Automatic Categorization of Patent Applications

1
Automatic Categorizationof Patent Applications

1. The need for automatic categorization of patent
   applications
2. The purpose of automatic categorization
3. How does automatic categorization work?
4. A quick word about the IPCCAT technology
5. Measuring categorization accuracy
6. Strategy to use an automatic categorizer
7. IPCCAT Demo

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The Need for Automatic Categorization of Patent
Applications

• Growth in number of patent applications
• 2140600 patent applications worldwide in 2011
• Up from 1050700 in 1995 (more than doubled in
  16 years)
• Source WIPO IP Indicators

• Large (and growing) number of IPC categories
• 631 Sub-Classes in IPC 2011
• 7392 Main Groups in IPC 2011
• Source IPCCAT Help File

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The Purpose of Automatic Categorization

• Accelerate patent application processing at
  Patent Offices

• Should not be used as a fully automated
  categorizer
• Average error rate at 5 to 10 at Class level, up
  to 20 or more at Main Group level
• Batch classification is possible but requires
  downstream elimination of predictions which are
  below a given confidence threshold

• Rather an assistant for human examiners
• Suggests most probable IPC categories
• Interactions with the examiner (asking for more
  predictions at a different IPC level, forcing a
  given domain, etc.)

4
How Does Automatic Categorization Work?

• Train an artificial intelligence program to
  recognize typical examples for each IPC category
• Provide already-classified patents for training
• Essential Balancing the number of examples
  across categories
• The more examples the better

• Test the program
• Submit patent applications whose IPC categories
  are already known
• Calculate categorization accuracy

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A Quick Word About The IPCCAT Technology

• The IPCCAT project was designed, managed and
  financed by WIPO from 2002 to 2004

• A strictly statistical approach
• No linguistic or other human-defined rules
• So it is language independent

• But an adaptation of the indexing method to the
  various languages supported (English, French,
  Spanish) so as to process collocations correctly

• Categorization algorithm Neural Networks of the
  Winnow type, improved by Simple Shift with the
  help of WIPO

• Validated through several competitions on the
  Internet (latest one the CLEF-2010 project)

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Measuring Categorization Accuracy

• To be really good we would only have to predict
  all the categories all the time! So we need two
  different ratios
• Precision On all the predictions made, how many
  were correct
• Recall On all the correct categories which
  should have been predicted, how many did we
  actually find

• The prediction accuracy is directly correlated to
  
• The number of categories at each IPC level
• The number of available training documents for
  each category

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Strategy To Use An Automatic Categorizer

• If you dont know which section or class is the
  most relevant
• Ask for a direct prediction at the finest
  possible level (Main Group) or
• Ask for a prediction at a coarser level (Class)
  and refine it down to Sub-Class, then to Main
  Group

• If you know which section or class is the most
  relevant
• Force a prediction under the relevant section or
  class (reduces the risk of error)
• Refine the prediction at the next level(s)

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IPCCAT Demo