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II-DV 2017: Averbis

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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.

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II-DV 2017: Averbis

  1. 1. WE AUTOMATE YOUR IP ACTIVITIES Kornél Markó Averbis GmbH 85% 75% 97%
  2. 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. 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. 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. 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. 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. 7. MACHINE LEARNING – A PARADIGM SHIFT Machine learning Data Query/Rules Computer Traditional approach Result Data Examples Computer Result
  8. 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. 9. YOUR BENEFITS
  10. 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. 11. COME VISIT OUR BOOTH
  12. 12. YOUR CONTACT Kornél Markó kornel.marko@averbis.com

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