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 .
Information Disclosure Statement
1. The information disclosure statements (IDS) submitted on 5/31/2024 and 7/11/2025 are in compliance with the provisions of 37 CFR 1.97. According, the information disclosure statement is being considered by the Examiner. Claim Objection
2. Claim 19 is objected to because of the following informalities:
Regarding claim 19, line 3 recites “generating data in a predetermined format…”. For clarification purposes, line 3 should recite “generating, by a processor, data in a predetermined format…”. Line 5 recites “generating a learning model…”. For clarification purposes, line 5 should recite “generating, a learning model generator, a learning model…”.
Claim Rejections - 35 USC § 101
3. The following is a quotation from 35 U.S.C. 101:
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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4. Claim 19 is rejected under 35 U.S.C. 101 as being directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding claim 19, the claim recites partial-discharge diagnostic method of performing determination of a factor of partial discharge in an insulator, comprising: generating data in a predetermined format with a pseudo partial-discharge signal reduced in an electric signal varying with a phase; and generating a learning model that performs, based on the data, at least either determination whether the partial discharge is present or determination of the factor of the partial discharge.
Step
Analysis
1: Statutory Category?
Yes. The claims recites a series of steps, therefore, its process
2A - Prong 1: Judicial Exception Recited?
Yes. The claim recites the limitation of generating data in a predetermined format with a pseudo partial-discharge signal reduced in an electric signal varying with a phase. This limitation, as drafted, is process that, under its broadest reasonable interpretation, cover performance of the limitation in the mind and nothing in the claim element precludes the step from practically being performed in the mind. The method claim encompasses mentally acquiring data in a predetermined format with a pseudo partial-discharge signal reduced in an electric signal varying with a phase. Thus, this claim limitation recites a mental process.
The claim recites the limitation of generating a learning model that performs, based on the data, at least either determination whether the partial discharge is present or determination of the factor of the partial discharge. This limitation recites a mathematical formula or calculation that is used to determine whether the partial discharge is present or determination of the factor of the partial discharge, the “modeling” step is determined to recite a mathematical concept because the claim explicitly recites a mathematical formula or calculation.
2A - Prong 2: Integrated into a Practical Application?
No. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is directed to the abstract idea.
2B: Claim provides an Inventive Concept?
No. The claim does not amount to significantly more than judicial exception, it does not provide enough to be an inventive concept because it merely amounts to applying a judicial exception to a well-known industry.
Examiner Notes
5. Examiner cites particular paragraphs, columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Claim Rejections - 35 USC § 102
6. 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
7. Claims 1-4, 6-13 and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pakr et al. (KR 20220043548; hereinafter “Pakr”).
Regarding claim 1, Pakr discloses a partial-discharge diagnostic device (a partial discharge detection apparatus in Fig. 4) capable of performing determination of a factor of partial discharge in an insulator (“by diagnosing the occurrence of partial discharge at an early stage, it is essential to monitor the degree of deterioration of the power cable insulator and to diagnose the partial discharge to prevent failure of power equipment”, see page 2), comprising: a processor (a data processing device 124, see page 6 and Fig. 4) configured to generate data in a predetermined format (pulse sequence input data is collected from signal detection by a sensor, a conversion process from pulse sequence data of pulse magnitude and phase to a feature vector data…, see page 4) with a pseudo partial-discharge signal reduced in an electric signal varying with a phase (detection of partial discharge occurrence is not significantly affected by noise-filtering performance, thus influenced by noise such as a pseudo partial-discharge signal is reduced, see page 3); and a learning model generator (a model management block 140 comprise a model generator 142 and the model learning unit 144, in Fig 4) configured to generate a learning model that determines, based on the data, at least either the factor of the partial discharge or whether the partial discharge is present (a model management block 140 for generating and learning a steady-state pattern model with the feature vector stream; and a comparison determination block 160 that determines an occurrence time of partial discharge using the steady-state pattern model. The partial discharge pattern classification model storage unit 148 for storing the partial discharge pattern classification model according to the related art may be further included. In this case, the model generator 42 may generate a partial discharge pattern classification model using data determined as abnormal (partial discharge is present) during actual use by the comparison determination block 160…. See page 7).
Regarding claim 2, Pakr discloses the device of claim 1, wherein the processor is configured to generate the data with the electric signal in a predetermined phase range reduced as the pseudo partial-discharge signal (see page 8).
Regarding claim 3, Pakr discloses the device of claim 2, wherein the processor is configured to generate the data with the electric signal having an absolute value equal to or larger than a predetermined magnitude reduced as the pseudo partial-discharge signal (see page 8).
Regarding claim 4, Pakr discloses the device of claim 1, wherein the processor is configured to generate the data with the electric signal in a predetermined frequency range reduced as the pseudo partial-discharge signal (see pages 8 and 11).
Regarding claim 6, Pakr discloses the device of claim 1, wherein the electric signal is a plurality of signals having phase differences, and the processor is configured to change the phases of the signals to be coincident with each other (see pages 4, 8).
Regarding claim 7, Pakr discloses the device of claim 1, wherein the processor is configured to generate the data as image data having numerical values arranged two-dimensionally, and the device further comprises a display controller configured to cause a display device to display the image data (see at least in pages 4, 8).
Regarding claim 8, Pakr discloses the device of claim 1, further comprising a data acquirer configured to acquire the electric signal measured by a sensor attached to or around an electric device (see page 6).
Regarding claim 9, Pakr discloses the device of claim 8, wherein the electric device is at least any of a generator, an electric motor, an inverter device, a switch gear, and a cable (a power cable in page 1), and the electric signal is a signal indicating at least any of a charge amount, a current, and a voltage corresponding to a phase of an applied voltage applied to the electric device (see Figs. 5-6).
Regarding claim 10, Pakr discloses the device of claim 9, further comprising an electric signal generator configured to perform at least either generation of an electric signal for learning or acquiring of the electric signal for learning via the data acquirer (see page 6 and Fig. 4).
Regarding claim 11, Pakr discloses the device of claim 10, wherein the processor is configured to generate the data based on at least either the electric signal measured or the electric signal for learning (see Fig. 4 and page 7).
Regarding claim 12, Pakr discloses the device of claim 11, wherein the electric signal generator is configured to generate the electric signal by using at least one of test data that simulates a condition of insulation deterioration and a result of simulation (see page 8).
Regarding claim 13, Pakr discloses the device of claim 12, further comprising a data augmenter configured to increase number of data pieces of the data by combining at least either the electric signal measured or the electric signal for learning (see page 8).
Regarding claim 19, Pakr discloses a partial-discharge diagnostic method (performing by a partial discharge detection apparatus in Fig. 4) of performing determination of a factor of partial discharge in an insulator (“by diagnosing the occurrence of partial discharge at an early stage, it is essential to monitor the degree of deterioration of the power cable insulator and to diagnose the partial discharge to prevent failure of power equipment”, see page 2), comprising: generating data in a predetermined format (pulse sequence input data is collected from signal detection by a sensor, a conversion process from pulse sequence data of pulse magnitude and phase to a feature vector data…, see page 4) with a pseudo partial-discharge signal reduced in an electric signal varying with a phase (detection of partial discharge occurrence is not significantly affected by noise-filtering performance, thus influenced by noise such as a pseudo partial-discharge signal is reduced, see page 3); and generating a learning model (generating by a model management block 140 comprised a model generator 142 and the model learning unit 144, in Fig 4) that performs, based on the data, at least either determination whether the partial discharge is present or determination of the factor of the partial discharge (a model management block 140 for generating and learning a steady-state pattern model with the feature vector stream; and a comparison determination block 160 that determines an occurrence time of partial discharge using the steady-state pattern model. The partial discharge pattern classification model storage unit 148 for storing the partial discharge pattern classification model according to the related art may be further included. In this case, the model generator 42 may generate a partial discharge pattern classification model using data determined as abnormal (partial discharge is present) during actual use by the comparison determination block 160…. See page 7).
Regarding claim 20, Pakr discloses a partial-discharge diagnostic system (a partial discharge detection apparatus in Fig. 4) that performs determination of a factor of partial discharge in an insulator (“by diagnosing the occurrence of partial discharge at an early stage, it is essential to monitor the degree of deterioration of the power cable insulator and to diagnose the partial discharge to prevent failure of power equipment”, see page 2), comprising: a measuring instrument configured to measure an electric signal by a sensor attached to or around an electric device (the signal measuring unit 121 is implemented as an input circuit that receives measurement signals of partial discharge detection sensors such as HFCT, UHF, and thin electrodes, and the pulse sequence collecting unit 122 includes the inputted measurement signals.); and a partial-discharge diagnostic device (a partial discharge detection apparatus in Fig. 4), wherein the partial-discharge diagnostic device includes a processor (a data processing device 124, see page 6 and Fig. 4) configured to generate data in a predetermined format (pulse sequence input data is collected from signal detection by a sensor, a conversion process from pulse sequence data of pulse magnitude and phase to a feature vector data…, see page 4) with a pseudo partial-discharge signal reduced in an electric signal varying with a phase (detection of partial discharge occurrence is not significantly affected by noise-filtering performance, thus influenced by noise such as a pseudo partial-discharge signal is reduced, see page 3), and a learning model generator (a model management block 140 comprise a model generator 142 and the model learning unit 144, in Fig 4) configured to generate a learning model that determines, based on the data, at least either the factor of the partial discharge or whether the partial discharge is present (a model management block 140 for generating and learning a steady-state pattern model with the feature vector stream; and a comparison determination block 160 that determines an occurrence time of partial discharge using the steady-state pattern model. The partial discharge pattern classification model storage unit 148 for storing the partial discharge pattern classification model according to the related art may be further included. In this case, the model generator 42 may generate a partial discharge pattern classification model using data determined as abnormal (partial discharge is present) during actual use by the comparison determination block 160…. See page 7).
8. Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yamada et al. (U.S. Pub. 2024/0183894; hereinafter “Yamada”).
Regarding claim 1, Yamada discloses a partial-discharge diagnostic device (a partial discharge determination apparatus in Fig. 1-4) capable of performing determination of a factor of partial discharge in an insulator (“a partial discharge determination apparatus that determines insulation degradation of an underground power transmission cable”, see [0001]), comprising: a processor (a partial discharge processing device 6, see Figs. 1, 3-4) configured to generate data in a predetermined format (The φ-q data generation unit 50 generates φ-q data D2 as illustrated in FIG. 6 in which a charge amount q of an applied voltage phase φ is represented for each applied voltage cycle based on the read measurement data D1, the φ-q data D2 is two-dimensional array data in which the horizontal axis represents an applied voltage phase angle number and the vertical axis represents the applied voltage cycle. The applied voltage phase angle number is an integer from 0 to 95, and means a phase angle in increments of 3.75° from 0° to 356.25… See [0062-65] and Figs. 4-20) with a pseudo partial-discharge signal reduced in an electric signal varying with a phase (the noise reduction unit 51 generates noise-reduced φ-q data D3 in which a noise component is reduced based on statistical information from the φ-q data D2 provided from the φ-q data generation unit 50, and outputs the generated noise-reduced φ-q data D3 to the φ-q-n data generation unit 52… See [0063-64]); and a learning model generator (a learning model generation unit 54 in Fig. 4) configured to generate a learning model that determines, based on the data, at least either the factor of the partial discharge or whether the partial discharge is present (by using the learning φ-q-n data D5, the learning model generation unit 54 performs machine learning using a phase angle distribution pattern of the charge amount of the partial discharge pulse PL, a phase angle distribution pattern of the noise, and a phase angle distribution pattern of the charge amount of the partial discharge pulse PL with noise for each degree of degradation of the underground power transmission cable 2, and generates a learning model 55 as a learning result. The partial discharge determination unit 56 generates φ-q-n data for determination whether or not the partial discharge pulse PL has been generated by inputting the generated determination φ-q-n data to the learning model 55, and determines the degree of degradation (the progress degree of the partial discharge) of the target underground power transmission cable 2 in a case where the partial discharge pulse PL has been generated…. See [0066-67]).
Regarding claim 2, Yamada discloses the device of claim 1, wherein the processor is configured to generate the data with the electric signal in a predetermined phase range reduced as the pseudo partial-discharge signal (see [0064, 66, 68, 83, 87]).
Regarding claim 3, Yamada discloses the device of claim 2, wherein the processor is configured to generate the data with the electric signal having an absolute value equal to or larger than a predetermined magnitude reduced as the pseudo partial-discharge signal (see [0012-, 15, 97, 101, 105]).
Regarding claim 4, Yamada discloses the device of claim 1, wherein the processor is configured to generate the data with the electric signal in a predetermined frequency range reduced as the pseudo partial-discharge signal (see [0013-14, 97, 98, 100]).
Regarding claim 5, Yamada discloses the device of claim 1, wherein the electric signal is a plurality of signals having phase differences, a partial-discharge signal at a timing of generation with respect to one of the signals corresponds to the pseudo partial-discharge signal generated with respect to different one of the signals, and the processor is configured to reduce, with respect to the different signal, the electric signal in a phase range based on a timing at which the partial-discharge signal is generated with respect to the one signal, as the pseudo partial-discharge signal (see [0017-18, 56, 62-63, 73, 95-96, 102-106]).
Regarding claim 6, Yamada discloses the device of claim 1, wherein the electric signal is a plurality of signals having phase differences, and the processor is configured to change the phases of the signals to be coincident with each other (see Figs. 5-14).
Regarding claim 7, Yamada discloses the device of claim 1, wherein the processor is configured to generate the data as image data having numerical values arranged two-dimensionally, and the device further comprises a display controller configured to cause a display device to display the image data (see at least in [0065]).
Regarding claim 8, Yamada discloses the device of claim 1, further comprising a data acquirer configured to acquire the electric signal measured by a sensor attached to or around an electric device (a data acquisition unit 22 in Fig. 4).
Regarding claim 9, Yamada discloses the device of claim 8, wherein the electric device is at least any of a generator, an electric motor, an inverter device, a switch gear, and a cable (a cable 20 in Fig. 1), and the electric signal is a signal indicating at least any of a charge amount, a current, and a voltage corresponding to a phase of an applied voltage applied to the electric device (see Fig. 5).
Regarding claim 10, Yamada discloses the device of claim 9, further comprising an electric signal generator configured to perform at least either generation of an electric signal for learning or acquiring of the electric signal for learning via the data acquirer (see Fig. 4).
Regarding claim 11, Yamada discloses the device of claim 10, wherein the processor is configured to generate the data based on at least either the electric signal measured or the electric signal for learning (see Fig. 4 and [0064-67]).
Regarding claim 12, Yamada discloses the device of claim 11, wherein the electric signal generator is configured to generate the electric signal by using at least one of test data that simulates a condition of insulation deterioration and a result of simulation (see [0064-67]).
Regarding claim 13, Yamada discloses the device of claim 12, further comprising a data augmenter configured to increase number of data pieces of the data by combining at least either the electric signal measured or the electric signal for learning (see [0077-81]).
Regarding claim 14, Yamada discloses the device of claim 13, further comprising a feature-amount extractor configured to extract a feature amount indicating a range of the partial-discharge signal based on the electric signal when insulation has deteriorated, generated by the electric signal generator, wherein the processor is configured to reduce the pseudo partial-discharge signal in the electric signal based on the feature amount (see at least in [0013]).
Regarding claim 15, Yamada discloses the device of claim 14, wherein the processor is configured to be able to adjust a signal intensity range of the electric signal (see [0046]).
Regarding claim 16, Yamada discloses the device of claim 15, wherein the electric signal is a signal indicating a charge amount corresponding to a phase of an applied voltage applied to the electric device, and the processor is configured to divide each of the phase range and a range of the charge amount into a plurality of sections and generate a generation frequency of the charge amount for each of regions defined by the sections of the phase range and the sections of the range of the charge amount, to obtain the data (see [0075-77 and 90-91]).
Regarding claim 17, Yamada discloses the device of claim 16, wherein the processor is configured to convert the generation frequency nonlinearly (see [0057-58]).
Regarding claim 18, Yamada discloses the 18. The device of claim 10, wherein the processor is configured to generate the data based on the electric signal acquired by the data acquirer when determination is performed, and the device further comprises a partial discharge determiner configured to perform at least either determination whether the partial discharge is present or determination of a generation factor by using the learning model based on the data (see Fig. 4 and [0062-67]).
Regarding claim 19, Yamada discloses a partial-discharge diagnostic method (performing by a partial discharge determination apparatus in Fig. 1-4) of performing determination of a factor of partial discharge in an insulator (“a partial discharge determination apparatus that determines insulation degradation of an underground power transmission cable”, see [0001]), comprising: generating data in a predetermined format (The φ-q data generation unit 50 generates φ-q data D2 as illustrated in FIG. 6 in which a charge amount q of an applied voltage phase φ is represented for each applied voltage cycle based on the read measurement data D1, the φ-q data D2 is two-dimensional array data in which the horizontal axis represents an applied voltage phase angle number and the vertical axis represents the applied voltage cycle. The applied voltage phase angle number is an integer from 0 to 95, and means a phase angle in increments of 3.75° from 0° to 356.25… See [0062-65] and Figs. 4-20) with a pseudo partial-discharge signal reduced in an electric signal varying with a phase (the noise reduction unit 51 generates noise-reduced φ-q data D3 in which a noise component is reduced based on statistical information from the φ-q data D2 provided from the φ-q data generation unit 50, and outputs the generated noise-reduced φ-q data D3 to the φ-q-n data generation unit 52… See [0063-64]); and generating a learning model (generating by a learning model generation unit 54 in Fig. 4) that performs, based on the data, at least either determination whether the partial discharge is present or determination of the factor of the partial discharge (by using the learning φ-q-n data D5, the learning model generation unit 54 performs machine learning using a phase angle distribution pattern of the charge amount of the partial discharge pulse PL, a phase angle distribution pattern of the noise, and a phase angle distribution pattern of the charge amount of the partial discharge pulse PL with noise for each degree of degradation of the underground power transmission cable 2, and generates a learning model 55 as a learning result. The partial discharge determination unit 56 generates φ-q-n data for determination whether or not the partial discharge pulse PL has been generated by inputting the generated determination φ-q-n data to the learning model 55, and determines the degree of degradation (the progress degree of the partial discharge) of the target underground power transmission cable 2 in a case where the partial discharge pulse PL has been generated…. See [0066-67]).
Regarding claim 20, Yamada discloses a partial-discharge diagnostic system (a partial discharge determination apparatus in Fig. 1-4) that performs determination of a factor of partial discharge in an insulator (“a partial discharge determination apparatus that determines insulation degradation of an underground power transmission cable”, see [0001]), comprising: a measuring instrument (a partial discharge measurement apparatus 5 in Fig. 1) configured to measure an electric signal by a sensor (a sensor 3 in Fig. 1) attached to or around an electric device (an electrical cable 2 in Fig. 1); and a partial-discharge diagnostic device (a partial discharge determination apparatus 1 in Fig. 1), wherein the partial-discharge diagnostic device includes a processor (a partial discharge processing device 6, see Figs. 1, 3-4) configured to generate data in a predetermined format (The φ-q data generation unit 50 generates φ-q data D2 as illustrated in FIG. 6 in which a charge amount q of an applied voltage phase φ is represented for each applied voltage cycle based on the read measurement data D1, the φ-q data D2 is two-dimensional array data in which the horizontal axis represents an applied voltage phase angle number and the vertical axis represents the applied voltage cycle. The applied voltage phase angle number is an integer from 0 to 95, and means a phase angle in increments of 3.75° from 0° to 356.25… See [0062-65] and Figs. 4-20) with a pseudo partial-discharge signal reduced in an electric signal varying with a phase (detection of partial discharge occurrence is not significantly affected by noise-filtering performance, thus influenced by noise such as a pseudo partial-discharge signal is reduced, see page 3), and a learning model generator (a learning model generation unit 54 in Fig. 4) configured to generate a learning model that determines, based on the data, at least either the factor of the partial discharge or whether the partial discharge is present (by using the learning φ-q-n data D5, the learning model generation unit 54 performs machine learning using a phase angle distribution pattern of the charge amount of the partial discharge pulse PL, a phase angle distribution pattern of the noise, and a phase angle distribution pattern of the charge amount of the partial discharge pulse PL with noise for each degree of degradation of the underground power transmission cable 2, and generates a learning model 55 as a learning result. The partial discharge determination unit 56 generates φ-q-n data for determination whether or not the partial discharge pulse PL has been generated by inputting the generated determination φ-q-n data to the learning model 55, and determines the degree of degradation (the progress degree of the partial discharge) of the target underground power transmission cable 2 in a case where the partial discharge pulse PL has been generated…. See [0066-67]).
Prior Art of Record
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dehghan (U.S Pub. 2017/0168024) discloses a partial discharge monitoring system for electrical machines (see specification for more details).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THANG LE whose telephone number is (571)272-9349. The examiner can normally be reached on Monday thru Friday 7:30AM-5:00PM EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Huy Phan can be reached on (571) 272-7924. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THANG X LE/Primary Examiner, Art Unit 2858
12/20/2025