Enhancing Patent Valuation with the Pay-Off Method, Journal of Intellectual Property Rights

http://nopr.niscair.res.in/bitstream/123456789/12687/1/JIPR%2016%285%29%20377-384.pdf

https://drive.google.com/drive/u/0/folders/1-6DKNy01wAqFGT86HwLVAEzO7Nmi1-xX

Numerical valuation of patents is a difficult task due to great uncertainty regarding the future and inaccuracy in

estimation. The pay-off method is an easy to use and understand analysis method that is based on using value scenarios and

real options-thinking. The method is designed for the analysis of assets that suffer from difficulties in estimation precision

and often face high uncertainty. This paper shows how patent valuation can be enhanced with the help of the pay-off

method, based on any of the three ‘conventional’ patent valuation methods. A numerical case about how the pay-off method

can be used together with the discounted cash flow method is presented. The method is already in use by a number of multi-

national companies for valuation of R&D and is on its way to be introduced into the IPR functions of a number of corporations.

This is also visible in the volume of qualitative methods for patent valuation (vs methods that use monetary terms); out of twenty-five observed valuation and measurement methods most are qualitative measures of patent ‘goodness’ and the ones that include valuation in terms of money rely on the conventional discounted cash flow.1,2

Consequently, patent valuation has a close connection to R&D valuation9 and can often utilize data collected from R&D managers; or data already collected for the analysis of projects in the R&D stage, if such data is available.

The pay-off method (POM)16 is an analysis method

that is suitable for cases, where value information is

in the form of scenarios. The main idea behind the

pay-off method is to create a distribution from values

of, usually three, value scenarios: best guess scenario,

minimum possible value scenario, and maximum

possible value scenario. This is done by:

(i) observing that the best guess scenario is the most

likely one and assigning it full degree

membership in the set of expected outcome;

(ii) deciding that the maximum possible (optimistic)

and the minimum possible (pessimistic) scenarios

are the upper and lower bounds of the

distribution – there is also a simplifying

assumption: to not consider values higher than

the optimistic scenario and lower than the

pessimistic scenario; and

(iii) assuming that the shape of the pay-off

distribution is triangular.