Appeal 2007-0987
Application 09/810,992
reasonably interpreted as corresponding to Forecast Pro’s “very short” and
“low volume” data quantity characterizations and “volatile” and “sparse”
profile element types associated with the Simple Methods and Low Volume
Models forecasting techniques. We also find no persuasive arguments from
Appellants that convince us of any error in the Examiner’s establishment of
proper motivation (Answer 6) for the proposed combination of Kadowaki
and Forecast Pro.2
We also make the observation that, in making the obviousness
rejection of claims 1 and 8, the Examiner has relied on Forecast Pro solely
for a teaching of the altering and refining of a selection of a personalization
engine based on profile element number and type. It is apparent to us,
however, from our own independent review of Forecast Pro, as well as the
Examiner’s analysis of Forecast Pro in relation to the anticipation rejection
of claims 18-20, that other of the features set forth in appealed claims 1 and
8 are also present in Forecast Pro.
For example, we find in Forecast Pro, as did the Examiner, a teaching
which corresponds to the claimed passing of a request object with a profile
element to an arbiter. The Examiner, in analyzing previously discussed
claim 19, directed attention to item 1 of Forecast Pro which describes the
passing of a profile element in the form of user historical data to an expert
system, i.e., an arbiter. Similarly, we find in item 1 of Forecast Pro a
disclosure of the analysis of the user historical data to select a particular
forecasting technique, i.e., personalization engine. Accessing content from a
2 Although Appellants contend (Reply Br. 6-7) that the Examiner has not
responded to Appellants’ arguments attacking the basis for the Examiner’s
proposed combination of Kadowaki and Forecast Pro, we find no such
arguments in Appellants’ principal Brief on appeal.
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