Expanded requirement types
Expanded requirement types use machine learning to allow a broad range of
responses instead of exact matches.
You can train Coleman by adding allowed values. The more values you input, the better the training. Coleman determines whether the user input is similar enough to the training values and accepts or rejects the input accordingly.
For example, email address can be defined as an expanded requirement type. You cannot anticipate all the email addresses that are specified by the users. You want Coleman to recognize whether the user input is an email address.
When you define the requirement type, select the option. Specify a large number of email addresses as allowed values and save. You may consider using the bulk edit feature.