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
The information disclosure statement (IDS) submitted on 12/14/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Claims 1-2 and 5-9 are pending. Claims 1-2, 5-6, and 8-9 are amended. Claim 7 is amended as being dependent on Claim 1. Claims 3-4 are cancelled.
Applicant’s arguments, filed 07/14/2026, with respect to drawing objections have been fully considered and are persuasive. The objections to the drawings have been withdrawn. New drawing objections have been issued for Figures 1 and 3.
Applicant’s arguments, filed 07/14/2026, with respect to specification objections have been fully considered. The objections of the specification have been withdrawn. New objections to the specification have been added for Para[0039], Para[0043] and Page 24 of the specification.
Applicant’s arguments, filed 07/14/2026, with respect to claim objections 3 have been fully considered and are persuasive. The objection of Claim 3 has been withdrawn. New claim objections have been issued for Claims 1 and 9.
Applicant’s arguments, filed 07/14/2026, with respect to claim rejections under 35 U.S.C 112(b) for Claims 1-9 have been fully considered and are persuasive. The rejection of Claims 1-9 have been withdrawn. New 35 U.S.C 112(b) rejections are issued in light of applicant’s amendments for Claims 1-2, 5-6, and 8-9.
Applicant’s arguments, filed 07/14/2026, with respect to claim rejections under 35 U.S.C 103 for Claims 1-9 have been fully considered and are not persuasive. A new rejection under 35 U.S.C 103 is applied for Claims 1, 5, 7-9 under prior arts Schweitzer (US 20090091441 A1), Kimura (US 20120257779 A1), Abe (US 7720235 B2), and Degges (US 9830924 B1), for Claim 2 under prior arts Schweitzer (US 20090091441 A1), Kimura (US 20120257779 A1), Abe (US 7720235 B2), Degges (US 9830924 B1), and Nandy (US 9711144 B2), and Claim 6 under Schweitzer (US 20090091441 A1), Kimura (US 20120257779 A1), Abe (US 7720235 B2), Degges (US 9830924 B1), and Park (US 20200388289 A1) in light of applicant’s amendments.
With regards to Claims 1 and 8-9, Applicant argues that the combination of Schweitzer, Kimura, Abe, Engh, Park, and Degges allegedly fail to teach the limitations of Claims 1 and 8-9. Applicant argues that Degges uses the Gaussian mixture model as a classifier for performing speech recognition but does not teach that an event model is expressed as a Gaussian mixture model nor that each event model for each respective event of a plurality of events is represented by a respective Gaussian mixture model as the respective first feature amount for the respective event.
Examiner respectfully disagrees. Applicant’s amended claims 1 and 8-9 recite “manage, in association with each respective event, a respective event model represented by a respective Gaussian mixture model as the respective first feature amount for the respective event”. Under a broadest reasonable interpretation, the claims are directed to a single Gaussian mixture model that represents the respective event models in Degges. As Degges classifies acoustic data using the Gaussian Mixture Model, the acoustic data in Degges is represented by the Gaussian Mixture Model for each speech data obtained in Degges, making the Gaussian Mixture Model a first feature amount for each of the speech data obtained in Degges.
Drawings
The drawings are objected to because
Fig. 1, 121 is labelled as “ACOUSTIC WAVEFORM”, which should be “ACOUSTIC WAVEFORM STORAGE AREA”.
Fig. 1, 122 is labelled as “DELAY CALCULATION WORK”, which should be “DELAY CALCULATION WORK AREA”.
Fig.3, 121 is labelled as “ACOUSTIC WAVEFORM” in both instances, which should both be “ACOUSTIC WAVEFORM STORAGE AREA”.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
Para[0039] line 2 and Para[0043] line 7 recite “acoustic waveform 121”, which should be “acoustic waveform storage area 121”.
Para[0043] lines 6-7 recite “acoustic sensor information”, which should be “acoustic sensor information 3121”.
Page 24 recites “115 diagnosis process”, which should be “115 state diagnosis process”.
Page 24 recites “121 acoustic waveform”, which should be “121 acoustic waveform storage area”.
Page 24 recites “122 delay calculation work”, which should be “122 delay calculation work area”.
Appropriate correction is required.
Claim Objections
Claims 1 and 9 are objected to because of the following informalities:
Claim 1 in page 3 lines 15-16 recite “first acoustic signal”, which should be “a first acoustic signal”.
Claim 9 in page 9 line 6 recites “the electric signal”, which should be “the electrical signal”. Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
a first feature amount extraction processing unit, a model creation unit, a second feature amount extraction processing unit, a similarity calculation processing unit, and a diagnosis processing unit in claim 8, which has the corresponding structure of being a part of a central processing unit or a processor, see Claim 1,
an event model storage unit in claim 8, which has the corresponding structure of being a storage device, see Claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claims 1-9 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.
Claims 1 and 8-9 recite “the respective event” in page 4 line 14 (for Claim 1), page 8 line 8 (For Claim 8), and page 10 line 12 (For Claim 9), even though “a respective event” is recited in Claims 1 and 8-9 in page 4 lines 10-11 (for Claim 1), page 8 lines 4-5 (For Claim 8), and page 10 lines 8-9 (For Claim 9) and “each respective event” is recited in Claims 1 and 8-9 in page 4 line 12 (for Claim 1), page 8 line 6(For Claim 8), and page 10 line 10 (For Claim 9). It is unclear which respective event is “the respective event”. Therefore, independent Claim 1 and dependent Claims 2, 5-7, and independent Claims 8-9 are indefinite.
Claims that depend on the above rejected claims are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
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, 5, and 7-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schweitzer (US 20090091441 A1) in view of Kimura (US 20120257779 A1), Abe (US 7720235 B2), and Degges (US 9830924 B1).
With regards to Claim 1, Schweitzer teaches
An acoustic diagnosis device comprising (See Abstract):
a microphone (See Figure 1, the microphone 104) configured to convert a sound pressure from a circuit breaker (See Figure 1 the Power System Device 102, which can be a circuit breaker, see Para[0028] “a typical power substation, which could include, for example, a monitoring intelligent electronic device, such as a distance relay, which controls one or more circuit breakers protecting a power line segment”) into an electrical signal (See Para[0022] “The microphone produces an analog electric signal (i.e. an electrical signal) corresponding to a sensed audio signal (i.e. a sound pressure)”);
an A/D converter configured to convert the sound pressure converted into the electric signal by the microphone into a digital value to obtain an acoustic signal (See Para[0022] “The electric signal is converted to digital form (i.e. a digital value, which corresponds to an acoustic signal output from ADC 106 in Figure 1) by analog-to-digital converter ("ADC") 106, which samples the signal at a constant frequency, such as 8 kHz”);
a central processing unit (See Figure 1 the processor 108); and
a storage device (See Figure 1 the memory 110),
wherein the central processing unit is configured by an executable program to (See Fig. 7, which contains the method steps for a set of instructions for the processor 108 to follow. See Para[0028] “For example, the HMI 118 could present the user with instructions, directing the user to produce certain events corresponding to the steps of FIG. 7.” Therefore the HMI comprises an executable program to make the processor conduct method steps in Fig. 7):
during a normal operation of the circuit breaker for various events of the circuit breaker, extract a first feature amount of first acoustic signal (See Para[0011] “the processor will store exemplary signals in a memory for comparison with acquired digital signals (i.e. the processor extracts via the comparison of the exemplary signals to the digital signals to obtain a first feature amount for a first acoustic signal for one of the exemplary signals). Stored exemplary signals can include the ambient sound signature present at the microphone as well as the sound signatures of normal events, such as breaker operations (i.e. during a normal operation of the circuit breaker for various events of the circuit breaker), and abnormal events, such as arcing (i.e. the first feature amount is a normal operation sound signature of the circuit breaker for a sound signature corresponding to the first acoustic signal)”), the delay being generated based on a relative distance between the circuit breaker and the microphone (See Para[0022] “the microphone may be placed near a device to be monitored”. Therefore, the microphone is set a certain distance from the device, which will cause a delay of the signal received by the microphone to be generated based on a relative distance between the circuit breaker and the microphone.), and
create a plurality of event models (See Para[0011] “the processor will store exemplary signals (i.e. event models) in a memory”. Examiner notes that the memory 110 actually stores the event models, the processor assigns these signals (i.e. interpreted here as create) as exemplary signals for future comparison.) for managing data regarding a respective first feature amount (See Para[0011] “Stored exemplary signals can include the ambient sound signature present at the microphone as well as the sound signatures of normal events” where the sound signature of normal operation of opening for each exemplary signal is a respective first feature amount i.e. they manage data regarding a respective first feature amount) corresponding to a acoustic signal for each event (See Para[0011] “Stored exemplary signals can include the ambient sound signature present at the microphone as well as the sound signatures of normal events”. In addition, these exemplary signals contain, or manage, the data associated with the corresponding open operation of the circuit breaker. Therefore each exemplary signal contains information about normal operations, making each first feature amount of each exemplary signal correspond to a acoustic signal, which are the exemplary signals, for each event),
wherein the storage device stores the plurality of event models (See Para[0011] “the processor will store exemplary signals in a memory (i.e. the memory 110)”. The plurality of event models are the exemplary signals.), and
wherein the plurality of event models stored in the storage device enable the central processing unit to (See Para[0011] “Generally, the processor will store exemplary signals in a memory for comparison with acquired digital signals.” Therefore, the exemplary signals, which are the plurality of event models, are stored in the storage device via the memory, which allows the processor to compare signals (i.e. wherein the plurality of event models stored in the storage device enable the central processing unit to:)):
manage, in association with a respective event ID, a respective event model obtained by extracting the first feature amount of a respective first acoustic signal collected by the microphone, wherein the respective event ID identifies a respective event of the various events of the circuit breaker (See Figures 2-5, where each figure corresponds to a different event of a circuit breaker, where Fig. 2 is for background noise, Fig. 3, is for a close operation, Fig. 4 is for a trip operation, and Fig. 5 is for arcing. For each event, the data is identified via the filename, which functions as a respective event ID (e.g. for Fig. 2 the event ID is background.DAT, for Fig. 3 the event ID is close.DAT, etc.). Therefore, each respective event model, as shown by the graphs in Figs. 2-5, are managed in association with a respective event ID. These graphs correspond to exemplary signals which are the respective first acoustic signals with a corresponding first feature amount (for closing or tripping in Figures 3 or 4 which is extracted and shown in the figures) for each figure, see Para[0024] “A system embodying the disclosed invention could store digital representations of the signals depicted in FIGS. 2-5, and use those signals as exemplary models”. Each of these exemplary signals are collected via a microphone, see Para[0011] “Stored exemplary signals can include the ambient sound signature present at the microphone as well as the sound signatures of normal events, such as breaker operations” and see Fig. 1 the microphone 104. Therefore, the respective event ID identifies a respective event of the various events of the circuit breaker.)
manage, in association with each respective event, a respective event model (See Para[0011] “the processor will store exemplary signals (i.e. the event models) in a memory for comparison with acquired digital signals (i.e. manage, in association with each respective event, a respective event model as it stores the exemplary signals, which are the respective event models, in the memory). Each exemplary signal corresponds to an event, such as normal operations of the circuit breaker, see above.)
the central processing unit is further configured to (See Para[0011] “Generally, the processor will store exemplary signals in a memory for comparison with acquired digital signals.”. Therefore, the processor is further configured to do multiple steps in the process of comparison of acquired digital signals.):
during diagnosis of the circuit breaker, extract a second feature amount of a second acoustic signal (See Para[0011] “The sound signatures may be captured at the installation with the aid of a user or through the use of an automation system. A pattern matching algorithm is used to determine whether or not an acquired digital signal matches a recorded exemplary event (i.e. during diagnosis of the circuit breaker, extract a second feature amount of a second acoustic signal, the second feature amount is an audio signature of the acquired digital signal, which is the second acoustic signal, corresponding to an operation of a circuit breaker, which is then compared to the first feature amount of the exemplary signals via the exemplary events).”), the delay of the second acoustic signal being generated from the relative distance between the circuit breaker and the microphone (See Para[0022] “the microphone may be placed near a device to be monitored”. Therefore, the microphone is set a certain distance from the device, which will cause a delay of the second acoustic signal received by the microphone and generated based on a relative distance between the circuit breaker and the microphone.),
calculate a similarity between the second feature amount of the second acoustic signal during the diagnosis (See Para[0024] “pattern matching algorithms (i.e. which calculate a similarity) could be applied to the time-domain representation of the captured signal”. The captured signal contains the audio signature for the second acoustic signal of the second feature amount.) and the first feature amount in the plurality of event models stored in the storage device for each time (See Para[0024] “the processor would compare a recently captured time signal (i.e. the comparison happens at each time) to the exemplary signal (i.e. the audio signatures which contain normal operations of the circuit breaker)” and “The same operation could be performed to determine if any of the events of FIGS. 3-5, i.e., contact closure, trip, or arcing, were occurring as well (i.e. compare using the plurality of event models, where the plurality in this case is the opening and closing specifically of the circuit breaker).”),
diagnose the circuit breaker based on a plurality of comparisons (See Para[0024] “The same operation could be performed to determine if any of the events of FIGS. 3-5, i.e., contact closure, trip, or arcing, were occurring as well (i.e. compare and diagnose using the plurality of event models, where the plurality in this case is the opening and closing specifically of the circuit breaker).” In other words, the diagnosis of the circuit breaker is based on a plurality of comparisons of the digital signal to the plurality of event models.).
Schweitzer is silent to the language of
wherein the feature amount is a timing synchronized based on a sensor signal from a sensor
in which a delay of the acoustic signal collected by the microphone is corrected based on a difference with the sensor signal,
and corrected for the delay of the acoustic signal, and
a respective event model represented by a respective Gaussian mixture model as the respective first feature amount for the respective event, and
in which a delay of the acoustic signal collected by the microphone is corrected based on a difference with a second sensor signal from the sensor, and
estimate the time at which the similarity becomes highest, as an occurrence timing of each event, and
diagnose the circuit breaker based on a plurality of estimated occurrence timings.
Kimura teaches
wherein the feature amount is a timing synchronized based on a sensor signal from a sensor (See Para[0128] “The image pickup apparatus 800 of this embodiment corrects the phase difference of the sound input to the microphone 106b by the delay units 805a and 805b, and the applicative delay units 806a and 806b. More specifically, the delay units 805a and 805b delay the input audio signals by predetermined amounts (i.e. wherein the feature amount is a timing synchronized as the predetermined amounts refer to the time delay of the microphone which is corrected or synchronized via the phase difference correction).” See also Para[0127] “the delay detection unit 804 detects a delay or an advance (phase difference) of the audio signals input to the microphones 502a and 502b (i.e. based on a sensor signal from a sensor, where the sensor signal from a sensor is the signal from microphone 502b, see Fig. 8. ), and the delay amount of the sound acquired by the microphone 106b is adjusted on the basis of the detected phase difference.”)
in which a delay of the acoustic signal collected by the microphone is corrected based on a difference with the sensor signal (See Para[0128] “The image pickup apparatus 800 of this embodiment corrects the phase difference (i.e. a delay, which is based on a difference with the sensor signal as the phase difference tracks the difference between the audio signals from microphones 502a and 502b, see Fig. 8. The acoustic signal collected by the microphone is the signal output by microphone 502a, the delay of which is corrected) of the sound input to the microphone 106b”),
and corrected for the delay of the acoustic signal (See above), and
in which a delay of the acoustic signal collected by the microphone is corrected based on a difference with a second sensor signal from the sensor (See Para[0128] “The image pickup apparatus 800 of this embodiment corrects the phase difference (i.e. a delay, which is based on a difference with a second sensor signal from the sensor as the phase difference tracks the difference between the audio signals (where the second sensor signal from the sensor is the audio signal from the microphone 502b, which is a sensor) from microphones 502a and 502b, see Fig. 8. The acoustic signal collected by the microphone is the signal output by microphone 502a, the delay of which is corrected) of the sound input to the microphone 106b”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Schweitzer wherein the feature amount is a timing synchronized based on a sensor signal from a sensor and in which a delay of the acoustic signal collected by the microphone is corrected based on a difference with the sensor signal, and corrected for the delay of the acoustic signal, and in which a delay of the acoustic signal collected by the microphone is corrected based on a difference with a second sensor signal from the sensor like in Kimura in order to more accurately diagnose the circuit breaker in Schweitzer when comparing the audio signals from a reference sensor.
Schweitzer and Kimura are silent to the language of
a respective event model represented by a respective Gaussian mixture model as the respective first feature amount for the respective event, and
estimate the time at which the similarity becomes highest, as an occurrence timing of each event, and
diagnose the circuit breaker based on a plurality of estimated occurrence timings.
Abe teaches
estimate the time at which the similarity becomes highest, as an occurrence timing of each event (See Abstract “The method finds a time difference, an expansion factor, and a similarity in one region (i.e. estimate the occurrence timing) in which the maximum similarity (i.e. when the similarity becomes highest) as the square of the correlation is obtained”), and
diagnose the circuit breaker based on a plurality of estimated occurrence timings (See Figure 1 the similarity determining unit 16 and Column 11 lines 59-61 “The similarity determining unit 16 performs similarity determination by comparing the maximum similarity sm with a threshold value sthsd.”. Therefore, the similarity determining unit 16 diagnoses whether a similarity sufficiently crosses a threshold based on the region of time, where the similarity is the highest as an occurrence timing.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Schweitzer and Kimura wherein estimate the time at which the similarity becomes highest, as an occurrence timing of each event, and diagnose the circuit breaker based on a plurality of estimated occurrence timings is done like in Abe. Examiner notes that Abe calculates one occurrence timing as Abe compares the tested acoustic signal against one event model (which is the second signal in Abe). Schweitzer can use the method in Abe to calculate the occurrence timings of the tested acoustic signal with each event model (the exemplary signals in Schweitzer), and using the corresponding occurrence timings to diagnose the circuit breaker, which will allow for a more quantifiable and consistent method to diagnose the circuit breaker.
Schweitzer, Kimura, and Abe are silent to the language of,
a respective event model represented by a respective Gaussian mixture model as the respective first feature amount for the respective event.
Degges teaches
a respective event model represented by a respective Gaussian mixture model as the respective first feature amount for the respective event (See Column 4 lines 13-15 “ The speech recognition engine 232 of the voice interaction module 230 processes received audio data. The speech voice interaction module may part of a classifier system 252 configured to perform speech recognition. The classifier system 252 may be, for example, a Support Vector Machine (SVM), although other machine learning techniques might be used instead of or to augment SVM. The classifier system 252 may utilize Hidden Markov Models (HMMs), Gaussian Mixture Models (GMMs)”. Therefore the speech data, which comprises the respective event, is classified via Gaussian Mixture Models, making each speech data a respective event model as it is represented by a respective Gaussian Mixture Model as Degges recites the Gaussian Mixture Model as a classification option. The Gaussian Mixture Model used to classify each speech data therefore acts as a respective first feature amount for each speech data as it classifies the data based on their features.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Schweitzer, Kimura, and Abe wherein a respective event model represented by a respective Gaussian mixture model as the respective first feature amount for the respective event is used like in Degges for managing feature amounts in order to have a more systematic and uniform methodology to classify and identify features in an acoustic signal.
With regards to Claim 5, Schweitzer, Kimura, Abe, and Degges teach the limitations of Claim 1. Schweitzer further teaches
wherein the first or second feature amount of the first or second acoustic signal, respectively, extracted by the central processing unit is a representation of feature amount vectors of the respective first or second acoustic signal (See Para[0011] “the processor (i.e. central processing unit) will store exemplary signals (i.e. one of which is the first acoustic signal) in a memory for comparison with acquired digital signals (i.e. one of which is the second acoustic signal, and both the first and second acoustic signals are extracted via the processor acquiring the exemplary signals and the digital signals to obtain a first feature amount and a second feature amount respectively, see Claim 1 above). Stored exemplary signals can include the ambient sound signature present at the microphone as well as the sound signatures of normal events, such as breaker operations, and abnormal events, such as arcing (i.e. the first feature amount and the second feature amount for the first and second acoustic signal respectively is a normal operation sound signature of the circuit breaker, and the first feature amount and the second feature amount is a representation of the feature amount vectors for the respective first and second acoustic signal, which are the individual data points associated with the sound signatures)”).
Schweitzer, Kimura, and Abe are silent to the language of,
an average value of feature amount vectors of the respective first or second acoustic signal.
Degges teaches
an average value of feature amount vectors of the respective first or second acoustic signal (See Column 8 lines 3-6 “The noise envelope 610 may be the average of all volume over time (i.e. an average value of the acoustic signal, where the individual values constitute feature amount vectors of a first acoustic signal, which is the noise envelope 610), or may be calculated by classifier system 252 based on an average amplitude of captured audio that is not recognized as speech”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Schweitzer, Kimura, and Abe wherein an average value of feature amount vectors of the respective first or second acoustic signal is used like in Degges for extracting feature amounts in order to have a more systematic and uniform methodology to classify and identify features in an acoustic signal.
With regards to Claim 7, Schweitzer, Kimura, Abe, and Degges teach the limitations of Claim 1. Schweitzer further teaches
the storage device stores a diagnosis parameter that manages a maximum value and a minimum value of an interval between specific events of the circuit breaker as threshold values (See Para[0022] “The digital signals are then stored by processor 108 in memory 110. When a sufficient time-period (i.e. a diagnosis parameter) has elapsed, and a complete window of digital samples have been captured (i.e. a maximum and minimum time is defined by the time-period, which are threshold times for obtaining the acoustic signal)”. Therefore, since the digital signals are stored in the storage device, and the digital signals are obtained via the time-period, the time-period of capturing the data is also stored in the memory 110.), and
the central processing unit diagnoses the circuit breaker based on the plurality of estimated occurrence timings (See Claim 1 above) and the diagnosis parameter (Examiner notes that the event models, which are the exemplary signals, are captured via the time-period of capture, hence the diagnosis of the circuit breaker also depends on the time-period of capture of the exemplary signals. In addition, for the combination of Schweitzer, Kimura, and Abe in Claim 1, the diagnosis of the circuit breaker done by Schweitzer still maintained the diagnosis parameter despite the modification of Schweitzer with Abe to diagnose with the estimated occurrence timings, as Schweitzer captures the exemplary signals via the diagnosis parameter, and then diagnoses the circuit breaker through comparison of the exemplary signals with the captured digital signal that is tested.).
With regards to Claim 8, Schweitzer teaches
An acoustic diagnosis device comprising (See Abstract):
a microphone (See Figure 1, the microphone 104) configured to convert a sound pressure from a circuit breaker (See Figure 1 the Power System Device 102, which can be a circuit breaker, see Para[0028] “a typical power substation, which could include, for example, a monitoring intelligent electronic device, such as a distance relay, which controls one o r more circuit breakers protecting a power line segment”) into an electrical signal (See Para[0022] “The microphone produces an analog electric signal (i.e. an electric signal) corresponding to a sensed audio signal (i.e. a sound pressure)”);
an A/D converter configured to convert the sound pressure converted into the electric signal by the microphone into a digital value to obtain an acoustic signal (See Para[0022] “The electric signal is converted to digital form (i.e. a digital value, which corresponds to an acoustic signal output from ADC 106 in Figure 1) by analog-to-digital converter ("ADC") 106, which samples the signal at a constant frequency, such as 8 kHz”);
that, during a normal operation of the circuit breaker for various events of the circuit breaker, is configured to extract a first feature amount of a first acoustic signal (See Para[0011] “the processor will store exemplary signals in a memory for comparison with acquired digital signals (i.e. the processor extracts via the comparison of the exemplary signals to the digital signals to obtain a first feature amount for a first acoustic signal for one of the exemplary signals, therefore is configured to extract)). Stored exemplary signals can include the ambient sound signature present at the microphone as well as the sound signatures of normal events, such as breaker operations (i.e. during a normal operation of the circuit breaker for various events of the circuit breaker), and abnormal events, such as arcing (i.e. the first feature amount is a normal operation sound signature of the circuit breaker for a sound signature corresponding to the first acoustic signal)”), the delay being generated based on a relative distance between the circuit breaker and the microphone (See Para[0022] “the microphone may be placed near a device to be monitored”. Therefore, the microphone is set a certain distance from the device, which will cause a delay of the signal received by the microphone be generated based on a relative distance between the circuit breaker and the microphone.);
a model creation unit (See Fig. 1 the processor 108) that is configured to create a plurality of event models (See Para[0011] “the processor will store exemplary signals (i.e. event models) in a memory”. Examiner notes that the memory 110 actually stores the event models, the processor assigns these signals (i.e. interpreted here as is configured to create) as exemplary signals for future comparison.) for managing data regarding a respective first feature amount (See Para[0011] “Stored exemplary signals can include the ambient sound signature present at the microphone as well as the sound signatures of normal events” where the sound signature of normal operation of opening for each exemplary signal is a respective first feature amount i.e. they manage data regarding a respective first feature amount) corresponding to a acoustic signal for each event (See Para[0011] “Stored exemplary signals can include the ambient sound signature present at the microphone as well as the sound signatures of normal events”. In addition, these exemplary signals contain, or manage, the data associated with the corresponding open operation of the circuit breaker. Therefore each exemplary signal contains information about normal operations, making each first feature amount of each exemplary signal correspond to a acoustic signal, which are the exemplary signals, for each event);
an event model storage unit (See Figure 1 the memory 110) that stores the plurality of event models, wherein the plurality of event models stored in the storage device enables (See Para[0011] “Generally, the processor will store exemplary signals (i.e. the plurality of event models) in a memory for comparison with acquired digital signals.” Therefore, the exemplary signals, which are the plurality of event models, are stored in the storage device via the memory, which allows the processor to compare signals (i.e. wherein the plurality of event models stored in the storage device enables:)):
in association with a respective event ID, management of a respective event model obtained by extracting the first feature amount of a respective first acoustic signal collected by the microphone, wherein the respective event ID identifies a respective event of the various events of the circuit breaker (See Figures 2-5, where each figure corresponds to a different event of a circuit breaker, where Fig. 2 is for background noise, Fig. 3, is for a close operation, Fig. 4 is for a trip operation, and Fig. 5 is for arcing. For each event, the data is identified via the filename, which functions as a respective event ID (e.g. for Fig. 2 the event ID is background.DAT, for Fig. 3 the event ID is close.DAT, etc.). Therefore, each respective event model, as shown by the graphs in Figs. 2-5, are managed in association with a respective event ID. These graphs correspond to exemplary signals which are the respective first acoustic signals with a corresponding first feature amount (for closing or tripping in Figures 3 or 4 which is extracted and shown in the figures) for each figure, see Para[0024] “A system embodying the disclosed invention could store digital representations of the signals depicted in FIGS. 2-5, and use those signals as exemplary models”. Each of these exemplary signals are collected via a microphone, see Para[0011] “Stored exemplary signals can include the ambient sound signature present at the microphone as well as the sound signatures of normal events, such as breaker operations” and see Fig. 1 the microphone 104. Therefore, the respective event ID identifies a respective event of the various events of the circuit breaker.), and
in association with each respective event, management of a respective event model (See Para[0011] “the processor will store exemplary signals (i.e. the event models) in a memory for comparison with acquired digital signals (i.e. in association with each respective event, management of a respective event model as it stores the exemplary signals, which are the respective event models, in the memory. Each exemplary signal corresponds to an event, such as normal operations of the circuit breaker, see above.).), and ;
that, during diagnosis of the circuit breaker, extracts a second feature amount of a second acoustic signal (See Para[0011] “The sound signatures may be captured at the installation with the aid of a user or through the use of an automation system. A pattern matching algorithm is used to determine whether or not an acquired digital signal (i.e. the processor extracts via the acquiring of the digital signals to obtain feature amounts) matches a recorded exemplary event (i.e. that, during diagnosis of the circuit breaker, extracts the second feature amount as an audio signature corresponding to an operation of a circuit breaker, which is then compared to the audio signatures of the event models).”), the delay of the second acoustic signal being generated from the relative distance between the circuit breaker and the microphone (See Para[0022] “the microphone may be placed near a device to be monitored”. Therefore, the microphone is set a certain distance from the device, which will cause a delay of the second acoustic signal received by the microphone be generated based on a relative distance between the circuit breaker and the microphone.);
that calculates a similarity between the second feature amount of the second acoustic signal during the diagnosis (See Para[0024] “pattern matching algorithms (i.e. which calculate a similarity) could be applied to the time-domain representation of the captured signal”. The captured signal contains the audio signature of the second feature amount of the second acoustic signal) and the first feature amount in the plurality of event models stored in the event model storage unit for each time (See Para[0024] “the processor would compare a recently captured time signal (i.e. the comparison happens at each time) to the exemplary signal (i.e. the audio signatures which contain normal operations of the circuit breaker)” and “The same operation could be performed to determine if any of the events of FIGS. 3-5, i.e., contact closure, trip, or arcing, were occurring as well (i.e. compare using the plurality of event models, where the plurality in this case is the opening and closing specifically of the circuit breaker, where these normal operations correspond to the first feature amount).”), and
diagnoses the circuit breaker based on a plurality of comparisons (See Para[0024] “The same operation could be performed to determine if any of the events of FIGS. 3-5, i.e., contact closure, trip, or arcing, were occurring as well (i.e. compare using the plurality of event models, where the plurality in this case is the opening and closing specifically of the circuit breaker).” In other words, the diagnosis is based on multiple comparisons of the digital signal to the plurality of event models.).
Schweitzer is silent to the language of
wherein the feature amount is a timing synchronized based on a sensor signal from a sensor
in which a delay of the acoustic signal collected by the microphone is corrected based on a difference with the sensor signal, and
and corrected for the delay of the acoustic signal, and
a respective event model represented by a respective Gaussian mixture model as the respective first feature amount for the respective event, and ;
in which a delay of the acoustic signal collected by the microphone is corrected based on a difference with a second sensor signal from the sensor, and
estimates the time at which the similarity becomes highest, as an occurrence timing of each event; and
that diagnoses the circuit breaker based on a plurality of the estimated occurrence timings, and
a first feature amount extraction processing unit, a second feature amount extraction processing unit, a similarity calculation processing unit, and a diagnosis processing unit.
Kimura teaches
wherein the feature amount is a timing synchronized based on a sensor signal from a sensor (See Para[0128] “The image pickup apparatus 800 of this embodiment corrects the phase difference of the sound input to the microphone 106b by the delay units 805a and 805b, and the applicative delay units 806a and 806b. More specifically, the delay units 805a and 805b delay the input audio signals by predetermined amounts (i.e. wherein the feature amount is a timing synchronized as the predetermined amounts refer to the time delay of the microphone which is corrected or synchronized via the phase difference correction).” See also Para[0127] “the delay detection unit 804 detects a delay or an advance (phase difference) of the audio signals input to the microphones 502a and 502b (i.e. based on a sensor signal from a sensor, where the sensor signal from a sensor is the signal from microphone 502b, see Fig. 8. ), and the delay amount of the sound acquired by the microphone 106b is adjusted on the basis of the detected phase difference.”)
in which a delay of the acoustic signal collected by the microphone is corrected based on a difference with the sensor signal (See Para[0128] “The image pickup apparatus 800 of this embodiment corrects the phase difference (i.e. a delay, which is based on a difference with the sensor signal as the phase difference tracks the difference between the audio signals from microphones 502a and 502b, see Fig. 8. The acoustic signal collected by the microphone is the signal output by microphone 502a, the delay of which is corrected) of the sound input to the microphone 106b”)
and corrected for the delay of the acoustic signal (See above), and
in which a delay of the acoustic signal collected by the microphone is corrected based on a difference with a second sensor signal from the sensor (See Para[0128] “The image pickup apparatus 800 of this embodiment corrects the phase difference (i.e. a delay, which is based on a difference with a second sensor signal from the sensor as the phase difference tracks the difference between the audio signals (where the second sensor signal from the sensor is the audio signal from the microphone 502b, which is a sensor) from microphones 502a and 502b, see Fig. 8. The acoustic signal collected by the microphone is the signal output by microphone 502a, the delay of which is corrected) of the sound input to the microphone 106b”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Schweitzer wherein the feature amount is a timing synchronized based on a sensor signal from a sensor and in which a delay of the acoustic signal collected by the microphone is corrected based on a difference with the sensor signal, and corrected for the delay of the acoustic signal, and in which a delay of the acoustic signal collected by the microphone is corrected based on a difference with a second sensor signal from the sensor is done like in Kimura in order to more accurately diagnose the circuit breaker in Schweitzer when comparing the audio signals from a reference sensor.
Schweitzer and Kimura are silent to the language of
a respective event model represented by a respective Gaussian mixture model as the respective first feature amount for the respective event, and ;
estimates the time at which the similarity becomes highest, as an occurrence timing of each event; and
that diagnoses the circuit breaker based on a plurality of the estimated occurrence timings, and
a first feature amount extraction processing unit, a second feature amount extraction processing unit, a similarity calculation processing unit, and a diagnosis processing unit.
Abe teaches
estimates the time at which the similarity becomes highest, as an occurrence timing of each event (See Abstract “The method finds a time difference, an expansion factor, and a similarity in one region (i.e. the occurrence timing) in which the maximum similarity (i.e. when the similarity becomes highest) as the square of the correlation is obtained”); and
that diagnoses the circuit breaker based on a plurality of the estimated occurrence timings (See Figure 1 the similarity determining unit 16 and Column 11 lines 59-61 “The similarity determining unit 16 performs similarity determination by comparing the maximum similarity sm with a threshold value sthsd.”. Therefore, the similarity determining unit 16 diagnoses whether a similarity sufficiently crosses a threshold using as a basis the region of time where the similarity is the highest, i.e. an occurrence timing.), and
a first feature amount extraction processing unit (See Figure 1 the first spectrum calculating unit 11), a second feature amount extraction processing unit (See Figure 1 the second spectrum calculating unit 13), a similarity calculation processing unit (See Figure 1 the similarity calculating unit 14), and a diagnosis processing unit (See Figure 1 the similarity determining unit 16 and Column 11 lines 59-61 “The similarity determining unit 16 performs similarity determination by comparing the maximum similarity sm with a threshold value sthsd.”. Therefore, the similarity determining unit 16 diagnoses whether a similarity sufficiently crosses a threshold.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Schweitzer and Kimura wherein Schweitzer estimates the time at which the similarity becomes highest, as an occurrence timing of each event; and that diagnoses the circuit breaker based on a plurality of the estimated occurrence timings, and uses a first feature amount extraction processing unit, a second feature amount extraction processing unit, a similarity calculation processing unit, and a diagnosis processing unit like in Abe as well. Estimating the time at which the similarity becomes highest, as an occurrence timing of each event will more accurately diagnose the circuit breaker (as in identifying more accurately which type of event corresponds to the tested acoustic signal) in accordance with the specific time the similarity of the tested acoustic signal is most similar to each event of the plurality of event models (which are the exemplary signals in Schweitzer). Examiner notes that Abe calculates one occurrence timing as Abe compares the tested acoustic signal against one event model (which is the second signal in Abe). Schweitzer can use the method in Abe to calculate the occurrence timings of the tested acoustic signal with each event model (the exemplary signals in Schweitzer), and using the corresponding occurrence timings to diagnose the circuit breaker, which will allow for a more quantifiable and consistent method to diagnose the circuit breaker. Also the segmentation of the processor in Schweitzer to have a first feature amount extraction processing unit, a second feature amount extraction processing unit, a similarity calculation processing unit, and a diagnosis processing unit like in Abe will allow Schweitzer to have a more compartmentalized design, allowing for a cleaner breakdown of the steps of diagnosing a circuit breaker.
Schweitzer, Kimura, and Abe are silent to the language of,
a respective event model represented by a respective Gaussian mixture model as the respective first feature amount for the respective event.
Degges teaches
a respective event model represented by a respective Gaussian mixture model as the respective first feature amount for the respective event (See Column 4 lines 13-15 “ The speech recognition engine 232 of the voice interaction module 230 processes received audio data. The speech voice interaction module may part of a classifier system 252 configured to perform speech recognition. The classifier system 252 may be, for example, a Support Vector Machine (SVM), although other machine learning techniques might be used instead of or to augment SVM. The classifier system 252 may utilize Hidden Markov Models (HMMs), Gaussian Mixture Models (GMMs)”. Therefore the speech data, which comprises the respective event, is classified via Gaussian Mixture Models, making each speech data a respective event model as it is represented by a respective Gaussian Mixture Model as Degges recites the Gaussian Mixture Model as a classification option. The Gaussian Mixture Model used to classify each speech data therefore acts as a respective first feature amount for each speech data as it classifies the data based on their features.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Schweitzer, Kimura, and Abe wherein a respective event model represented by a respective Gaussian mixture model as the respective first feature amount for the respective event is used like in Degges for managing feature amounts in order to have a more systematic and uniform methodology to classify and identify features in an acoustic signal.
With regards to Claim 9, Schweitzer teaches
an acoustic diagnosis method of an acoustic diagnosis device (See Abstract) including a microphone (See Figure 1, the microphone 104) that converts a sound pressure from a circuit breaker (See Figure 1 the Power System Device 102, which can be a circuit breaker, see Para[0028] “a typical power substation, which could include, for example, a monitoring intelligent electronic device, such as a distance relay, which controls one or more circuit breakers protecting a power line segment”)into an electrical signal (See Para[0022] “The microphone produces an analog electric signal (i.e. an electric signal) corresponding to a sensed audio signal (i.e. a sound pressure)”), and an A/D converter that converts the sound pressure converted into the electric signal by the microphone into a digital value to obtain an acoustic signal (See Para[0022] “The electric signal is converted to digital form (i.e. a digital value, which corresponds to an acoustic signal output from ADC 106 in Figure 1)by analog-to-digital converter ("ADC") 106, which samples the signal at a constant frequency, such as 8 kHz”), the acoustic diagnosis method comprising:
extracting, by a central processing unit of the acoustic diagnosis device, during a normal operation of the circuit breaker for various events of the circuit breaker, a first feature amount of a first acoustic signal (See Para[0011] “the processor will store exemplary signals in a memory for comparison with acquired digital signals (i.e. the processor extracts via the comparison of the exemplary signals to the digital signals to obtain a first feature amount for a first acoustic signal for one of the exemplary signals, therefore extracting, by a central processing unit of the acoustic diagnosis device). Stored exemplary signals can include the ambient sound signature present at the microphone as well as the sound signatures of normal events, such as breaker operations, and abnormal events, such as arcing (i.e. the first feature amount is a normal operation sound signature of the circuit breaker, which corresponds to during a normal operation of the circuit breaker for various events of the circuit breaker)”), the delay being generated based on a relative distance between the circuit breaker and the microphone (See Para[0022] “the microphone may be placed near a device to be monitored”. Therefore, the microphone is set a certain distance from the device, which will cause a delay of the signal received by the microphone be generated based on a relative distance between the circuit breaker and the microphone.);
creating a plurality of event models (See Para[0011] “the processor will store exemplary signals (i.e. event models) in a memory”. Examiner notes that the memory 110 actually stores the event models, the processor assigns these signals (i.e. interpreted here as creates) as exemplary signals for future comparison.) for managing data regarding a respective first feature amount (See Para[0011] “Stored exemplary signals can include the ambient sound signature present at the microphone as well as the sound signatures of normal events” where the sound signature of normal operation of opening for each exemplary signal is a respective first feature amount i.e. they manage data regarding a respective first feature amount) corresponding to a acoustic signal for each event (See Para[0011] “Stored exemplary signals can include the ambient sound signature present at the microphone as well as the sound signatures of normal events”. In addition, these exemplary signals contain, or manage, the data associated with the corresponding open operation of the circuit breaker. Therefore each exemplary signal contains information about normal operations, making each first feature amount of each exemplary signal correspond to a acoustic signal, which are the exemplary signals, for each event),
storing, by a storage device of the acoustic diagnosis device, the plurality of event models (See Para[0011] “the processor will store exemplary signals in a memory (i.e. the memory 110, which causes storing, by a storage device of the acoustic diagnosis device, where the memory 110 is part of the acoustic diagnosis device, see the entire Fig. 1)”. The plurality of event models are the exemplary signals.) created by the central processing unit, wherein the plurality of event models stored in the storage device enable the central processing unit to (See Para[0011] “Generally, the processor will store exemplary signals in a memory for comparison with acquired digital signals.” Examiner notes that the memory 110 actually stores the event models, the processor assigns these signals (i.e. interpreted here as create) as exemplary signals for future comparison. Therefore, the processor created the plurality of event models, which are stored in the storage device via the memory, allowing the processor to compare signals (i.e. wherein the plurality of event models stored in the storage device enable the central processing unit to:)):
manage, in association with a respective event ID, a respective event model obtained by extracting the first feature amount of a respective first acoustic signal collected by the microphone, wherein the respective event ID identifies a respective event of the various events of the circuit breaker (See Figures 2-5, where each figure corresponds to a different event of a circuit breaker, where Fig. 2 is for background noise, Fig. 3, is for a close operation, Fig. 4 is for a trip operation, and Fig. 5 is for arcing. For each event, the data is identified via the filename, which functions as a respective event ID (e.g. for Fig. 2 the event ID is background.DAT, for Fig. 3 the event ID is close.DAT, etc.). Therefore, each respective event model, as shown by the graphs in Figs. 2-5, are managed in association with a respective event ID. These graphs correspond to exemplary signals which are the respective first acoustic signals with a corresponding first feature amount (for closing or tripping in Figures 3 or 4 which is extracted and shown in the figures) for each figure, see Para[0024] “A system embodying the disclosed invention could store digital representations of the signals depicted in FIGS. 2-5, and use those signals as exemplary models”. Each of these exemplary signals are collected via a microphone, see Para[0011] “Stored exemplary signals can include the ambient sound signature present at the microphone as well as the sound signatures of normal events, such as breaker operations” and see Fig. 1 the microphone 104. Therefore, the respective event ID identifies a respective event of the various events of the circuit breaker.), and
manage, in association with each respective event, a respective event model (See Para[0011] “the processor will store exemplary signals (i.e. the event models) in a memory for comparison with acquired digital signals (i.e. manage, in association with each respective event, a respective event model as it stores the exemplary signals, which are the respective event models, in the memory). Each exemplary signal corresponds to an event, such as normal operations of the circuit breaker, see above.), and;
during diagnosis of the circuit breaker, extracting, by the central processing unit, a second feature amount of a second acoustic signal (See Para[0011] “The sound signatures may be captured at the installation with the aid of a user or through the use of an automation system. A pattern matching algorithm is used to determine whether or not an acquired digital signal (i.e. the processor extracts via the acquiring of the digital signals, which is a second acoustic signal to obtain a second feature amount, therefore during diagnosis of the circuit breaker, extracting, by the central processing unit, a second feature amount of a second acoustic signal ) matches a recorded exemplary event (i.e. the second feature amount is an audio signature corresponding to an operation of a circuit breaker, which is then compared to the audio signatures of the event models).”), the delay of the second acoustic signal being generated from the relative distance between the circuit breaker and the microphone (See Para[0022] “the microphone may be placed near a device to be monitored”. Therefore, the microphone is set a certain distance from the device, which will cause a delay of the second acoustic signal received by the microphone be generated based on a relative distance between the circuit breaker and the microphone.);
calculating a similarity between the second feature amount of the second acoustic signal during the diagnosis (See Para[0024] “pattern matching algorithms (i.e. which calculate a similarity) could be applied to the time-domain representation of the captured signal”. The captured signal contains the audio signature for the second acoustic signal of the second feature amount.) and the first feature amount in the plurality of event models stored in the storage device for each time (See Para[0024] “the processor would compare a recently captured time signal (i.e. the comparison happens at each time) to the exemplary signal (i.e. the audio signatures which contain normal operations of the circuit breaker which correspond to the first feature amount)” and “The same operation could be performed to determine if any of the events of FIGS. 3-5, i.e., contact closure, trip, or arcing, were occurring as well (i.e. compare using the plurality of event models, where the plurality in this case is the opening and closing specifically of the circuit breaker).”);
diagnosing the circuit breaker based on a plurality of comparisons (See Para[0024] “The same operation could be performed to determine if any of the events of FIGS. 3-5, i.e., contact closure, trip, or arcing, were occurring as well (i.e. compare using the plurality of event models, where the plurality in this case is the opening and closing specifically of the circuit breaker).” In other words, the diagnosis is based on multiple comparisons of the digital signal to the plurality of event models.).
Schweitzer is silent to the language of
wherein the feature amount is a timing synchronized based on a sensor signal from a sensor
in which a delay of the acoustic signal collected by the microphone is corrected based on a difference with the sensor signal,
and corrected for the delay of the acoustic signal, and
a respective event model represented by a respective Gaussian mixture model as the respective first feature amount for the respective event, and;
in which a delay of the acoustic signal collected by the microphone is corrected based on a difference with a second sensor signal from the sensor,
and performing an event timing estimation of estimating the time at which the similarity becomes highest, as an occurrence timing of each event; and
diagnosing the circuit breaker based on a plurality of estimated occurrence timings.
Kimura teaches
wherein the feature amount is a timing synchronized based on a sensor signal from a sensor (See Para[0128] “The image pickup apparatus 800 of this embodiment corrects the phase difference of the sound input to the microphone 106b by the delay units 805a and 805b, and the applicative delay units 806a and 806b. More specifically, the delay units 805a and 805b delay the input audio signals by predetermined amounts (i.e. wherein the feature amount is a timing synchronized as the predetermined amounts refer to the time delay of the microphone which is corrected or synchronized via the phase difference correction).” See also Para[0127] “the delay detection unit 804 detects a delay or an advance (phase difference) of the audio signals input to the microphones 502a and 502b (i.e. based on a sensor signal from a sensor, where the sensor signal from a sensor is the signal from microphone 502b, see Fig. 8.), and the delay amount of the sound acquired by the microphone 106b is adjusted on the basis of the detected phase difference.”)
in which a delay of the acoustic signal collected by the microphone is corrected based on a difference with the sensor signal (See Para[0128] “The image pickup apparatus 800 of this embodiment corrects the phase difference (i.e. a delay, which is based on a difference with the sensor signal as the phase difference tracks the difference between the audio signals from microphones 502a and 502b, see Fig. 8. The acoustic signal collected by the microphone is the signal output by microphone 502a, the delay of which is corrected) of the sound input to the microphone 106b”),
and corrected for the delay of the acoustic signal (See above), and
in which a delay of the acoustic signal collected by the microphone is corrected based on a difference with a second sensor signal from the sensor (See Para[0128] “The image pickup apparatus 800 of this embodiment corrects the phase difference (i.e. a delay, which is based on a difference with a second sensor signal from the sensor as the phase difference tracks the difference between the audio signals (where the second sensor signal from the sensor is the audio signal from the microphone 502b, which is a sensor) from microphones 502a and 502b, see Fig. 8. The acoustic signal collected by the microphone is the signal output by microphone 502a, the delay of which is corrected) of the sound input to the microphone 106b”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Schweitzer wherein the feature amount is a timing synchronized based on a sensor signal from a sensor in which a delay of the acoustic signal collected by the microphone is corrected based on a difference with the sensor signal, and corrected for the delay of the acoustic signal, and in which a delay of the acoustic signal collected by the microphone is corrected based on a difference with a second sensor signal from the sensor like in Kimura in order to more accurately diagnose the circuit breaker when comparing the event models with the tested acoustic signal by using a reference sensor.
Schweitzer and Kimura are silent to the language of
a respective event model represented by a respective Gaussian mixture model as the respective first feature amount for the respective event, and;
and performing an event timing estimation of estimating the time at which the similarity becomes highest, as an occurrence timing of each event; and
diagnosing the circuit breaker based on a plurality of estimated occurrence timings.
Abe teaches
and performing an event timing estimation of estimating the time at which the similarity becomes highest, as an occurrence timing of each event (See Abstract “The method finds a time difference, an expansion factor, and a similarity in one region (i.e. the occurrence timing) in which the maximum similarity (i.e. when the similarity becomes highest) as the square of the correlation is obtained”); and
diagnosing the acoustic signals based on the estimated occurrence timing (See Figure 1 the similarity determining unit 16 and Column 11 lines 59-61 “The similarity determining unit 16 performs similarity determination by comparing the maximum similarity sm with a threshold value sthsd.”. Therefore, the similarity determining unit 16 diagnoses whether a similarity sufficiently crosses a threshold using as a basis the region of time where the similarity is the highest, i.e. an occurrence timing.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Schweitzer and Kimura wherein Schweitzer performs an event timing estimation of estimating the time at which the similarity becomes highest, as an occurrence timing of each event; and diagnoses the circuit breaker based on a plurality of estimated occurrence timings like in Abe. Examiner notes that Abe calculates one occurrence timing as Abe compares the tested acoustic signal against one event model (which is the second signal in Abe). Schweitzer can use the method in Abe to calculate the occurrence timings of the tested acoustic signal with each event model (the exemplary signals in Schweitzer), and using the corresponding occurrence timings to diagnose the circuit breaker, which will allow for a more quantifiable and consistent method to diagnose the circuit breaker.
Schweitzer, Kimura, and Abe are silent to the language of,
a respective event model represented by a respective Gaussian mixture model as the respective first feature amount for the respective event.
Degges teaches
a respective event model represented by a respective Gaussian mixture model as the respective first feature amount for the respective event (See Column 4 lines 13-15 “ The speech recognition engine 232 of the voice interaction module 230 processes received audio data. The speech voice interaction module may part of a classifier system 252 configured to perform speech recognition. The classifier system 252 may be, for example, a Support Vector Machine (SVM), although other machine learning techniques might be used instead of or to augment SVM. The classifier system 252 may utilize Hidden Markov Models (HMMs), Gaussian Mixture Models (GMMs)”. Therefore the speech data, which comprises the respective event, is classified via Gaussian Mixture Models, making each speech data a respective event model as it is represented by a respective Gaussian Mixture Model as Degges recites the Gaussian Mixture Model as a classification option. The Gaussian Mixture Model used to classify each speech data therefore acts as a respective first feature amount for each speech data as it classifies the data based on their features.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Schweitzer, Kimura, and Abe wherein a respective event model represented by a respective Gaussian mixture model as the respective first feature amount for the respective event is used like in Degges for managing feature amounts in order to have a more systematic and uniform methodology to classify and identify features in an acoustic signal.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schweitzer (US 20090091441 A1), Kimura (US 20120257779 A1), Abe (US 7720235 B2), and Degges (US 9830924 B1), further in view of Nandy (US 9711144 B2).
With regards to Claim 2, Schweitzer, Kimura, Abe, and Degges teach the limitations of Claim 1.
Schweitzer further teaches
wherein the sensor is an electrical sensor provided in the circuit breaker, and the central processing unit acquires the signal input from the electrical sensor provided in the circuit breaker (See Para[0022] “The microphone 104 may be mounted directly to a sidewall of the power system device 102”. And Para[0022] “The microphone produces an analog electric signal corresponding to a sensed audio signal.” In other words, the microphone is an electrical sensor as it outputs an electric signal based on vibrational signals (which is an audio signal) and can be a part of the power system device 102 (i.e the circuit breaker), therefore making it being provided in the circuit breaker, and Fig. 6, where this microphone would be the second microphone 104b and the central processing unit, which is processor 108, acquires the signal input from the electrical sensor.) with a digital value of the microphone (See Fig. 6 the microphone 104a).
the first or second sensor signal, respectively, received from the electrical sensor (See Fig. 6, where the first sensor signal is output by microphone 104a to be received by processor 108.).
Schweitzer, Kimura, Abe, and Degges are silent to the language of
wherein the central processing unit synchronizes the signal input from the electrical sensor with a digital value of the microphone, and calculates the delay of the acoustic signal collected by the microphone with respect to the sensor signal.
Nandy teaches
wherein the central processing unit synchronizes the signal input from the electrical sensor with a digital value of the microphone (See Column 6 lines 49-52 “A voice trigger algorithm starts recognition on the buffered data, x(m) over a first processing interval (i.e. the digital value of the microphone, see Column 4 lines 15-16 “The host 206 is, in one example, a processing element such as a codec or digital signal processor”, in other words, the host 206 (or 300 in Fig. 3), obtains digital data input.), while the real-time data x(n) (i.e. the signal input) is saved in a buffer on the host 300” and see Column 6 lines 56-59 “Buffer data of the buffer in the first microphone (e.g., buffer 228 in first microphone 202) is extended (e.g., by a length less than 256 ms) to allow the stitch algorithm operated by the processor 304 (i.e. the central processing unit) to synchronize x(m) and x(n) and eliminate signal discontinuity.”), and calculates the delay of the acoustic signal collected by the microphone with respect to the sensor signal (See Column 6 lines 47-48 “Discontinuity can be represented as x(m)-x(n) and is approximately equal to 256 ms”. In other words, a delay is calculated, which is correspondingly removed by the synchronization. See also Fig. 2, where the buffer 228 acts as the electrical sensor that outputs a buffered electrical signal based on vibrational data which is the acoustic data. The microphone 202 produces the digital value, and Nandy teaches to synchronize buffered data from the buffer with the digital data of the microphone.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Schweitzer, Kimura, Abe and Degges wherein the central processing unit synchronizes the signal input from the electrical sensor with a digital value of the microphone, and calculates the delay of the acoustic signal collected by the microphone with respect to the sensor signal like in Nandy in order to accurately compare the signals from the signal input from an electrical sensor and the digital value from the microphone.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schweitzer (US 20090091441 A1), Kimura (US 20120257779 A1), Abe (US 7720235 B2), and Degges (US 9830924 B1), further in view of Park (US 20200388289 A1).
With regards to Claim 6, Schweitzer, Kimura, Abe, and Degges teach the limitations of Claim 1. Schweitzer further teaches
wherein the first or second feature amount of the first or second acoustic signal, respectively, extracted by the central processing unit is a representation of feature amount vectors of the respective first or second acoustic signal (See Para[0011] “the processor (i.e. central processing unit) will store exemplary signals (i.e. one of which is the first acoustic signal) in a memory for comparison with acquired digital signals (i.e. one of which is the second acoustic signal, and both the first and second acoustic signals are extracted via the processor acquiring the exemplary signals and the digital signals to obtain a first feature amount and a second feature amount respectively, see Claim 1 above). Stored exemplary signals can include the ambient sound signature present at the microphone as well as the sound signatures of normal events, such as breaker operations, and abnormal events, such as arcing (i.e. the first feature amount and the second feature amount for the first and second acoustic signal respectively is a normal operation sound signature of the circuit breaker, and the first feature amount and the second feature amount is a representation of the feature amount vectors for the respective first and second acoustic signal, which are the individual data points associated with the sound signatures)”).
Schweitzer, Kimura, Abe, and Degges are silent to the language of,
a median value of a distribution of feature amount vectors of the acoustic signal.
Park teaches
a median value of a distribution of feature amount vectors of the acoustic signal (See Para[0120] “the reference value may be a median value of the size of the amplitude (i.e. a median of a distribution of feature amount vectors, where feature amount vectors is interpreted as individual values of the amplitude) of the audio signal (i.e. the acoustic signal)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Schweitzer, Kimura, Abe, and Degges wherein a median value of a distribution of feature amount vectors of the acoustic signal is used like in Park in order to have a more systematic and uniform methodology to classify and identify features in an acoustic signal.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MOSTOFA AHMED HISHAM/Examiner, Art Unit 2857 /YOSHIHISA ISHIZUKA/Primary Examiner, Art Unit 2857
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