DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3-4, 6-7, and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Peter et al. (BE 1026844) in view of Ho (KR 101532885).
Regarding to claims 1, 6-7:
Peter et al. discloses a method for determining the service life of a switching device (FIG. 3, element 1000), the method comprising the steps of:
providing a neural network having at least two input variables and an output variable (FIG. 6 shows a neural network with inputs and outputs);
determining at least a current variable, wherein the current variable is a continuous variable representing a current flowing through the switching device (FIG. 6 shows at least the ISpule that reads on the claimed current variable because it is the measured current flowing through the switch 1000);
determining a switching device state variable (page 3, paragraphs 1-5: This usage history or the number of switching cycles represent the state/condition of the switching device);
inputting at least the current variable and the switching device state variable as an input variable into the neural network (FIG. 6: At least the current ISpulse is inputted to the neural network. The neural network also takes into account the usage history of the switching device or the number of switching cycles (page 3, paragraphs 1-5). This usage history or the number of switching cycles is considered as a discrete variable); and
determining a remaining service life of the switching device by means of the neural network (page 9, 3rd-4th paragraphs: One of possible output portions of the artificial neural network is the expected remaining life of the switch).
Peter et al. however is silent wherein the switching device state variable represents an occurrence of a sticking, jammed, or fused switching device event.
Ho discloses a method for issuing a state of a switching relay comprising a meter to detect whether a fusion state of the relay occurs (yes or no: discrete state) to notify an abnormality in the switching relay (Abstract).
Therefore, it would have been obvious for one having ordinary skill in the art at the time of the filing date to modify Peter’s determination the remaining service life to also be based on the fusion state of the switch condition to minimize problems caused by the fusing phenomenon as taught by Ho (page 1).
Peter also discloses the following claims:
Regarding to claim 3: wherein the neural network is trained by means of monitored learning (page 2, lines 25-32: The artificial neural network is operated based on training data used in a learning process).
Regarding to claim 4: wherein at least the method steps are carried out in a cloud-based device (It is well-known that training a machine learning model or a neural network is performed on a cloud-based service provided by major technology companies. Please see Mohassel et al. (US 2021/0209247), paragraph [0002]).
Regarding to claim 9: further comprising: i) inputting a service life variable as an input variable into the neural network, and ii) comparing the service life variable with the output variable prior to determining the remaining service life of the switching device by means of the neural network (FIG. 6 and page 9, paragraphs 1-9: The neural network determines the remaining service life of the switching device based on the aging history and the number of switching cycles that have already been carried out (service life variable) in comparison to the data in the training phase of the neural network).
Response to Arguments
Applicant’s arguments with respect to the claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAM S NGUYEN whose telephone number is (571)272-2151.
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/LAM S NGUYEN/ Primary Examiner, Art Unit 2853
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