1. CLAIMS
CLassification Automated InforMation System
Computer-Assisted Categorisation of Patent Documents in the International Patent Classification
Patrick Fiévet, CLAIMS Project Manager WIPO
& Caspar J. Fall, CLAIMS Consultant ELCA
ICIC’03, Nîmes, 22 October 2003

2. Agenda Introduction to CLAIMS project (PF)
Computer-assisted categorization prototypes (CJF)
CLAIMS Categorizer perspectives (PF)

3. 1. Introduction to CLAIMS Project

4. 1.1 CLAIMS Context World Intellectual Property Organization (WIPO)
International Patent Classification (IPC)
Classification Automated Information System (CLAIMS)

5. 1.2 CLAIMS Project Objectives
IT support for the promotion of the IPC
IPC Reform and revision support
IPC Tutorials
Translation and Natural Language Search in the IPC
IPC Categorization assistance to Patent Offices

6. 2. Computer-assisted Categorization

7. 2.1 Objectives
Develop a solution for predicting International Patent Classification (IPC) codes
Facilitate accurate classification in small and medium patent offices
Support for documents in multiple languages
Categorization assistance tool
Open questions
Depth of computer-assisted categorization
What accuracy?

8. 2.1 Key issues Survey of automated categorization research
Patent categorization
The IPC is a hierarchical classification
120 classes, 628 subclasses, 69’000 groups
Patents have secondary IPC codes
The categories are modified over time
Vocabulary very diverse and technical

9. 2.1 Patent categorization approach
Machine-learning method to recognize categories
Statistical distribution of words
Establish training data
Training documents with good IPC codes
210’000 to 830’000 documents
Disadvantages
No need for keywords
Easy to train the tools
Can support many languages
Never absolute certainty in the results
Difficult to have reliable full automation
Advantages

10. 2.2 Prototype Custom development Measure categorization success
State-of-the-art algorithm
Language independent
Measure categorization success
Compare the predictions with other manually classified documents

11. 2.2 Prototype results

12. 2.2 Improving accuracy with category refining
Scenario 1
Scenario 2
validate
refine
direct

13. 2.3 Conclusions It works well! To get accurate results, one needs:
Useful user assistance
Direct categorization at subclass level possible
IPC codes can be refined accurately to main group level
To get accurate results, one needs:
Large datasets
Good category coverage
Accurate IPC codes
Read the proceedings for more details
Demonstration available after the presentation

14. 3. IPCCAT

15. 3.1 CLAIMS Categorizer Perspectives
1. Implementation : IPCCAT
2. Training sets for IPC Categorization: English, French, Spanish and Russian, German possibly chinese
3. IPC Data sets improvement & Categorizer Retraining

16. 3.2 CLAIMS Categorizer Perspectives
4. Improve integration of the IPC Categorizer with other CLAIMS tools
5. CLAIMS policy for distribution of data sets in various Languages

17. 3.2 Access to IPCCAT for PCT
Login: IBGST01
Password: clobterib

18. Questions / Answers
Patrick Fiévet:

19. Thank you for your attention
CLAIMS
CLassification Automated InforMation System
Thank you for your attention