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Customizing Statistics for
Sharper Analysis
Laurent Hill
Renaud Garat
April 15, 2013
II-SDV 2013 Nice 2
Companies’
Acceleration
Conceptual Model Of A Patent Based
Analysis
Top
Companies
Technology
Changes
Co...
Why Customize Analysis?
• Data quality
• Productivity
• Technical Specific Needs
II-SDV 2013 Nice 3
Why Customize Analysis?
• Data quality
• Names: normalisation – M&A
bank collaterals
• Concepts: normalisation
languages
I...
Why Customize Analysis?
• Productivity
• Guideline with predefined set of charts
• Re-usable templates:
charts, axis, colo...
II-SDV 2013 Nice 6
Why Customize Analysis?
II-SDV 2013 Nice 7
• Technical Specific Needs
• Concepts grouping / hiding
• Classification codes ...
II-SDV 2013 Nice 8
Why Customize Analysis?
• Problems to solve
• Grouping Rules: Permanent vs Contextual
• Technical Use vs Goals: Customized...
II-SDV 2013 Nice 10
Why Customize Analysis?
• Conclusion
☺☺☺☺ Powerful and precise
Time consuming (system programing?)
? Future…
II-SDV 2013 N...
II-SDV 2013 Nice 12
? Future…
Guided activities
Preconfigured
Analysis Metrics
Case study: building a
technology/use matrix
• The goal is to understand which
technologies are used to achieve a given
go...
First try: concept/concept matrix
• No clear picture emerges, the concepts
just reflect important notions in the corpus
II...
Concepts after clean up / Data rules
• Getting better, drilling down on each cell
very powerful for understanding
II-SDV 2...
We need a better solution
• Clearly this is not enough, we need to
separate problem domain concepts from
solution concepts...
Goal / mean matrix, concept
version
• Simple and
clear:
• Good for
communication
• Precision and
completeness
could be
imp...
Slice & Dice through boolean queries
• We need a sharper tool to dissect the corpus
• We already have a sharp tool with th...
Technology crossing from Custom
analysis axis
19
Another view, using independent claims
II-SDV 2013 Nice 20
Less noise,
some true
white space
emerges
Analyzing white space on
technology crossings
II-SDV 2013 Nice 21
• No
pending
patents
• Most of the
art is
lapsed or
expi...
Case study wrap up
• Editing data, customizing analysis views
and axis let us go beyond simple analysis
• By leveraging th...
Thank you
Laurent Hill
Renaud Garat
April 15, 2013
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Transcript of "II-SDV 2013 Customising Statistics for Sharper Analysis"

  1. 1. Customizing Statistics for Sharper Analysis Laurent Hill Renaud Garat April 15, 2013
  2. 2. II-SDV 2013 Nice 2 Companies’ Acceleration Conceptual Model Of A Patent Based Analysis Top Companies Technology Changes Company Changes 1. Understand the Overall Landscape Strengths: Despite the Company’s apparently weak patent base, the Company is well positioned, especially when teamed with Company C Weaknesses: Company B is a generalist competitor for the Company to watch Opportunities: Company A and Company C are weak generalist competitors that could represent licensing opportunities for the Company Threats: Company A’s patenting efforts are concentrated in the area of Yarn Constructions, so the Company needs to understand their business plans SWOT Analysis White-Space Opportunities University Opportunities 2. Understand Recent Trends 4. Understand Available Art 5. Estimate Freedom To Practice 6. Determine Who To Watch And Leverage Strategic Direction 3. Understand Close Art IP Portfolio Gap Analysis Geographic Coverage Companies’ Focus Areas Investment Rate Business Landscape Companies’ Momentum Top Cited-by Art Similarity Search IP Sharks & Trolls Data Information Knowledge + Experience Insight
  3. 3. Why Customize Analysis? • Data quality • Productivity • Technical Specific Needs II-SDV 2013 Nice 3
  4. 4. Why Customize Analysis? • Data quality • Names: normalisation – M&A bank collaterals • Concepts: normalisation languages II-SDV 2013 Nice 4
  5. 5. Why Customize Analysis? • Productivity • Guideline with predefined set of charts • Re-usable templates: charts, axis, colors, grouping rules (assignees, concepts, classification codes, inventors…), real- time interaction (refining/expanding) II-SDV 2013 Nice 5
  6. 6. II-SDV 2013 Nice 6
  7. 7. Why Customize Analysis? II-SDV 2013 Nice 7 • Technical Specific Needs • Concepts grouping / hiding • Classification codes grouping / hiding
  8. 8. II-SDV 2013 Nice 8
  9. 9. Why Customize Analysis? • Problems to solve • Grouping Rules: Permanent vs Contextual • Technical Use vs Goals: Customized Axis • Full Text content: Boolean Axis II-SDV 2013 Nice 9
  10. 10. II-SDV 2013 Nice 10
  11. 11. Why Customize Analysis? • Conclusion ☺☺☺☺ Powerful and precise Time consuming (system programing?) ? Future… II-SDV 2013 Nice 11
  12. 12. II-SDV 2013 Nice 12 ? Future… Guided activities Preconfigured Analysis Metrics
  13. 13. Case study: building a technology/use matrix • The goal is to understand which technologies are used to achieve a given goal • Corpus : Swimming pool cleaners • ~ 1100 patents • Let’s explore a few alternatives on how to do it II-SDV 2013 Nice 13
  14. 14. First try: concept/concept matrix • No clear picture emerges, the concepts just reflect important notions in the corpus II-SDV 2013 Nice 14
  15. 15. Concepts after clean up / Data rules • Getting better, drilling down on each cell very powerful for understanding II-SDV 2013 Nice 15
  16. 16. We need a better solution • Clearly this is not enough, we need to separate problem domain concepts from solution concepts • Solution: custom axis • Let’s start by manually sorting out concepts • One axis for Goals , one axis for Means II-SDV 2013 Nice 16
  17. 17. Goal / mean matrix, concept version • Simple and clear: • Good for communication • Precision and completeness could be improved II-SDV 2013 Nice 17
  18. 18. Slice & Dice through boolean queries • We need a sharper tool to dissect the corpus • We already have a sharp tool with the full text search engine: fields, proximity… • Each value in a custom axis can be a search engine query. • Example: combining independent claims and CPC • ((water 3D (filter??? or purify???))/ICLM or C02F- 2103/42/CPC) II-SDV 2013 Nice 18
  19. 19. Technology crossing from Custom analysis axis 19
  20. 20. Another view, using independent claims II-SDV 2013 Nice 20 Less noise, some true white space emerges
  21. 21. Analyzing white space on technology crossings II-SDV 2013 Nice 21 • No pending patents • Most of the art is lapsed or expired • Dead end?
  22. 22. Case study wrap up • Editing data, customizing analysis views and axis let us go beyond simple analysis • By leveraging the precision of a full featured search engine on all patent text, we were able to build a goals/Mean technology matrix • Domain expertise still needed, no magic bullet II-SDV 2013 Nice 22
  23. 23. Thank you Laurent Hill Renaud Garat April 15, 2013
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