The WIPO Patent Analytics Handbook
Note to Readers
About the Author
Acknowlegements
Preface
How to use the Handbook
0.0.1
Datasets
1
Introduction
2
Scientific Literature
2.1
Accessing the Scientific Literature
2.2
Searching Literature Databases
2.2.1
Stemming
2.2.2
Using Search Operators
2.2.3
Proximity Operators
2.2.4
Regular Expressions
2.3
Precision vs. Recall
2.4
Processing Scientific Literature
2.5
Visualizing the Scientific Literature
2.5.1
Dashboards
2.5.2
Network Visualisation
2.5.3
Other forms of visualisation
2.6
Linking the Scientific Literature with Patent Analysis
2.6.1
Mapping Authors to Inventors
2.7
Linking Citations with Patent Literature
2.8
Conclusion
3
Counting Patent Data
3.1
The structure of patent numbers
3.1.1
The country code
3.1.2
The numeric identifier
3.1.3
Kind Codes
3.2
Preparing to Count Patent Data
3.3
Counting Priority or First Filings
3.4
Counting Priority Applications
3.5
Counting Applications
3.5.1
Mapping Publications (Family Members)
3.6
Trends by Country using Publication Data
3.7
Patent Families
3.8
Forecasting Patent Activity
4
PATSTAT (placeholder)
4.0.1
Getting Started
4.0.2
Recipe 1
4.0.3
Recipe 2 IPC based analysis
4.0.4
Recipe 3 Patent Family Analysis
4.0.5
Recipe 4 Applicants
4.0.6
Recipe 5 Inventors
4.0.7
Recipe 6 Non Patent Literature
5
Indicators (placeholder)
6
Text Mining (placeholder)
7
Geocoding
7.0.1
Getting Started
7.0.2
Getting set up with the Google Maps API
7.0.3
Using the API
7.0.4
The Source Data
7.1
Lookup the Records
7.1.1
Using placement
7.1.2
Using ggmap
7.1.3
Using Googleway
7.2
Reviewing Initial Results
7.2.1
Tackling Abbreviations
7.2.2
Lookup edited names
7.2.3
Bringing the data together
7.2.4
Assessing the Quality of Geocoding
7.3
Preprocess the Data and Rerun the Query
7.3.1
Duplicated Affiliation Names
7.3.2
Quickly Mapping the Data
7.4
Round Up
8
References
9
Machine Learning (placeholder)
9.0.1
Artificial Intelligence & Machine Learning
9.0.2
Word Vectors
9.0.3
Word Vectors with fastext
9.0.4
Training Word Vectors for Drones
9.0.5
Using Word Vectors
9.0.6
Exploring Analogies
9.0.7
Patent Specific Word Embeddings
9.0.8
Machine learning in Classification
10
Patent Classfication (placeholder)
11
Patent Citations
11.0.1
Non Patent Literature
11.0.2
Literature and Patent Citation Data with the Lens
11.0.3
Patent Citations
11.0.4
Navigating Patent Networks
11.0.5
Forward Citations
11.0.6
Counting Citations by Patent Families
11.0.7
Citations and Knowledge Spillovers
11.1
Conclusion
12
Social Media and Patent Analytics (placeholder)
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The WIPO Patent Analytics Handbook
Chapter 5
Indicators (placeholder)
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