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

3. PORTFOLIO
PRODUCTS:
CORE TECHNOLOGIES:

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

7. PATENT ANALYTICS

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

11. TEXT MINING
tumors tumour
cancer
carcinoma
lymphoma
endometrioma
astrocytoma
glioblastoma
seminoma
ALL
leukemia

12. TEXT MINING

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

18. CONFUSION MATRIX

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

22. VERAPAMIL

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