Averbis is specialized in the area of text mining and machine-learning-based patent monitoring. We help our clients screen large numbers of patents in no time, estimate their relevancy for the company and automatically classify them into customer-specific categories. Our approach is based on artificial intelligence – with the result that it learns from and imitates the behavior of IP professionals. Compared to conventional rule-based approaches, our approach is up to 400% more accurate and achieves the same accuracy offered by manual monitoring. At the same time, it reduces manual patent monitoring intervention by up to 80%. Thanks to  Information Discovery, we enable IP professionals to reduce backlogs, improve staff efficiency and minimize inconsistencies associated with patent monitoring, ultimately improving the experience both for you and your customers.

1. WE AUTOMATE YOUR IP ACTIVITIES
Kornél Markó
Averbis GmbH
85%
75%
97%

2. OUR MISSION
Understand. Automate. Predict.
W E H E L P Y O U T O
turn text into
actionable
information
automate
cognitive
processes
make
correct
predictions

3. MARKETS AND CUSTOMERS
H E A L T H C A R E
O T H E R S
P H A R M A
P AT E N T S

4. CHALLENGE
With ~3 mio new patent applications per year, there is a high demand to:
stop wasting time on an
endless amount of
manual routine work
massively improve patent
identification accuracy
enable IP professionals to
make data-driven decisions
empower IP
professionals to focus
on the interesting work

5. OUR SOLUTION
• is a machine-learning based patent classification software
• analyzes large numbers of patents, estimate their relevancy with high
accuracy and automatically classify them into customer-specific categories
• continuously learns from and imitates the behavior of IP professionals

6. Lung Cancer
UNDER THE HOOD
T E X T M I N I N G
T E R M I N O L O G I E S
S E M A N T I C S E A R C H
M ACHINE LEARNING
Lung Cancer
Lung Adenocarcinoma
Pulmonal Mass
“A biomarker
panel comprising an microRNA”
Lung Adenocarcinoma
• Use of Cabazitraxel in patients
with metastatic NSCLC (WO-
2015036507-A1)
• Treatment of Melanoma (EP-
3021848-A2)
Oncology
Therapy
Lung Cancer
“A biomarker
panel comprising an microRNA”
Lung Adenocarcinoma

7. MACHINE LEARNING – A PARADIGM SHIFT
Machine learning
Data
Query/Rules
Computer
Traditional approach
Result
Data
Examples
Computer Result

8. MACHINE LEARNING FOR CLASSIFICATION IN A NUTSHELL
Define Categories
Summarzie your most important technology areas,
department affiliations and more into user-defined
categories
1
Provide Examples
Provide pre-categorized documents or create manual
manual categorizations via the Graphical User
Interface. These documents will train the system.
2
Let the System Categorize Documents
The system automatically categorizes new
documents. It also provides a level of confidence for
its predictions.
3
Review Results
Review the results with the lowest level of confidence
and add them to the training documents. This is how
the system learns best.
4
Active
Learning
GO

9. YOUR BENEFITS

10. HOW OUR CUSTOMERS USE IT
In principle, patent classification can be applied to all questions that
appear periodically and/or require a lot of patents to be assessed:
✓ send relevant patents to the right people in the right department
✓ improve patent prior art search
✓ classify patents and non patent literature into company-internal
classification schemes
✓ conduct continuous patent landscaping analyses
✓ do competitor analysis
✓ identify new drug targets
✓ identify patents about gene editing technologies
✓ …

11. COME VISIT OUR BOOTH

12. YOUR CONTACT
Kornél Markó
kornel.marko@averbis.com