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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on May 20, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
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Claims 1, 3-11 and 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
(Step 1)
Independent claim 1 recites a non-contact voltage measuring method (a process).
Independent claim 11 recites a non-contact voltage measuring system (a machine).
(Step 2A: Prong 1)
The limitations of claim 1 of generating an output signal, analyzing the output signal, and outputting the sampled signal along with their respective limitations; and the limitations of claim 11 of a signal processing circuit to generate an output signal based on the measurement signal, a sampling unit used to analyzing the output signal to generate a sampled signal, and a trained artificial intelligence model used to receive the sampled signal and output the recovery signal along with their respective limitations, as drafted, under its broadest reasonable interpretation, covers the performance of the limitation in the mind. That is, other than reciting “a signal processing circuit”, “a sampling unit” and “a trained artificial intelligence model” for claims 1 and 6, nothing in the claim elements precludes the steps/limitations above from practically being performed in the mind. For example, but for the “a signal processing circuit”, “a sampling unit” and “a trained artificial intelligence model” language, the “generating”, “analyzing” and “outputting” along with their respective limitations of claims 1 and 6; and the “performing” and “deriving” steps of claim 7 encompasses a user to mentally (or with aid of pen and paper) to generate an output signal based on the measurement signal, analyze the output signal to generate a sampled signal and receive the sampled signal to output a recovery signal. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, claims 1 and 11 recite an abstract idea.
(Step 2A: Prong 2)
This judicial exception is not integrated into a practical application because claims 1 and 11 do not contain any additional elements that integrate the abstract idea into a practical application. Claim 1 recites the additional elements of a measuring a voltage signal source through a capacitive couple structure to generate a measurement signal, a signal processing circuit, a sampling unit and a trained artificial intelligence model. Claim 11 recites the additional elements of a capacitive coupling structure to measure a voltage signal source to generate a measurement signal, a signal processing circuit, an operational circuit, a sampling unit and a trained artificial intelligence model. The measuring a voltage signal source through a capacitive couple structure to generate a measurement signal of claim 1 and the capacitive coupling structure to measure a voltage signal source to generate a measurement signal of claim 11 are recited at a high level of generality (i.e., as a general means of measuring the voltage signal source to generate a measurement signal), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The signal processing circuit, the operational circuit, the sampling unit and the trained artificial intelligence model of claims 1 and 11 are recited at a high-level of generality (i.e., as a generic processor/computer performing generic computer functions of generating an output signal, analyzing the output signal to generate a sampled signal and outputting/receive the sampled signal to then output a recovery signal) such that it amounts no more than mere instructions to apply the exception using a generic computer component and the trained artificial intelligence model is used to generally apply the abstract idea and it is recited at a high level of generality that it amounts to using a generic computer/processor with a generic trained artificial intelligence model. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus, claims 1 and 11 are directed to an abstract idea.
(Step 2B)
Claim(s) 1 and 11 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above (see Step 2A: Prong 2), claims 1 and 11 do contain additional elements. The additional element of measuring a voltage signal source through a capacitive couple structure to generate a measurement signal of claim 1 and the additional element of a capacitive coupling structure to measure a voltage signal source to generate a measurement signal of claim 11 provide no indication that the additional elements are anything other than a generic computer component or process used in a well-understood, routine, and conventional function, recognized by one skilled in the art, when claimed in a generic manner. Claims 1 and 11 further recites the additional elements of a signal processing circuit, an operational circuit, a sampling unit and a trained artificial intelligence model which amounts to no more than mere instructions to apply the exception using a generic computer component the trained artificial intelligence model is used to generally apply the abstract idea and it is recited at a high level of generality that it amounts to using a generic computer/processor with a generic trained artificial intelligence model. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, the additional elements are not sufficient to amount to significantly more than the judicial exception because it does not impose any meaningful limits on practicing the abstract idea. Claims 1 and 11 are not patent eligible.
Regarding claim 3, this claim further adds to the generating the output signal of claim 1. Thus, this claim still falls under the “Mental Processes” grouping of abstract ideas (for similar reasons as disclosed for claim 1 above) and does not include additional elements that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 4, this claim further adds to the generating the output signal of claim 1. Thus, this claim still falls under the “Mental Processes” grouping of abstract ideas (for similar reasons as disclosed for claim 1 above) and does not include additional elements that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 5, this claim further adds to the generating the output signal step of claim 1. Thus, this claim still falls under the “Mental Processes” grouping of abstract ideas (for similar reasons as disclosed for claim 1 above) and does not include additional elements that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 6, this claim further adds to the generating the sampled signal step of claim 1. Thus, this claim still falls under the “Mental Processes” grouping of abstract ideas (for similar reasons as disclosed for claim 1 above) and does not include additional elements that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 7, this claim further adds to the trained artificial intelligence model outputting the recovery signal step of claim 1. Thus, this claim still falls under the “Mental Processes” grouping of abstract ideas (for similar reasons as disclosed for claim 1 above) and does not include additional elements that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 8, this claim further adds a comparing step. Thus, this claim still falls under the “Mental Processes” grouping of abstract ideas (for similar reasons as disclosed for claims 1 above) and does not include additional elements that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 9, this claim further adds to the trained artificial intelligence model outputting the recovery signal step of claims 1 and 7. Thus, this claim still falls under the “Mental Processes” grouping of abstract ideas (for similar reasons as disclosed for claim 1 above) and does not include additional elements that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 10, this claim further adds to the trained artificial intelligence model outputting the recovery signal step of claim 1. Thus, this claim still falls under the “Mental Processes” grouping of abstract ideas (for similar reasons as disclosed for claim 1 above) and does not include additional elements that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 13, this claim further adds to the generating the output signal of claim 11. Thus, this claim still falls under the “Mental Processes” grouping of abstract ideas (for similar reasons as disclosed for claim 11 above) and does not include additional elements that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 14, this claim further adds to the generating the output signal of claim 11. Thus, this claim still falls under the “Mental Processes” grouping of abstract ideas (for similar reasons as disclosed for claim 11 above) and does not include additional elements that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 15, this claim further adds to the generating the output signal step of claim 11. Thus, this claim still falls under the “Mental Processes” grouping of abstract ideas (for similar reasons as disclosed for claim 11 above) and does not include additional elements that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 16, this claim further adds to the generating the sampled signal step of claim 11. Thus, this claim still falls under the “Mental Processes” grouping of abstract ideas (for similar reasons as disclosed for claim 11 above) and does not include additional elements that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 17, this claim further adds to the trained artificial intelligence model outputting the recovery signal step of claim 11. Thus, this claim still falls under the “Mental Processes” grouping of abstract ideas (for similar reasons as disclosed for claim 11 above) and does not include additional elements that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 18, this claim further adds a comparing step. Thus, this claim still falls under the “Mental Processes” grouping of abstract ideas (for similar reasons as disclosed for claims 11 and 17 above) and does not include additional elements that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 19, this claim further adds to the trained artificial intelligence model outputting the recovery signal step of claims 11 and 17. Thus, this claim still falls under the “Mental Processes” grouping of abstract ideas (for similar reasons as disclosed for claim 11 above) and does not include additional elements that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding claim 20, this claim further adds to the trained artificial intelligence model outputting the recovery signal step of claims 11. Thus, this claim still falls under the “Mental Processes” grouping of abstract ideas (for similar reasons as disclosed for claim 11 above) and does not include additional elements that are sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
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) 1-6, 10-16 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. CN211905516U (called Xu hereinafter, applicant disclosed art and the examiner has provided a English machine translation) in view of Peter et al. US2021/0164808 (called Peter hereinafter).
Regarding independent claim 1, Xu teaches a non-contact voltage measuring method (Figs. 3 and 9; para [0007]), comprising:
measuring a voltage signal source (para [0039]; first voltage signal of the cable) to be measured through a capacitive coupling structure (Figs. 3 and 9; metal plate 1) to generate a measurement signal (para [0039]; first voltage signal of the cable is converted into a second voltage signal);
generating an output signal (para [0040]) based on the measurement signal through a signal processing circuit (Figs. 3 and 9; signal processing circuit 2);
analyzing the output signal through a sampling unit (Fig. 9; digital processing circuit 32) to generate a sampled signal (para [0074]; equivalent capacitance value); and
Xu fails to teach outputting the sampled signal to a trained artificial intelligence model, so that the trained artificial intelligence model outputs a recovery signal.
Peter teaches outputting the sampled signal to a trained artificial intelligence model (Figs. 1 and 3; para [0067-0068]; machine learning methods), so that the trained artificial intelligence model outputs a recovery signal (Figs. 1 and 3, 306; para [0067-0068 and 0079]).
Therefore, it would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the structure as described by Xu with the machine learning method applied to sensor signals as described by Peter for the purpose of providing a robust sensor that has high Sn value, F1 behavior, and low installation dependence and use a machine learning method to improve the results outputted by the machine learning method (para [0009 and 0068]).
Regarding claim 2, Xu and Peter teach the non-contact voltage measuring method according to claim 1, Xu further teaches wherein the capacitive coupling structure comprises: a plate capacitor (Figs. 3 and 9; metal plate 1), having a first insulating surface (Figs. 3 and 9; space/insulator between metal plate 1 and the cable to form capacitance C2) and a second insulating surface (Figs. 3 and 9; space/insulator between metal plate 1 and ground to form capacitance CP), wherein the first insulating surface of the plate capacitor is in contact with a first line segment of a signal line of the voltage signal source to be measured (Figs. 3 and 9; top plate of metal plate 1; para [0039]), and the second insulating surface of the plate capacitor is in contact with a second line segment of the signal line of the voltage signal source to be measured (Figs. 3 and 9; bottom plate of metal plate 1; para [0039]).
Regarding claim 3, Xu and Peter teach the non-contact voltage measuring method according to claim 1, Xu further teaches wherein the step of generating the output signal comprises: performing analog signal processing on the output signal through the signal processing circuit (Figs. 3 and 9; A/D converter 31 is after the signal processing circuit 2, thus the signals through components 21-24 are analog).
Regarding claim 4, Xu and Peter teach the non-contact voltage measuring method according to claim 1, Xu further teaches wherein the step of generating the output signal comprises: filtering the output signal through the signal processing circuit (Figs. 3 and 9; para [0068]; frequency divider 24 filters out frequences not needed).
Regarding claim 5, Xu and Peter teach the non-contact voltage measuring method according to claim 1, Xu further teaches wherein the step of generating the output signal comprises: amplifying the output signal through the signal processing circuit (Figs. 3 and 9; para [0062]; amplifier 21 regulates the second voltage signal output from the metal plate 1).
Regarding claim 6, Xu and Peter teach the non-contact voltage measuring method according to claim 1, Xu further teaches wherein the step of generating the sampled signal comprises: performing an analog-to-digital conversion on the output signal to generate the sampled signal (Fig. 9; A/D converter 31).
Regarding claim 10, Xu and Peter teach the non-contact voltage measuring method according to claim 1, Peter further teaches wherein the step of outputting the recovery signal comprises: generating an amplitude parameter, a phase parameter and a frequency parameter based on the sampled signal through the trained artificial intelligence model (Fig. 3; para [0007]; receiving sensor data that has frequencies, amplitudes and phase positions will have the machine learning method use that data); and generating the recovery signal based on the amplitude parameter, the phase parameter and the frequency parameter through the trained artificial intelligence model (Fig. 3; para [0007]; receiving sensor data that has frequencies, amplitudes and phase positions will have the machine learning method use the data to output a signal with frequencies, amplitudes, and phase positions).
Regarding independent claim 11, Xu teaches a non-contact voltage measuring system (Figs. 3 and 9), comprising:
a capacitive coupling structure (Figs. 3 and 9; metal plate 1), used to measure a voltage signal source to be measured to generate a measurement signal (para [0039]; first voltage signal of the cable is converted into a second voltage signal);
a signal processing circuit (Figs. 3 and 9; signal processing circuit 2), electrically connected to the capacitive coupling structure to generate an output signal based on the measurement signal (para [0040]); and
an operational circuit (Figs. 3 and 9; voltage measuring circuit 3), electrically connected to the signal processing circuit and comprising:
a sampling unit (Fig. 9; digital processing circuit 32), used to analyze the output signal to generate a sampled signal (para [0074]; equivalent capacitance value); and
Xu fails to teach a trained artificial intelligence model (Figs. 1, 3 and 4; para [0067-0068]; machine learning methods), used to receive the sampled signal and output a recovery signal.
a trained artificial intelligence model (Figs. 1 and 3; para [0067-0068]; machine learning methods), used to receive the sampled signal and output a recovery signal (Figs. 1 and 3, 306; para [0067-0068 and 0079]).
Therefore, it would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the structure as described by Xu with the machine learning method applied to sensor signals as described by Peter for the purpose of providing a robust sensor that has high Sn value, F1 behavior, and low installation dependence and use a machine learning method to improve the results outputted by the machine learning method (para [0009 and 0068]).
Regarding claim 12, Xu and Peter teach the non-contact voltage measuring system according to claim 11, Xu further teaches wherein the capacitive coupling structure comprises: a plate capacitor (Figs. 3 and 9; metal plate 1), having a first insulating surface (Figs. 3 and 9; space/insulator between metal plate 1 and the cable to form capacitance C2) and a second insulating surface (Figs. 3 and 9; space/insulator between metal plate 1 and ground to form capacitance CP), wherein the first insulating surface of the plate capacitor is in contact with a first line segment of a signal line of the voltage signal source to be measured (Figs. 3 and 9; top plate of metal plate 1; para [0039]), and the second insulating surface of the plate capacitor is in contact with a second line segment of the signal line of the voltage signal source to be measured (Figs. 3 and 9; bottom plate of metal plate 1; para [0039]).
Regarding claim 13, Xu and Peter teach the non-contact voltage measuring system according to claim 11, Xu further teaches wherein the signal processing circuit performs analog signal processing on the output signal (Figs. 3 and 9; A/D converter 31 is after the signal processing circuit 2, thus the signals through components 21-24 are analog).
Regarding claim 14, Xu and Peter teach the non-contact voltage measuring system according to claim 11, Xu further teaches wherein the signal processing circuit filters the output signal (Figs. 3 and 9; para [0068]; frequency divider 24 filters out frequences not needed).
Regarding claim 15, Xu and Peter teach the non-contact voltage measuring system according to claim 11, Xu further teaches wherein the signal processing circuit amplifies the output signal (Figs. 3 and 9; para [0062]; amplifier 21 regulates the second voltage signal output from the metal plate 1).
Regarding claim 16, Xu and Peter teach the non-contact voltage measuring system according to claim 11, Xu further teaches wherein the sampling unit performs an analog-to-digital conversion on the output signal to generate the sampled signal (Fig. 9; A/D converter 31).
Regarding claim 20, Xu and Peter teach the non-contact voltage measuring system according to claim 11, Peter further teaches wherein the trained artificial intelligence model generates an amplitude parameter, a phase parameter and a frequency parameter based on the sampled signal (Fig. 3; para [0007]; receiving sensor data that has frequencies, amplitudes and phase positions will have the machine learning method use that data), and the trained artificial intelligence model generates the recovery signal based on the amplitude parameter, the phase parameter and the frequency parameter (Fig. 3; para [0007]; receiving sensor data that has frequencies, amplitudes and phase positions will have the machine learning method use the data to output a signal with frequencies, amplitudes, and phase positions).
Allowable Subject Matter
Claims 7-9 and 17-19 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 7, the prior arts of record taken alone or in combination fail to teach or suggest:
“wherein the trained artificial intelligence model comprises a signal recovery model, the signal recovery model comprises a plurality of adjustable parameters, and the adjustable parameters comprises at least one of a weight parameter, an offset parameter and a nonlinear function, wherein the signal recovery model is used to generate the recovery signal based on the sampled signal.”
Claims 8 and 9 are indicated as allowable subject matter for depending on claim 7.
Regarding claim 17, the prior arts of record taken alone or in combination fail to teach or suggest:
“wherein the trained artificial intelligence model comprises a signal recovery model, the signal recovery model comprises a plurality of adjustable parameters, and the adjustable parameters comprise at least one of a weight parameter, an offset parameter, and a nonlinear function, wherein the signal recovery model is used to generate the recovery signal based on the sampled signal.”
Claims 18 and 19 are indicated as allowable subject matter for depending on claim 17.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lorek et al. discloses “Non-contact voltage sensing method and apparatus” (see US2022/0178970)
Imai et al. discloses “Non-contact voltage measurement device” (see US2017/0059619)
Epperson et al. discloses “Sensor subsystems for non-contact voltage measurement devices” (see US2018/0136259)
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/DAVID B FREDERIKSEN/Examiner, Art Unit 2858
/HUY Q PHAN/Supervisory Patent Examiner, Art Unit 2858
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