Data Science Application in“Business Portfolio & Risk Management” Maethee Chandavimol, PhD, RFC, RMA

1. Data Science Application in
“Business Portfolio & Risk Management”
Maethee Chandavimol, PhD, RFC, RMA
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2. Sharing Sessions
o Business Stategies, Tomorrow ?
o Engineer, Research, Data
o Data Analytic + Consultant
o What’s next?
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3. Yesterday’s Business Strategies
Industrial
Intellectual
Property
Operational Plans
Patents (+Designs)
Copyrights
Trademarks
Trade Secrets
Actionable Intelligence
B
Research
Industrial
Sectors
Obsolete idea, Obsolete Results.
Strategy
Execution
Product Lifecycle
IP & Product Portfolio
Searching, Mining
Acquiring, In/Cross-licensing
Infringement
Competitive Intelligence
Business Intelligence
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4. Patent Infringement
“Natural language interface
using constrained
intermediate dictionary of
results." The document says
the invention "relates to
user interfaces, and more
specifically, to userspecifically, to user
interfaces that recognize
natural language."
Source: http://www.engadget.com/2016/04/20/apple-settles-siri-patent-lawsuit/
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5. DATA
Today’s Business Strategies
SCIENCE
o Patents
o Non-Patent Content
ANALYTICAL
o Human Driven
o Computational Driven
Strategic
Planning
Competitive
Intelligence
Merger
Acquisition
Out-Licensing
In-Licensing
Patentability
Technology
Roadmap
Opportunity to
Practice
Opportunity to
Exclude
ACTIONABLE INTELLIGENCE
Create the actionable information relevant
to the identified question or issue.
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6. Tomorrow’s Business Strategy
o Protection of intellectual property assets
o Reducing the risks associated with costly patent
= f(x, x’, y, y”, IP Strategy, Risk/Return)
o Reducing the risks associated with costly patent
o Infringement litigation
o Competitor Environment, call for action!
o Risks associated with not having a IP strategy
“A Business strategy without IP strategy is no strategy”
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7. Engineer, Research, Data = ?
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8. What do you know about PATENT?
• Patent
– What is a Patent?
– What is in the Patent?
– Where/how do you find them?– Where/how do you find them?
• Once you get them,
– How do you read them?
– What do you get from Patent?
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9. Why did I search Patent Database first?
o Source of technological information!
o Overview of the prior invention
o Background history of the problem
o Claims and boundary of the invention
Possible solution for given problemo Possible solution for given problem
Gather business intelligence: Inventor, competitors,
potential partners, strategies
Avoid redeveloping existing invention & duplication
of R&D work
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10. A Patent consists of
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11. A Patent consists of
o Drawing
o Technical field
o Background of the invention
o Summary of Invention
??
o Summary of Invention
o Claims
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12. Patent: Wearable Device
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13. Patent DATABASES
o Most up-to-date source of information on applied
technology.
o Up to 80% of current technical knowledge can only be
discovered in patent documents.
o This information is rapidly available, as most patento This information is rapidly available, as most patent
applications are published 18 months after the first filing.
o All documents are classified by technological areas on the
basis of the International Patent Classification (IPC) which
is the world-wide standard.
o Patent Databases:
o WIPO, USPTO, ESPACENET, JPO, IPICThailand, etc.

14. Smart Questions to ask…
o How many patents do we have concerning technology ‘x’?
o How does our portfolio compare with company ‘ABC’ ?
o Who is citing our portfolio?
o Which patents do business unit ‘xyz’ own?
o Which patents should we divest as a result of selling division XYZ?o Which patents should we divest as a result of selling division XYZ?
o How do we track our key technologies/competitors?
o As we develop new products what is IP landscape and what is closest art?
o How do our invention disclosures compare with current granted patents?
ABC Intelligence can help you to avoid unnecessary risks and maximize your company bottom line.
Google
Samsung
Amazon
Alibaba
Kodak
Nokia
Apple
Nike
Glaxo
Denso
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15. How did I do Patent Mapping Project?
1. Define scopes of work (Objectives)
2. Conduct data collection:
– Patent search
– Data Cleansing
3. Buildup a local Patent database3. Buildup a local Patent database
4. Analyze Patents
– Data Mining (Macro Patent Maps)
– Text Mining (Micro Patent Maps)
5. Valuable results/Reports

16. Patent Maps
Guideline…Guideline…
• Patent Count Analysis
– Technology life cycle
– Patent Quantity comparison
• Country Analysis• Country Analysis
• Assignee Analysis
– R&D capability
– Citation Analysis (FW/BW)
– Citation Rate Analysis
• Classification Analysis
– IPC patent activity analysis
– No. of IPC by competitors

17. Patent Maps
EXAMPLES …EXAMPLES …
o Assignee Patent Map
o Assignee Country Patent Map
o Attorney-Agent-Firm Patent Map
o Application/Filing Year Patent Map
o Inventor Patent Map
o Technology Class (IPC) Patent Map
Sub-Technology (IPC) Patent Map
B01
F04
A09
F06
Year
o Sub-Technology (IPC) Patent Map
o Publication/Grant Year Patent Map
o Priority Year Patent Map
o Technology Class (UPC) Patent Map
o Sub-Technology (UPC) Patent Map
o Backward Citation Patent Map
o Forward Citation Patent Map
o etc…

18. Infringement search
• Patent Search
– Technical Keywords
– Inventor
– Assignee– Assignee
– Company
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19. Problem Solving
• Invention analysis
• Forward/backward citation analysis
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20. Technology Roadmap
o Basic Search Technical Keywords
o Background of Invention
o IPC Search / IPC Chart
Key patentso Key patents
o Forward/Backward Citation analysis
o Patent Map...
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21. Example I: Overview of technology trend
• Patent database: US Patent
• Results:
– 1900 to 2005 664 Granted
Patents (XXX technology)
– 2001 to Present 199 Pending
Patents (Future technology)
• F – Mechanical Engineering
– F04 – Positive Displacement
Machines for Liquids
• F04B – Pumps
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Patents (Future technology)
• F04B – Pumps
• F04C – Rotary-piston or
Oscillation-piston
• F04D – Non-positive
displacement pumps
• F04F – Siphons

22. Example II: Nanotechnology
• Class 700 – Nanostructure
– Class 733 – Nanodiaphragm
– Class 762 – Nanowire or quantum wire
• Class 768 – Bent wire
– Class 769 – Helical wire– Class 769 – Helical wire
• Class 840 – Manufacture, Treatment or detection of
Nanostructure
– Class 842 – Carbon nanotubes of fullerenes
– Class 855 – For manufacture of nanostructure
• Class 977 – Nanotechnology
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23. Whitespace for R&D
• Grouping technical knowledge
• Core Patents: by company, by inventor, by group
researchers, …
Example

24. Data Analytic + Consultant = ?
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25. Credit Scoring Model
o Estimate whether applicant will successfully repay loan
based on various information
o Develop models (called “scorecards”) estimating the
probability of default of a customer
o Typically, assign points to each piece of information, add
all points and compare with a threshold (cut-off)all points and compare with a threshold (cut-off)
Benefits of developing model:
• Speed, Accuracy, Consistency
• Reduce operating cost, Bad Loan
• Improved portfolio management
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26. Example of Application Scorecards
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27. Data Variable used in Application Scorecard
Development
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28. How did I develop Scorecard?
1. Data Definition
2. Explore Data: Missing Value & Data Cleansing
3. Initial Characteristic Analysis (Known Good/Bad),
Attribute
4. Primary Scorecard: Logistic Regression, Decision4. Primary Scorecard: Logistic Regression, Decision
Tree, Neural Network
5. Reject Reference (All Good/Bad)
6. Final Scorecard: Scaling & Assessment
7. Model Validation
8. Management Report
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29. Gauging the Strength of Characteristics
o Weight of Evidence (WOE) – Predictive power of
each attribute
o Information Value (IV) – Predictive power of the
characteristiccharacteristic
o Range & Trend of WOE across grouped attributes
within a characteristic
o Operational & Business consideration
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30. Example of Scorecard Development
Attribute, WOE, IV, Ranging Trend
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31. Example of Scorecard Development
Bad Rate Model Linearity Kolmogorov-Smirnov statistic
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32. In summary
o As an Engineer/Consultant:
o Whitespace Analytic using USPTO US patent database
(prescreening idea before startup projects/new innovation)
o Analysis of technology trends ( identify area of strengths and
future intentions of other player in the same field)
o Infringement Searcho Infringement Search
o As a Risk Analytics:
o Credit Scoring Model: Expert Judgment + data mining +
Mathematical model
o Data … + … + … = Business Strategy?
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33. Tomorrow’s Business Strategy
o Protection of intellectual property assets (including your
existing and future Data)
= f(x, x’, y, y”, IP Strategy, Data Analytic, Risk/Return,)
existing and future Data)
o Reducing the risks associated with costly patent & copyrights
o Infringement litigation (Business flow diagram included!)
o Competitor Environment, call for action! (Startup, Fintech?)
o Risks associated with not having a IP strategy
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