DETAILED ACTION
This office action is in response to communication filed on June 16, 2026.
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 June 16, 2026 has been entered.
Response to Amendment
Amendments filed on June 16, 2026 have been entered.
Claims 1-2, 11, 21 and 26 have been amended.
Claims 4-5, 10, 14-15 and 20 remain canceled.
Claims 27-31 have been added.
Claims 1-3, 6-9, 11-13, 16-19 and 21-31 have been examined.
Response to Arguments
Applicant’s arguments, see Remarks (p. 10), filed on 06/16/2026, with respect to the objections to the claims have been fully considered. In view of the amendments to the claims addressing the informalities raised in the previous office action, the objections to the claims have been withdrawn.
Applicant’s arguments, see Remarks (p. 11-14), filed on 06/16/2026, with respect to the rejection of claims 1-3, 6-9, 11-13, 16-19 and 21-26 under 35 U.S.C. 103 have been fully considered but are moot in view of new grounds of rejection.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 26 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 26 recites “the identifier of the at least one equipment item includes at least one of a name of the at least one equipment item or a serial number of the at least one equipment item” which is already incorporated in claim 1, which claim 26 depends from.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Examiner’s Note
Claims 1-3, 6-9, 11-13, 16-19 and 21-31 were evaluated for patent eligibility under 35 U.S.C. 101 using the SUBJECT MATTER ELIGIBILITY TEST FOR PRODUCTS AND PROCESSES described in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (see also 2019 Revised Patent Subject Matter Eligibility Guidance) to determine patent eligibility under 35 U.S.C. 101.
Regarding claim 1, the examiner submits that under Step 1 of the test for evaluating claims for eligibility under 35 U.S.C. 101, the claim is to a machine/manufacture, which is one of the statutory categories of invention.
Continuing with the analysis, under Step 2A - Prong One of the test:
the limitation “perform pre-processing on the signal, the pre-processing including at least one of: signal mixing, signal augmentation, signal time characteristic extraction, signal filtration, signal Fourier transformation, feature extraction pipeline, dimensionality reduction mechanism, or signal spectral analysis” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mathematical concepts (i.e., filtering, Fourier transform, etc.; see specification at [0006], [0104], [0117]) to manipulate data and obtain additional information (e.g., pre-processed signal). The limitation in the context of this claim mainly refers to applying mathematical concepts to transform data.
the limitation “receive, based on the pre-processed signal and the machine learning algorithm, a classification of the pre-processed signal, the classification being associated with an acoustic profile of the leakage of the pressurized gas from a source of the leakage into the three-dimensional open space, the classification indicating at least a three-dimensional direction of the source of the leakage in the three-dimensional open space relative to the one or more acoustic sensors, the source of the leakage being positioned at a second location within the three-dimensional open space, the second location being different from the first location” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mathematical concepts (see specification at [0006], [0104], [0117], [0190]) to obtain additional information (i.e., classification). Except for the recitation of the extra-solution activities (e.g., source/type of data being evaluated) and/or the particular technological environment or field of use, the limitation in the context of this claim mainly refers to applying mathematical concepts to manipulate data and obtain additional information.
the limitation “determine, based on the three-dimensional direction, the second location within the three-dimensional open space” is a process that, under its broadest reasonable interpretation in light of the specification, covers performance of the limitation using mathematical concepts (see specification at [0015], [0054]) to obtain additional information (i.e., second location). The limitation in the context of this claim mainly refers to applying mathematical concepts to manipulate data and obtain additional information.
Therefore, the claim recites a judicial exception under Step 2A - Prong One of the test.
Furthermore, under Step 2A - Prong Two of the test, the claim recites:
“A system for acoustically detecting leakage of a pressurized gas” which generally links the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h));
“one or more acoustic sensors positioned at a first location within a particular physical environment, the particular physical environment being a three-dimensional open space, the three-dimensional open space including: at least one conduit containing the pressurized gas, and a plurality of equipment items connected to the at least one conduit, wherein the first location is spatially separated from the at least one conduit” which adds extra-solution activities (e.g., mere data gathering, source/type of data to be manipulated) (see MPEP 2106.05(g));
“at least one processing unit” which adds the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f));
“receive a signal from the one or more acoustic sensors” which adds extra-solution activities (e.g., mere data gathering, source/type of data to be manipulated) (see MPEP 2106.05(g));
“input the pre-processed signal to a machine learning algorithm, the machine learning algorithm having been trained using training data at least partially collected at the first location within the three-dimensional open space” which adds extra-solution activities (e.g., mere data inputting into a model) (see MPEP 2106.05(g)) and mere computer implementation (e.g., training a machine learning model); and
“cause an output associated with the classification to be displayed on a user device, the output including: a map representation of the three-dimensional open space, the map representation including representations of the at least one conduit and the plurality of equipment items, the representations of the plurality of items including footprint outlines of the plurality of equipment items, a marker within the map representation of the three-dimensional open space indicating the second location within the three-dimensional open space, the marker being overlaid on at least one of the footprint outlines; and an identifier of at least one equipment item of the plurality of equipment items impacted by the leakage, the identifier of the at least one equipment item including at least one of a name of the at least one equipment item or a serial number of the at least one equipment item” which integrates the judicial exception into a practical application, when considering the claim as a whole, by reflecting an improvement to other technology or technical field (e.g., classifying a fluid leakage based on an acoustic signal and determining the source of the fluid leakage and impacted equipment) (see MPEP 2106.05(a)).
Therefore, these additional elements, when considered individually and in combination, integrate the judicial exception into a practical application when viewing the claim as a whole. The claim is eligible at Prong Two of the Revised Step 2A (see 2019 Revised Patent Subject Matter Eligibility Guidance – Revised Step 2A, see also MPEP 2106.04(d)).
Similarly, independent claim 11 is directed to patent eligible subject matter as explained above with regards to claim 1.
Regarding the dependent claims 2-3, 6-9, 12-13, 16-19 and 21-31, they were found to be patent eligible under 35 U.S.C. 101 by incorporating the eligible subject matter of their corresponding independent claims.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
Claims 1-3, 6-9, 11-13, 16-19 and 21-31 are rejected under 35 U.S.C. 103 as being unpatentable over Batany (US 20230184620 A1), hereinafter ‘Batany’, in view of Bart (DE 102019215653 A1, see translation), hereinafter ‘Bart’, and in further view of Yokono (US 20170307465 A1), hereinafter ‘Yokono’, and Hasselbeck (US 20170307464 A1), hereinafter ‘Hasselbeck’.
Regarding claim 1.
Batany discloses:
A system (Fig. 1, item 1 – “fluid network”) for acoustically detecting leakage of a fluid ([0100]: a method for characterizing a leak is applied in a fluid distribution network (see [0001]-[0002])), the system comprising:
one or more acoustic sensors (Fig. 1, items 3 – “vibro-acoustic sensors”) positioned at a first location within a particular physical environment ([0100]: fluid distribution network includes sensors mounted on pipes (see also [0013] and [0035])), the particular physical environment being a three-dimensional space (Fig. 1; [0001]-[0002]: leak characterization is applied in a fluid distribution network, which is implied to be a 3D space), the three-dimensional space including:
at least one conduit (Fig. 1, item 2 – ‘pipe’) containing the fluid ([0100]: fluid distribution network includes pipes containing the fluid for distribution), and
a plurality of equipment items connected to the at least one conduit ([0102]: fluid distribution network includes equipment such as valves, junction collars, etc., which are represented in a digital map of the network (see [0041]; see also [0043], [0074] regarding types of leaks)); and
at least one processing unit (Figs. 1-2, item 10 – “leak characterization module”; [0100]-[0101]: a leak characterization module is hosted within a computer or remote server and includes multiple components for characterization of leaks) configured to:
receive a signal from the one or more acoustic sensors ([0117]: the leak characterization module receives vibro-acoustic signals (Fig. 3, items 21) from the sensors (see also [0108]) for leak characterization analysis (see also [0051], [0054], [0120]));
perform pre-processing on the signal, the pre-processing including at least one of: signal mixing, signal augmentation, signal time characteristic extraction, signal filtration, signal Fourier transformation, feature extraction pipeline, dimensionality reduction mechanism, or signal spectral analysis ([0117]-[0118]: signals are standardized using transfer functions and filtered for evaluation (see also [0067]-[0071]));
input the pre-processed signal to a machine learning algorithm (Fig. 2, item 13 – “neural network”), the machine learning algorithm having been trained using training data at least partially collected at the first location within the three-dimensional space (Fig. 5; [0120]: after pre-processing, the signals are input into a statistical learning model or neural network that has been previously trained using signals from the sensors (see also [0010], [0038]-[0040], [0047], [0054], [0067]-[0071], [0081], [0114]-[0116]));
receive, based on the pre-processed signal and the machine learning algorithm, a classification of the pre-processed signal ([0119]-[0120]: the statistical learning model receives the sensor signals and outputs leak characterization data (see also [0045], [0051]-[0052], [0074])), the classification being associated with an acoustic profile of the leakage of the fluid from a source of the leakage into the three-dimensional space (Fig, 5, [0019], [0024]-[0025], [0104], [0114], [0116]: a database stores signals and corresponding leak information, the database being used for training the statistical learning model in order to obtain the characterization data (see also [0033], [0043], [0061], [0064], [0108])), the classification indicating at least information of the source of the leakage in the three-dimensional space relative to the one or more acoustic sensors ([0019], [0024], [0061], [0064], [0119]: information regarding the location of the leak based on the distance from the sensors (see [0033]) is also obtained), the source of the leakage being positioned at a second location within the three-dimensional space, the second location being different from the first location (Fig. 3, item 20; [0108]: leaks are located within the distribution network);
determine, based on the information, the second location within the three-dimensional space ([0019], [0024], [0061], [0064], [0119]: information regarding the location of the leak based on the distance from the sensors (see [0033]) is obtained); and
cause an output associated with the classification to be displayed on a user device ([0100]-[0101], [0110]: a leak characterization module is hosted within a computer or remote server, with information regarding the leak characterization allowing for repairs (see also [0024], [0053], [0061]), which implies information to be displayed on a user device of the computer), the output including:
a map representation of the three-dimensional space, the map representation including representations of the at least one conduit and the plurality of equipment items (Fig. 1; [0102]: a digital map of the fluid distribution network including the pipes, sensors and other equipment is used during the analysis (see also [0014], [0041], [0055], [0072])),
a marker within the map representation of the three-dimensional space indicating the second location within the three-dimensional open space (Fig. 3, item 20; [0110]: once leak is located, maintenance agent is send to right address (see [0024] and [0061]; see also [0064])); and
an identifier of at least one equipment item of the plurality of equipment items impacted by the leakage ([0074], [0102], [0110], [0119]-[0120]: type of leak corresponding to the impacted equipment is determined as part of the analysis using the digital map information, the digital map including the position of the equipment for performing maintenance or repairs (see also [0024], [0035], [0041], [0061], [0072])).
Batany does not explicitly disclose:
the fluid is a pressurized gas;
the three-dimensional space is a three-dimensional open space;
wherein the first location is spatially separated from the at least one conduit;
the information is three-dimensional direction;
the representations of the plurality of equipment items including footprint outlines of the plurality of equipment items;
the marker being overlaid on at least one of the footprint outlines; and
the identifier of the at least one equipment item including at least one of a name of the at least one equipment item or a serial number of the at least one equipment item.
Regarding “the fluid is a pressurized gas”, Bart teaches:
“A measuring system (20; 120) for monitoring a line system (10; 110) which carries gas under positive or negative pressure is proposed, the measuring system (20; 120) having at least one measuring unit (22; 122) for installation on or in the line system (10; 110), with at least one acoustic sensor (24; 124), a processing unit (28; 128), and a data transmission device (30) for transmitting measurement data from the measuring unit (22; 122) to the processing unit; having. The measuring unit (22; 122) is set up to digitize data recorded with the acoustic sensor and to transmit it as a data stream to the processing unit (28; 128) via the data transmission device. The processing unit (28; 128) is set up to subject the data received from the measuring unit (22; 122) to a Fourier transformation, in particular a fast Fourier transformation (FFT), to use a machine-learned model to decide whether the processed data is a indicate leakage in the line system (10; 110) and output this determination in the event of an indicated leakage” (Abstract: a measuring system for monitoring a line carrying gas under positive or negative pressure (pressurized gas), and determining leaks in the line using acoustic data and machine learning is presented (see also page 1, last par. regarding leaks on pressurized gas)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Batany in view of Bart to incorporate the fluid as a pressurized gas, in order to provide a robust system that can monitor different fluid conditions present in actual environments.
Regarding “the three-dimensional space is a three-dimensional open space”, Yokono teaches:
“To perform a fluid leak diagnosis, first, an area map image Pa (image such as that shown in FIG. 10) of a diagnosis area in a plant to be diagnosed is stored in the storage unit 18 of the mobile computer 2. After that, while a diagnosis staff moves around the diagnosis area, leak portions are diagnosed based on detected ultrasonic wave values and detection sounds using the mobile detector 1 as shown in FIG. 2” ([0086]: fluid leak diagnosis is performed in a plant by staff moving around the area and using a mobile detector (see also Batany at [0024], [0061] regarding sending maintenance agent for repairing the leak)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Batany in view of Bart, and in further view of Yokono, to incorporate the three-dimensional space as a three-dimensional open space, in order to easily access diagnosis areas for improved analysis and prompt response.
Regarding “wherein the first location is spatially separated from the at least one conduit; and the information is three-dimensional direction”, Hasselbeck teaches:
“The sensor is placed in proximity to plumbing or plumbing fixtures, where it periodically monitors for high frequency signals associated with leaks. This acoustic signal may propagate many meters from the leak, both within the plumbing conduit and/or in free space. The closer the sensor is to the leak, the better its sensitivity. Detection will depend on i) the rate of flow, ii) the physical geometry of the emitting orifice, and iii) the acoustic quality of the path between leak and detector. Direct contact with pipe is not a requirement for effective sensor operation. Because the leak signals propagate with small loss in air, it is better to have the acoustic sensor mounted above the pipe (e.g., a PVC/plastic pipe that is part of the plumbing) than directly on it because it will hear water escaping outside the pipe, not within it” ([0035]: sensors can be located in proximity to (spatially separated from) conduits); and
“To summarize, disclosed is a water leak sensing method that includes one or more acoustic transducers, one or more sound collection devices including on-axis or off-axis parabolic reflectors to focus acoustic energy on the acoustic transducer(s) to discern the direction of the leak source …” ([0050]: a water leak sensing method includes directional sound collection devices (see [0039]) to discern direction (analogous to three-dimensional direction) of the leak source (see also abstract; see further Batany at [0064] regarding estimating distance between sensor and leak); examiner notes that by knowing the distance between multiple sensing devices and the source of the leak relative to the acoustic sensors, the direction of the leak source can be calculated; examiner also notes that distance/direction/location of leaks can be represented in three dimensions).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Batany in view of Bart and Yokono, and in further view of Hasselbeck, to incorporate the first location being spatially separated from the at least one conduit, and the information as three-dimensional direction, in order to provide a faster and enhanced localization of leaks.
Regarding “the representations of the plurality of equipment items including footprint outlines of the plurality of equipment items; the marker being overlaid on at least one of the footprint outlines; and the identifier of the at least one equipment item including at least one of a name of the at least one equipment item or a serial number of the at least one equipment item”, Yokono further teaches:
“The leak portion map producing unit 34 accesses the database Db via the database management unit 32, produces, as shown in FIG. 15a, a map image (leak portion
display image) Pe in which, with respect to the positional data indicating the positions of respective fluid leak portions in the plant, indicators representing the respective fluid leak
portions are displayed superimposed on the overall configuration diagram image (or the partial configuration diagram image) of the target plant at locations corresponding to the
positional data of the respective fluid leak portions on the overall configuration diagram image, and displays the produced map image Pe on the display unit 30. Specifically,
coordinate values, which represent the position indicated by the mark Ma, in the area map image Pa are converted into coordinate values in the overall configuration diagram image, the converted coordinate values are used as the positional data, and an indicator is disposed at the location indicated by the converted coordinate values … the indicators may also be displayed associated with other data, such as, for example, the area or the floor to which each fluid leak portion belongs, the device that is leaking fluid or the site of the fluid leak (i.e., target member or target site of the collected data D), the date and time of detection, or the name of the diagnosis staff ” ([0121]: leaks in the piping system of a plant (see [0017]) are detected using a portable detecting device (see [0074]) and corresponding information regarding the leak is displayed superimposed on a configuration plant of the diagram (footprint outlines of the plurality of equipment items), the information including target member (see [0095] - name of the at least one equipment item) (see also [0098]-[0099], [0106], [0116])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Batany in view of Bart, Yokono and Hasselbeck, to incorporate the representations of the plurality of equipment items including footprint outlines of the plurality of equipment items; and the marker being overlaid on at least one of the footprint outlines; and the identifier of the at least one equipment item including at least one of a name of the at least one equipment item or a serial number of the at least one equipment item, in order to manage data on fluid leaks individually, as discussed by Yokono ([0120]), while easily identifying leaks for improving efficiency of repairs.
Regarding claim 2.
Batany in view of Bart, Yokono and Hasselbeck, discloses all the features of claim 1 as described above.
Batany does not disclose:
at least one acoustic sensor of the one or more acoustic sensors is configured to dynamically change its orientation.
Yokono further teaches:
“As shown in FIGS. 3 and 4, directional microphones 3 for detecting ultrasonic waves generated at a fluid leak portion and a light source 4 for emitting a light beam are arranged on a front end portion of the mobile detector 1, and a display unit 5 for displaying detected ultrasonic wave values (specifically, detected sound pressures) in bar-graphic representation and digital representation as well as various keys 6 are arranged on a rear end portion of the mobile detector 1” ([0079]: a mobile detector includes directional microphones that are used for detecting ultrasonic waves generated at a fluid leak portion in pipes (see Fig. 2)); and
“As shown in FIG. 5, the plurality of microphones 3 are arranged oriented in the same direction while being dispersed at the vertex positions of a regular polygon K (regular hexagon in the present example) in a state in which their directional ranges S have common overlapping portions SS. On the other hand, the light source 4 for emitting a light beam is disposed at the center of gravity of the regular polygon K as viewed in the directivity direction of the microphones such that the light beam is emitted to the common overlapping portions SS of the directional ranges S of the microphones. Thus, as shown in FIG. 2, in detecting a leak portion based on detected ultrasonic wave values and detection sounds while changing the directivity direction of the microphones 3 by changing the orientation of the front end of the mobile detector 1, the detection of a leak portion can be performed while visually observing light beam irradiation points and visually checking detection target portions in a clear manner at the thus respectively irradiated points one after another” ([0081]: directivity direction of microphones is changed during detection process).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Batany in view of Bart, Yokono and Hasselbeck, to configure at least one acoustic sensor of the one or more acoustic sensors to dynamically change its orientation, in order to provide a robust data acquisition system that can accommodate to the real conditions of the field (e.g., sensor can be automatically focused to different areas in the environment for improved data acquisition capability).
Regarding claim 3.
Batany in view of Bart, Yokono and Hasselbeck, discloses all the features of claim 1 as described above.
Batany further discloses:
the machine learning algorithm comprises a deep learning algorithm ([0105]: convolutional neural networks are employed for the analysis (see also [0048], [0078])).
Regarding claim 6.
Batany in view of Bart, Yokono and Hasselbeck, discloses all the features of claim 1 as described above.
Batany further discloses:
the machine learning algorithm is uniquely trained for the three-dimensional open space ([0114]-[0116]: statistical learning model or neural network has been previously trained using signals from the sensors in fluid distribution network (see also [0010], [0038]-[0040], [0054], [0067]-[0071], [0081]; see further claim 1 for three-dimensional open space feature)).
Regarding claim 7.
Batany in view of Bart, Yokono and Hasselbeck, discloses all the features of claim 1 as described above.
Batany further discloses:
the machine learning algorithm is a generalized algorithm tuned to the three-dimensional open space ([0114]-[0116], [0127]: statistical learning model or neural network has been previously trained using signals from the sensors in fluid distribution network as well as interfering noise (see also [0010], [0028], [0038]-[0040], [0054], [0067]-[0071], [0081]; see further claim 1 for three-dimensional open space feature)).
Regarding claim 8.
Batany in view of Bart, Yokono and Hasselbeck, discloses all the features of claim 1 as described above.
Batany does not explicitly disclose:
the prompt output is at least one of a message, graphical user interface content, or data sent to a different system.
However, Batany teaches:
“Once the leak 20 is located, it is possible to go on site to excavate it and repair it” ([0110]: leak information is used for sending agents for performing maintenance (see also [0004], [0024], [0053], [0061]); examiner interprets that leak information must be presented to the agents in a form (e.g., text or graphical form) in order to direct them to the right address).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Batany in view of Bart, Yokono and Hasselbeck, to incorporate the prompt output as at least one of a message, graphical user interface content, or data sent to a different system, in order to facilitate the communication of essential information regarding the detected leaks for accurately implementing remedy actions.
Regarding claim 9.
Batany in view of Bart, Yokono and Hasselbeck, discloses all the features of claim 1 as described above.
Batany further discloses:
the at least one processing unit is configured to receive a plurality of signals from a plurality of acoustic sensors ([0117], [0119]-[0120]: the statistical learning model receives the sensor signals and outputs leak characterization data (see also [0051]-[0052], [0054])).
Regarding claim 11.
Batany discloses:
A computer-implemented method for acoustically detecting leakage of a fluid ([0100]-[0101]: a method for characterizing a leak is applied in a fluid distribution network (Fig. 1, item 1; see [0001]-[0002]) using a leak characterization module (Figs. 1-2, item 10) hosted within a computer or remote server) using one or more acoustic sensors (Fig. 1, items 3 – “vibro-acoustic sensors”), the computer-implemented method comprising:
receiving a signal from the one or more acoustic sensors ([0117]: the leak characterization module receives vibro-acoustic signals (Fig. 3, items 21) from the sensors (see also [0108]) for leak characterization analysis (see also [0051], [0054], [0120])) positioned at a first location within a particular physical environment ([0100]: fluid distribution network includes sensors mounted on pipes (see also [0013] and [0035])), the particular physical environment being a three-dimensional space (Fig. 1; [0001]-[0002]: leak characterization is applied in a fluid distribution network, which is implied to be a 3D space), the three-dimensional space including:
at least one conduit (Fig. 1, item 2 – ‘pipe’) containing the fluid ([0100]: fluid distribution network includes pipes containing the fluid for distribution), and
a plurality of equipment items connected to the at least one conduit ([0102]: fluid distribution network includes equipment such as valves, junction collars, etc., which are represented in a digital map of the network (see [0041]; see also [0043], [0074] regarding types of leaks));
performing pre-processing on the signal, the pre-processing including at least one of: signal mixing, signal augmentation, signal time characteristic extraction, signal filtration, signal Fourier transformation, feature extraction pipeline, dimensionality reduction mechanism, or signal spectral analysis ([0117]-[0118]: signals are standardized using transfer functions and filtered for evaluation (see also [0067]-[0071]));
inputting the pre-processed signal to a machine learning algorithm (Fig. 2, item 13 – “neural network”), the machine learning algorithm having been trained using training data at least partially collected at the first location within the three-dimensional space (Fig. 5; [0120]: after pre-processing, the signals are input into a statistical learning model or neural network that has been previously trained using signals from the sensors (see also [0010], [0038]-[0040], [0047], [0054], [0067]-[0071], [0081], [0114]-[0116]));
receiving, based on the pre-processed signal and the machine learning algorithm, a classification of the pre-processed signal ([0119]-[0120]: the statistical learning model receives the sensor signals and outputs leak characterization data (see also [0045], [0051]-[0052], [0074])), the classification being associated with an acoustic profile of the leakage of the fluid from a source of the leakage into the three-dimensional space (Fig, 5, [0019], [0024]-[0025], [0104], [0114], [0116]: a database stores signals and corresponding leak information, the database being used for training the statistical learning model in order to obtain the characterization data (see also [0033], [0043], [0061], [0064], [0108])), the classification indicating at least information of the source of the leakage in the three-dimensional space relative to the one or more acoustic sensors ([0019], [0024], [0061], [0064], [0119]: information regarding the location of the leak based on the distance from the sensors (see [0033]) is also obtained), the source of the leakage being positioned at a second location within the three-dimensional space, the second location being different from the first location (Fig. 3, item 20; [0108]: leaks are located within the distribution network);
determining, based on the information, the second location within the three-dimensional space ([0019], [0024], [0061], [0064], [0119]: information regarding the location of the leak is based on the distance from the sensors (see [0033]) is also obtained); and
causing an output associated with the classification to be displayed on a user device ([0100]-[0101], [0110]: a leak characterization module is hosted within a computer or remote server, with information regarding the leak characterization allowing for repairs (see also [0024], [0053], [0061]), which implies information to be displayed on a user device of the computer), the output including:
a map representation of the three-dimensional space, the map representation including representations of the at least one conduit and the plurality of equipment items (Fig. 1; [0102]: a digital map of the fluid distribution network including the pipes, sensors and other equipment is used during the analysis (see also [0014], [0041], [0055], [0072])),
a marker within the map representation of the three-dimensional space indicating the second location within the three-dimensional space (Fig. 3, item 20; [0110]: once leak is located, maintenance agent is send to right address (see [0024] and [0061]; see also [0064])); and
an identifier of at least one equipment item of the plurality of equipment items impacted by the leakage ([0074], [0102], [0110], [0119]-[0120]: type of leak corresponding to the impacted equipment is determined as part of the analysis using the digital map information, the digital map including the position of the equipment for performing maintenance or repairs (see also [0024], [0035], [0041], [0061], [0072])).
Batany does not explicitly disclose:
the fluid is a pressurized gas;
the three-dimensional space is a three-dimensional open space;
wherein the first location is spatially separated from the at least one conduit;
the information is three-dimensional direction;
the representations of the plurality of equipment items including footprint outlines of the plurality of equipment items;
the marker being overlaid on at least one of the footprint outlines; and
the identifier of the at least one equipment item including at least one of a name of the at least one equipment item or a serial number of the at least one equipment item.
Regarding “the fluid is a pressurized gas”, Bart teaches:
“A measuring system (20; 120) for monitoring a line system (10; 110) which carries gas under positive or negative pressure is proposed, the measuring system (20; 120) having at least one measuring unit (22; 122) for installation on or in the line system (10; 110), with at least one acoustic sensor (24; 124), a processing unit (28; 128), and a data transmission device (30) for transmitting measurement data from the measuring unit (22; 122) to the processing unit; having. The measuring unit (22; 122) is set up to digitize data recorded with the acoustic sensor and to transmit it as a data stream to the processing unit (28; 128) via the data transmission device. The processing unit (28; 128) is set up to subject the data received from the measuring unit (22; 122) to a Fourier transformation, in particular a fast Fourier transformation (FFT), to use a machine-learned model to decide whether the processed data is a indicate leakage in the line system (10; 110) and output this determination in the event of an indicated leakage” (Abstract: a measuring system for monitoring a line carrying gas under positive or negative pressure (pressurized gas), and determining leaks in the line using acoustic data and machine learning is presented (see also page 1, last par. regarding leaks on pressurized gas)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Batany in view of Bart to incorporate the fluid as a pressurized gas, in order to provide a robust system that can monitor different fluid conditions present in actual environments.
Regarding “the three-dimensional space is a three-dimensional open space”, Yokono teaches:
“To perform a fluid leak diagnosis, first, an area map image Pa (image such as that shown in FIG. 10) of a diagnosis area in a plant to be diagnosed is stored in the storage unit 18 of the mobile computer 2. After that, while a diagnosis staff moves around the diagnosis area, leak portions are diagnosed based on detected ultrasonic wave values and detection sounds using the mobile detector 1 as shown in FIG. 2” ([0086]: fluid leak diagnosis is performed in a plant by staff moving around the area and using a mobile detector (see also Batany at [0024], [0061] regarding sending maintenance agent for repairing the leak)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Batany in view of Bart, and in further view of Yokono, to incorporate the three-dimensional space as a three-dimensional open space, in order to easily access diagnosis areas for improved analysis and prompt response.
Regarding “wherein the first location is spatially separated from the at least one conduit; and the information is three-dimensional direction”, Hasselbeck teaches:
“The sensor is placed in proximity to plumbing or plumbing fixtures, where it periodically monitors for high frequency signals associated with leaks. This acoustic signal may propagate many meters from the leak, both within the plumbing conduit and/or in free space. The closer the sensor is to the leak, the better its sensitivity. Detection will depend on i) the rate of flow, ii) the physical geometry of the emitting orifice, and iii) the acoustic quality of the path between leak and detector. Direct contact with pipe is not a requirement for effective sensor operation. Because the leak signals propagate with small loss in air, it is better to have the acoustic sensor mounted above the pipe (e.g., a PVC/plastic pipe that is part of the plumbing) than directly on it because it will hear water escaping outside the pipe, not within it” ([0035]: sensors can be located in proximity to (spatially separated from) conduits); and
“To summarize, disclosed is a water leak sensing method that includes one or more acoustic transducers, one or more sound collection devices including on-axis or off-axis parabolic reflectors to focus acoustic energy on the acoustic transducer(s) to discern the direction of the leak source …” ([0050]: a water leak sensing method includes directional sound collection devices (see [0039]) to discern direction (analogous to three-dimensional direction) of the leak source (see also abstract; see further Batany at [0064] regarding estimating distance between sensor and leak); examiner notes that by knowing the distance between multiple sensing devices and the source of the leak relative to the acoustic sensors, the direction of the leak source can be calculated; examiner also notes that distance/direction/location of leaks can be represented in three dimensions).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Batany in view of Bart and Yokono, and in further view of Hasselbeck, to incorporate the first location being spatially separated from the at least one conduit, and the information as three-dimensional direction, in order to provide a faster and enhanced localization of leaks.
Regarding “the representations of the plurality of equipment items including footprint outlines of the plurality of equipment items; the marker being overlaid on at least one of the footprint outlines; and the identifier of the at least one equipment item including at least one of a name of the at least one equipment item or a serial number of the at least one equipment item”, Yokono further teaches:
“The leak portion map producing unit 34 accesses the database Db via the database management unit 32, produces, as shown in FIG. 15a, a map image (leak portion
display image) Pe in which, with respect to the positional data indicating the positions of respective fluid leak portions in the plant, indicators representing the respective fluid leak
portions are displayed superimposed on the overall configuration diagram image (or the partial configuration diagram image) of the target plant at locations corresponding to the
positional data of the respective fluid leak portions on the overall configuration diagram image, and displays the produced map image Pe on the display unit 30. Specifically,
coordinate values, which represent the position indicated by the mark Ma, in the area map image Pa are converted into coordinate values in the overall configuration diagram image, the converted coordinate values are used as the positional data, and an indicator is disposed at the location indicated by the converted coordinate values … the indicators may also be displayed associated with other data, such as, for example, the area or the floor to which each fluid leak portion belongs, the device that is leaking fluid or the site of the fluid leak (i.e., target member or target site of the collected data D), the date and time of detection, or the name of the diagnosis staff ” ([0121]: leaks in the piping system of a plant (see [0017]) are detected using a portable detecting device (see [0074]) and corresponding information regarding the leak is displayed superimposed on a configuration plant of the diagram (footprint outlines of the plurality of equipment items), the information including target member (see [0095] - name of the at least one equipment item) (see also [0098]-[0099], [0106], [0116])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Batany in view of Bart, Yokono and Hasselbeck, to incorporate the representations of the plurality of equipment items including footprint outlines of the plurality of equipment items; the marker being overlaid on at least one of the footprint outlines; and the identifier of the at least one equipment item including at least one of a name of the at least one equipment item or a serial number of the at least one equipment item, in order to manage data on fluid leaks individually, as discussed by Yokono ([0120]), while easily identifying leaks for improving efficiency of repairs.
Regarding claim 12.
Batany in view of Bart, Yokono and Hasselbeck, discloses all the features of claim 11 as described above.
Batany does not disclose:
at least one acoustic sensor of the one or more acoustic sensors is configured to dynamically change its orientation.
Yokono further teaches:
“As shown in FIGS. 3 and 4, directional microphones 3 for detecting ultrasonic waves generated at a fluid leak portion and a light source 4 for emitting a light beam are arranged on a front end portion of the mobile detector 1, and a display unit 5 for displaying detected ultrasonic wave values (specifically, detected sound pressures) in bar-graphic representation and digital representation as well as various keys 6 are arranged on a rear end portion of the mobile detector 1” ([0079]: a mobile detector includes directional microphones that are used for detecting ultrasonic waves generated at a fluid leak portion in pipes (see Fig. 2)); and
“As shown in FIG. 5, the plurality of microphones 3 are arranged oriented in the same direction while being dispersed at the vertex positions of a regular polygon K (regular hexagon in the present example) in a state in which their directional ranges S have common overlapping portions SS. On the other hand, the light source 4 for emitting a light beam is disposed at the center of gravity of the regular polygon K as viewed in the directivity direction of the microphones such that the light beam is emitted to the common overlapping portions SS of the directional ranges S of the microphones. Thus, as shown in FIG. 2, in detecting a leak portion based on detected ultrasonic wave values and detection sounds while changing the directivity direction of the microphones 3 by changing the orientation of the front end of the mobile detector 1, the detection of a leak portion can be performed while visually observing light beam irradiation points and visually checking detection target portions in a clear manner at the thus respectively irradiated points one after another” ([0081]: directivity direction of microphones is changed during detection process).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Batany in view of Bart, Yokono and Hasselbeck, to configure at least one acoustic sensor of the one or more acoustic sensors to dynamically change its orientation, in order to provide a robust data acquisition system that can accommodate to the real conditions of the field (e.g., sensor can be focused to different areas in the environment for improved data acquisition capability).
Regarding claim 13.
Batany in view of Bart, Yokono and Hasselbeck, discloses all the features of claim 11 as described above.
Batany further discloses:
the machine learning algorithm comprises a deep learning algorithm ([0105]: convolutional neural networks are employed for the analysis (see also [0048], [0078])).
Regarding claim 16.
Batany in view of Bart, Yokono and Hasselbeck, discloses all the features of claim 11 as described above.
Batany further discloses:
the machine learning algorithm is uniquely trained for the three-dimensional open space ([0114]-[0116]: statistical learning model or neural network has been previously trained using signals from the sensors in fluid distribution network (see also [0010], [0038]-[0040], [0054], [0067]-[0071], [0081]; see further claim 11 for three-dimensional open space feature)).
Regarding claim 17.
Batany in view of Bart, Yokono and Hasselbeck, discloses all the features of claim 11 as described above.
Batany further discloses:
the machine learning algorithm is a generalized algorithm tuned to the three-dimensional open space ([0114]-[0116], [0127]: statistical learning model or neural network has been previously trained using signals from the sensors in fluid distribution network as well as interfering noise (see also [0010], [0028], [0038]-[0040], [0054], [0067]-[0071], [0081]; see further claim 11 for three-dimensional open space feature)).
Regarding claim 18.
Batany in view of Bart, Yokono and Hasselbeck, discloses all the features of claim 11 as described above.
Batany does not explicitly disclose:
the prompt output is at least one of a message, graphical user interface content, or data sent to a different system.
However, Batany teaches:
“Once the leak 20 is located, it is possible to go on site to excavate it and repair it” ([0110]: leak information is used for sending agents for performing maintenance (see also [0004], [0024], [0053], [0061]); examiner interprets that leak information must be presented to the agents in a form (e.g., text or graphical form) to in order to direct them to the right address).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Batany in view of Bart, Yokono and Hasselbeck, to incorporate the prompt output as at least one of a message, graphical user interface content, or data sent to a different system, in order to facilitate the communication of essential information regarding the detected leaks for accurately implementing remedy actions.
Regarding claim 19.
Batany in view of Bart, Yokono and Hasselbeck, discloses all the features of claim 11 as described above.
Batany further discloses:
receiving a plurality of signals from a plurality of acoustic sensors ([0117], [0119]-[0120]: the statistical learning model receives the sensor signals and outputs leak characterization data (see also [0051]-[0052], [0054])).
Regarding claim 21.
Batany in view of Bart, Yokono and Hasselbeck, discloses all the features of claim 1 as described above.
Batany does not disclose:
the three-dimensional open space is a space within a building.
Bart further teaches:
“The invention thus offers a simple and inexpensive way of monitoring line systems with gas under positive or negative pressure. The concept of the line system is to be understood broadly. By way of example, but not completely, this includes systems (e.g. in production, hospitals, office buildings, parking garages) tools (including mobile tools), means of transport (here e.g. compressed air cars, or any objects equipped with gas expansion motors) if they contain pressurized gases are supplied. Leakages can occur in the entire pneumatic / compressed air system / gas system (e.g. consisting of compressor / compressor, pipes and actuators). Measuring units can be attached variably there (for example on couplings, maintenance units, valve terminals, inside / outside of systems / tools / machines)” (page 5, par. 2-3: monitored line systems include office buildings (a space within a building)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Batany in view of Bart, Yokono and Hasselbeck, to incorporate the three-dimensional open space as a space within a building, in order to provide a flexible system that can monitor leak conditions occurring in different environments.
Regarding claim 22.
Batany in view of Bart, Yokono and Hasselbeck, discloses all the features of claim 1 as described above.
Batany further discloses:
the classification further indicates a cause of the leakage and wherein the output further includes an indication of the cause of the leakage ([0110]: the characterization information includes the cause of the leak (see also [0043], [0074]; see further [0024], [0061] regarding sending maintenance agent for repairing the leak which implies outputting cause of leakage for maintenance)).
Regarding claim 23.
Batany in view of Bart, Yokono and Hasselbeck, discloses all the features of claim 22 as described above.
Batany further discloses:
the cause of the leakage includes at least one of excessive pressure, a broken seal, corrosion, a hole, a crack, a loose connection, an open nozzle or a damaged joint ([0043], [0074], [0110]: characterization of a leak includes the type (cause) of the leak such as crack or defective seal).
Regarding claim 24.
Batany in view of Bart, Yokono and Hasselbeck, discloses all the features of claim 1 as described above.
Batany does not explicitly disclose:
the classification further includes an estimated amount of the pressurized gas lost due to the leakage.
However, Batany teaches:
“The present disclosure relates to a method for training a statistical learning model intended for the characterization of a leak in a fluid network, in which the fluid network is equipped with a plurality of vibro-acoustic sensors configured to provide vibro-acoustic signals, including the construction of a database associating, at least for a plurality of documented leaks, at least one leak characterization data actually determined among the leak type and the leak flow rate with at least one vibro-acoustic signal obtained directly or indirectly from at least one vibro-acoustic sensor, and including the training of the statistical learning model on the thus constructed database” ([0010]: characterization of a leak in a fluid network (see also [0002] regarding gas networks) is achieved by using vibro-acoustic signals and training a statistical learning model, the characterization including the flow rate of the leak (analogous to an estimated amount of the pressurized gas lost due to the leakage; see [0029], [0045]-[0046] and [0111])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Batany in view of Bart, Yokono and Hasselbeck, to incorporate the classification to further include an estimated amount of the pressurized gas lost due to the leakage, in order to obtain information on the severity of the leak without investing significant resources as well as to prioritize the repairs, optimizing maintenance costs and increasing the overall performance of the fluid network, as discussed by Batany ([0052]-[0053]).
Regarding claim 25.
Batany in view of Bart, Yokono and Hasselbeck, discloses all the features of claim 24 as described above.
Batany does not explicitly disclose:
the output further includes an indication of the estimated amount of the pressurized gas lost due to the leakage.
However, Batany teaches:
“The present disclosure relates to a method for training a statistical learning model intended for the characterization of a leak in a fluid network, in which the fluid network is equipped with a plurality of vibro-acoustic sensors configured to provide vibro-acoustic signals, including the construction of a database associating, at least for a plurality of documented leaks, at least one leak characterization data actually determined among the leak type and the leak flow rate with at least one vibro-acoustic signal obtained directly or indirectly from at least one vibro-acoustic sensor, and including the training of the statistical learning model on the thus constructed database” ([0010]: characterization of a leak in a fluid network (see also [0002] regarding gas networks) is achieved by using vibro-acoustic signals and training a statistical learning model, the characterization including the flow rate of the leak (analogous to an estimated amount of the pressurized gas lost due to the leakage; see [0029], [0045]-[0046] and [0111])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Batany in view of Bart, Yokono and Hasselbeck, to incorporate the output further including an indication of the estimated amount of the pressurized gas lost due to the leakage, in order to obtain information on the severity of the leak without investing significant resources as well as to prioritize the repairs, optimizing maintenance costs and increasing the overall performance of the fluid network, as discussed by Batany ([0052]-[0053]).
Regarding claim 26.
Batany in view of Bart, Yokono and Hasselbeck, discloses all the features of claim 1 as described above.
Batany does not explicitly disclose:
the identifier of the at least one equipment item includes at least one of a name of the at least one equipment item or a serial number of the at least one equipment item.
Yokono further teaches:
“The leak portion map producing unit 34 accesses the database Db via the database management unit 32, produces, as shown in FIG. 15a, a map image (leak portion
display image) Pe in which, with respect to the positional data indicating the positions of respective fluid leak portions in the plant, indicators representing the respective fluid leak
portions are displayed superimposed on the overall configuration diagram image (or the partial configuration diagram image) of the target plant at locations corresponding to the
positional data of the respective fluid leak portions on the overall configuration diagram image, and displays the produced map image Pe on the display unit 30. Specifically,
coordinate values, which represent the position indicated by the mark Ma, in the area map image Pa are converted into coordinate values in the overall configuration diagram image, the converted coordinate values are used as the positional data, and an indicator is disposed at the location indicated by the converted coordinate values … the indicators may also be displayed associated with other data, such as, for example, the area or the floor to which each fluid leak portion belongs, the device that is leaking fluid or the site of the fluid leak (i.e., target member or target site of the collected data D), the date and time of detection, or the name of the diagnosis staff ” ([0121]: leaks in the piping system of a plant (see [0017]) are detected using a portable detecting device (see [0074]) and corresponding information regarding the leak is displayed superimposed on a configuration plant of the diagram, the information including target member (see [0095] - name of the at least one equipment item) (see also [0098]-[0099], [0106], [0116])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Batany in view of Bart, Yokono and Hasselbeck, to incorporate the identifier of the at least one equipment item including at least one of a name of the at least one equipment item or a serial number of the at least one equipment item, in order to manage data on fluid leaks individually, as discussed by Yokono ([0120]), while easily identifying leaks for improving efficiency of repairs.
Regarding claim 27.
Batany in view of Bart, Yokono and Hasselbeck, discloses all the features of claim 1 as described above.
Batany further discloses:
the one or more acoustic sensors comprises a single acoustic sensor ([0013]: at least one sensor can be used (see also [0034] regarding at least one sensor being used for monitoring some length of the fluid network)).
Regarding claim 28.
Batany in view of Bart, Yokono and Hasselbeck, discloses all the features of claim 27 as described above.
Batany further discloses:
the single acoustic sensor comprises a microphone ([0013]: sensor is implemented as a microphone (see also [0108])).
Regarding claim 29.
Batany in view of Bart, Yokono and Hasselbeck, discloses all the features of claim 1 as described above.
Batany does not explicitly disclose:
the three-dimensional direction is determined based on sound features extracted from the signal.
Yokono further teaches:
“As shown in FIGS . 3 and 4 , directional microphones 3 for detecting ultrasonic waves generated at a fluid leak portion and a light source 4 for emitting a light beam are arranged on a front end portion of the mobile detector 1, and a display unit 5 for displaying detected ultrasonic wave values (specifically, detected sound pressures) in bar-graphic representation and digital representation as well as various keys 6 are arranged on a rear end portion of the mobile detector 1” ([0079]: directional microphones are used for detecting ultrasonic waves generated at the leak portion for leak detection (see [0078]), with ultrasonic wave values (analogous to sound features extracted from the signal) being displayed; examiner interprets that by detecting ultrasonic waves using directional microphones, 3D direction of leak can be obtained).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Batany in view of Bart, Yokono and Hasselbeck, to incorporate the three-dimensional direction being determined based on sound features extracted from the signal, in order to facilitate the identification of leaks for rapidly taking appropriate actions.
Regarding claim 30.
Batany in view of Bart, Yokono and Hasselbeck, discloses all the features of claim 1 as described above.
Batany does not explicitly disclose:
the three-dimensional direction is determined without a beamforming technique across a plurality of microphones.
However, Batany further teaches:
“In the present disclosure, by “vibro-acoustic sensor” it is meant a sensor coupled to any type of liquid or solid medium and capable of recording a displacement, a speed, an acceleration or a higher-order time derivative in one or several directions, and in particular in the three special directions. It can therefore be in particular an accelerometer, a seismometer, a geophone, a microphone or a hydrophone, to mention but a few. These may be permanently mounted sensors and/or mobile sensors temporarily applied by an operator” ([0013]: different types of sensors can be applied, with particular information being recorded in different directions, as mobile sensors can also be implemented (see also [0041] regarding signals propagating through equipment, which implies beamforming technique not being used)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Batany in view of Bart, Yokono and Hasselbeck, to incorporate the three-dimensional direction being determined without a beamforming technique across a plurality of microphones, in order to provide a robust and efficient monitoring system that diagnoses different equipment.
Regarding claim 31.
Batany in view of Bart, Yokono and Hasselbeck, discloses all the features of claim 1 as described above.
Batany further discloses:
the one or more acoustic sensors comprise a plurality of acoustic sensors ([0013]: at least one sensor can be used, which implies more than one sensor can be applied (see also [0034] regarding at least one sensor being used for monitoring some length of the fluid network)).
Batany does not explicitly disclose:
wherein the three-dimensional direction is determined based at least in part on directionality information obtained from the plurality of acoustic sensors.
Yokono further teaches:
“As shown in FIGS . 3 and 4 , directional microphones 3 for detecting ultrasonic waves generated at a fluid leak portion and a light source 4 for emitting a light beam are arranged on a front end portion of the mobile detector 1, and a display unit 5 for displaying detected ultrasonic wave values (specifically, detected sound pressures) in bar-graphic representation and digital representation as well as various keys 6 are arranged on a rear end portion of the mobile detector 1” ([0079]: directional microphones are used for detecting ultrasonic waves generated at the leak portion for leak detection (see [0078]); examiner interprets that by detecting ultrasonic waves using directional microphones, 3D direction of leak can be obtained).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Batany in view of Bart, Yokono and Hasselbeck, to incorporate the three-dimensional direction being determined based at least in part on directionality information obtained from the plurality of acoustic sensors, in order to facilitate the identification of leaks for rapidly taking appropriate actions.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bowen; Jay C et al., US 20220128427 A1, METHODS AND SYSTEMS TO INTERNALLY AND EXTERNALLY LOCATE OBSTRUCTIONS AND LEAKS IN CONVEYANCE PIPE
Reference discloses locating leaks or obstruction in pipes.
Dharmadhikari; Ninad et al., US 20240035630 A1¸ Method And System Of Leak Detection
Reference discloses mapping source of leaks in a pipeline.
LAI, Zhen-ming et al., CN 117570382 A¸ Distributed water leakage detection method and system of hydropower plant factory
Reference discloses a leak detection method that collects data from detection points and uses pipeline structure to create a 3D model to map the data for the user to locate water leakage.
Shand; Zachary et al., US 20210238991 A1, SYSTEM, METHOD AND DEVICE FOR FLUID CONDUIT INSPECTION
Reference discloses sensors devices that use passive magnetometry to inspect fluid conduits.
SODA; Giuseppe, US 20150308917 A1¸ SYSTEM AND METHOD FOR SUPERVISING, MANAGING, AND MONITORING THE STRUCTURAL INTEGRITY OF A FLUID-TRANSPORTATION PIPELINE NETWORK, FOR LOCATING THE LEAKING POINT, AND FOR EVALUATING THE EXTENT OF THE FAILURE
Reference discloses monitoring structural integrity of a pipeline network and locating leak points and extent of failure.
van Pol; Johannes Hubertus Gerardus et al., US 20180177064 A1, SENSOR DEVICE, SYSTEMS, AND METHODS FOR DETERMINING FLUID PARAMETERS
Reference discloses a sensor device used for collecting information about fluid and conduit carrying fluid.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINA CORDERO whose telephone number is (571)272-9969. The examiner can normally be reached 9:30 am - 6:00 pm.
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/LINA CORDERO/Primary Examiner, Art Unit 2857
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