DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The rejections from the Office Action of 1/27/2026 are hereby withdrawn. New grounds for rejection are presented below.
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/31/2026 has been entered.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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, 8, 12-16, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lie et al. (US 20100268489 A1)[hereinafter “Lie”], Felemban et al. (US 20160356665 A1)[hereinafter “Felemban”], and Lan et al. (US 20240369514 A1)[hereinafter “”Lan”].
Regarding Claims 1, 12, and 20, Lie discloses a method (implemented using a memory, processors, and CRM instructions [Paragraphs [0061]-[0062]]) comprising:
performing an evaluation to identify a plurality of characteristics of a set of sensed acoustic data from two distinct sound sources [Paragraph [0066] – “The following description of the present invention comprises a method and a system for detecting leaks or flows of liquid, gasses, or particles by means of acoustic transducers. The invention is suited for measuring leaks or flows of fluid, gasses, or particles in connection with, for example a petroleum producing well in one or another phase of the process by complementing the well, or by production of oil or gas, or by use of the well for injecting gas or fluid into a reservoir. The tool is shaped as a closed pipe, a so called down hole "log tool", which may be inserted into the well, for example by way of being supported on a wire or a coil pipe. Thus, the tool is localized in operation inside the pipe itself that is being used for production or injection of fluids during the measurements. The tool logs data continuously by utilizing one or more passive acoustic transducers that detect, that is "listens to", the noise profile from the surroundings whereat the one or more transducers are localised at any point in time. This is done at the same time as the tool is guided through the pipe, in order to facilitate detection of a leak or flow when the tool is near the specific leak or flow, irrespective of the nature of the leak or flow. There will always be several different kinds of noise present, such as electrical, mechanic, or thermal noise, or noise from flowing mediums and possibly from the presence of particles like sand and other mechanical objects, which may be brought in contact with the structure.”];
wherein the plurality of characteristics includes a center frequency and a frequency bandwidth that are unique to the two distinct sound sources [Paragraph [0094] – “An output signal from amplifier 160 is then connected with one or more frequency filters that each has a bandwidth that is smaller or equal to that of the transducers, namely the total bandwidth of sensor element 150. High and low pass frequencies for these filters are set digitally from DSP 170, and thus both the bandwidths and center frequencies for these filters may be set by changing the software in the DSP 170. By such an approach, several different types of signal processing are susceptible to being implemented according to need. Such software may comprise signal-processing routines that are suited for suppressing undesired background noise.” Using bandwidth and center frequency in filtering out noise.];
comparing the plurality of characteristics of the sensed acoustic data with one or more characteristics of a first type of sound source; identifying, based on the comparison, that a first subset of the plurality of characteristics corresponds to the one or more characteristics of the first type of sound source [Paragraph [0066] – “This noise, which exists in the normal conditions where there are no leaks present, is here called "background noise". It will have its own special spectral characteristics, which may be detected, analysed, and saved prior to implementing the measuring process itself.”];
initiating a filtering function to separate the first subset of the plurality of characteristics from the set of acoustic data based on the comparison of the center frequency and the frequency bandwidth of the two distinct sound sources [Paragraph [0085] – “The background noise, such as noise from the surroundings and mechanical contact noise that is always present during motion of the tool 10, is in the present invention eliminated by utilizing filtering software installed in a digital signal processor (DSP) 170.”]; and
performing an analysis on a second subset of the plurality of characteristics to identify metrics to associate with the second subset of the plurality of characteristics, wherein the second subset of the plurality of characteristics is associated with one or more characteristics of a second type of sound source [Paragraph [0066] – “A leak or flow of fluid or gasses or particles in the structure will, however, generate noise with a different characteristic than the background noise, as this noise will be localised in one or more specific frequency ranges, different from the frequency distribution of the background noise. Thus, the tool is specially designed to collect and process this information.”] and an actionable assessment is initiated based on the metrics identified by the analysis on the second subset of the plurality of characteristics being associated with the one or more characteristics of the second type of sound source [Paragraph [0066] – “The acoustic signals are locally and immediately digitised and processed inside of the tool itself, and then transferred to a suitable computer on the surface that stores and visually presents the results of the logging. The tool may thus give the operator information about even small leaks, as well the specific position of the leak in real time.”].
Lie fails to disclose that the two distinct sound sources comprise fluid flow through an orifice in tubing or casing in a downhole environment, a channel or fracture in an annulus or formation of the downhole environment, a permeable matrix of the formation, or a combination thereof; or filtering the two distinct sound sources so that they can be separately analyzed.
However, Felemban discloses a pipeline monitoring system where pre-processing is performed to remove noise and unnecessary data to provide noise-free data [Paragraph [0037] – “Embodiments for a pipeline monitoring system use signal processing and machine learning techniques to detect the presence of leakage in the pipelines. Sensor nodes acquire NPW data from the pipeline network. Pre-processing is performed to remove noise and unnecessary data to provide noise-free data. Noise signals can be removed using a low-pass filter and Daubechies wavelet transform.”]. Felemban classifies pipeline signal data into benign and non-benign groups [Paragraph [0038] – “Binary classification can be used in which the pressure signal is classified into one of two classes.”], which include fluid flow through an orifice in tubing or casing in a downhole environment [Paragraph [0038] – “A first class includes non-leak or benign objects, which shows the fluid flow in the pipeline is normal and there is no defect present in the pipeline.” Fluid flow through an orifice is inherent.], a channel or fracture in an annulus or formation of the downhole environment [Paragraph [0038] – “A second class is a leak or non-benign object, which indicates the presence of a fault, deformation, or crack in the pipeline.”], and a permeable matrix of the formation [Paragraph [0038] – “A first class includes non-leak or benign objects, which shows the fluid flow in the pipeline is normal and there is no defect present in the pipeline.” Such fluid flow from the formation and into the pipeline is inherent.].
It would have been obvious to classify the pipeline data in such a manner and to filter out data corresponding to benign sources in order to be more readily able to determine the cause and location of non-benign data (i.e., focus on data corresponding to leaks to identify the cause and location of leaks).
Lie fails to disclose that the plurality of characteristics includes a spectral shape and performing the filtering based on spectral shape.
However, Lan discloses distinguishing acoustic sources in a pipe based on their different spectral shapes [See the spectral shapes 806, 808, and 810 of Fig. 8 and Paragraphs [0052] and [0080]]. It would have been obvious to use spectral shape as a parameter in distinguishing acoustic sources because this would have presented a viable manner for doing so.
Regarding Claim 20, Lie discloses a set of sensors that sense a plurality of characteristics of a set of sensed acoustic data [Paragraph [0094] – “In a preferred embodiment of the present invention, a single broadband acoustic transducer is utilized for implementing the sensor element 150. Alternatively, use of several transducers simultaneously is also possible pursuant to the present invention, wherein these several transducers have their best sensitivity in mutually different frequency ranges; by use of several transducers, it is feasible to expand the total sensitivity and/or frequency range for the tool 10.”].
Regarding Claims 2 and 13, Lie discloses identifying spectral content of the set of sensed acoustic data; accessing stored data that includes the one or more characteristics of the first type of sound source [Paragraph [0066] – “This noise, which exists in the normal conditions where there are no leaks present, is here called "background noise". It will have its own special spectral characteristics, which may be detected, analysed, and saved prior to implementing the measuring process itself.”]; and
identifying spectral content associated with the first type of sound source after accessing the stored data, wherein the filtering function separates the spectral content associated with the first type of sound source from the spectral content of the set of sensed acoustic data [Paragraph [0085] – “The background noise, such as noise from the surroundings and mechanical contact noise that is always present during motion of the tool 10, is in the present invention eliminated by utilizing filtering software installed in a digital signal processor (DSP) 170.”].
Regarding Claims 3 and 14, Lie discloses that the first subset of the plurality of characteristics includes a set of frequencies of the first type of sound source [Paragraph [0066] – “A leak or flow of fluid or gasses or particles in the structure will, however, generate noise with a different characteristic than the background noise, as this noise will be localised in one or more specific frequency ranges, different from the frequency distribution of the background noise.”].
Regarding Claims 4 and 15, Lie discloses that the first subset of the plurality of characteristics of the type of sound source includes a magnitude of the set of frequencies of the sound source [Paragraph [0106] – “The signal from the sensor element 150 can be represented by a signal R.sub.T. The signal R.sub.T includes numerous Fourier components at various Fourier frequencies .omega.. By way of example, it is feasible that the tool 10 is brought in a vicinity of a region along the well 230 where there are simultaneously three faults giving rise to four sub-signals as expressed in Equation 5 (Eq. 5): R.sub.T=R.sub.1+R.sub.2+R.sub.3+R.sub.en”Paragraph [0106] – “R.sub.en=a sub-signal corresponding to background noise-generating processes occurring along the well 230 and electronic noise arising in electronic circuits of the tool 10.”Fourier components inherently having magnitudes corresponding to frequencies.].
Regarding Claims 5 and 16, Lie discloses identifying an estimated location to associate with the second type of sound source based on an analysis of the one or more characteristics of the second type of sound source [Paragraph [0066] – “A leak or flow of fluid or gasses or particles in the structure will, however, generate noise with a different characteristic than the background noise, as this noise will be localised in one or more specific frequency ranges, different from the frequency distribution of the background noise. Thus, the tool is specially designed to collect and process this information. The acoustic signals are locally and immediately digitised and processed inside of the tool itself, and then transferred to a suitable computer on the surface that stores and visually presents the results of the logging. The tool may thus give the operator information about even small leaks, as well the specific position of the leak in real time.”].
Regarding Claims 8 and 19, Lie discloses classifying the second subset of the plurality of characteristics as belonging to a wellbore defect, wherein the actional assessment includes providing an alert to administrative staff that identifies the wellbore defect [Paragraph [0066] – “A leak or flow of fluid or gasses or particles in the structure will, however, generate noise with a different characteristic than the background noise, as this noise will be localised in one or more specific frequency ranges, different from the frequency distribution of the background noise. Thus, the tool is specially designed to collect and process this information. The acoustic signals are locally and immediately digitised and processed inside of the tool itself, and then transferred to a suitable computer on the surface that stores and visually presents the results of the logging. The tool may thus give the operator information about even small leaks, as well the specific position of the leak in real time.”].
Claim(s) 6, 7, 17, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lie et al. (US 20100268489 A1)[hereinafter “Lie”], Felemban et al. (US 20160356665 A1)[hereinafter “Felemban”], Lan et al. (US 20240369514 A1)[hereinafter “”Lan”], and Greensted, Delay Sum Beamforming, The Lab Book Pages, 2012.
Regarding Claims 6 and 17, Lie fails to disclose:
identifying a first sum of acoustic energy that is associated with a first set of delay times by:
delaying a first portion of the acoustic energy by a first delay based on the first portion of the acoustic energy being received by a first acoustic sensor of an acoustic array, wherein the first delay is included in the first set of delay times,
delaying a second portion of the acoustic energy by a second delay based on the second portion of the acoustic energy being received by a second acoustic sensor of the acoustic array, wherein the second delay is included in the first set of delay times, and
adding the first portion of the acoustic energy to the second portion of the acoustic energy and to a third portion of the acoustic energy;
comparing the first sum of the acoustic energy, a second sum of the acoustic energy associated with a second set of delay times, and a third sum of acoustic energy that is associated with a third set of delay times; and
identifying a location of the first type of sound source based on the first sum of acoustic energy being greater than the second sum of acoustic energy and the third sum of acoustic energy and based on the first set of delay times.
However, Greensted discloses a manner of using different delay sets with three acoustic receivers where the delayed acoustic signal responses are summed and the acoustic signals producing the highest sum correspond to the angle of arrival of the acoustic source [See the figure at the bottom of Page 1 and the figure at the bottom of Page 6]. The delay sets can be selected to steer the acoustic receivers in doing so [See the “Steering” section at Page 6]. Two or three sums are produced [See the figure at the bottom of Page 1 and the figure at the bottom of Page 6]. It would have been obvious to utilize such a technique by varying the steering delay times and assessing the summing response in order to determine a corresponding angle of arrival from an acoustic source of interest so as to better identify the source of the acoustic emission in ascertaining the location of a leak.
Regarding Claims 7 and 18, Lie fails to disclose:
identifying a first magnitude of a first portion of sound energy received by a first sensor of a sensor array;
identifying a second magnitude of a second portion of the sound energy received by a second sensor of the sensor array;
identifying a third magnitude of a third portion of the sound energy received from a third sensor of the sensor array; and
identifying the estimated location of the type of sound source based on an evaluation that compares the first magnitude of the first portion of sound energy with the second magnitude of the second portion of sound energy and with the third magnitude of the third portion of the sound energy.
However, Greensted discloses a manner of using different delay sets with three acoustic receivers where the delayed acoustic signal responses are summed and the acoustic signals producing the highest sum (magnitude) correspond to the angle of arrival of the acoustic source [See the figure at the bottom of Page 1 and the figure at the bottom of Page 6]. The delay sets can be selected to steer the acoustic receivers in doing so [See the “Steering” section at Page 6]. Two or three sums are produced [See the figure at the bottom of Page 1 and the figure at the bottom of Page 6]. It would have been obvious to utilize such a technique by varying the steering delay times and assessing the summing response in order to determine a corresponding angle of arrival from an acoustic source of interest so as to better identify the source of the acoustic emission in ascertaining the location of a leak.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lie et al. (US 20100268489 A1)[hereinafter “Lie”], Felemban et al. (US 20160356665 A1)[hereinafter “Felemban”], Lan et al. (US 20240369514 A1)[hereinafter “”Lan”], and Li et al., MFCC-DSR: A Novel Feature Extraction Approach for Small Leak Identification of Gas Pipes, IEEE, 2018 [hereinafter “Li”].
Regarding Claim 9, Lie fails to disclose:
calculating a first power spectral density associated with a sound emitted by the first type of sound source by identifying power levels associated with a plurality of frequencies included in the sound emitted by the first type of sound source; and
calculating a second power spectral density associated with a sound emitted by the second type of sound source by identifying power levels associated with a plurality of frequencies included in the sound emitted by the first type of sound source.
However, Li discloses doing so as a part of optimizing the usefulness of an acoustic signal in determining whether a leak is present [See Page 2408, first column – “Parameter a,b and h can adjust signal to noise ratio (SNR) of leak acoustic signals. Signal to Noise Ratio (SNR) is an important indicator of SR performances, which addresses the relationship between useful signals and noises. When SNR is small, the noises are strong. SNR is given using the following formula: SNR = 10 ∗ ln S/NWhere S is power spectral density of leak information and N is power spectral density of noises.
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150
501
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”].
It would have been obvious to take such an approach in order to better assess the presence of a leak in the presence of background noise.
Response to Arguments
Applicant argues:
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336
785
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636
782
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Examiner’s Response:
Lan is not relied on with regards to the recited “center frequency.” The use of center frequencies is disclosed by Lie [Paragraph [0094]].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Berlin et al., Comparison of Information Criteria for Detection of Useful Signals in Noisy Environments, MDPI, 2.14.2023
Timofeev et al., Adaptive Acoustic Beamformer With Source Tracking Capabilities, IEEE, 2008
US 20220179117 A1 – DEEP LEARNING METHODS FOR WELLBORE LEAK DETECTION
US 20220163420 A1 – METHOD AND SYSTEM FOR DETECTING A STRUCTURAL ANOMALY IN A PIPELINE NETWORK
US 20210304730 A1 – BEAMFORMING SYSTEM BASED ON DELAY DISTRIBUTION MODEL USING HIGH FREQUENCY PHASE DIFFERENCE
US 20210033742 A1 – ACOUSTIC INTEGRITY IMAGING
US 20190203585 A1 – Wellbore Sand Detection Using Passive Acoustic Array
US 20180230797 A1 – ESTIMATION OF FLOW RATES USING ACOUSTICS IN A SUBTERRANEAN BOREHOLE AND/OR FORMATION
US 20180137853 A1 – PASSIVE BEAMFORMER
US 20180003810 A1 – MULTILINE RECEIVE BEAMFORMERS AND RELATED SYSTEMS AND METHODS
US 20160356666 A1 – INTELLIGENT LEAKAGE DETECTION SYSTEM FOR PIPELINES
US 9482736 B1 – Cascaded Adaptive Beamforming System
US 20140269198 A1 – Beamforming Sensor Nodes And Associated Systems
US 20110188346 A1 – METHOD FOR DETECTING AND LOCATING FLUID INGRESS IN A WELLBORE
US 20100249594 A1 – ITERATIVE TIME DELAY VALUES FOR ULTRASOUND BEAMFORMING
US 20100226501 A1 – BACKGROUND NOISE ESTIMATION
US 20100010351 A1 – TIME OF FLIGHT ESTIMATION METHOD USING BEAMFORMING FOR ACOUSTIC TOMOGRAPHY
US 5668778 A – Method For Detecting Acoustic Signals From An Underwater Source
US 5247302 A – Entropy-based Signal Receiver
US 4170766 A – Beamformer
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/KYLE R QUIGLEY/Primary Examiner, Art Unit 2857
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