© Jouve 2013 Document strictement confidentiel 1 J-Class : an hybrid patent classification system IPC Workshop 25/02/2013 1

© Jouve 2013Document strictement confidentiel

1

J-Class : an hybrid patent classification system

IPC Workshop

25/02/2013

1


Jean-Pierre Raysz26/02/2013 2

Plan

Jouve

J-class

Pre-processing

Similarity method

Semantic method

Combined method

Evaluations

Naïve Questions?



Jouve

The Jouve Group provides customers with cross-media solutions for designing, enriching, showcasing and distributing content. By offering innovative turnkey solutions for publishing, digitization, business process outsourcing, IT and printing, we help our customers develop flexible strategies to gain the competitive edge in the digital market.

3,000 employees25 locations, 15 in FranceAround 150,000,000 € turnover25% of sales are export



pre-processing

Classic linguistic pre-processing: phrase segmentation, tokenization, POS-tagging, lemmatization

Patent-specific pre-processing: « key-phrase » tagging

Key-phrase = part of the description that concisely describes the patent document topic

Detection of language inconsistencies => small number of documents have been ignored



Semantic method

1. Construction of semantic modelsterm extraction;semantic relation verification;different filtering methods : representative terms, polysemy reduction etcLanguage-specific methods used (linguistic pre-processing, term extraction, lexical resources)

2. Trainingannotation of patent documents with extracted terms;value calculation according to frequency and position;feed an SVM classifier.



Learning

Selected Terms

Classified Documents

InternationalPatent Classification

Terms Extraction

Semantic Network (Wordnet)

Semantic Consolidation

Relevant Terms Sub Semantic network



Run

to be classified Documents

Semantic Annotation

Annotated documents

Classifier

Classified Documents

Relevant Terms Relevant PatternsRelevant Concepts



Similarity method

Indexing – retrieval method using the Lemur System

Principle: 1. Build an index using the target data2. Query using the test data: Retrieval of the most similar patents 3. Calculate the query patent class, using the classes of the indexed documents

1 index per language; language-specific stop-words lists;



Hybrid system

Input: 3 best candidates for the 2 methods abovefrom 3 to 6 candidatesbuild classifiers on the fly, through 1 vs 1 training

Final score = sum of probability values obtained for each binary classifier

SimilaritySimilarity

SemanticSemantic

DecisionDecisionDocumentDocumentClassifiedClassifiedDocumentDocument



Fine grained Classification

Bio-technologies domain (A01H)19 classesLearning with 4005 docs, evaluation with 1251 docs.

Use of a validated terminology of the domain (INRA/MIG)System 1: Semantic Networks System 2 : Similarity System 5 : Terminology

77.91% 76.59% 51.08%

System 3: Terminology + System 1

System 4: Terminology + System 2

77.34% 76.86%



CLEF IP Results

2,7M documents used for learning stage (1,3 M patents)600 classes Learning with documents < 2002, Evaluation with documents > 2002

Nb candidates Similarity Method (1)Semantic and statistic Method

Combined Method (3)

Delta between (1) and (3)

First candidate 77,5% 75,15% 82,1% +4,6

Two candidates 86,6% 86,7% 92,05% +5,45

3 candidates 91,15% 91,05% 95,35% +4,2

4 candidates 93,5% 93,65% 96,6% +3,1

5 candidates 94,8% 94,7% 97% +2,2

6 candidates 95,8 % 95,25 % 97,05 % +1,25

10 candidates 97,05% 97,25%

20 candidates 98,4% 98,55%



Pre-classification results

4.5 M patents used for learning stage100 classesEvaluation with 130 000 patents

Method PrecisionSemantic Networks (233 000 terms)Semantic Networks (233 000 terms) 85,4 %85,4 %

Similarity MethodSimilarity Method 81,8600 %

Patent Office Staff 80 %



Naïve Questions

How can we improve our system performance ?more patents -> better results

What does it mean 80% for patent office staff ?

What is the inter annotator agreement between examiners ?

What is the best achievable performance for an automatic classification system ?

it is not 100%, that is for sure



Suspicious references

US2007215593 : Diaper rash prevention apparatus

IPC : A21B1/00 (Bakers’ ovens)ECLA : A47K11/02 (Sanitary equipment)

US6459426B1 : Monolithic integrated circuit implemented in a digital display unit for generating digital data elements from an analog display signal received at high frequencies. “The present invention relates to digital display units used in computer systems”

IPC : B60R25/10F; B62H5/00; B62H5/20 (Vehicles; Cycles)



Non mutually exclusive classes

ECLA : E05D13/04 -> IPC class E05C17/60but class ECLA E05C17/60 also exist !!!

ECLA : E05D13/04 Fasteners specially adapted for holding sliding wings open

E05D13/06 with notches E05D13/06 acting by friction

ECLA : E05C17/60 holding sliding wings open E05C17/62 using notches E05C17/64 by friction



Some confusions

US2006140886 : Tanning Aids - Claim 1. A tanning aid comprising a polymethyl methacrylate shaped body which comprises 0.1 to 1.5% …

EPO class : A61K8 Cosmetic preparationsJ-Class : C08F Macromolecular compounds…

US2007282244 - Glaucoma Implant with Anchor - Claim 1. A method for reducing intraocular pressure…

EPO class : A61M27 Implants devices for drainage of body fluids from one part of the body to the other (intraocular A61F9/00)J-Class : A61F9 Method or devices for treatment of the eyes

A61F9/007V Apparatus for modifying intraocular pressure, e.g. for glaucoma treatment



Some confusions

GB1077771 : Improvements in or relating to hot water storage containers - Claim 1. A hot water storage container of double walled construction, the walls being 70 spaced apart to provide a cavity to receive heat insulation material…

EPO class : E03B11 Arrangements or adaptations of tanks for water supplyJ-Class : F24D Domestic hot-water supply systems; Elements or Components therefor



True confusions

EPO : F24H1/18 water-storage heatersJ-Class : F17C Liquified gaz containers



True confusion

EPO : B62K25 Axle suspensionJ-Class : B61F RAIL Vehicle suspension


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