Invention Information Retrieval and Visualization

Contents:1. Introduction2. Background3. IR Framework4. Visualization Framework5. Conclusion

Honggu Lin(u6135394)

User:• Person

Query:• Keywords• short

Goal of search:• Precision-Oriented• Few top relevant

document are sufficient

User:• Patent

analysts

Query:• Patent document• Long

Goal of search:• Recall-Oriented• Top 100-200

documents are examined

Web Search Prior Art Search

1. Introduction

Figure 1: Comparison between Web Search and Prior Art Search

2.Background2.1Structure of Patent

Figure 2 .1: A sample XML file for a patent document from the EPO[1]

• Title• Abstract• Description• Claims• International Patent

Classification Code (IPCR)

• Citations

2.Background2.2 Elasticsearch

• A search engine based on Lucene.

• Open source.

• Neal-time search.

• HTTP web interface and schema-free JSON documents.

• Elasticsearch is developed alongside a data-collection and log-parsing engine called Logstash,

and an analytics and visualisation platform called Kibana.

3. IR Framework3.1 Patent Retrieval Overall Process

Query Patents

Query

Patents in Collection

Indexed Documents

Retrieved Documents

Query (Re)formulation Indexing

Retrieval Model(Elasticsearch)

Feedback

Figure 3.1: Illustration of the process in my patent retrieval system

Patent Preprocess Indexing Patent Preprocess

Index statistic(TF-IDF)

3. IR Framework3.2 Data Collection

• Cross Language Evaluation Forum for Intellectual Property evaluation track (CLEF-IP).

• CLEF-IP 2010 contains 2.6 million patent documents, 2000 topics

68%

24%

8%

Language

EN

DE

FR

Figure 3 .1: Percentage of English, German, and French patents in CLEF-IP 2010 collection

22%

10%

16%

52%

Completeness

Title

Title+Abstract

Title+Claims+[Abstract]

Title+Description+Claims+[Abstract]

Figure 3 .2: Completeness of the presence of English text in the CLEF-IP 2010 patent collection

3. IR Framework3.3 Data Preprocess

.XML .JSON Format UnifySection SelectionLanguage Filter

Index the .JSON file in Elasticsearch

Figure 3.3 Illustration of the process of Date Preprocess

3. IR Framework3.4 Query Reduction

Section selection

Term extraction(TF-IDF)

technical phrase formation

Metadata usage (IPCR)

Section Combination

Figure 3.4 Process of Query Formation

4. Visualization FrameworkQuery and Related Patents Selected from the Results

MongoDB

Django Web Framework• Highlight Common Area

between query and its related patent.

• Common Word Word-Net

Put in

Use

Effects

Figure 4.1:Illustration of the process in a Query and Related Patent Visualization System

5. Conclusion

• Explore the differences of results when we use different query formulation method and find out he optimal one.

• Visualize the retrieval result in a more intuitive way.

Reference:[1]Walid Magdy . (2012). Toward Higher Effectiveness for Recall- Oriented Information Retrieval: A Patent Retrieval Case Study . Retrieved from http://doras.dcu.ie/16814/1/WalidMagdyThesis.pdf

Q & A