Commercialization Options for a set of Wireless Patents
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Transcript of Commercialization Options for a set of Wireless Patents
COMMERCIALIZATION OPTIONS FOR A SET OF COMMERCIALIZATION OPTIONS FOR A SET OF
WIRELESS PATENTSWIRELESS PATENTS
Project Advisor : Prof. Mary Mathew
Industry Supervisor: Mr. Mihir Mahajan
Shanmukha Sreenivas P , M.MGT II Year
Department of Management Studies, IISc
ContentsContentsProblem StatementObjectivesLiterature ReviewMethodologyResultsConclusionsBibliography
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Problem StatementProblem Statement To compare a given set of patents to class similar
benchmark patents of the world and suggest commercialization options for a selected set of patents.
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ObjectivesObjectives1) To compare the given set of wireless patents with the
identified sample of wireless world patents.
2) To identify a benchmark sample of wireless patents in the world, given the patent classes of the given set of wireless patents.
3) To evolve an elimination model to select a sample of patents with higher commercial potential.
4) Suggest commercialization options for the selected set of patents.
Literature ReviewLiterature Review
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Variable Insights Author
No of US classes Breadth, Extent of core technology diffusion Shapiro (2003)No of IPC classes Breadth, Extent of core technology diffusion Shapiro (2003)
No of InventorsDegree of inventor collaboration, Opposition Survivability
Reitzig (2003), Gibbs (2008)
No of family members Patent value and Market size Gambardella et al. (2008)Patent Grant Lag Degree of Technology Complexity, Patent Value Popp (2003), Retzig (2003)Age Probability of Patent trade, Patent Value Serrano (2008)
Backward citations count Quantitative indicator of prior art, Market sizeReitzig (2003), Lemley (2013)
Forward citations count Patent Value, Probability of patent trade Serrano (2008)No of claims Patent Value Ughetto (2011)No of words in first claim Scope of Claim, Patent Quality Osenga (2012)No of elements in first claim Scope of Claim, Patent Quality Osenga (2012)
MethodologyMethodology
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Demographic analysis
Patent Bucketing exercise(3 methods)
Three search techniques for potential licensees
Set of 175 Wireless communication patents
World benchmark patents obtained for each bucket.
(n5 = 135)
Selected set of patents(n4 = 135)
Demographic comparison of benchmark and given set of
patents
Discriminant analysis to identify patents similar to benchmark
Commercialization strategy
Shortlist of frequently appearing companies
Cont…
Sampling of world patents from above
companies(n2 = 252);(n3 = 252)
25 Buckets
Patents of high potential
List of potential licensees for each Bucket
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Given set of patents Sample of world patents
from above
(n2 = 252);(n3= 252) (n1 = 175)
Cont…
Comparison of 2 random samples & given set of patents
Data SetsData SetsGiven Set of Patents (n1= 175)
Two samples of patents from short listed companies of random world sample (n2= 252, n3= 252)
Patents from the given set considered after bucketing (n4= 135)
World benchmark patents obtained for each bucket (n5= 135)
Ocean Tomo auctioned patents (n6= 10)Extracted from – Innography(2013) ; Relecura(2013) )
USP class wise analysis of given data setStatus of the applications (awarded v/s filed)Patents by inventor type ( academic v/s non-academic)Patent class by key inventorsAnalysis of familiesDescriptive statistics on :Patent lag and Age Number of Citations – forward and backwardNumber of Foreign FilingsNumber of InventorsNumber of words and elements in the first claim
1.1 Patent Demographic Analysis1.1 Patent Demographic Analysis
1.2 Patent Bucketing Exercise1.2 Patent Bucketing Exercise Identified 5 broad categories.
The above 5 categories further into 25 buckets.
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1.3 Search techniques employed on 1.3 Search techniques employed on bucketsbuckets
Search Techniques:IPC classification based search
Forward and Backward citations based search (Two - level)
Keyword based search
Outcome:List of potential licensees for each bucketShortlist of frequently appearing companies (14)
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2.1 Model to shortlist patents with 2.1 Model to shortlist patents with commercial potential - LDAcommercial potential - LDA
Classification methodD= v1X1+v2X2+v3X3+v4X4+…+c
D= Discriminate function
v=Discriminant coefficient or weight for that variableX=Variable consideredc=Constant
Patents of the given set (n4) having a greater extent of similarity with the benchmark patents (n5) are considered for further analysis.
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2.2 Positioning of shortlisted patents 2.2 Positioning of shortlisted patents on Innovation chain & Wireless chainon Innovation chain & Wireless chain
Concept / Device basedBasic R&D / Applied R&D / NPDAcademic / Industry based inventors
Device value chainNetwork value chain Infrastructure value chainApplication value chainContent value chain
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} Wireless Value Chain
RESULTSRESULTS
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Demographic Analysis of Given Set (Demographic Analysis of Given Set (nn1 1 =175)=175)
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Demographic Analysis…1Demographic Analysis…1
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No. of patents/patent applications filed in each USP class
Patent filings w.r.t Filing Year and Publication Year
Based on data extracted from Innography
Demographic Analysis…2Demographic Analysis…2Jurisdictional Spread and Family analysis
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Publication_Country No of
Applications CA 1
CN 35
DE 7
EP 6
JP 33
KR 23
US 175
WO 89
Total 369
No. of Filings No. of applications
1 68
2 61
3 19
4 16
5 10
8 1
Total 175
Based on data extracted from InnographyFew country filings are not included in Innography
Patent buckets identified :Patent buckets identified :
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Telecommunications Software 34Digital Image Processing 5Quality of Service 7Security 13Data Management 5Other 4
Communication 60Massive MIMO 16Modulation 2Space Time Coding 7Energy Optimization & Error detection
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Cognitive Radios 8Multi hop Communication 4Other 14
Networking 64Location Discovery 7Node authentication 7Internetwork Load balancing 3Quality of Experience determination
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Device handoff 3Scheduling in adhoc networks 5Optical Networks 5Packet Management 9Sensor Management 4Resource – Network Management 8Other 9
OTHER 10Hardware-Medical Devices 7
Category-Buckets No. of patents
Category-Buckets No. of patents
Category-Buckets No. of patents
Category-Buckets No. of patents
Search techniques applied on buckets identified…1Search techniques applied on buckets identified…1
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Ex: Mass ive MIMO IPC Classification based search
Forward & Backward citations based search Keyword based search
Airgo Networks Inc. Alcatel-lucent Alcatel-lucentAtheros Communications, Inc. Deere & Company Broadcom Corporation
Broadcom Corporation Ems Technologies, Inc.Electronics And Telecommunications Research Institute
Electronics And Telecommunications Research Institute Fujitsu Limited Fujitsu LimitedFujitsu Limited General Telecommunications Institute Huawei Technologies Co., Ltd.Hitachi, Ltd. Google Inc. Intel CorporationIntel Corporation Htc Corporation Interdigital, Inc.Interdigital, Inc. Ikanos Communications, Inc. Koninklijke Philips Electronics NvLg Corp. Intel Corporation Lg Corp.Marvell Technology Group Ltd. Kathrein-werke Kg Mimos, BerhadNippon Telegraph & Telephone Corp. Microsoft Corporation Nec CorporationPanasonic Corporation Motorola Solutions Inc Nippon Telegraph & Telephone Corp.Qualcomm, Inc. Nokia Corporation Nokia CorporationSony Corporation Qualcomm, Inc. Panasonic CorporationSharp Corporation Rockwell Collins, Inc. Qualcomm, Inc.Samsung Electronics Co., Ltd. Samsung Electronics Co., Ltd. Samsung Electronics Co., Ltd.Telefonaktiebolaget Lm Ericsson Toshiba Corporation Sony CorporationToshiba Corporation Unwired Planet Inc Telefonaktiebolaget Lm EricssonWionics Res Xr Communications, Llc Zte Corporation
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Potent ia l l i censees - Mass ive MIMO Airgo Networks Inc.Atheros Communications, Inc.Deere & CompanyEms Technologies, Inc.Ikanos Communications, Inc.Kathrein-werke KgMarvell Technology Group Ltd.Mimos, BerhadRockwell Collins, Inc.Unwired Planet IncWionics ResXr Communications, Llc
AppleBroadcomCisco SystemsFujitsu LimitedHuaweiKoninklijke Philips ElectronicsLG ElectronicsMotorola Mobility (acquired by Google)NEC corporationNokia corporationQualcommResearch in Motion (Blackberry)Samsung ElectronicsZTE corporation
Potent ia l l i censees for the port fo l io
Search techniques applied on buckets identified…2Search techniques applied on buckets identified…2
Patent Demographic comparisons - Patent Demographic comparisons - nn1 1 (Given Set) v/s n(Given Set) v/s n22 & n & n3 3 (World Samples)(World Samples)
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Patent Demographics Patent Demographics nn1 1 v/s v/s nn22 & & nn33
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Mean of Given Set(1)
Mean of World Set-I(2)
Mean of World Set-II(3) F statistic
t12
statistict13
statistict23
statistic
No of IPC classes 1.57 3.29 2.98 21.934** -6.54** -6.25** 1.14
No of Inventors 1.67 2.88 2.79 32.75** -7.95** -7.68** 0.558
No of family members 1.96 5.52988 4.438247 53.29** 10.343** -8.872** 1.553
Patent Grant Lag 1128.46 1507.12 1582.15 10.58** 3.872** -5.122** -0.95
Backward Citations Count
6.31 29.27 20.75 29.889** -7.81** -6.085** 2.744*
Forward Citations Count
0.57 2.69 3.02 17.551** -5.377** -6.266** -0.71
No of words in first claim 209.5 158.5 171.89 7.27** 3.6223* 2.49* -1.566
No of claims 22.01 22.12 21.33 0.269 -0.09 0.59 0.615
No of elements in first claim
4.3571 4.4936 4.6107 0.23 0.387 0.626 0.402
No of US classes 2.53 2.92 3.04 2.167 -1.62 -1.962 -0.493
Significance level: * for 0.05, ** for 0.01
Discrimination of given set ( nn44) and world benchmark (nn55) to identify commercially potential patents in the given set
Data input to the LDA modelData input to the LDA model
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Category-Buckets No. of patents from given set
No. of world benchmark
patents considered
Telecommunications Software 30 30Digital Image Processing 5 5Quality of Service 7 7Security 13 13Data Management 5 5
Similarly, we extracted data for all the remaining buckets
Variables chosen for analysis - LDAVariables chosen for analysis - LDA
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Forward Citations Count
V01
Backward Citations Count
V02
No of US classes V03No of IPC classes V04No of Inventors V05No of family members V06Patent Grant Lag V07No of claims V08Strength V09Age V10No of words in first claim
V11
No of elements in first claim
V12
{0, if patent belongs to benchmark set1, if patent belongs to given set
The class variable =
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Comparison of means between the two groupsComparison of means between the two groups
Mean of Given Set(1)
Mean of Benchmark Set(2)
t12 statistic
Forward Citations Count 0.6 5.8 -10.79**
Backward Citations Count 5.95 19.26 4.59**
No of IPC classes 1.52 1.94 -2.98*No of Inventors 1.64 3.23 -8.12**
No of family members 1.61 2.93 6.086**
Patent Grant Lag 708.86 848.32 -2.602*No of claims 22.33 32.83 6.733**
Strength 31.07 75.07 -19.31**Age 1307.28 1736.07 7.822**
No of words in first claim 142.25 139.58 0.247
No of elements in first claim 4.14 4.17 0.119
No of US classes 2.3 2.73 -1.33
Significance level: * for 0.05, ** for 0.01
28Box Plot of the Strength variable for the two sets
Box Plot of the strength variableBox Plot of the strength variable
Low Strength benchmark patents
High Strength -Given set patents
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Linear Discriminant AnalysisLinear Discriminant Analysis
1. Classification Results without strength variable2. Classification Results with outliers removed without strength variable
Membership misfit of benchmark patents = 28.1 % Membership misfit of benchmark patents = 25.5 %
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LDA functionLDA function
Is_given_set
Predicted Group
Membership
Total0 1Cross-validatedResults
Count 0 111 24 1351 10 125 135
% 0 82.4 17.6 100.01 7.4 92.6 100.0
87.5% (236/270) of cross validated grouped cases are correctly classified
Membership misfit of benchmark patents = 17.6 %
3. Classification Results with strength variable
Goodness of fit of the modelGoodness of fit of the model
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Function Eigenvalue% of
VarianceCumulative
%Canonical
Correlation1 1.606 100.0 100.0 .785
Test of Function(s)
Wilks' Lambda Chi-square df Sig.
1 .384 253.302 9 .000
62 % of the variability is explained
Model is significant
Fairly good discrimination is achieved between the two groups
Shortlisted PatentsShortlisted Patents
32The last three patents are the additional ones that are obtained using model 3
S.No Publication No
Forward Citations
Count
Backward Citations
Count
No of US
classes
No of IPC
classesNo of
Inventors
No of family
members
Patent Grant Lag
No of claims
Patent Granted Strength Age Bucket
P1 US8266256 6 19 7 1 1 4 1148 10 1 85 1403Sensor mngmnt
P2 US20100217345 4 0 2 2 2 2 547 20 0 55 1547 Health
P3 US20100226491 3 5 3 3 2 1 549 20 0 55 1535 Health
P4 US20100271994 3 5 1 1 1 0 552 20 0 55 1489 node auth
P5 US20120014424 2 3 1 1 2 0 553 33 0 65 1042 MIMO
P6 US20110003612 1 13 1 1 2 0 553 33 0 55 1420 node auth
P7 US20120068845 1 0 1 1 1 1 566 29 0 65 992 QoSP8 US8126486 0 26 10 8 2 1 1278 18 1 55 1727 MIMO
P9 US8327367 0 39 9 5 3 3 1370 12 1 55 1539Sensor mngmnt
P10 US8193941 0 26 5 2 2 2 1126 28 1 65 1477 Health
Comparative Analysis with sold Comparative Analysis with sold patentspatentsShortlisted patents of given set – 10Ocean Tomo Auctioned Patents – 10Sample of benchmark patents – 10Sample of patents from given set – 10
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Comparative AnalysisComparative Analysis
(days)
35Source : “Value network dynamics in 3G–4G wireless communications: A systems thinking approach to strategic value assessment” Pagani, 2008
Suggested positioning in –Suggested positioning in – Wireless Value Chain Wireless Value Chainfor the shortlisted patents for the shortlisted patents of given setof given set
P1P1 P2P2
P3P3
P4P4
P5P5
P6P6 P7P7
P8P8
P9P9
P10P10
P8P8
P9P9P7P7
Suggested positioning in –Suggested positioning in –Innovation Value Chain Innovation Value Chain for the shortlisted patents of given setfor the shortlisted patents of given set
36Source : Innovation Value Chain , Hansen (2007)
P2P2P3P3P4P4 P5P5 P6P6 P7P7
P8P8 P9P9
P10P10
P1P1
Rationale for commercialization Rationale for commercialization optionsoptionsCommercialization options available are : New product development investments:
Valid if the patent is a concept suitable to enter the device development stage.
License the patent/s : Valid if a potential licensee keen to work further on the patent and based on a mutually agreed royalty model.
Sell out of the patent/s: Selling a patent may not be substantial unless the product has been on the market for a long time. The patent buyer usually won't want to spend a lot for an unproven product that might not generate a big profits.
Initiate a startup: Valid if the patent is at that stage where it can be manifested into a service or product for a customer and revenues are in sight within 6 months. It should also have a willing entrepreneur keen to take it out.
Cross Licensing: Usually, this type of agreement happens between two parties in order to avoid litigation or to settle an infringement dispute. Very often, the patents that each party owns covers different essential aspects of a given commercial product.
37Source: Shapiro, Carl, “Navigating the Patent Thicket: Cross Licenses, Patent Pools, and Standard Setting )”,2010
Suggested commercialization options for Suggested commercialization options for the shortlisted Patents/Patent Applicationsthe shortlisted Patents/Patent Applications
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Publication NoPatent
GrantedDevice based
Concept based
Academic Inventor
Industrial Inventor
Suggested positioning in - Wireless Value
Chain
Suggested positioning in -
Innovation Value Chain
Suggested – Bucket
Suggested - Commercialization
Route
US8266256 • • • Infrastructure Applied R&DSensor mgmt Technology Licensing
US20100217345 • • InfrastructureDevelopment &
Design Health NPD
US20100226491 • • DeviceDevelopment &
Design Health NPD
US20100271994 • • Infrastructure Applied R&D Node Auth Technology Licensing
US20120014424 • • Infrastructure Applied R&D MIMO Technology Licensing
US20110003612 • • Infrastructure Applied R&D Node Auth Technology Licensing
US20120068845 • •Network &
InfrastructureDevelopment &
Design QoS NPD
US8126486 • • • Infrastructure Applied R&D MIMO Technology Licensing
US8327367 • • •Network &
Infrastructure Applied R&DSensor mgmt Technology Licensing
US8193941 • • • InfrastructureDevelopment &
Design Health NPD
Snoring Treatment
ConclusionsConclusionsObjectives1) To compare the given set of wireless
patents with the identified sample of world wireless patents.
2) To identify a benchmark sample of wireless patents in the world, given the patent classes of the given set of wireless patents.
3) To evolve an elimination model to select a sample of patents with higher commercial potential.
4) Suggest commercialization options for the selected set of patents.
Results1) Analyzed the gap between the given set of
wireless patents with the identified sample of world wireless patents based on Patent Latent Variables.
2) Identified benchmark patents of 14 companies shortlisted on applying search techniques on the buckets identified.
3) Used Linear Discriminant Analysis to identify the patents with higher commercial potential.
4) Positioned the shortlisted patents on the Wireless Value Chain and on the Innovation Value Chain and suggested commercialization options.
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