How to deal with new features values in my classification model? Lets say i have a categorical feature having a set of values equal to ['Single','Married','Divorced','Unknown']. Okay, so with the help of the other features, i create my model, i test it, all is fine and i deploy it.

Now , some user using my model as a webservice, types in 'Widow' for that feature value.

What's the proper way of dealing with it? Is there a way to ignore it? and predict based on the rest of the features only? or do i have to handle unknown feature values by assigning Unknown to them too? or worst case scenario, i handle the exception, return a message saying : hey man (or woman) use one of the available values on that feature! ?? 

Note : I'm trying to avoid assigning Unknown to it, because 'Unknown' has its weight. ( My real question is : if there is a way to set a feature's weight to 0 when doing inference )
Depending on the implementation, that variable could have been one-hot-encoded and in that way, the "widow" value could be assigned to "zero" in all the encoded variables (maybe equal to unknown) and the path designed to that 0 will be followed.