DETAILED ACTION
This office action is in response to communication filed on December 10, 2025.
Response to Amendment
Amendments filed on December 10, 2025 have been entered.
Claims 1, 11 and 21 have been amended.
Claims 4-5, 10, 14-15 and 20 remain canceled.
Claim 26 has been added.
Claims 1-3, 6-9, 11-13, 16-19 and 21-26 have been examined.
Response to Arguments
Applicant’s arguments, see Remarks (p. 10-11), filed on 12/10/2025, with respect to the objections to the specification have been fully considered. In view of the amendments to the specification addressing the informalities raised in the previous office action, the objections to the specification have been withdrawn.
Applicant’s arguments, see Remarks (p. 11), filed on 12/10/2025, 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to the claims addressing the informalities raised in the previous office action, the objections to the claims have been withdrawn. However, upon further consideration, new objections to the claims are presented below in order to address additional informalities.
Applicant’s arguments, see Remarks (p. 11), filed on 12/10/2025, with respect to the rejection of claim 21 under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, have been fully considered. In view of the amendments to the claims addressing the issues raised in the previous office action, the rejection has been withdrawn.
Applicant’s arguments, see Remarks (p. 14-17), filed on 12/10/2025, with respect to the rejection of claims 1-3, 6-9, 11-13, 16-19 and 21 under 35 U.S.C. 103 have been fully considered but are moot in view of new grounds of rejection.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/05/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim language “a map representation of the three-dimensional open space, the map representation including representations of the conduit and the plurality of equipment items” should read “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” in order to provide appropriate antecedence basis.
Claim language “an identifier of at least one equipment item of the plurality of equipment items impacted by the leak” should read “an identifier of at least one equipment item of the plurality of equipment items impacted by the leakage” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 2 is objected to because of the following informalities:
Claim language should read “The system of claim 1, wherein [[the]] at least one acoustic sensor of the one or more acoustic sensors is configured to dynamically change its orientation” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 11 is objected to because of the following informalities:
Claim language “a map representation of the three-dimensional open space, the map representation including representations of the conduit and the plurality of equipment items” should read “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” in order to provide appropriate antecedence basis.
Claim language “an identifier of at least one equipment item of the plurality of equipment items impacted by the leak” should read “an identifier of at least one equipment item of the plurality of equipment items impacted by the leakage” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Claim 26 is objected to because of the following informalities:
Claim language should read “The system of claim 1, wherein 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” in order to provide appropriate antecedence basis.
Appropriate correction is required.
Examiner’s Note
Claims 1-3, 6-9, 11-13, 16-19 and 21-26 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” 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 conduit and 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; and an identifier of at least one equipment item of the plurality of equipment items impacted by the leak” 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 21-26, 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 21-26 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 Tan (CN 109859320 A, see translation), hereinafter ‘Tan’ 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 is based on the distance from the sensors (see [0033]) is also 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 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 leak ([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; and
the information is three-dimensional direction.
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”, Tan teaches:
“This invention belongs to pipe network of the visible image creating field, specifically claims a generating system of three-dimensional visual image according to the pipe network attribute data, comprising a storing module for storing the pipe network attribute data, the attribute data includes coordinates, locating module; for obtaining the current coordinate, a matching module for matching the pipe network in the coordinate in the storage module according to the current coordinate, and transferring module for transferring to the memory module matching module matching the pipe network attribute data, modeling module. for three-dimensional modelling according to the calling module attribute data of pipe network, the pipe network of the three-dimensional model display module for modelling module builds a pipe network for visual display. Using this system, the three-dimensional model the worker can quickly obtain the current position of pipe network and perform maintenance work. The invention further claims a method for generating three-dimensional visual image according to the pipe network attribute data” (abstract: three-dimensional image of a pipe network is generated for visual display and for performing maintenance by a worker, which suggests the space to be open for the worker to perform maintenance (see also p. 4 regarding augmented reality technology for combining virtual and real environments; see further 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 Tan, to incorporate the three-dimensional space as a three-dimensional open space, in order to quickly obtain the current position of the pipe network and perform maintenance work, as discussed by Tan (abstract).
Regarding “the information is three-dimensional direction”, Hasselbeck teaches:
“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 Tan, and in further view of Hasselbeck, to incorporate the information as three-dimensional direction, in order to provide a faster and enhanced localization of leaks.
Regarding claim 3.
Batany, in view of Bart, Tan 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, Tan 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])).
Regarding claim 7.
Batany, in view of Bart, Tan 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])).
Regarding claim 8.
Batany, in view of Bart, Tan 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) 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, Tan 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, Tan 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);
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 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 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 leak ([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; and
the information is three-dimensional direction.
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”, Tan teaches:
“This invention belongs to pipe network of the visible image creating field, specifically claims a generating system of three-dimensional visual image according to the pipe network attribute data, comprising a storing module for storing the pipe network attribute data, the attribute data includes coordinates, locating module; for obtaining the current coordinate, a matching module for matching the pipe network in the coordinate in the storage module according to the current coordinate, and transferring module for transferring to the memory module matching module matching the pipe network attribute data, modeling module. for three-dimensional modelling according to the calling module attribute data of pipe network, the pipe network of the three-dimensional model display module for modelling module builds a pipe network for visual display. Using this system, the three-dimensional model the worker can quickly obtain the current position of pipe network and perform maintenance work. The invention further claims a method for generating three-dimensional visual image according to the pipe network attribute data” (abstract: three-dimensional image of a pipe network is generated for visual display and for performing maintenance by a worker, which suggests the space to be open for the worker to perform maintenance (see also p. 4 regarding augmented reality technology for combining virtual and real environments; see further 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 Tan, to incorporate the three-dimensional space as a three-dimensional open space, in order to quickly obtain the current position of the pipe network and perform maintenance work, as discussed by Tan (abstract).
Regarding “the information is three-dimensional direction”, Hasselbeck teaches:
“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 Tan, and in further view of Hasselbeck, to incorporate the information as three-dimensional direction, in order to provide a faster and enhanced localization of leaks.
Regarding claim 13.
Batany in view of Bart, Tan 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, Tan 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])).
Regarding claim 17.
Batany, in view of Bart, Tan 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])).
Regarding claim 18.
Batany, in view of Bart, Tan 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, Tan 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, Tan 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, Tan 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, Tan 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, Tan 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, Tan 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, Tan 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, Tan 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, Tan 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, Tan 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, Tan and Hasselbeck, discloses all the features of claim 1 as described above.
Batany further discloses:
the identifier of 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 ([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])).
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Batany in view of Bart, Tan and Hasselbeck, in further view of Yokono (US 11422053 B2), hereinafter ‘Yokono’.
Regarding claim 2.
Batany in view of Bart, Tan 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 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” (col. 8, lines 50-57: 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” (col. 9, lines 3-21: 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, Tan and Hasselbeck, and in further view of Yokono, 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 12.
Batany in view of Bart, Tan 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 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” (col. 8, lines 50-57: 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” (col. 9, lines 3-21: 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, Tan and Hasselbeck, and in further view of Yokono, 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).
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.
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