0 © wipo – 2003 pf & cjf claims computer-assisted categorisation of patent documents in the...

1 © WIPO – 2003 PF & CJF

CLAIMS

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

CLassification Automated InforMation System

2 © WIPO – 2003 PF & CJF

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

• IPC Reform and revision support

• IPC Categorization assistance to Patent Offices

• IPC Tutorials

• Translation and Natural Language Search in the IPC

IT support for the promotion of the IPC

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2. Computer-assisted Categorization

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2.1 Objectives

Develop a solution for predicting International Patent Classification (IPC) codesFacilitate accurate classification in small and medium

patent officesSupport for documents in multiple languagesCategorization assistance tool

Open questionsDepth of computer-assisted categorizationWhat accuracy?

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2.1 Key issues

Survey of automated categorization research

Patent categorizationThe IPC is a hierarchical classification

» 120 classes, 628 subclasses, 69’000 groups» Patents have secondary IPC codes

The categories are modified over timeVocabulary very diverse and technical

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2.1 Patent categorization approachMachine-learning method to recognize categories

»Statistical distribution of wordsEstablish 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

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2.2 PrototypeCustom development

State-of-the-art algorithmLanguage independent

Measure categorization successCompare the predictions with other manually

classified documents

1

2

3

ic

ic

realguess

1

2

3

ic

ic

realguess

1

2

3

ic

ic

realguess

Top prediction Three guesses All classes

mc mc mc

11 © WIPO – 2003 PF & CJF

88

.7%

88

.1%

89

.1%

90

.2%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

English French Russian German

Pre

cisi

on

Class Subclass Main group

2.2 Prototype results

12 © WIPO – 2003 PF & CJF

2.2 Improving accuracy with category refining

A 01

A 61B A 61C

A 61D1 /00 A 61D3 /00 A 61D5 /00

A 61D

A 61 A 62

A

...

B

...

C

...

...

IP C

direct

validate

refine

Scenario 1 Scenario 2

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2.3 Conclusions

It works well!Useful user assistanceDirect 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 coverageAccurate IPC codes

Read the proceedings for more detailsDemonstration available after the presentation

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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