ii-sdv 2014 automated relevancy check of patents and scientific literature (katrin tomanek and...
TRANSCRIPT
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Dr. Philipp Daumke
Analyze Text, Gain Answers
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ABOUT AVERBIS
Founded: 2007
Location: Freiburg im Breisgau
Team: Domain- & IT-Experts
Focus: Leverage structured & unstructured information
Current Sectors: Pharma, Health, Automotive, Publishers & Libraries
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PORTFOLIO
PRODUCTS:
CORE TECHNOLOGIES:
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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:
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� (Semi-)Automate patent categorization
with high precision
� Learning system
imitates the behavior of IP professionals
� Semantic search
Search for meanings, not just keywords
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PATENT ANALYTICS
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PATENT ANALYTICS
TerminologiesText Mining Rules
Text Mining Machine Learning
Patent Collection
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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
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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
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TEXT MINING
tumors tumour
cancer
carcinoma
lymphoma
endometrioma
astrocytoma
glioblastoma
seminoma
ALL
leukemia
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TEXT MINING
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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
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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
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POWERFUL FRONTEND
Linguistic full text search
Lingustic
Filters
Patent Summary
Additional info, e.g. picture
Multilabel Classification
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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)
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USE CASE1: LARGE-SCALE PATENT LANDSCAPING
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CONFUSION MATRIX
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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!
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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“
– …
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PATENT ANALYTICS
Rule SetText Mining,
Machine LearningSearch, Analysis
Medline, Embase
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VERAPAMIL
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USE CASE2: RESEARCH LITERATURE RELEVANCY
Rule: Testosterone, but only given externally
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USE CASE2: RESEARCH LITERATURE RELEVANCY
Rule: Ignore products listed in enumerations
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USE CASE 3: SOCIAL MEDIA ANALYTICS
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USE CASE 3: SOCIAL MEDIA ANALYTICS
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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 …“
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USE CASE4: PATIENT RECRUITMENT/DIAGNOSIS SUPPORT
Disease ProfilesInclusion/Exclusion Criteria
Categorization Visualization
Electronic Health Records
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USE CASE4: PATIENT RECRUITMENT/DIAGNOSIS SUPPORT
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USE CASE4: PATIENT RECRUITMENT/DIAGNOSIS SUPPORT