Appeal No. 1999-1856
Application No. 08/686,792
the same period of time. Appellants argue that the controller and the neural network of
Samad are trained by “the same data,” not by “different” data. (See brief at pages 7-8.)
This argument is not persuasive since claim 15 does not recite that “different” data sets
are required for each control network.
Appellants argue that the combination of Samad and Broese would produce two
neural networks which would be the same, and further contend that claim 15 requires
two “different” sets or “another set” of data for training the control network. (See brief at
page 8.) We disagree with appellants as discussed above. We find no express
support for this argument in the language of claim 15.
Appellants further argue that claim 15 generates two different process models and
that the combination of Samad and Broese would produce the same model. We
disagree with appellants’ interpretation of the language of independent claim 15. (See
brief at page 9.) While the models may be similar, they necessarily would not be exactly
the same. The language of independent claim 15 does not recite any specific detail of
the data sets used in training the networks or any specific details or characteristics of the
process models generated. Therefore, this argument is not persuasive. Since
appellants have not rebutted the examiner’s prima facie case of obviousness, we will
sustain the rejection of independent claims 15, 16 and their dependent claims 17-26.
CONCLUSION
To summarize, the decision of the examiner to reject claims 15-26 under
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