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Create the new label by assigning it at least once. This is the initial step to introduce a new category or classification within your data taxonomy.

Search for instances in the reviewed data where the new label should have been assigned, and apply the label accordingly. This step is crucial for maintaining consistency across your data set.

Use 'Missed Label' to find all additional missing examples in the reviewed data. This action helps in identifying and rectifying any instances that may have been overlooked during the initial review.

Check validation to ensure the label is performing as expected, and follow recommended actions if further training is required. Validation is key to assess the accuracy and performance of the new label within the system.

 According to the UiPath documentation, the RegEx Extractor is a data extraction method that uses regular expressions to define and capture data from documents1. The RegEx Extractor does not consume any page units, which are the units of measurement for the consumption of Document Understanding services2. Therefore, the page unit cost per extracted page for the RegEx Extractor is 0.

References:

1: RegEx Extractor 2: Document Understanding - Metering & Charging Logic

In this scenario, the rules defined in the Taxonomy Manager are as follows:

IsNotEmpty: Ensures that the field is not empty.

EndsWithUSA: Checks if the extracted address ends with "USA".

ContainsNY: Ensures that the extracted address contains "NY".

The address extracted in this case is: "42 W 80th St. West New York, NJ 1234, USA". While the address ends with "USA" (passing the EndsWithUSA rule), it includes "West New York, NJ", which satisfies the ContainsNY rule even though "NY" is part of "New York". However, the exact behavior of the Contains rule can generate a warning message because "NY" is part of a larger string ("New York"). Since this does not constitute an error but simply a rule conflict, the validation can proceed.

Therefore, the most accurate outcome would be a warning, but validation can still be performed

The DocumentUnderstanding ML package is a generic, retrainable model designed to handle various document types that are not covered by out-of-the-box models. It allows for the extraction of data from structured and semi-structured documents by building a model from scratch through training. This package is highly flexible and can be tailored to fit different document formats, making it ideal when specific document types are not pre-configured in UiPath’s out-of-the-box offerings.(Source: UiPath Documentation on ML Packages