Paper Info
Title
On Predictive Patent Valuation: Forecasting Patent Citations and Their Types.
Abstract
Patents are widely regarded as a proxy for inventive output which is valuable and can be commercialized by various means. Individual patent information such as technology field, classification, claims, application jurisdictions are increasingly available as released by different venues. This work has relied on a long-standing hypothesis that the citation received by a patent is a proxy for knowledge flows or impacts of the patent thus is directly related to patent value. This paper does not fall into the line of intensive existing work that test or apply this hypothesis, rather we aim to address the limitation of using so-far received citations for patent valuation. By devising a point process based patent citation type aware (self-citation and non-self-citation) prediction model which incorporates the various information of a patent, we open up the possibility for performing predictive patent valuation which can be especially useful for newly granted patents with emerging technology. Study on real-world data corroborates the efficacy of our approach. Our initiative may also have policy implications for technology markets, patent systems and all other stakeholders. The code and curated data will be available to the research community.
Year
Venue
Field
2017
THIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE
Patent valuation,Actuarial science,Computer science,Artificial intelligence,Machine learning
DocType
Citations 
PageRank 
Conference
4
0.39
References 
Authors
0
6
Name
Order
Citations
PageRank
Xin Liu128774.92
Junchi Yan289183.36
Shuai Xiao3569.55
Xiangfeng Wang428021.75
Hongyuan Zha56703422.09
Stephen M. Chu637226.33