1. A computer implemented method for automatic categorization of samples according to learnt patterns of labeling, the method comprising steps a computer is programmed to perform, the steps comprising: a) on a computer, receiving a plurality of data units labeling a plurality of physical substance samples such that each one of the data units labels only a respective one of the physical substance samples and each one of the physical substance samples is labeled by only one of the data units; b) parsing each one of the data units into at least one respective ordered list of one or more specific tokens; c) for at least one subgroup of said ordered lists, the subgroup consisting of ordered lists of a same number of specific tokens, merging at least two ordered lists in which each pair of specific tokens of a same order of in-ordered-list positioning are at least one of a group consisting of: a pair of identical specific tokens, and a pair of non-identical specific tokens matching a same general token, into an ordered list defining a labeling pattern common to the ordered lists being merged and therefore to more than one of the labeled physical substance samples, the ordered list defining the labeling pattern by including the general token in place of the specific tokens matching the general token; and d) categorizing a sample by matching a data unit labeling the sample being categorized, to at least one of the labeling patterns, the matching being based on a matching criterion, the matching criterion being that each specific token included in an ordered list that is a result of parsing the data unit labeling the sample being categorized, is either identical to a token that is of a same order of in-ordered-list positioning and is included in the ordered list that defines the labeling pattern, or is in conformity to a general pattern defined by the token of the same order of in-ordered-list positioning.