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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
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
Allison JR, Lemley MA, Moore KA, Trunkey RD (2003) Valuable patents. Geo Lj 92:435
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):217–241
Denton FR, Heald PJ (2002) Random walks, non-cooperative games, and the complex mathematics of patent pricing. Rutgers L Rev 55:1175
de Saint-Georges M, de la Potterie BP (2013) A quality index for patent systems. Res Policy 42(3):704–719
Ercan S, Kayakutlu G (2014) Patent value analysis using support vector machines. Soft Comput 18(2):313–328
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):519–529
Gal Y, Ghahramani Z (2015) Dropout as a bayesian approximation: representing model uncertainty in deep learning. arXiv preprint arXiv:150602142
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
Garson DG (1991) Interpreting neural-network connection weights. AI Expert 6(4):46–51. https://doi.org/10.5555/129449.129452
Grimaldi M, Cricelli L (2019) Indexes of patent value: a systematic literature review and classification. Knowl Manag Res Pract. https://doi.org/10.1080/14778238.2019.1638737
Han EJ, Sohn SY (2015) Patent valuation based on text mining and survival analysis. J Technol Transf 40(5):821–839
Hernández-Lobato JM, Adams R (2015) Probabilistic backpropagation for scalable learning of bayesian neural networks. In: International conference on machine learning, pp 1861–1869
Lemley MA, Shapiro C (2005) Probabilistic patents. J Econ Perspect 19(2):75–98
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
Liu S, Liu G, Zhou H (2019) A robust parallel object tracking method for illumination variations. Mobile Netw Appl 24(1):5–17
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 138:106537
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 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
Munari F, Oriani R (2011) The economic valuation of patents: methods and applications. Edward Elgar Publishing, Cheltenham
Murphy WJ, Orcutt JL, Remus PC (2012) Patent valuation: improving decision making through analysis, vol 571. Wiley, New York
Neal RM (2012) Bayesian learning for neural networks, vol 118. Springer, New York
Suzuki J (2011) Structural modeling of the value of patent. Res Policy 40(7):986–1000
Thoma G (2014) Composite value index of patent indicators: factor analysis combining bibliographic and survey datasets. World Patent Inf 38:19–26
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
Acknowledgements
The research work in this paper was supported by the National Science Foundation of China (grant no. 61801251) and Natural Science Foundation of Inner Mongolia (2018BS06002).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Liu, W., Qiao, W. & Liu, X. Discovering the realistic paths towards the realization of patent valuation from technical perspectives: defense, implementation or transfer. Neural Comput & Applic 33, 577–590 (2021). https://doi.org/10.1007/s00521-020-04964-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-020-04964-x