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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 5 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 5, the phrase "for example" renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 10 and 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because regarding claim 10, a computer program is non-statutory subject matter. The Examiner recommends stating a “non-transitory computer readable medium storing a computer program…”. Regarding claim 11, a computer-readable data carrier can be interpreted as an electrical signal, which is non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-11 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sheng
CN-108390380-A (hereinafter “Sheng”, citations refer to provided English Translation).
Regarding claim 1, Sheng discloses a method of monitoring a voltage grid (12) (via monitoring transformer state of a voltage grid), comprising the steps of: acquiring at least a first characteristic parameter (e.g. transformer state) of the voltage grid (12) at a first point in time (e.g. pg. 2-3, via time series/time sequence); acquiring the first characteristic parameter of the voltage grid (12) at a second point in time that is different from the first point in time (e.g. pg. 2-3, via time series/time sequence); feeding the first characteristic parameter acquired at the first point in time and the first characteristic parameter acquired at the second point in time into a processor unit (16), which processes the first characteristic parameter acquired at the first point in time and the first characteristic parameter acquired at the second point in time together such that a future state of the voltage grid (12) is predicted (e.g. state parameter trend prediction) on the basis of the first characteristic parameter acquired at the at least two different points in time (e.g. pg. 2-3 and 6-10); and outputting the predicted future state of the voltage grid (12) (e.g. pg. 2-3 and 6-10).
Regarding claim 2, Sheng discloses the method according to claim 1, characterized in that the first characteristic parameter is a voltage, a current (e.g. pg. 6-7), a power (e.g. pg. 6-7), a frequency, a distortion, a harmonic, a reactive power and/or an energy value, in particular for a phase of a multiphase voltage grid (12).
Regarding claims 3, Sheng discloses the method according to claim 1, characterized in that the first characteristic parameter is acquired multiple times, so that a time sequence comprising more than two points in time of the first characteristic parameter is available, which is processed (e.g. pg. 6-10, multiple detections via the quantity matrix).
Regarding claim 4, Sheng discloses the method according to claim 1, characterized in that the processor unit (16) comprises an artificial intelligence (24) which receives at least the first characteristic parameter acquired at the at least two different points in time as an input quantity and outputs the future state of the voltage grid (12) as an output quantity (e.g. pg. 6-10).
Regarding claim 5, Sheng discloses the method according to claim 4, characterized in that the artificial intelligence (24) includes at least one artificial neural network, for example an artificial convolutional neural network or an artificial recurrent neural network, in particular wherein the artificial intelligence includes a long short-term memory (LSTM) network or a gated recurrent unit (GRU) (e.g. pg. 6-10).
Regarding claim 6, Sheng discloses the method according to claim 1, characterized in that a multidimensional vector (via the quantity matrix) is generated which comprises the first characteristic parameter acquired at the at least two different points in time, the multidimensional vector being processed by the processor unit (16) (e.g. pg. 6-10).
Regarding claim 7, Sheng discloses the method according to claim 1, characterized in that at least one future development over time of a characteristic parameter is predicted as the future state of the voltage grid (12) (e.g. pg. 6-10).
Regarding claim 8, Sheng discloses a method of training an artificial intelligence (24) for predicting a future state of a voltage grid (12), comprising the steps of: providing a training data set (via the state quantity matrix) for the artificial intelligence (24), which comprises at least a first characteristic parameter of the voltage grid (12) at a first point in time, the first characteristic parameter of the voltage grid (12) at a second point in time, and an actual state of the voltage grid (12) at a third point in time, which is later in time than the first point in time and the second point in time (e.g. pg. 6-10); feeding the first characteristic parameter at the first point in time and the first characteristic parameter at the second point in time into a processor unit (16) which includes the artificial intelligence (24) to be trained, wherein the processor unit (16) including the artificial intelligence (24) processes the first characteristic parameter acquired at the different points in time together and outputs a predicted future state of the voltage grid (12) at the third point in time (e.g. pg. 6-10); comparing the predicted future state of the voltage grid (12) at the third point in time with the actual state of the voltage grid (12) at the third point in time, which is part of the training data set, in order to determine a deviation (e.g. prediction error) between the predicted future state of the voltage grid (12) at the third point in time and the actual state of the voltage grid (12) at the third point in time (e.g. pg. 6-10); and feeding back (via back propagation) the deviation between the predicted future state of the voltage grid (12) at the third point in time and the actual state of the voltage grid (12) at the third point in time in order to adjust weighting factors of the artificial intelligence (24) to be trained, if the deviation is outside a tolerance range (e.g. pg. 6-10).
Regarding claim 9, Sheng discloses a system (10) for monitoring a voltage grid (12), comprising at least one processor unit (16) (e.g. data pre-processing module) configured to carry out a method according to claim 1 (e.g. pg. 2-7).
Regarding claim 10, Sheng discloses a computer program (18) comprising program code means for carrying out the steps of a method according to claim 1 when the computer program (18) is executed on a processor unit (16) (e.g. pg. 2-7).
Regarding claim 11, Sheng discloses a computer-readable data carrier (20) having the computer program (18) according to claim 10 stored thereon (e.g. pg. 2-7).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES R KASENGE whose telephone number is (571)272-3743. The examiner can normally be reached Monday - Friday 7:30am to 4pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kenneth Lo can be reached at (571) 272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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CKJune 22, 2026
/CHARLES R KASENGE/Primary Examiner, Art Unit 2116
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