A study on patent term prediction by survival time analysis using neural hazard model

Koji Marusaki, Kensei Nakai, Shotaro Kataoka, Seiya Kawano, Asahi Hentona, Takeshi Sakumoto, Yuta Yamamoto, Kaede Mori and Hirofumi Nonaka

Technological Forecasting and Social Change, 2024, vol. 203, issue C

Abstract: Patent term is considered one of the factors that determine the private value of a patent. Predicting it can therefore be used as an indicator of corporate management. However, since ordinary regression analysis methods use time series data as the objective variable, it was common to apply the survival time analysis such as the Cox proportional hazards model (CPH) for patent term prediction. On the other hand, CPH cannot incorporate the nonlinear elements of the explanatory variables in the estimation of the risk function, and there is a risk that it may be too simple as a model to predict patent terms from each explanatory variable.

Keywords: Patent term prediction; Survival time analysis; Deepsurv; Neural hazard model; Japan (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162524001860
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:203:y:2024:i:c:s0040162524001860

DOI: 10.1016/j.techfore.2024.123390

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().