Appeal No. 97-0507
Application 08/202,609
the claimed apparatus to differentiate it from prior art
apparatus satisfying structural limitations of that which have
been recited on the claims on appeal. In an analogous manner
here, we and the examiner take the view that the nature of the
data to be processed by the neural network is not dispositive or
does not differentiate a prior art neural network apparatus
processing different types of raw data, and particularly data
that has been known and admitted to be known in the prior art by
appellants here. Id. 2 USPQ2d at 1648. Accord, Ex parte
Wikdahl, 10 USPQ2d 1546, 1548 (Bd. Pat. App. & Int. 1989) and In
re Casey, 370 F.2d 576, 580, 152 USPQ 235, 238 (CCPA 1967).
Appellants’ positions set forth at page 2 of the reply brief
are also not persuasive. We are not persuaded by appellants’
reasoning that the structure of the neural network is changed
when the weighting functions are changed as asserted here. As
the examiner’s reasoning makes clear, the references relied on
make clear as well as appellants’ own admission with respect to
prior art neural networks makes clear, the backpropagation
“trainability” of a neural network inherently requires that the
weighting functions be changed in accordance with the training
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