Patent Valuation Using Citations: A Review and Sensitivity Analysis

Author(s):  
Dan Werner ◽  
Huy Dang

Abstract As a result of studies demonstrating a correlation between a patent’s value and its forward citation count, patent valuation using forward citations has been increasingly used by practitioners when a patent’s value has not been otherwise established. Although potential limitations of patent citation analysis have been discussed in the past, there is little empirical research demonstrating the sensitivity of estimated patent values to various assumptions embedded within the method. We first summarize an approach that has been used by prior practitioners to estimate the relative value of patents within a portfolio using forward citations, and then perform various analyses to investigate the sensitivity of the approach to certain assumptions. We find that some concerns of prior literature are well-founded, while others are less so. For example, we confirm that biased valuations will result from failure to properly control for patent age and technology. Our analysis also finds that truncation bias is a problem when analyzing recently issued patents, which confirms findings from existing literature. We estimate the rate at which such truncation bias dissipates as patents age and find that the bias for the median patent is reduced to below 10% within five years from the date of publication, although additional variation can remain on an individualized level. Regarding the issue of self-citations, we find that the valuation approach using forward citation analysis can be (but is not always) sensitive to the issue of self-citations, with a median difference of 16.8%. Finally, the valuation approach using forward citation analysis appears to be robust to assumptions underlying patent cohort construction.

Author(s):  
Taoran Ji ◽  
Zhiqian Chen ◽  
Nathan Self ◽  
Kaiqun Fu ◽  
Chang-Tien Lu ◽  
...  

Modeling and forecasting forward citations to a patent is a central task for the discovery of emerging technologies and for measuring the pulse of inventive progress. Conventional methods for forecasting these forward citations cast the problem as analysis of temporal point processes which rely on the conditional intensity of previously received citations. Recent approaches model the conditional intensity as a chain of recurrent neural networks to capture memory dependency in hopes of reducing the restrictions of the parametric form of the intensity function. For the problem of patent citations, we observe that forecasting a patent's chain of citations benefits from not only the patent's history itself but also from the historical citations of assignees and inventors associated with that patent. In this paper, we propose a sequence-to-sequence model which employs an attention-of-attention mechanism to capture the dependencies of these multiple time sequences. Furthermore, the proposed model is able to forecast both the timestamp and the category of a patent's next citation. Extensive experiments on a large patent citation dataset collected from USPTO demonstrate that the proposed model outperforms state-of-the-art models at forward citation forecasting.


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