Appeal No. 2006-0945
Application No. 10/245,888
comprehended that an air conditioner is yet another instance of a load factor that depends on the
specific model and age of the unit, similar to the circumstances calling for the learning capability
in Narita I and would therefore have been motivated to apply the bifurcated adjustment of Narita
II to the learning tables of Narita I. Alternatively, a person of ordinary skill in the art would have
reached the same conclusion by seeing that Narita II’s teaching that such a ON/OFF decision
path for air conditioners arises because there is no direct monitoring of reverse torque. As Narita
II goes on to teach, an approximation from a look up table is needed instead, and the effect of an
air conditioner on actual performance is going to cause any data in the table, derived from
driving conditions absent an air conditioner, to err significantly. Narita II then provides the
solution of an adjustment for the air conditioning load. But upon also reading Narita I, a person
of ordinary skill would immediately comprehend that a single fixed value to represent device
performance, such as that of an air conditioner, results in less than optimal performance because
of actual operating characteristic differences among various devices and even of the same device
over time. Narita I then teaches that an effective mechanism to correct such performance
degradation is to establish a learning mechanism based on the variables that are measured.
Given that to apply Narita II to Narita I would involve little more than adding the test for
whether an air conditioner is ON or OFF and two sets of learning look up tables instead of one,
this would appear to have been well within the capacity of one of ordinary skill in the art.
Because we find that the breadth of the claimed “operation load of the air conditioner”
encompasses Narita’s embodiment of the ON/OFF operation load and that when Narita’s air
conditioner is turned ON, the operation load increases as in claim 1, and that Narita provides a
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