules. In: Proceedings of the 20th international conference on very large data bases, VLDB, vol 1215, pp 487–499 Agrawal R, Srikant R et al (1994) Fast algorithms for mining association rules. In: Proceedings of the 20th international conference on very large data bases, VLDB, vol 1215, pp 487–499
2.
go back to reference Allison JR, Lemley MA, Moore KA, Trunkey RD (2003) Valuable patents. Geo Lj 92:435 Allison JR, Lemley MA, Moore KA, Trunkey RD (2003) Valuable patents. Geo Lj 92:435
3.
go back to reference Bass S, Kurgan L (2009) Discovery of factors influencing patent value based on machine learning in patents in the field of nanotechnology. Scientometrics 82(2):217–241CrossRef Bass S, Kurgan L (2009) Discovery of factors influencing patent value based on machine learning in patents in the field of nanotechnology. Scientometrics 82(2):217–241CrossRef
4.
go back to reference Denton FR, Heald PJ (2002) Random walks, non-cooperative games, and the complex mathematics of patent pricing. Rutgers L Rev 55:1175 Denton FR, Heald PJ (2002) Random walks, non-cooperative games, and the complex mathematics of patent pricing. Rutgers L Rev 55:1175
5.
go back to reference de Saint-Georges M, de la Potterie BP (2013) A quality index for patent systems. Res Policy 42(3):704–719CrossRef de Saint-Georges M, de la Potterie BP (2013) A quality index for patent systems. Res Policy 42(3):704–719CrossRef
6.
go back to reference Ercan S, Kayakutlu G (2014) Patent value analysis using support vector machines. Soft Comput 18(2):313–328CrossRef Ercan S, Kayakutlu G (2014) Patent value analysis using support vector machines. Soft Comput 18(2):313–328CrossRef
7.
go back to reference Fischer T, Leidinger J (2014) Testing patent value indicators on directly observed patent valuetion: an empirical analysis of ocean tomo patent auctions. Res Policy 43(3):519–529CrossRef Fischer T, Leidinger J (2014) Testing patent value indicators on directly observed patent valuetion: an empirical analysis of ocean tomo patent auctions. Res Policy 43(3):519–529CrossRef
8.
go back to reference Gal Y, Ghahramani Z (2015) Dropout as a bayesian approximation: representing model uncertainty in deep learning. arXiv preprint arXiv:150602142 Gal Y, Ghahramani Z (2015) Dropout as a bayesian approximation: representing model uncertainty in deep learning. arXiv preprint arXiv:150602142
9.
go back to reference Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: representing model uncertainty in deep learning. In: International conference on machine learning, pp 1050–1059 Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: representing model uncertainty in deep learning. In: International conference on machine learning, pp 1050–1059
12.
go back to reference Han EJ, Sohn SY (2015) Patent valuation based on text mining and survival analysis. J Technol Transf 40(5):821–839CrossRef Han EJ, Sohn SY (2015) Patent valuation based on text mining and survival analysis. J Technol Transf 40(5):821–839CrossRef
13.
go back to reference Hernández-Lobato JM, Adams R (2015) Probabilistic backpropagation for scalable learning of bayesian neural networks. In: International conference on machine learning, pp 1861–1869 Hernández-Lobato JM, Adams R (2015) Probabilistic backpropagation for scalable learning of bayesian neural networks. In: International conference on machine learning, pp 1861–1869
14.
go back to reference Lemley MA, Shapiro C (2005) Probabilistic patents. J Econ Perspect 19(2):75–98CrossRef Lemley MA, Shapiro C (2005) Probabilistic patents. J Econ Perspect 19(2):75–98CrossRef
15.
go back to reference Lin H, Wang H, Du D, Wu H, Chang B, Chen E (2018) Patent quality valuation with deep learning models. In: International conference on database systems for advanced applications, Springer, New York, pp 474–490 Lin H, Wang H, Du D, Wu H, Chang B, Chen E (2018) Patent quality valuation with deep learning models. In: International conference on database systems for advanced applications, Springer, New York, pp 474–490
16.
go back to reference Liu S, Liu G, Zhou H (2019) A robust parallel object tracking method for illumination variations. Mobile Netw Appl 24(1):5–17CrossRef Liu S, Liu G, Zhou H (2019) A robust parallel object tracking method for illumination variations. Mobile Netw Appl 24(1):5–17CrossRef
17.
go back to reference Liu S, Guo C, Al-Turjman F, Muhammad K, de Albuquerque VHC (2020) Reliability of response region: a novel mechanism in visual tracking by edge computing for iiot environments. Mech Syst Signal Process 138:106537CrossRef Liu S, Guo C, Al-Turjman F, Muhammad K, de Albuquerque VHC (2020) Reliability of response region: a novel mechanism in visual tracking by edge computing for iiot environments. Mech Syst Signal Process 138:106537CrossRef
18.
go back to reference Liu X, Yan J, Xiao S, Wang X, Zha H, Chu SM (2017) On predictive patent valuation: forecasting patent citations and their types. In: Thirty-first AAAI conference on artificial intelligence Liu X, Yan J, Xiao S, Wang X, Zha H, Chu SM (2017) On predictive patent valuation: forecasting patent citations and their types. In: Thirty-first AAAI conference on artificial intelligence
19.
go back to reference Liu Y, Hseuh P, Lawrence R, Meliksetian S, Perlich C, Veen A (2011) Latent graphical models for quantifying and predicting patent quality. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1145–1153 Liu Y, Hseuh P, Lawrence R, Meliksetian S, Perlich C, Veen A (2011) Latent graphical models for quantifying and predicting patent quality. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1145–1153
20.
go back to reference Munari F, Oriani R (2011) The economic valuation of patents: methods and applications. Edward Elgar Publishing, CheltenhamCrossRef Munari F, Oriani R (2011) The economic valuation of patents: methods and applications. Edward Elgar Publishing, CheltenhamCrossRef
21.
go back to reference Murphy WJ, Orcutt JL, Remus PC (2012) Patent valuation: improving decision making through analysis, vol 571. Wiley, New York Murphy WJ, Orcutt JL, Remus PC (2012) Patent valuation: improving decision making through analysis, vol 571. Wiley, New York
22.
go back to reference Neal RM (2012) Bayesian learning for neural networks, vol 118. Springer, New York Neal RM (2012) Bayesian learning for neural networks, vol 118. Springer, New York
23.
go back to reference Suzuki J (2011) Structural modeling of the value of patent. Res Policy 40(7):986–1000CrossRef Suzuki J (2011) Structural modeling of the value of patent. Res Policy 40(7):986–1000CrossRef
24.
go back to reference Thoma G (2014) Composite value index of patent indicators: factor analysis combining bibliographic and survey datasets. World Patent Inf 38:19–26CrossRef Thoma G (2014) Composite value index of patent indicators: factor analysis combining bibliographic and survey datasets. World Patent Inf 38:19–26CrossRef
25.
go back to reference Wang S, Lei Z, Lee WC (2014) Exploring legal patent citations for patent valuation. In: Proceedings of the 23rd ACM international conference on conference on information and knowledge management. ACM, pp 1379–1388 Wang S, Lei Z, Lee WC (2014) Exploring legal patent citations for patent valuation. In: Proceedings of the 23rd ACM international conference on conference on information and knowledge management. ACM, pp 1379–1388
Metadata
Title
Discovering the realistic paths towards the realization of patent valuation from technical perspectives: defense, implementation or transfer
Authors
Weidong Liu
Wenbo Qiao
Xin Liu
Publication date
15-05-2020
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 2/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-04964-x

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