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Dr. Philipp Daumke
Analyze Text, Gain Answers
ABOUT AVERBIS
Founded: 2007
Location: Freiburg im Breisgau
Team: Domain- & IT-Experts
Focus: Leverage structured & unstructured information
Current Sectors: Pharma, Health, Automotive, Publishers & Libraries
PORTFOLIO
PRODUCTS:
CORE TECHNOLOGIES:
II-SDV 2014 Automated Relevancy Check of Patents and Scientific Literature (Katrin Tomanek and Philipp Daumke - Averbis, Germany)
CHALLENGE
Exponential growth of data
• need for data-driven decisions
• limited human resources for analysis
New analytics tools needed for
• Semantic search and discovery
• Competitor analysis
• Identification of market trends
• IP landscaping
• Portfolio analysis
• …
Patent applications:
Medline articles:
(Semi-)Automate patent categorization
with high precision
Learning system
imitates the behavior of IP professionals
Semantic search
Search for meanings, not just keywords
PATENT ANALYTICS
PATENT ANALYTICS
Terminologies
Text Mining Rules
Text Mining Machine Learning
Patent Collection
TERMINOLOGY MANAGEMENT
Define the ‚semantic
space‘ of your
technology fields
• Keywords
• Categories
• Hierarchies
• ….
Include relevant word
lists from your
company
• Products
• Devices
• Companies
• Components
• Indications
• …
Reuse already existing
terminologies on the
market
TEXT MINING
Lung metastasis lung metastasis
lung metastases
metastases in the lung
metastases in the lower lobe of the lung
pulmonal metastates
pulmonal relapse of a metastasis
pulmonal filia
pulmonal filiae
lung filiae
lower lobe filiae
TEXT MINING
tumors tumour
cancer
carcinoma
lymphoma
endometrioma
astrocytoma
glioblastoma
seminoma
ALL
leukemia
TEXT MINING
PATENT CLASSIFICATION – MACHINE LEARNING
System learns how to fine-classify patents
Observes and imitates human decision making
Advantages
• No explicit externalization of knowledge needed
• No rule-writing
• Better results
• System generalizes (higher recall)
• Statistical model can handle „noise“ better than rules
• Ambiguity and textual variations better handled
THE PROCESS OF MACHINE LEARNING
Labeling
• Up to 100 categories
• ~10-50 patents per category
• Hierarchical categories
• Multi-labeling
Learning
• Learn characteristic patterns in labeled data
• Lots of different classification algorithms
Prediction & Review
• Automatically map new patents to categories
• Confidence value for each category
• Different selection criteria
14
POWERFUL FRONTEND
Linguistic full
text search
Lingustic
Filters
Patent Summary
Additional info,
e.g. picture
Multilabel
Classification
USE CASE1: LARGE-SCALE PATENT LANDSCAPING
• Goal: to semi-automatically categorize patents to the
company‘s technology landscape
• Technology Landscape: 35 Classes (8 main classes, 27 sub-
classes)
• 7.000 patents, 10 competitors
• Evaluation
– between automated judgement with expert judgement
– between two expert judgements (Interrator-Agreement)
USE CASE1: LARGE-SCALE PATENT LANDSCAPING
CONFUSION MATRIX
USE CASE1: LARGE-SCALE PATENT LANDSCAPING
Results Accuracy Time Savings
Automated, Scenario I 85% 70%
Automated, Scenario II 82% 80%
Manual (2 expert judges) 80%
Averbis Patent Analytics save up to 80% of time with
accuracy being on par with manual judges!
USE CASE2: RESEARCH LITERATURE RELEVANCY
• Goal: to automatically identify company‘s relevant
literature
• Rule set:
– Mentionings of company‘s indications, products, etc.
– Competitor products and indications
– „Testosterone, but only given externally“
– „Products shall not be found in an enumeration“
– …
PATENT ANALYTICS
Rule Set
Text Mining,
Machine Learning
Search,
Analysis
Medline, Embase
VERAPAMIL
USE CASE2: RESEARCH LITERATURE RELEVANCY
Rule: Testosterone, but only given externally
USE CASE2: RESEARCH LITERATURE RELEVANCY
Rule: Ignore products listed in enumerations
USE CASE 3: SOCIAL MEDIA ANALYTICS
USE CASE 3: SOCIAL MEDIA ANALYTICS
USE CASE 3: SOCIAL MEDIA ANALYTICS
Main Challenge: what is positive, what is
negative?
– „Could somebody please remove the dead bird from the
balcony“?
– „From the breadcrumbs lying under the bed one could live for
ages“
– „The hotel is situated in the crowdiest party district of the town“
– „The toilets were that big that I couldn‘t sit down for …“
USE CASE4: PATIENT RECRUITMENT/
DIAGNOSIS SUPPORT
Disease Profiles
Inclusion/Exclusion Criteria
Categorization Visualization
Electronic Health
Records
USE CASE4: PATIENT RECRUITMENT/
DIAGNOSIS SUPPORT
USE CASE4: PATIENT RECRUITMENT/
DIAGNOSIS SUPPORT
For further questions, please contact
Dr. Philipp Daumke
philipp.daumke@averbis.com
+49 761 - 203 9769 0

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II-SDV 2014 Automated Relevancy Check of Patents and Scientific Literature (Katrin Tomanek and Philipp Daumke - Averbis, Germany)

  • 1. Dr. Philipp Daumke Analyze Text, Gain Answers
  • 2. ABOUT AVERBIS Founded: 2007 Location: Freiburg im Breisgau Team: Domain- & IT-Experts Focus: Leverage structured & unstructured information Current Sectors: Pharma, Health, Automotive, Publishers & Libraries
  • 5. CHALLENGE Exponential growth of data • need for data-driven decisions • limited human resources for analysis New analytics tools needed for • Semantic search and discovery • Competitor analysis • Identification of market trends • IP landscaping • Portfolio analysis • … Patent applications: Medline articles:
  • 6. (Semi-)Automate patent categorization with high precision Learning system imitates the behavior of IP professionals Semantic search Search for meanings, not just keywords
  • 8. PATENT ANALYTICS Terminologies Text Mining Rules Text Mining Machine Learning Patent Collection
  • 9. TERMINOLOGY MANAGEMENT Define the ‚semantic space‘ of your technology fields • Keywords • Categories • Hierarchies • …. Include relevant word lists from your company • Products • Devices • Companies • Components • Indications • … Reuse already existing terminologies on the market
  • 10. TEXT MINING Lung metastasis lung metastasis lung metastases metastases in the lung metastases in the lower lobe of the lung pulmonal metastates pulmonal relapse of a metastasis pulmonal filia pulmonal filiae lung filiae lower lobe filiae
  • 13. PATENT CLASSIFICATION – MACHINE LEARNING System learns how to fine-classify patents Observes and imitates human decision making Advantages • No explicit externalization of knowledge needed • No rule-writing • Better results • System generalizes (higher recall) • Statistical model can handle „noise“ better than rules • Ambiguity and textual variations better handled
  • 14. THE PROCESS OF MACHINE LEARNING Labeling • Up to 100 categories • ~10-50 patents per category • Hierarchical categories • Multi-labeling Learning • Learn characteristic patterns in labeled data • Lots of different classification algorithms Prediction & Review • Automatically map new patents to categories • Confidence value for each category • Different selection criteria 14
  • 15. POWERFUL FRONTEND Linguistic full text search Lingustic Filters Patent Summary Additional info, e.g. picture Multilabel Classification
  • 16. USE CASE1: LARGE-SCALE PATENT LANDSCAPING • Goal: to semi-automatically categorize patents to the company‘s technology landscape • Technology Landscape: 35 Classes (8 main classes, 27 sub- classes) • 7.000 patents, 10 competitors • Evaluation – between automated judgement with expert judgement – between two expert judgements (Interrator-Agreement)
  • 17. USE CASE1: LARGE-SCALE PATENT LANDSCAPING
  • 19. USE CASE1: LARGE-SCALE PATENT LANDSCAPING Results Accuracy Time Savings Automated, Scenario I 85% 70% Automated, Scenario II 82% 80% Manual (2 expert judges) 80% Averbis Patent Analytics save up to 80% of time with accuracy being on par with manual judges!
  • 20. USE CASE2: RESEARCH LITERATURE RELEVANCY • Goal: to automatically identify company‘s relevant literature • Rule set: – Mentionings of company‘s indications, products, etc. – Competitor products and indications – „Testosterone, but only given externally“ – „Products shall not be found in an enumeration“ – …
  • 21. PATENT ANALYTICS Rule Set Text Mining, Machine Learning Search, Analysis Medline, Embase
  • 23. USE CASE2: RESEARCH LITERATURE RELEVANCY Rule: Testosterone, but only given externally
  • 24. USE CASE2: RESEARCH LITERATURE RELEVANCY Rule: Ignore products listed in enumerations
  • 25. USE CASE 3: SOCIAL MEDIA ANALYTICS
  • 26. USE CASE 3: SOCIAL MEDIA ANALYTICS
  • 27. USE CASE 3: SOCIAL MEDIA ANALYTICS Main Challenge: what is positive, what is negative? – „Could somebody please remove the dead bird from the balcony“? – „From the breadcrumbs lying under the bed one could live for ages“ – „The hotel is situated in the crowdiest party district of the town“ – „The toilets were that big that I couldn‘t sit down for …“
  • 28. USE CASE4: PATIENT RECRUITMENT/ DIAGNOSIS SUPPORT Disease Profiles Inclusion/Exclusion Criteria Categorization Visualization Electronic Health Records
  • 29. USE CASE4: PATIENT RECRUITMENT/ DIAGNOSIS SUPPORT
  • 30. USE CASE4: PATIENT RECRUITMENT/ DIAGNOSIS SUPPORT
  • 31. For further questions, please contact Dr. Philipp Daumke philipp.daumke@averbis.com +49 761 - 203 9769 0