Appeal No. 2006-2240
Application No. 10/232,015
We are not persuaded by appellant’s arguments. Initially we note that claim 1 states
“a method … comprising:” and as such the language of the claim is open ended and does not
preclude additional steps. Thus, we do not find that the scope of the limitation “selecting one
parameter pair signal of said plurality of parameter pair signals” as limited to selecting only one
parameter pair. Rather, we consider the scope of the limitation includes a step where one
parameter pair is selected, however, we do not find a limitation that precludes further selection
of parameter pairs. Similarly, we do not find that the scope of the limitation “generating a
measured parameter signal responsive to said selecting” to be limited to generating a measured
parameter signal to only the one selected parameter pair. Rather, we consider the scope of the
limitation is that the generated measured parameter signal is responsive the selecting step and
does not preclude the generation of measured parameters from also being responsive to other
steps.
Turning to Larkin, we find that Larkin teaches a monitoring system which makes use of
redundant signals representative of a parameter being monitored, thus providing an accurate
estimate of the parameter if one of the sensors is faulty. See column 2, line 66 through column
3, line 4. The system monitors the output of the sensors and generates several difference signals,
the magnitude of which represents the difference between the sensor signals. See column 7,
lines 1 through 18, see also step 74 in Figure 7. The difference signals are then fuzzified to
assign degrees of membership to each of the values. See column 8, lines 41 through 55, also step
76 in Figure 7. We consider the steps of fuzzifying to require selection of an input and
determining the input value’s (a crisp value) degree of membership with each of a series of
rules.1 These fuzzy values and the difference values are used in a de-fuzzification process in
which sums of the product of the values and their degree of membership are used to determine
the estimate of the parameter being monitored by the sensors. Thus, the system provides
1 E.g. for the crisp input of 10, the degree of membership of small is 1.0, the degree of
membership for medium is 0, and the degree of membership for large is 0, or stated differently,
10 is a fully in the fuzzy group small and not in the fuzzy groups for medium or large.
4
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