SlideShare une entreprise Scribd logo
1  sur  30
Télécharger pour lire hors ligne
The Art of Patent Landscaping
II-SDV - Nice
Aalt van de Kuilen
April, 18th 2016
Definitions
 Patent landscape reports provide a snap-shot of
the patent situation of a specific technology or
company, either within a given country or region,
or globally.
WIPO definition
Why?
Patent Landscapes can give an overview of
 technology sector
 competitors
 economic value of patent portfolios
 strategic position
Impact of Patent Landscapes on R&D:
 shorten R&D time by 60%
 save R&D costs by 40%
Limitations
 Very important:
 Be aware that not all companies, active in a market, have
a similar patent filing strategy
 Example  TESLA (hardly any patents on electric cars)
Before we start
Before starting the Patent Landscape it is
important to know the reason for creating a
patent landscape!!!
Every Landscape has a purpose why it has to
be created!!
Creating a landscape
 Don’t underestimate how difficult it can be to create
a landscape
 Do not underestimate time needed to plan and
execute a landscape!!
Step 1: Ask for the reason for creating this landscape
The purpose for the landscape has impact on what
decision(s) is/are going to be taken based on the
information.  impact on the scope!!!
Step 2: Become familiar with the topic
Before you start you have to become familiar with
the topic by reading some general articles or
reviews.
Step 3: Considerations before starting the search
a) Patent families
b) Citation search
c) Patent assignee names
d) Classifications
e) Regions
f) Broad or small topic
a) Patent Families
What is the most useful patent family structure for
landscaping
Databases have different definitions
Most used are 2 definitions
 Extended families  INPADOC (about 89% is based on one priority!)
 Strict families  WPI, Espacenet.(100% based on one priority)
 Citation analysis can give additional records. (be
aware of different policies especially between EU
en US)
 A manual check is needed.
b) Citation search
 No standardization of company names.
 Manually corrections are necessary!
Helpful 
 Corporate Trees (most providers have one)
 Derwent Company Codes ( Link )
 Standardization initiatives (Univ. of Leuven)
KUL/EUROSTAT: Method for harmonizing applicants names
c) Patent assignee names
d) Classifications
Absolutely necessary.
Keywords only is insufficient and will miss a lot of records,
especially in new technologies.
A global classification system is most preferred, like IPC and CPC.
Warning 1: Not all documents have a CPC ,10-30% of the families
do not have a CPC assigned, mainly records coming from Japan,
China, Korea and Russia.
For details about coverage see the CPC website www.cpcinfo.org
Warning 2: be aware that not all relevant documents are classified
correctly!
e) Regions
Which countries do you want to include  depend on
the topic!!
1. Where are the main competitors located?
2. Are all technology areas as important for all regions?
3. For some countries it is hard to retrieve patent
information
f) Broad or small topic
Broad topics are seldom useful for landscaping.
Topics like:
Pharma industry, Green energy, Automotive industry,
Nanotechnology are distributed over several classes and
hard to retrieve
Small topics, within a small set of classes, are more
reliable for landscaping.
Step 4: Search
Most critical step!
Broad approach is most often the best!
(Include both classes and keywords)
Which database(s)??
(be aware  documents missed at this stage will
never show up later!!)
Step 5: Clean up
in order to create a reliable landscape
80-90% relevancy is required
Sometimes a manual clean-up is necessary
Step 6: Checking the dataset
Check whether or not your set make sense.
Look if some key patents are included in the set.
 Step 7: analyzing, visualization and reporting
 Many tools are available for analyzing the data . Choose
the tool that fits best for purpose
 Visualize only global trends and not specific data. Too
detailed reports will not be read.
 The most important part of the report is the summary!!
(most clients do not read more than the summary page)
Summary
(How to create a patent landscape in 7 steps)
 Step 1: Ask the reason for the landscape
 Step 2: Become familiar with the topic
 Step 3: Considerations before starting the search
a) Patent families
b) Citation search
c) Patent assignee names
d) Classifications
e) Regions
f) Broad or small topic
 Step 4: Search
 Step 5: Clean up
 Step 6: Checking the dataset
 Step 7: Analyzing, visualization and reporting
Example
 Tsunami Warning systems
 Your company wants to develop a new Tsunami
Warning System. Before starting, they want to know
what is already available on the market, who are the
competitors and what described in IP.
https://en.wikipedia.org/wiki/Tsunami
https://en.wikipedia.org/wiki/Tsunami_warning_system
To become familiar with the topic Wikipedia is
often a good source
Gives, in this case, the very important
information that a tsunami is an earthquake.
https://en.wikipedia.org/wiki/Main_Page
Considerations and search
 Classifications
 Citations
 Database selection (more than one?)
Proposed choices
Text search: example!!
Most probably in Title and abstract and claims
(earthquake) w5? (warning or alarm)
Most probably in full text
(tsunami) w5? (warning or alarm)
Classifications: example!!
(CPC=(G08B21/10 or H04W4/22 or G01V1/008 or H04W76/007 OR
H04W4/06 OR H04W76/002 OR H04W68/00))
or (CPC=NO (records with no CPC)! and (IPC=G08B21/10 or G08B21/00 or
G08B27/00 or G01V1/00 or H04W/22 OR H04W4/22 OR H04W4/06
OR H04M11/04 OR G08B27/00))
Possibilities for data clean up
 Exclude old documents
 Exclude Chinese, Taiwanese and Korean records with
only one family member (or other less relevant countries?)
 Exclude German Utility Models, with only one family
member DEU
In this case, at this stage, approx. 2000 records
 Further limitations and clean-up based on the
scope/purpose of the landscape!!
Analyzing, visualization and
reporting
After having created the set, there is no preference in the
tool used for analyzing and visualization
Be sure the report (and even the summary) gives answers to
the questions that has been asked.
Conclusion
 Patent Landscaping is complex and time consuming!!
 Patent Landscaping has it’s limitations
www.patentinformationservices.nl

Contenu connexe

Tendances

II-SDV 2015, 20 - 21 April, in Nice
II-SDV 2015, 20 - 21 April, in NiceII-SDV 2015, 20 - 21 April, in Nice
II-SDV 2015, 20 - 21 April, in NiceDr. Haxel Consult
 
II-SDV 2015, 20 - 21 April, in Nice
II-SDV 2015, 20 - 21 April, in NiceII-SDV 2015, 20 - 21 April, in Nice
II-SDV 2015, 20 - 21 April, in NiceDr. Haxel Consult
 
ICIC 2017: New product presentation minesoft
ICIC 2017: New product presentation minesoftICIC 2017: New product presentation minesoft
ICIC 2017: New product presentation minesoftDr. Haxel Consult
 
ICIC 2014 Patent Citation Analysis: Tools and Techniques
ICIC 2014 Patent Citation Analysis: Tools and Techniques ICIC 2014 Patent Citation Analysis: Tools and Techniques
ICIC 2014 Patent Citation Analysis: Tools and Techniques Dr. Haxel Consult
 
II-SDV 2016 Manish Sinka - Taking Patent Research platforms beyond Search
II-SDV 2016 Manish Sinka - Taking Patent Research platforms beyond SearchII-SDV 2016 Manish Sinka - Taking Patent Research platforms beyond Search
II-SDV 2016 Manish Sinka - Taking Patent Research platforms beyond SearchDr. Haxel Consult
 
ICIC 2017: Publication Analysis and Publication Strategy
ICIC 2017: Publication Analysis and Publication Strategy  ICIC 2017: Publication Analysis and Publication Strategy
ICIC 2017: Publication Analysis and Publication Strategy Dr. Haxel Consult
 
ICIC 2017: The Use of Patent Information for Innovation and Competitive Intel...
ICIC 2017: The Use of Patent Information for Innovation and Competitive Intel...ICIC 2017: The Use of Patent Information for Innovation and Competitive Intel...
ICIC 2017: The Use of Patent Information for Innovation and Competitive Intel...Dr. Haxel Consult
 
II-SDV 2017: Spotting the Stars in your Galaxy of Patent Data
II-SDV 2017: Spotting the Stars in your Galaxy of Patent DataII-SDV 2017: Spotting the Stars in your Galaxy of Patent Data
II-SDV 2017: Spotting the Stars in your Galaxy of Patent DataDr. Haxel Consult
 
ICIC 2017: Technology Scouting: Decision Support in Strategic Analyses for Te...
ICIC 2017: Technology Scouting: Decision Support in Strategic Analyses for Te...ICIC 2017: Technology Scouting: Decision Support in Strategic Analyses for Te...
ICIC 2017: Technology Scouting: Decision Support in Strategic Analyses for Te...Dr. Haxel Consult
 
II-PIC 2017: Porduct presentation minesoft
II-PIC 2017: Porduct presentation minesoftII-PIC 2017: Porduct presentation minesoft
II-PIC 2017: Porduct presentation minesoftDr. Haxel Consult
 
II-PIC 2017: Gain insight into technical, legal and business information thro...
II-PIC 2017: Gain insight into technical, legal and business information thro...II-PIC 2017: Gain insight into technical, legal and business information thro...
II-PIC 2017: Gain insight into technical, legal and business information thro...Dr. Haxel Consult
 
II-SDV 2016 Bob Stembridge We have all the Time in the World; a Review of ho...
II-SDV 2016 Bob Stembridge  We have all the Time in the World; a Review of ho...II-SDV 2016 Bob Stembridge  We have all the Time in the World; a Review of ho...
II-SDV 2016 Bob Stembridge We have all the Time in the World; a Review of ho...Dr. Haxel Consult
 
II-SDV 2016 Aleksandar Kapisoda, Klaus Kater - Deep Web Search
II-SDV 2016 Aleksandar Kapisoda, Klaus Kater - Deep Web SearchII-SDV 2016 Aleksandar Kapisoda, Klaus Kater - Deep Web Search
II-SDV 2016 Aleksandar Kapisoda, Klaus Kater - Deep Web SearchDr. Haxel Consult
 
II-SDV 2014 The Challenges of Managing “Big Data” in the Patent Field: Patent...
II-SDV 2014 The Challenges of Managing “Big Data” in the Patent Field: Patent...II-SDV 2014 The Challenges of Managing “Big Data” in the Patent Field: Patent...
II-SDV 2014 The Challenges of Managing “Big Data” in the Patent Field: Patent...Dr. Haxel Consult
 
II-SDV 2016 Michael Iarrobino - Improving Text Mining Results with Access to ...
II-SDV 2016 Michael Iarrobino - Improving Text Mining Results with Access to ...II-SDV 2016 Michael Iarrobino - Improving Text Mining Results with Access to ...
II-SDV 2016 Michael Iarrobino - Improving Text Mining Results with Access to ...Dr. Haxel Consult
 
ICIC 2017: New product presentationsLighthouse IP
ICIC 2017: New product presentationsLighthouse IPICIC 2017: New product presentationsLighthouse IP
ICIC 2017: New product presentationsLighthouse IPDr. Haxel Consult
 
II-PIC 2017: Product Presentation Gridlogics
II-PIC 2017: Product Presentation GridlogicsII-PIC 2017: Product Presentation Gridlogics
II-PIC 2017: Product Presentation GridlogicsDr. Haxel Consult
 
ICIC 2014 Valuing IP in the Chemical Space – Science, Art and Special Conside...
ICIC 2014 Valuing IP in the Chemical Space – Science, Art and Special Conside...ICIC 2014 Valuing IP in the Chemical Space – Science, Art and Special Conside...
ICIC 2014 Valuing IP in the Chemical Space – Science, Art and Special Conside...Dr. Haxel Consult
 

Tendances (20)

II-SDV 2015, 20 - 21 April, in Nice
II-SDV 2015, 20 - 21 April, in NiceII-SDV 2015, 20 - 21 April, in Nice
II-SDV 2015, 20 - 21 April, in Nice
 
II-SDV 2015, 20 - 21 April, in Nice
II-SDV 2015, 20 - 21 April, in NiceII-SDV 2015, 20 - 21 April, in Nice
II-SDV 2015, 20 - 21 April, in Nice
 
ICIC 2017: New product presentation minesoft
ICIC 2017: New product presentation minesoftICIC 2017: New product presentation minesoft
ICIC 2017: New product presentation minesoft
 
II-SDV 2016 Expert System
II-SDV 2016 Expert SystemII-SDV 2016 Expert System
II-SDV 2016 Expert System
 
ICIC 2014 Patent Citation Analysis: Tools and Techniques
ICIC 2014 Patent Citation Analysis: Tools and Techniques ICIC 2014 Patent Citation Analysis: Tools and Techniques
ICIC 2014 Patent Citation Analysis: Tools and Techniques
 
II-SDV 2016 Manish Sinka - Taking Patent Research platforms beyond Search
II-SDV 2016 Manish Sinka - Taking Patent Research platforms beyond SearchII-SDV 2016 Manish Sinka - Taking Patent Research platforms beyond Search
II-SDV 2016 Manish Sinka - Taking Patent Research platforms beyond Search
 
ICIC 2017: Publication Analysis and Publication Strategy
ICIC 2017: Publication Analysis and Publication Strategy  ICIC 2017: Publication Analysis and Publication Strategy
ICIC 2017: Publication Analysis and Publication Strategy
 
ICIC 2017: The Use of Patent Information for Innovation and Competitive Intel...
ICIC 2017: The Use of Patent Information for Innovation and Competitive Intel...ICIC 2017: The Use of Patent Information for Innovation and Competitive Intel...
ICIC 2017: The Use of Patent Information for Innovation and Competitive Intel...
 
II-SDV 2017: Spotting the Stars in your Galaxy of Patent Data
II-SDV 2017: Spotting the Stars in your Galaxy of Patent DataII-SDV 2017: Spotting the Stars in your Galaxy of Patent Data
II-SDV 2017: Spotting the Stars in your Galaxy of Patent Data
 
II-SDV 2016 Minesoft
II-SDV 2016 MinesoftII-SDV 2016 Minesoft
II-SDV 2016 Minesoft
 
ICIC 2017: Technology Scouting: Decision Support in Strategic Analyses for Te...
ICIC 2017: Technology Scouting: Decision Support in Strategic Analyses for Te...ICIC 2017: Technology Scouting: Decision Support in Strategic Analyses for Te...
ICIC 2017: Technology Scouting: Decision Support in Strategic Analyses for Te...
 
II-PIC 2017: Porduct presentation minesoft
II-PIC 2017: Porduct presentation minesoftII-PIC 2017: Porduct presentation minesoft
II-PIC 2017: Porduct presentation minesoft
 
II-PIC 2017: Gain insight into technical, legal and business information thro...
II-PIC 2017: Gain insight into technical, legal and business information thro...II-PIC 2017: Gain insight into technical, legal and business information thro...
II-PIC 2017: Gain insight into technical, legal and business information thro...
 
II-SDV 2016 Bob Stembridge We have all the Time in the World; a Review of ho...
II-SDV 2016 Bob Stembridge  We have all the Time in the World; a Review of ho...II-SDV 2016 Bob Stembridge  We have all the Time in the World; a Review of ho...
II-SDV 2016 Bob Stembridge We have all the Time in the World; a Review of ho...
 
II-SDV 2016 Aleksandar Kapisoda, Klaus Kater - Deep Web Search
II-SDV 2016 Aleksandar Kapisoda, Klaus Kater - Deep Web SearchII-SDV 2016 Aleksandar Kapisoda, Klaus Kater - Deep Web Search
II-SDV 2016 Aleksandar Kapisoda, Klaus Kater - Deep Web Search
 
II-SDV 2014 The Challenges of Managing “Big Data” in the Patent Field: Patent...
II-SDV 2014 The Challenges of Managing “Big Data” in the Patent Field: Patent...II-SDV 2014 The Challenges of Managing “Big Data” in the Patent Field: Patent...
II-SDV 2014 The Challenges of Managing “Big Data” in the Patent Field: Patent...
 
II-SDV 2016 Michael Iarrobino - Improving Text Mining Results with Access to ...
II-SDV 2016 Michael Iarrobino - Improving Text Mining Results with Access to ...II-SDV 2016 Michael Iarrobino - Improving Text Mining Results with Access to ...
II-SDV 2016 Michael Iarrobino - Improving Text Mining Results with Access to ...
 
ICIC 2017: New product presentationsLighthouse IP
ICIC 2017: New product presentationsLighthouse IPICIC 2017: New product presentationsLighthouse IP
ICIC 2017: New product presentationsLighthouse IP
 
II-PIC 2017: Product Presentation Gridlogics
II-PIC 2017: Product Presentation GridlogicsII-PIC 2017: Product Presentation Gridlogics
II-PIC 2017: Product Presentation Gridlogics
 
ICIC 2014 Valuing IP in the Chemical Space – Science, Art and Special Conside...
ICIC 2014 Valuing IP in the Chemical Space – Science, Art and Special Conside...ICIC 2014 Valuing IP in the Chemical Space – Science, Art and Special Conside...
ICIC 2014 Valuing IP in the Chemical Space – Science, Art and Special Conside...
 

En vedette

II-SDV 2014 Automated Relevancy Check of Patents and Scientific Literature (K...
II-SDV 2014 Automated Relevancy Check of Patents and Scientific Literature (K...II-SDV 2014 Automated Relevancy Check of Patents and Scientific Literature (K...
II-SDV 2014 Automated Relevancy Check of Patents and Scientific Literature (K...Dr. Haxel Consult
 
Big Data Analysis for Standard Essential Patents
Big Data Analysis for Standard Essential PatentsBig Data Analysis for Standard Essential Patents
Big Data Analysis for Standard Essential PatentsAlex G. Lee, Ph.D. Esq. CLP
 
Patent: Presentation on Patent Mining
Patent: Presentation on Patent MiningPatent: Presentation on Patent Mining
Patent: Presentation on Patent MiningBananaIP Counsels
 
Anthony Trippe Harnessing the Power of Patent Analytics A Policy Maker's Pers...
Anthony Trippe Harnessing the Power of Patent Analytics A Policy Maker's Pers...Anthony Trippe Harnessing the Power of Patent Analytics A Policy Maker's Pers...
Anthony Trippe Harnessing the Power of Patent Analytics A Policy Maker's Pers...atripper
 
Green startups basck ip landscape & tools 241012
Green startups basck ip landscape & tools 241012Green startups basck ip landscape & tools 241012
Green startups basck ip landscape & tools 241012Christian Bunke
 
Treparel - KMX Patent Analytics 2014
Treparel - KMX Patent Analytics 2014Treparel - KMX Patent Analytics 2014
Treparel - KMX Patent Analytics 2014Treparel
 
Emerging patent thickets and standards in the medical devices and telehealth ...
Emerging patent thickets and standards in the medical devices and telehealth ...Emerging patent thickets and standards in the medical devices and telehealth ...
Emerging patent thickets and standards in the medical devices and telehealth ...Quentin Tannock
 
Relecura - Features Overview
Relecura - Features OverviewRelecura - Features Overview
Relecura - Features OverviewRelecura Inc.
 
PIIP_Patent search & Analysis_20160829
PIIP_Patent search & Analysis_20160829PIIP_Patent search & Analysis_20160829
PIIP_Patent search & Analysis_20160829YiTien Liao
 

En vedette (10)

II-SDV 2014 Automated Relevancy Check of Patents and Scientific Literature (K...
II-SDV 2014 Automated Relevancy Check of Patents and Scientific Literature (K...II-SDV 2014 Automated Relevancy Check of Patents and Scientific Literature (K...
II-SDV 2014 Automated Relevancy Check of Patents and Scientific Literature (K...
 
Big Data Analysis for Standard Essential Patents
Big Data Analysis for Standard Essential PatentsBig Data Analysis for Standard Essential Patents
Big Data Analysis for Standard Essential Patents
 
Patent: Presentation on Patent Mining
Patent: Presentation on Patent MiningPatent: Presentation on Patent Mining
Patent: Presentation on Patent Mining
 
Anthony Trippe Harnessing the Power of Patent Analytics A Policy Maker's Pers...
Anthony Trippe Harnessing the Power of Patent Analytics A Policy Maker's Pers...Anthony Trippe Harnessing the Power of Patent Analytics A Policy Maker's Pers...
Anthony Trippe Harnessing the Power of Patent Analytics A Policy Maker's Pers...
 
Green startups basck ip landscape & tools 241012
Green startups basck ip landscape & tools 241012Green startups basck ip landscape & tools 241012
Green startups basck ip landscape & tools 241012
 
PatSnap Landscaping
PatSnap LandscapingPatSnap Landscaping
PatSnap Landscaping
 
Treparel - KMX Patent Analytics 2014
Treparel - KMX Patent Analytics 2014Treparel - KMX Patent Analytics 2014
Treparel - KMX Patent Analytics 2014
 
Emerging patent thickets and standards in the medical devices and telehealth ...
Emerging patent thickets and standards in the medical devices and telehealth ...Emerging patent thickets and standards in the medical devices and telehealth ...
Emerging patent thickets and standards in the medical devices and telehealth ...
 
Relecura - Features Overview
Relecura - Features OverviewRelecura - Features Overview
Relecura - Features Overview
 
PIIP_Patent search & Analysis_20160829
PIIP_Patent search & Analysis_20160829PIIP_Patent search & Analysis_20160829
PIIP_Patent search & Analysis_20160829
 

Similaire à II-SDV 2016 Aalt van de Kuilen - The Art of Patent Landscaping

Data Science Application in Business Portfolio & Risk Management
Data Science Application in Business Portfolio & Risk ManagementData Science Application in Business Portfolio & Risk Management
Data Science Application in Business Portfolio & Risk ManagementData Science Thailand
 
ISO 15926 series standard and its business value
ISO 15926 series standard and its business valueISO 15926 series standard and its business value
ISO 15926 series standard and its business valueHiroshi Okada
 
Microbattery Patent Landscape Sample
Microbattery Patent Landscape SampleMicrobattery Patent Landscape Sample
Microbattery Patent Landscape SampleKnowmade
 
TAPUniversity 8 Steps for Requirements Capture with Use Cases
TAPUniversity 8 Steps for Requirements Capture with Use CasesTAPUniversity 8 Steps for Requirements Capture with Use Cases
TAPUniversity 8 Steps for Requirements Capture with Use CasesDave Kohrell
 
Creation of Software Focusing on Patent Analysis
Creation of Software Focusing on Patent AnalysisCreation of Software Focusing on Patent Analysis
Creation of Software Focusing on Patent AnalysisIRJET Journal
 
Patent scope ompi
Patent scope ompiPatent scope ompi
Patent scope ompiLATIPAT
 
Interoperability.pptx
Interoperability.pptxInteroperability.pptx
Interoperability.pptxRahul720416
 
Patent Portfolios and Invention Sessions in Growth Stage Tech Companies - Dav...
Patent Portfolios and Invention Sessions in Growth Stage Tech Companies - Dav...Patent Portfolios and Invention Sessions in Growth Stage Tech Companies - Dav...
Patent Portfolios and Invention Sessions in Growth Stage Tech Companies - Dav...Dave Litwiller
 
GaN Technology Top-100 IP profiles - Sample
GaN Technology Top-100 IP profiles - SampleGaN Technology Top-100 IP profiles - Sample
GaN Technology Top-100 IP profiles - SampleKnowmade
 
2010 Patent Information
2010 Patent Information2010 Patent Information
2010 Patent Informationsmallhead
 
Industry4.0 IoT Vincent Thavonekham - Azure Day Ukraine
Industry4.0 IoT Vincent Thavonekham - Azure Day UkraineIndustry4.0 IoT Vincent Thavonekham - Azure Day Ukraine
Industry4.0 IoT Vincent Thavonekham - Azure Day UkraineFactoVia
 
3D Cell Culture technologies Patent Landscape Sample 2016
3D Cell Culture technologies Patent Landscape Sample 2016 3D Cell Culture technologies Patent Landscape Sample 2016
3D Cell Culture technologies Patent Landscape Sample 2016 Knowmade
 
Miniaturized Gas Sensors Patent Landscape 2016 Sample
Miniaturized Gas Sensors Patent Landscape 2016 SampleMiniaturized Gas Sensors Patent Landscape 2016 Sample
Miniaturized Gas Sensors Patent Landscape 2016 SampleKnowmade
 
Atomico Need-to-Know 24 August 2017
Atomico Need-to-Know 24 August 2017Atomico Need-to-Know 24 August 2017
Atomico Need-to-Know 24 August 2017Atomico
 
Technology analysis intro
Technology analysis introTechnology analysis intro
Technology analysis introFilip_Poulsen
 
Instant Tech Insights Lidar on June 21st 2022
Instant Tech Insights Lidar on June 21st 2022Instant Tech Insights Lidar on June 21st 2022
Instant Tech Insights Lidar on June 21st 2022decodemai
 
Netw 583 strategic management of technology entire class
Netw 583 strategic management of technology entire classNetw 583 strategic management of technology entire class
Netw 583 strategic management of technology entire classPatrickrasacs
 
WORLD CONFERENCE: TRIZ FUTURE 2008 5-7 November 2008, University of Twente, ...
WORLD CONFERENCE: TRIZ FUTURE 2008  5-7 November 2008, University of Twente, ...WORLD CONFERENCE: TRIZ FUTURE 2008  5-7 November 2008, University of Twente, ...
WORLD CONFERENCE: TRIZ FUTURE 2008 5-7 November 2008, University of Twente, ...Roberto Nani
 

Similaire à II-SDV 2016 Aalt van de Kuilen - The Art of Patent Landscaping (20)

Data Science Application in Business Portfolio & Risk Management
Data Science Application in Business Portfolio & Risk ManagementData Science Application in Business Portfolio & Risk Management
Data Science Application in Business Portfolio & Risk Management
 
ISO 15926 series standard and its business value
ISO 15926 series standard and its business valueISO 15926 series standard and its business value
ISO 15926 series standard and its business value
 
Microbattery Patent Landscape Sample
Microbattery Patent Landscape SampleMicrobattery Patent Landscape Sample
Microbattery Patent Landscape Sample
 
TAPUniversity 8 Steps for Requirements Capture with Use Cases
TAPUniversity 8 Steps for Requirements Capture with Use CasesTAPUniversity 8 Steps for Requirements Capture with Use Cases
TAPUniversity 8 Steps for Requirements Capture with Use Cases
 
Creation of Software Focusing on Patent Analysis
Creation of Software Focusing on Patent AnalysisCreation of Software Focusing on Patent Analysis
Creation of Software Focusing on Patent Analysis
 
Patent scope ompi
Patent scope ompiPatent scope ompi
Patent scope ompi
 
Interoperability.pptx
Interoperability.pptxInteroperability.pptx
Interoperability.pptx
 
iRobot_Case.docx.pdf
iRobot_Case.docx.pdfiRobot_Case.docx.pdf
iRobot_Case.docx.pdf
 
Patent Portfolios and Invention Sessions in Growth Stage Tech Companies - Dav...
Patent Portfolios and Invention Sessions in Growth Stage Tech Companies - Dav...Patent Portfolios and Invention Sessions in Growth Stage Tech Companies - Dav...
Patent Portfolios and Invention Sessions in Growth Stage Tech Companies - Dav...
 
GaN Technology Top-100 IP profiles - Sample
GaN Technology Top-100 IP profiles - SampleGaN Technology Top-100 IP profiles - Sample
GaN Technology Top-100 IP profiles - Sample
 
2010 Patent Information
2010 Patent Information2010 Patent Information
2010 Patent Information
 
Industry4.0 IoT Vincent Thavonekham - Azure Day Ukraine
Industry4.0 IoT Vincent Thavonekham - Azure Day UkraineIndustry4.0 IoT Vincent Thavonekham - Azure Day Ukraine
Industry4.0 IoT Vincent Thavonekham - Azure Day Ukraine
 
3D Cell Culture technologies Patent Landscape Sample 2016
3D Cell Culture technologies Patent Landscape Sample 2016 3D Cell Culture technologies Patent Landscape Sample 2016
3D Cell Culture technologies Patent Landscape Sample 2016
 
Miniaturized Gas Sensors Patent Landscape 2016 Sample
Miniaturized Gas Sensors Patent Landscape 2016 SampleMiniaturized Gas Sensors Patent Landscape 2016 Sample
Miniaturized Gas Sensors Patent Landscape 2016 Sample
 
Atomico Need-to-Know 24 August 2017
Atomico Need-to-Know 24 August 2017Atomico Need-to-Know 24 August 2017
Atomico Need-to-Know 24 August 2017
 
Patent database
Patent databasePatent database
Patent database
 
Technology analysis intro
Technology analysis introTechnology analysis intro
Technology analysis intro
 
Instant Tech Insights Lidar on June 21st 2022
Instant Tech Insights Lidar on June 21st 2022Instant Tech Insights Lidar on June 21st 2022
Instant Tech Insights Lidar on June 21st 2022
 
Netw 583 strategic management of technology entire class
Netw 583 strategic management of technology entire classNetw 583 strategic management of technology entire class
Netw 583 strategic management of technology entire class
 
WORLD CONFERENCE: TRIZ FUTURE 2008 5-7 November 2008, University of Twente, ...
WORLD CONFERENCE: TRIZ FUTURE 2008  5-7 November 2008, University of Twente, ...WORLD CONFERENCE: TRIZ FUTURE 2008  5-7 November 2008, University of Twente, ...
WORLD CONFERENCE: TRIZ FUTURE 2008 5-7 November 2008, University of Twente, ...
 

Plus de Dr. Haxel Consult

AI-SDV 2022: Henry Chang Patent Intelligence and Engineering Management
AI-SDV 2022: Henry Chang Patent Intelligence and Engineering ManagementAI-SDV 2022: Henry Chang Patent Intelligence and Engineering Management
AI-SDV 2022: Henry Chang Patent Intelligence and Engineering ManagementDr. Haxel Consult
 
AI-SDV 2022: Creation and updating of large Knowledge Graphs through NLP Anal...
AI-SDV 2022: Creation and updating of large Knowledge Graphs through NLP Anal...AI-SDV 2022: Creation and updating of large Knowledge Graphs through NLP Anal...
AI-SDV 2022: Creation and updating of large Knowledge Graphs through NLP Anal...Dr. Haxel Consult
 
AI-SDV 2022: The race to net zero: Tracking the green industrial revolution t...
AI-SDV 2022: The race to net zero: Tracking the green industrial revolution t...AI-SDV 2022: The race to net zero: Tracking the green industrial revolution t...
AI-SDV 2022: The race to net zero: Tracking the green industrial revolution t...Dr. Haxel Consult
 
AI-SDV 2022: Accommodating the Deep Learning Revolution by a Development Proc...
AI-SDV 2022: Accommodating the Deep Learning Revolution by a Development Proc...AI-SDV 2022: Accommodating the Deep Learning Revolution by a Development Proc...
AI-SDV 2022: Accommodating the Deep Learning Revolution by a Development Proc...Dr. Haxel Consult
 
AI-SDV 2022: Domain Knowledge makes Artificial Intelligence Smart Linda Ander...
AI-SDV 2022: Domain Knowledge makes Artificial Intelligence Smart Linda Ander...AI-SDV 2022: Domain Knowledge makes Artificial Intelligence Smart Linda Ander...
AI-SDV 2022: Domain Knowledge makes Artificial Intelligence Smart Linda Ander...Dr. Haxel Consult
 
AI-SDV 2022: Embedding-based Search Vs. Relevancy Search: comparing the new w...
AI-SDV 2022: Embedding-based Search Vs. Relevancy Search: comparing the new w...AI-SDV 2022: Embedding-based Search Vs. Relevancy Search: comparing the new w...
AI-SDV 2022: Embedding-based Search Vs. Relevancy Search: comparing the new w...Dr. Haxel Consult
 
AI-SDV 2022: Rolling out web crawling at Boehringer Ingelheim - 10 years of e...
AI-SDV 2022: Rolling out web crawling at Boehringer Ingelheim - 10 years of e...AI-SDV 2022: Rolling out web crawling at Boehringer Ingelheim - 10 years of e...
AI-SDV 2022: Rolling out web crawling at Boehringer Ingelheim - 10 years of e...Dr. Haxel Consult
 
AI-SDV 2022: Machine learning based patent categorization: A success story in...
AI-SDV 2022: Machine learning based patent categorization: A success story in...AI-SDV 2022: Machine learning based patent categorization: A success story in...
AI-SDV 2022: Machine learning based patent categorization: A success story in...Dr. Haxel Consult
 
AI-SDV 2022: Machine learning based patent categorization: A success story in...
AI-SDV 2022: Machine learning based patent categorization: A success story in...AI-SDV 2022: Machine learning based patent categorization: A success story in...
AI-SDV 2022: Machine learning based patent categorization: A success story in...Dr. Haxel Consult
 
AI-SDV 2022: Finding the WHAT – Will AI help? Nils Newman (Search Technology,...
AI-SDV 2022: Finding the WHAT – Will AI help? Nils Newman (Search Technology,...AI-SDV 2022: Finding the WHAT – Will AI help? Nils Newman (Search Technology,...
AI-SDV 2022: Finding the WHAT – Will AI help? Nils Newman (Search Technology,...Dr. Haxel Consult
 
AI-SDV 2022: New Insights from Trademarks with Natural Language Processing Al...
AI-SDV 2022: New Insights from Trademarks with Natural Language Processing Al...AI-SDV 2022: New Insights from Trademarks with Natural Language Processing Al...
AI-SDV 2022: New Insights from Trademarks with Natural Language Processing Al...Dr. Haxel Consult
 
AI-SDV 2022: Extracting information from tables in documents Holger Keibel (K...
AI-SDV 2022: Extracting information from tables in documents Holger Keibel (K...AI-SDV 2022: Extracting information from tables in documents Holger Keibel (K...
AI-SDV 2022: Extracting information from tables in documents Holger Keibel (K...Dr. Haxel Consult
 
AI-SDV 2022: Scientific publishing in the age of data mining and artificial i...
AI-SDV 2022: Scientific publishing in the age of data mining and artificial i...AI-SDV 2022: Scientific publishing in the age of data mining and artificial i...
AI-SDV 2022: Scientific publishing in the age of data mining and artificial i...Dr. Haxel Consult
 
AI-SDV 2022: AI developments and usability Linus Wretblad (IPscreener / Uppdr...
AI-SDV 2022: AI developments and usability Linus Wretblad (IPscreener / Uppdr...AI-SDV 2022: AI developments and usability Linus Wretblad (IPscreener / Uppdr...
AI-SDV 2022: AI developments and usability Linus Wretblad (IPscreener / Uppdr...Dr. Haxel Consult
 
AI-SDV 2022: Where’s the one about…? Looney Tunes® Revisited Jay Ven Eman (CE...
AI-SDV 2022: Where’s the one about…? Looney Tunes® Revisited Jay Ven Eman (CE...AI-SDV 2022: Where’s the one about…? Looney Tunes® Revisited Jay Ven Eman (CE...
AI-SDV 2022: Where’s the one about…? Looney Tunes® Revisited Jay Ven Eman (CE...Dr. Haxel Consult
 
AI-SDV 2022: Copyright Clearance Center
AI-SDV 2022: Copyright Clearance CenterAI-SDV 2022: Copyright Clearance Center
AI-SDV 2022: Copyright Clearance CenterDr. Haxel Consult
 
AI-SDV 2022: New Product Introductions: CENTREDOC
AI-SDV 2022: New Product Introductions: CENTREDOCAI-SDV 2022: New Product Introductions: CENTREDOC
AI-SDV 2022: New Product Introductions: CENTREDOCDr. Haxel Consult
 
AI-SDV 2022: Possibilities and limitations of AI-boosted multi-categorization...
AI-SDV 2022: Possibilities and limitations of AI-boosted multi-categorization...AI-SDV 2022: Possibilities and limitations of AI-boosted multi-categorization...
AI-SDV 2022: Possibilities and limitations of AI-boosted multi-categorization...Dr. Haxel Consult
 
AI-SDV 2022: Big data analytics platform at Bayer – Turning bits into insight...
AI-SDV 2022: Big data analytics platform at Bayer – Turning bits into insight...AI-SDV 2022: Big data analytics platform at Bayer – Turning bits into insight...
AI-SDV 2022: Big data analytics platform at Bayer – Turning bits into insight...Dr. Haxel Consult
 

Plus de Dr. Haxel Consult (20)

AI-SDV 2022: Henry Chang Patent Intelligence and Engineering Management
AI-SDV 2022: Henry Chang Patent Intelligence and Engineering ManagementAI-SDV 2022: Henry Chang Patent Intelligence and Engineering Management
AI-SDV 2022: Henry Chang Patent Intelligence and Engineering Management
 
AI-SDV 2022: Creation and updating of large Knowledge Graphs through NLP Anal...
AI-SDV 2022: Creation and updating of large Knowledge Graphs through NLP Anal...AI-SDV 2022: Creation and updating of large Knowledge Graphs through NLP Anal...
AI-SDV 2022: Creation and updating of large Knowledge Graphs through NLP Anal...
 
AI-SDV 2022: The race to net zero: Tracking the green industrial revolution t...
AI-SDV 2022: The race to net zero: Tracking the green industrial revolution t...AI-SDV 2022: The race to net zero: Tracking the green industrial revolution t...
AI-SDV 2022: The race to net zero: Tracking the green industrial revolution t...
 
AI-SDV 2022: Accommodating the Deep Learning Revolution by a Development Proc...
AI-SDV 2022: Accommodating the Deep Learning Revolution by a Development Proc...AI-SDV 2022: Accommodating the Deep Learning Revolution by a Development Proc...
AI-SDV 2022: Accommodating the Deep Learning Revolution by a Development Proc...
 
AI-SDV 2022: Domain Knowledge makes Artificial Intelligence Smart Linda Ander...
AI-SDV 2022: Domain Knowledge makes Artificial Intelligence Smart Linda Ander...AI-SDV 2022: Domain Knowledge makes Artificial Intelligence Smart Linda Ander...
AI-SDV 2022: Domain Knowledge makes Artificial Intelligence Smart Linda Ander...
 
AI-SDV 2022: Embedding-based Search Vs. Relevancy Search: comparing the new w...
AI-SDV 2022: Embedding-based Search Vs. Relevancy Search: comparing the new w...AI-SDV 2022: Embedding-based Search Vs. Relevancy Search: comparing the new w...
AI-SDV 2022: Embedding-based Search Vs. Relevancy Search: comparing the new w...
 
AI-SDV 2022: Rolling out web crawling at Boehringer Ingelheim - 10 years of e...
AI-SDV 2022: Rolling out web crawling at Boehringer Ingelheim - 10 years of e...AI-SDV 2022: Rolling out web crawling at Boehringer Ingelheim - 10 years of e...
AI-SDV 2022: Rolling out web crawling at Boehringer Ingelheim - 10 years of e...
 
AI-SDV 2022: Machine learning based patent categorization: A success story in...
AI-SDV 2022: Machine learning based patent categorization: A success story in...AI-SDV 2022: Machine learning based patent categorization: A success story in...
AI-SDV 2022: Machine learning based patent categorization: A success story in...
 
AI-SDV 2022: Machine learning based patent categorization: A success story in...
AI-SDV 2022: Machine learning based patent categorization: A success story in...AI-SDV 2022: Machine learning based patent categorization: A success story in...
AI-SDV 2022: Machine learning based patent categorization: A success story in...
 
AI-SDV 2022: Finding the WHAT – Will AI help? Nils Newman (Search Technology,...
AI-SDV 2022: Finding the WHAT – Will AI help? Nils Newman (Search Technology,...AI-SDV 2022: Finding the WHAT – Will AI help? Nils Newman (Search Technology,...
AI-SDV 2022: Finding the WHAT – Will AI help? Nils Newman (Search Technology,...
 
AI-SDV 2022: New Insights from Trademarks with Natural Language Processing Al...
AI-SDV 2022: New Insights from Trademarks with Natural Language Processing Al...AI-SDV 2022: New Insights from Trademarks with Natural Language Processing Al...
AI-SDV 2022: New Insights from Trademarks with Natural Language Processing Al...
 
AI-SDV 2022: Extracting information from tables in documents Holger Keibel (K...
AI-SDV 2022: Extracting information from tables in documents Holger Keibel (K...AI-SDV 2022: Extracting information from tables in documents Holger Keibel (K...
AI-SDV 2022: Extracting information from tables in documents Holger Keibel (K...
 
AI-SDV 2022: Scientific publishing in the age of data mining and artificial i...
AI-SDV 2022: Scientific publishing in the age of data mining and artificial i...AI-SDV 2022: Scientific publishing in the age of data mining and artificial i...
AI-SDV 2022: Scientific publishing in the age of data mining and artificial i...
 
AI-SDV 2022: AI developments and usability Linus Wretblad (IPscreener / Uppdr...
AI-SDV 2022: AI developments and usability Linus Wretblad (IPscreener / Uppdr...AI-SDV 2022: AI developments and usability Linus Wretblad (IPscreener / Uppdr...
AI-SDV 2022: AI developments and usability Linus Wretblad (IPscreener / Uppdr...
 
AI-SDV 2022: Where’s the one about…? Looney Tunes® Revisited Jay Ven Eman (CE...
AI-SDV 2022: Where’s the one about…? Looney Tunes® Revisited Jay Ven Eman (CE...AI-SDV 2022: Where’s the one about…? Looney Tunes® Revisited Jay Ven Eman (CE...
AI-SDV 2022: Where’s the one about…? Looney Tunes® Revisited Jay Ven Eman (CE...
 
AI-SDV 2022: Copyright Clearance Center
AI-SDV 2022: Copyright Clearance CenterAI-SDV 2022: Copyright Clearance Center
AI-SDV 2022: Copyright Clearance Center
 
AI-SDV 2022: Lighthouse IP
AI-SDV 2022: Lighthouse IPAI-SDV 2022: Lighthouse IP
AI-SDV 2022: Lighthouse IP
 
AI-SDV 2022: New Product Introductions: CENTREDOC
AI-SDV 2022: New Product Introductions: CENTREDOCAI-SDV 2022: New Product Introductions: CENTREDOC
AI-SDV 2022: New Product Introductions: CENTREDOC
 
AI-SDV 2022: Possibilities and limitations of AI-boosted multi-categorization...
AI-SDV 2022: Possibilities and limitations of AI-boosted multi-categorization...AI-SDV 2022: Possibilities and limitations of AI-boosted multi-categorization...
AI-SDV 2022: Possibilities and limitations of AI-boosted multi-categorization...
 
AI-SDV 2022: Big data analytics platform at Bayer – Turning bits into insight...
AI-SDV 2022: Big data analytics platform at Bayer – Turning bits into insight...AI-SDV 2022: Big data analytics platform at Bayer – Turning bits into insight...
AI-SDV 2022: Big data analytics platform at Bayer – Turning bits into insight...
 

Dernier

Magic exist by Marta Loveguard - presentation.pptx
Magic exist by Marta Loveguard - presentation.pptxMagic exist by Marta Loveguard - presentation.pptx
Magic exist by Marta Loveguard - presentation.pptxMartaLoveguard
 
『澳洲文凭』买詹姆士库克大学毕业证书成绩单办理澳洲JCU文凭学位证书
『澳洲文凭』买詹姆士库克大学毕业证书成绩单办理澳洲JCU文凭学位证书『澳洲文凭』买詹姆士库克大学毕业证书成绩单办理澳洲JCU文凭学位证书
『澳洲文凭』买詹姆士库克大学毕业证书成绩单办理澳洲JCU文凭学位证书rnrncn29
 
PHP-based rendering of TYPO3 Documentation
PHP-based rendering of TYPO3 DocumentationPHP-based rendering of TYPO3 Documentation
PHP-based rendering of TYPO3 DocumentationLinaWolf1
 
A Good Girl's Guide to Murder (A Good Girl's Guide to Murder, #1)
A Good Girl's Guide to Murder (A Good Girl's Guide to Murder, #1)A Good Girl's Guide to Murder (A Good Girl's Guide to Murder, #1)
A Good Girl's Guide to Murder (A Good Girl's Guide to Murder, #1)Christopher H Felton
 
Git and Github workshop GDSC MLRITM
Git and Github  workshop GDSC MLRITMGit and Github  workshop GDSC MLRITM
Git and Github workshop GDSC MLRITMgdsc13
 
Call Girls Near The Suryaa Hotel New Delhi 9873777170
Call Girls Near The Suryaa Hotel New Delhi 9873777170Call Girls Near The Suryaa Hotel New Delhi 9873777170
Call Girls Near The Suryaa Hotel New Delhi 9873777170Sonam Pathan
 
Font Performance - NYC WebPerf Meetup April '24
Font Performance - NYC WebPerf Meetup April '24Font Performance - NYC WebPerf Meetup April '24
Font Performance - NYC WebPerf Meetup April '24Paul Calvano
 
Intellectual property rightsand its types.pptx
Intellectual property rightsand its types.pptxIntellectual property rightsand its types.pptx
Intellectual property rightsand its types.pptxBipin Adhikari
 
定制(Lincoln毕业证书)新西兰林肯大学毕业证成绩单原版一比一
定制(Lincoln毕业证书)新西兰林肯大学毕业证成绩单原版一比一定制(Lincoln毕业证书)新西兰林肯大学毕业证成绩单原版一比一
定制(Lincoln毕业证书)新西兰林肯大学毕业证成绩单原版一比一Fs
 
办理(UofR毕业证书)罗切斯特大学毕业证成绩单原版一比一
办理(UofR毕业证书)罗切斯特大学毕业证成绩单原版一比一办理(UofR毕业证书)罗切斯特大学毕业证成绩单原版一比一
办理(UofR毕业证书)罗切斯特大学毕业证成绩单原版一比一z xss
 
『澳洲文凭』买拉筹伯大学毕业证书成绩单办理澳洲LTU文凭学位证书
『澳洲文凭』买拉筹伯大学毕业证书成绩单办理澳洲LTU文凭学位证书『澳洲文凭』买拉筹伯大学毕业证书成绩单办理澳洲LTU文凭学位证书
『澳洲文凭』买拉筹伯大学毕业证书成绩单办理澳洲LTU文凭学位证书rnrncn29
 
Packaging the Monolith - PHP Tek 2024 (Breaking it down one bite at a time)
Packaging the Monolith - PHP Tek 2024 (Breaking it down one bite at a time)Packaging the Monolith - PHP Tek 2024 (Breaking it down one bite at a time)
Packaging the Monolith - PHP Tek 2024 (Breaking it down one bite at a time)Dana Luther
 
Q4-1-Illustrating-Hypothesis-Testing.pptx
Q4-1-Illustrating-Hypothesis-Testing.pptxQ4-1-Illustrating-Hypothesis-Testing.pptx
Q4-1-Illustrating-Hypothesis-Testing.pptxeditsforyah
 
Call Girls In The Ocean Pearl Retreat Hotel New Delhi 9873777170
Call Girls In The Ocean Pearl Retreat Hotel New Delhi 9873777170Call Girls In The Ocean Pearl Retreat Hotel New Delhi 9873777170
Call Girls In The Ocean Pearl Retreat Hotel New Delhi 9873777170Sonam Pathan
 
Top 10 Interactive Website Design Trends in 2024.pptx
Top 10 Interactive Website Design Trends in 2024.pptxTop 10 Interactive Website Design Trends in 2024.pptx
Top 10 Interactive Website Design Trends in 2024.pptxDyna Gilbert
 
定制(UAL学位证)英国伦敦艺术大学毕业证成绩单原版一比一
定制(UAL学位证)英国伦敦艺术大学毕业证成绩单原版一比一定制(UAL学位证)英国伦敦艺术大学毕业证成绩单原版一比一
定制(UAL学位证)英国伦敦艺术大学毕业证成绩单原版一比一Fs
 
SCM Symposium PPT Format Customer loyalty is predi
SCM Symposium PPT Format Customer loyalty is prediSCM Symposium PPT Format Customer loyalty is predi
SCM Symposium PPT Format Customer loyalty is predieusebiomeyer
 
Contact Rya Baby for Call Girls New Delhi
Contact Rya Baby for Call Girls New DelhiContact Rya Baby for Call Girls New Delhi
Contact Rya Baby for Call Girls New Delhimiss dipika
 

Dernier (20)

Magic exist by Marta Loveguard - presentation.pptx
Magic exist by Marta Loveguard - presentation.pptxMagic exist by Marta Loveguard - presentation.pptx
Magic exist by Marta Loveguard - presentation.pptx
 
『澳洲文凭』买詹姆士库克大学毕业证书成绩单办理澳洲JCU文凭学位证书
『澳洲文凭』买詹姆士库克大学毕业证书成绩单办理澳洲JCU文凭学位证书『澳洲文凭』买詹姆士库克大学毕业证书成绩单办理澳洲JCU文凭学位证书
『澳洲文凭』买詹姆士库克大学毕业证书成绩单办理澳洲JCU文凭学位证书
 
PHP-based rendering of TYPO3 Documentation
PHP-based rendering of TYPO3 DocumentationPHP-based rendering of TYPO3 Documentation
PHP-based rendering of TYPO3 Documentation
 
Hot Sexy call girls in Rk Puram 🔝 9953056974 🔝 Delhi escort Service
Hot Sexy call girls in  Rk Puram 🔝 9953056974 🔝 Delhi escort ServiceHot Sexy call girls in  Rk Puram 🔝 9953056974 🔝 Delhi escort Service
Hot Sexy call girls in Rk Puram 🔝 9953056974 🔝 Delhi escort Service
 
A Good Girl's Guide to Murder (A Good Girl's Guide to Murder, #1)
A Good Girl's Guide to Murder (A Good Girl's Guide to Murder, #1)A Good Girl's Guide to Murder (A Good Girl's Guide to Murder, #1)
A Good Girl's Guide to Murder (A Good Girl's Guide to Murder, #1)
 
Git and Github workshop GDSC MLRITM
Git and Github  workshop GDSC MLRITMGit and Github  workshop GDSC MLRITM
Git and Github workshop GDSC MLRITM
 
Call Girls Near The Suryaa Hotel New Delhi 9873777170
Call Girls Near The Suryaa Hotel New Delhi 9873777170Call Girls Near The Suryaa Hotel New Delhi 9873777170
Call Girls Near The Suryaa Hotel New Delhi 9873777170
 
Font Performance - NYC WebPerf Meetup April '24
Font Performance - NYC WebPerf Meetup April '24Font Performance - NYC WebPerf Meetup April '24
Font Performance - NYC WebPerf Meetup April '24
 
young call girls in Uttam Nagar🔝 9953056974 🔝 Delhi escort Service
young call girls in Uttam Nagar🔝 9953056974 🔝 Delhi escort Serviceyoung call girls in Uttam Nagar🔝 9953056974 🔝 Delhi escort Service
young call girls in Uttam Nagar🔝 9953056974 🔝 Delhi escort Service
 
Intellectual property rightsand its types.pptx
Intellectual property rightsand its types.pptxIntellectual property rightsand its types.pptx
Intellectual property rightsand its types.pptx
 
定制(Lincoln毕业证书)新西兰林肯大学毕业证成绩单原版一比一
定制(Lincoln毕业证书)新西兰林肯大学毕业证成绩单原版一比一定制(Lincoln毕业证书)新西兰林肯大学毕业证成绩单原版一比一
定制(Lincoln毕业证书)新西兰林肯大学毕业证成绩单原版一比一
 
办理(UofR毕业证书)罗切斯特大学毕业证成绩单原版一比一
办理(UofR毕业证书)罗切斯特大学毕业证成绩单原版一比一办理(UofR毕业证书)罗切斯特大学毕业证成绩单原版一比一
办理(UofR毕业证书)罗切斯特大学毕业证成绩单原版一比一
 
『澳洲文凭』买拉筹伯大学毕业证书成绩单办理澳洲LTU文凭学位证书
『澳洲文凭』买拉筹伯大学毕业证书成绩单办理澳洲LTU文凭学位证书『澳洲文凭』买拉筹伯大学毕业证书成绩单办理澳洲LTU文凭学位证书
『澳洲文凭』买拉筹伯大学毕业证书成绩单办理澳洲LTU文凭学位证书
 
Packaging the Monolith - PHP Tek 2024 (Breaking it down one bite at a time)
Packaging the Monolith - PHP Tek 2024 (Breaking it down one bite at a time)Packaging the Monolith - PHP Tek 2024 (Breaking it down one bite at a time)
Packaging the Monolith - PHP Tek 2024 (Breaking it down one bite at a time)
 
Q4-1-Illustrating-Hypothesis-Testing.pptx
Q4-1-Illustrating-Hypothesis-Testing.pptxQ4-1-Illustrating-Hypothesis-Testing.pptx
Q4-1-Illustrating-Hypothesis-Testing.pptx
 
Call Girls In The Ocean Pearl Retreat Hotel New Delhi 9873777170
Call Girls In The Ocean Pearl Retreat Hotel New Delhi 9873777170Call Girls In The Ocean Pearl Retreat Hotel New Delhi 9873777170
Call Girls In The Ocean Pearl Retreat Hotel New Delhi 9873777170
 
Top 10 Interactive Website Design Trends in 2024.pptx
Top 10 Interactive Website Design Trends in 2024.pptxTop 10 Interactive Website Design Trends in 2024.pptx
Top 10 Interactive Website Design Trends in 2024.pptx
 
定制(UAL学位证)英国伦敦艺术大学毕业证成绩单原版一比一
定制(UAL学位证)英国伦敦艺术大学毕业证成绩单原版一比一定制(UAL学位证)英国伦敦艺术大学毕业证成绩单原版一比一
定制(UAL学位证)英国伦敦艺术大学毕业证成绩单原版一比一
 
SCM Symposium PPT Format Customer loyalty is predi
SCM Symposium PPT Format Customer loyalty is prediSCM Symposium PPT Format Customer loyalty is predi
SCM Symposium PPT Format Customer loyalty is predi
 
Contact Rya Baby for Call Girls New Delhi
Contact Rya Baby for Call Girls New DelhiContact Rya Baby for Call Girls New Delhi
Contact Rya Baby for Call Girls New Delhi
 

II-SDV 2016 Aalt van de Kuilen - The Art of Patent Landscaping

  • 1. The Art of Patent Landscaping II-SDV - Nice Aalt van de Kuilen April, 18th 2016
  • 2. Definitions  Patent landscape reports provide a snap-shot of the patent situation of a specific technology or company, either within a given country or region, or globally. WIPO definition
  • 3. Why? Patent Landscapes can give an overview of  technology sector  competitors  economic value of patent portfolios  strategic position Impact of Patent Landscapes on R&D:  shorten R&D time by 60%  save R&D costs by 40%
  • 4. Limitations  Very important:  Be aware that not all companies, active in a market, have a similar patent filing strategy  Example  TESLA (hardly any patents on electric cars)
  • 5. Before we start Before starting the Patent Landscape it is important to know the reason for creating a patent landscape!!! Every Landscape has a purpose why it has to be created!!
  • 6. Creating a landscape  Don’t underestimate how difficult it can be to create a landscape  Do not underestimate time needed to plan and execute a landscape!!
  • 7. Step 1: Ask for the reason for creating this landscape The purpose for the landscape has impact on what decision(s) is/are going to be taken based on the information.  impact on the scope!!!
  • 8. Step 2: Become familiar with the topic Before you start you have to become familiar with the topic by reading some general articles or reviews.
  • 9. Step 3: Considerations before starting the search a) Patent families b) Citation search c) Patent assignee names d) Classifications e) Regions f) Broad or small topic
  • 10. a) Patent Families What is the most useful patent family structure for landscaping Databases have different definitions Most used are 2 definitions  Extended families  INPADOC (about 89% is based on one priority!)  Strict families  WPI, Espacenet.(100% based on one priority)
  • 11.  Citation analysis can give additional records. (be aware of different policies especially between EU en US)  A manual check is needed. b) Citation search
  • 12.  No standardization of company names.  Manually corrections are necessary! Helpful   Corporate Trees (most providers have one)  Derwent Company Codes ( Link )  Standardization initiatives (Univ. of Leuven) KUL/EUROSTAT: Method for harmonizing applicants names c) Patent assignee names
  • 13. d) Classifications Absolutely necessary. Keywords only is insufficient and will miss a lot of records, especially in new technologies. A global classification system is most preferred, like IPC and CPC. Warning 1: Not all documents have a CPC ,10-30% of the families do not have a CPC assigned, mainly records coming from Japan, China, Korea and Russia. For details about coverage see the CPC website www.cpcinfo.org Warning 2: be aware that not all relevant documents are classified correctly!
  • 14. e) Regions Which countries do you want to include  depend on the topic!! 1. Where are the main competitors located? 2. Are all technology areas as important for all regions? 3. For some countries it is hard to retrieve patent information
  • 15. f) Broad or small topic Broad topics are seldom useful for landscaping. Topics like: Pharma industry, Green energy, Automotive industry, Nanotechnology are distributed over several classes and hard to retrieve Small topics, within a small set of classes, are more reliable for landscaping.
  • 16. Step 4: Search Most critical step! Broad approach is most often the best! (Include both classes and keywords) Which database(s)?? (be aware  documents missed at this stage will never show up later!!)
  • 17. Step 5: Clean up in order to create a reliable landscape 80-90% relevancy is required Sometimes a manual clean-up is necessary
  • 18. Step 6: Checking the dataset Check whether or not your set make sense. Look if some key patents are included in the set.
  • 19.  Step 7: analyzing, visualization and reporting  Many tools are available for analyzing the data . Choose the tool that fits best for purpose  Visualize only global trends and not specific data. Too detailed reports will not be read.  The most important part of the report is the summary!! (most clients do not read more than the summary page)
  • 20.
  • 21. Summary (How to create a patent landscape in 7 steps)  Step 1: Ask the reason for the landscape  Step 2: Become familiar with the topic  Step 3: Considerations before starting the search a) Patent families b) Citation search c) Patent assignee names d) Classifications e) Regions f) Broad or small topic  Step 4: Search  Step 5: Clean up  Step 6: Checking the dataset  Step 7: Analyzing, visualization and reporting
  • 22. Example  Tsunami Warning systems  Your company wants to develop a new Tsunami Warning System. Before starting, they want to know what is already available on the market, who are the competitors and what described in IP.
  • 23. https://en.wikipedia.org/wiki/Tsunami https://en.wikipedia.org/wiki/Tsunami_warning_system To become familiar with the topic Wikipedia is often a good source Gives, in this case, the very important information that a tsunami is an earthquake. https://en.wikipedia.org/wiki/Main_Page
  • 24. Considerations and search  Classifications  Citations  Database selection (more than one?)
  • 25. Proposed choices Text search: example!! Most probably in Title and abstract and claims (earthquake) w5? (warning or alarm) Most probably in full text (tsunami) w5? (warning or alarm) Classifications: example!! (CPC=(G08B21/10 or H04W4/22 or G01V1/008 or H04W76/007 OR H04W4/06 OR H04W76/002 OR H04W68/00)) or (CPC=NO (records with no CPC)! and (IPC=G08B21/10 or G08B21/00 or G08B27/00 or G01V1/00 or H04W/22 OR H04W4/22 OR H04W4/06 OR H04M11/04 OR G08B27/00))
  • 26.
  • 27. Possibilities for data clean up  Exclude old documents  Exclude Chinese, Taiwanese and Korean records with only one family member (or other less relevant countries?)  Exclude German Utility Models, with only one family member DEU In this case, at this stage, approx. 2000 records  Further limitations and clean-up based on the scope/purpose of the landscape!!
  • 28. Analyzing, visualization and reporting After having created the set, there is no preference in the tool used for analyzing and visualization Be sure the report (and even the summary) gives answers to the questions that has been asked.
  • 29. Conclusion  Patent Landscaping is complex and time consuming!!  Patent Landscaping has it’s limitations