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
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
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“
– …
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