Duane Shelton and Sam MonboWTEC

STI 2012 Montreal

September 6-8, 2012

Input-Output Modelling and Simulation of Scientometric Indicators:

A Focus on Patents

Outline

Patents as important indicators of science

Best input-output models, systems with memory

Simulation and validation on past data

What-if games for the future Conclusions

Why are Patents Important Indicators?

Patents are a proxy for the output of applied research, as papers are for basic research

Stakes are high: As professors publish or perish, inventors seek serious money from inventions

Lots of data is available, sometimes more than for papers

There have been big changes in patent regimes

Applications to patent offices of Japan, USA, Europe, S. Korea, and PRC. From Zhou & Stembridge, Patented in China, Thompson Reuters World IP Today, 2010. The Chinese took the lead in 2011.

Big Changes: China is Now Rising in Patents, a New USPTO Regime and Switch to “First to File”

Q: Why the US Patent and Trademark Office?

A: All Inventors Like USPTO patents

This is the first of 6 pages from the most valuable patent ever. Bell lived in Scotland and the US, but did some of his best work in Canada. Note the 3 weeks at USPTO.

World Share of USPTO Applications

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Data from the USPTO is an International Indicator

There are now more foreign applications (and grants) at the USPTO than domestic ones. US data needs to be separate, because of its home advantage.

Patents in USPTO

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Where Do Those Foreign Applications Come From?

About 99% come from the 40 “OECD Group” countries

Input-Output Models

Obviously resources are needed to invent and pay for patent applications

Which national resource indicators are most strongly correlated with patents: Overall “GERD,” gross expenditures on R&D GERD sources: Government, Industry, Abroad (funding from

abroad), Other GERD spending: HERD, BERD, Non-Profit (other than

universities), and GOVERD (government labs) Number of researchers

Triadic

USPTO

1999 2007 1999 2007

Capital vs. Labor

GERD 0.924 0.895 0.947 0.830

Researchers 0.847 0.680 0.664 0.428

Funding Components

Industry 0.934 0.913 0.970 0.861

Government 0.881 0.818 0.834 0.628

Spending Components

HERD 0.949 0.890 0.910 0.791

BERD 0.921 0.905 0.966 0.852

Same Year Correlations: Patents vs. Inputs

Step-Wise Regression for USPTO Patent Share

Step 1 2 3 4Constant 0.007885 0.106753 0.053946 0.018833

Ind 0.737 1.182 1.119 0.980T-Value 21.41 22.67 17.52 10.39P-Value 0.000 0.000 0.000 0.000

Gov -0.419 -0.684 -1.141T-Value -9.09 -4.06 -3.99P-Value 0.000 0.000 0.000

Gov_1 0.34 0.60T-Value 1.63 2.50P-Value 0.114 0.019

HERD 0.29T-Value 1.93P-Value 0.064

S 1.05 0.544 0.529 0.505R-Sq 93.85 98.40 98.54 98.72R-Sq(adj) 93.65 98.29 98.39 98.53

US omitted because of home advantage

Ind

USPTO

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Scatterplot of USPTO07 vs Ind

The Best Same-Year Predictor of USPTO Applications is Industry R&D. US is Omitted.

USpatents = 0.038 + 0.798IndustryR&D

p = 0.000, R2 = 92%, Good, but, can lags improve this?

Cross Correlation of Papers with Government R&D

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Years of Lag

Not Patents, but Papers From the SCI in 2007

The correlations are very high, and there seems to be very little delay in getting published papers out from R&D investment in.

There is a very high correlation between industrial R&D and USPTO applications (in 2007). And it gets better with a lag of 5 or so years, but the peak is broad.

Lags are Important for Patents, Even for Applications

Cross Correlation of Patent Applications With Industrial R&D

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Years of Lag

Without US

With US

log I02

log A

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Scatterplot of log Apps10 vs log I02

USPTO applications in 2010 vs. industrial R&D in 2002 on a log-log scale. The U.S. would be at (5.38, 5.02)

Example of a Lagged Regression

USPTO Input and Output

Disposals (grants + abandonments) are the total output. This is an unstable system: arrival rates > departure rates. How can these additional delays be modelled?

USPTO StatsFiscal Year Basis

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Allowed (Grants)

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Normalized System Delay

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Traffic Intensity

The Dreaded Instability Curve for the M/M/1 Queue

Traffic intensity is arrival rate / service rate. Delays increase without bound when this goes to 1.

Foreign Industry R&D Funding

U.S. Industry R&D Funding

Patent Office

Applications

Backlog Queue Server

Grants

Multiplier

Delay

Multiplier

Delay

Abandonments

GPSS/H is used for simulation

Model for Applications and Grants from R&D Inputs

Patent ApplicationsSimulations From R&D Investments 5 Years Earlier

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Actual

Simulation19

Industry R&D is input. Dataset is OECD Group of 40 countries. Fixed ratio based on 5-year average, then actual R&D used.

Simulation Results from US and Foreign Sources —Validation to 2008

The actual service rate is used in the queue for this historical data. Thus they should agree closely after the initialization prior to 1998.

USPTO24 Simulation ValidationDisposals (Grants + Abandonments)

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Simulation Results--Continued

The USPTO says their “backlog” is about 750K, but this only counts applications awaiting first action. This estimate uses USPTO data, adds allowances, and deducts design patents.

Average Queue Length and Delay Time Usually Do Not Agree So Well

Total in System

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Simulaton24

“Traditional Total Pendency” includes time waiting at the USPTO plus time the inventor spends preparing a resubmission. Thus the single queue model is a simplification. This graph suggests that the USPTO fudges this calculation.

Delay Time in the USPTO. Big difference here, why?

Time Spent in USPTO

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Reported Pendency

What-If Results

The model was validated through 2008 It can be used to forecast through 2014 at least,

since R&D data is available to 2009 But, first there have been big changes at the

USPTO in 2009 - 2011

Recent Changes at USPTO

David Kappos, Director in 2009

Patent reform of 2011, “America Invents Act”

First to file (instead of first to invent)

International harmonization

Better access to fees to hire examiners (Congress has a habit of raiding these)

USPTO Stats (Utility+Plant+Reissue)

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Big Changes in 2009

The model was built through 2008 for an unstable system. If this can be kept up, it will stabilize the USPTO.

WhatIf Exercises--Disposals

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Actuals

Model Allows Forecasts Based on Scenarios. Applications Can be Accurately Forecast Until 2017

#1 Same disposal rate as 2008

#2 Improvement as done in 2009-2010

Share of USPTO Patent Grants

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One trend is that US applications are being crowded out by foreign applications. In 2008 for the first time, there were more foreign grants than domestic ones. The trends are slow, so it will be a long time before no Americans get US patents. The model permits tracking of US vs. foreign applications.

Further Work Ideas: Extrapolation to 2017, Plus

Conclusions

The simulation shows that lagged R&D investments as an input can accurately model patent applications, grant outputs, and total backlog

Until 2009 USPTO was an unstable queue with arrival rate > service rate

However, its performance only gradually deteriorates—there is no imminent danger of collapse

The simulation allows what-if games, complicated by the big changes in 2009

Further work: forecasts to 2017, US vs. foreign patents, time delay discrepancy

References

https://sites.google.com/site/patentology/annexure-usptoqueue An queueing analysis of the USPTO

Publish or Patent: Bibliometric Evidence for Empirical Trade-offs in National Funding Strategies. JAIST 2012. With L. Leydesdorff.

http://www.uspto.gov/patents/stats/index.jsp