CLAIMS

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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 ICIC03,
Nîmes, 22 October 2003
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Agenda

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

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1. Introduction to CLAIMS Project
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1.1 CLAIMS Context
World Intellectual Property Organization (WIPO)
International Patent Classification (IPC)
Classification Automated Information System
(CLAIMS)
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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

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2. Computer-assisted Categorization
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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?

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2.1 Key issues

• Survey of automated categorization research
• Patent categorization
• The IPC is a hierarchical classification
• 120 classes, 628 subclasses, 69000 groups
• Patents have secondary IPC codes
• The categories are modified over time
• Vocabulary very diverse and technical

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2.1 Patent categorization approach

• Machine-learning method to recognize categories
• Statistical distribution of words
• Establish training data
• Training documents with good IPC codes
• 210000 to 830000 documents

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2.2 Prototype

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

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2.2 Prototype results
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2.2 Improving accuracy with category refining
Scenario 1
Scenario 2
validate
direct
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2.3 Conclusions

• It works well!
• 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

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3. IPCCAT
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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
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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
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3.2 Access to IPCCAT for PCT
Login IBGST01
Password clobterib
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Questions / Answers
Patrick Fiévet patrick.fievet_at_wipo.in
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CLAIMS
CLassification Automated InforMation System
Thank you for your attention