Appeal 2009-3941
Application 10/334,370
steps. These process steps involve analyzing the document which may be
performed outside a computer using no more than human intelligence.
2. Dependent claims 3 and 4 require that the steps of the method
recited in their base claim be performed by the first and second computing
devices, respectively.
3. Brown relates to an automatic text classification system which
extracts words and word sequences from the texts to be analyzed and
compares them with training data having a measure of probability with
respect to a plurality of qualities. Each plurality of qualities may be
represented by an axis, whose two end points correspond to mutually
exclusive characteristics. Based on the comparison, the texts to be analyzed
are then classified in terms of the plurality of qualities (Abstract).
4. Brown provides a system and method of generating
classification data for text. The method comprises: identifying semantic
content bearing lexical units in the data representing the text to be classified
and determining classification data as a score for the text to be classified
with respect to each of a plurality of qualities. Brown does so by comparing
the identified lexical units with stored lexical units having a distribution of
lexical scores associated therewith for each of a plurality of qualities (¶ 0018
- ¶ 0024).
5. Brown’s training system comprises two parts - first, a
classification of a plurality of pre-selected training texts in terms of each of a
plurality of qualities and second, an automatic text analysis of each of the
classified training texts. The object of the training system is to generate an
output of singles, doubles, and triples of word stems and word stem
4
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