Once you have at least ten positive and ten negative patent examples each of both in your Training Set, click 'Build Classifier'. 

This will introduce a new data column to the lists of patents you are reviewing. A score, represented by a number between 0 to 1.0, will be assigned to each patent based on how relevant to the training set the machine believes it to be.

The higher the score, the more related the machine deems the patent to be. For example, a patent with a Score of 0.88 is considered to be more relevant to what you are interested in than a patent with a Score of 0.45. 

The column 'Classifier class' will populate to mark the patent family as positive or negative based on the score. 

To continue populating your training set, you will need to either mark a patent as Positive or Negative, or reassign it. This will populate the 'User class' column.

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