DETAILED ACTIONS
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This office action is in response to the amendments/arguments submitted by the Applicant(s) on 06/10/2026.
Status of the Claims
Claims 1-20 are pending.
Claims 1-14 and 17-20 are amended.
Response to Arguments
Rejections Under 35 U.S.C. §101:
Applicant’s argument/amendment, see remarks page 9-11, filed 06/10/2026
with respect to the rejection(s) of claims under 35 U.S.C. §101 has been considered, and are persuasive. Therefore, 35 U.S.C. § 101 rejections of claims have been withdrawn.
Rejections Under 35 U.S.C. §102
Applicant's arguments, see remarks page 11-12, filed 06/10/2026
with respect to the rejection(s) of Claims under 35 U.S.C. §102 (a)(1) has been considered, and are moot because the amendment has necessitated a new ground of rejection. The new rejections are set forth below.
Rejections Under 35 U.S.C. §103
Applicant's arguments, see remarks page 11-12, filed 06/10/2026
with respect to the rejection(s) of Claims under 35 U.S.C.§103 has been considered, and are moot because the amendment has necessitated a new ground of rejection of independent claims. Therefore, new rejections are set forth below.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hauge et al. (US 2019/0169982 A1, hereinafter Hauge, previously cited IDS ref.)
and in view of Sadovnychiy et al. (US 2022/0276116 A1, hereinafter Sadovnychiy, previously cited).
Regarding Claim 1, Hauge teaches,
(Currently Amended) A system for detecting fluid leaks (Hauge, Figure 6, 622, leak detection system), the system comprising:
a display (Hauge, Figure 2, Display 226); and
one or more physical processors configured by machine-readable instructions (Hauge, Figure 1, 170 processors, [0003], A system can include a processor; memory accessible by the processor; and processor-executable instructions stored in the memory where the instructions include instructions to instruct the system”) to:
obtain multi-sensor information, the multi-sensor information (Hauge, Figure 3, [0059] “The oilfield network 302 may also include one or more surface units (e.g., a surface unit 1 316, a surface unit 318, etc.), for example, a surface unit for each wellsite. Such surface units may include functionality to collect data from sensors”) characterizing separate fluid leak probability levels (detected at a location by multiple sensors of different types for a fluid facility (Hauge, Figure 8, a probability block 840, [0178], a method includes performing a probability analysis for fluid leak probability”) (Hauge, Figure 6, [0133] “An LDS (leak detection system) can help to provide operational support by detecting a pipeline leak and estimating the location of a leak.” [ 0158] “As shown in FIG. 6, the LDS 622 can receive measured flow (FM) values and simulated flow (FS) values as well as measured pressure (PM) values and simulated pressure (PS) values and optionally measured temperature (TM) values and simulated temperature (TS) values. As shown, along a length of the simulator online pipeline 610”),
reconcile the different fluid leak probability levels detected by different ones of the multiple sensors (Hauge, Figure 7. [0189], “. A detection pair can have a corresponding location value. model-calculated value and a corresponding field-measured value may be identical in location in that the model-calculated value corresponds identically with a sensor or sensors in a fluid production network, Figure 8, [0190] “a plurality of probability calculations may be combined in a weighted average for a total probability calculation that can be used for raising leak warnings and/or alarm”); using a Bayesian model probability table, wherein the Bayesian model probability table defines determines likelihoods of multiple fluid leak probability levels at the location based on for different combinations of the separate fluid leak probability levels detected at the location by the multiple sensors of different type (the method 800 of FIG. 8. Step 840, As an example, a detection pair can be sent through to a Bayesian Changepoint Analysis (BCA) routine. [0188]” FIG. 8 shows an example of a method 800 that includes performing a Bayesian changepoint analysis (BCA). FIG. 8. Step 840, “Total Probability of Pairs”, and step 860 “ Estimate I Output Leak Location(s) 860”) NOTE: “each pair” of data represent different sensor measurement at different locations.)
wherein the Bayesian model probability table defines a first likelihood of a first fluid leak probability level and a second likelihood of a second fluid leak probability level for a given combination of the separate fluid leak probability levels detected at the location by the multiple sensors of different types; (Hauge, Figure 8, [0190], the method 800 of FIG. 8. As an example, a detection pair can be sent through to a Bayesian Changepoint Analysis (BCA) routine where the output from the routine is a probability calculation for a breach of one or more defined detection thresholds. In such an approach, each of a plurality of probability calculations may be combined in a weighted average for a total probability calculation that can be used for raising leak warnings and/or alarms. The combined approach, as in a method that includes combining the probability calculation from each of a plurality of detection pairs (e.g., N total pairs), can provide robustness and reduce the amount of false alarms. As an example, a user may adjust one or more weights, optionally during runtime of a leak detection fran1ework in order to strike an appropriate balance between detection sensitivity and robustness based on field performance experience”)
and
facilitate one or more operations at the fluid facility based on the likelihoods of the multiple fluid leak probability levels at the location, (Hauge, [0182]” As an example, a method can include rendering at least one graphical user interface to a display that includes a graphic of leak-related information” [0195] “leak location estimation during flowing conditions” [0290] As an example, an LDS may analyze a plurality of field measurements from a plurality of locations of a production network where such an analysis may aim to verify whether a leak exists or does not exist”.) wherein facilitating the one or more operations at the fluid facility based on the likelihoods of the multiple fluid leak probability levels at the location includes presenting the likelihoods of the multiple fluid leak probability levels at the location within a graphical user interface on the display for use in operating the fluid facility. (Hauge, [0282] In FIG. 14, the GUI 1400 shows a gas pipeline overview where a leak alarm can be transferred to a SCADA system or other suitable system. As shown, probability information may be rendered to the display, as to a probability of a leak at one or more locations. As m1 example, an
LDS may generate a leak rate estimate and trm1sfer such information to, for example, a SCADA system. As an example, an LDS may provide for land and/or sea operation monitoring”).
Huge is silent on the multiple sensors of the different types including an image sensor and a sound sensor,
the muti-sensor information characterizing a first an image sensor fluid leak probability level detected at the location by the image sensor of the first type and a second sound sensor fluid leak probability level detected at the location by the second sound sensor, wherein the image sensor fluid leak probability level detected by the image sensor is different from the sound sensor fluid leak probability level detected by the sound sensor; of the second type.
However, Sadovnychiy teaches multiple sensors of the different types including an image sensor (Sadovnychiy, Figure 2, perimeter surveillance module 6, [0022] , “[0022] One more object of the present disclosure is to provide a pipeline monitoring system, which is complemented by a perimeter surveillance equipment, integrated by motion sensors and a camera, with the option of night vision, with which an alarm can be activated, providing the ability to view the site remotely.”) and a sound sensor, (Sadovnychiy [0051] The location of the hydrophones 9, 10 according to FIG. 2, can record acoustic phenomena upstream and downstream, so it can be installed at intermediate points along the pipe”);
the muti-sensor information characterizing a first an image sensor (Sadovnychiy, Figure 2, perimeter surveillance module 6, Figure 3, 7 see [0053] a perimeter surveillance camera 7,) and fluid leak probability level detected at the location (Hauge teaches, method 800 of FIG. 8. Step 840) by the image sensor of the first type and a second sound sensor fluid leak probability level detected at the location by the second sound sensor, (Sadovnychiy, Figure 2, perimeter surveillance module 6, The location of the hydrophones 9, 10 ) wherein the image sensor fluid leak probability level detected by the image sensor is different from the sound sensor fluid leak probability level (Hauge teaches probability estimation for each sensor data and use Bayesian model to integrate all the sensor information to obtain total probability see figure 8, 13-14. And Hauge [0188]-[0190].)”detected by the sound sensor; of the second type;
It would have been obvious to a person of ordinary skill before the effective filing date to modify Hauge multiple monitoring sensor system to include with the surveillance system with camera and acoustic sensors as taught by Sadovnychiy with the benefit of monitoring pipeline system leaks non-intrusively, allowing the operation of the pipeline without any alteration of the flow and operate with night vision and works 24/7, preventing risk of the system and allowing an optimal operation of pipelines that transport hydrocarbons.( Sadovnychiy,[0022],[0051], [0044]-[0045]).
Regarding Claim 2, combination of Hauge and Sadovnychiy, teaches the system of claim 1,
Hauge further teaches, wherein the likelihoods of the multiple fluid leak probability levels at the location are presented on the display for use in fixing a fluid leak at the fluid facility. (Hauge, Figure 2, Display 226, figure 14, (Hauge, [0282] In FIG. 14, the GUI 1400 shows a gas pipeline overview where a leak alarm can be transferred to a SCADA system or other suitable system. As shown, probability information may be rendered to the display, as to a probability of a leak at one or more locations);
Regarding Claim 3, combination of Hauge and Sadovnychiy, teaches the system of claim 1,
Hauge further teaches wherein facilitating the one or more operations at the fluid facility based on the likelihoods of the multiple fluid leak probability levels at the location includes automatically stopping or changing the one or more operations at the fluid facility to stop or change generating, processing, storing, and/or transporting fluid at the fluid facility. (Hague, [0130] upon detection of a leak or some probability of a leak, one or more actions may be taken to mitigate leakage of fluid or [0183] As an example, a method can include transmitting leak-related information to a data transmission system that is operatively coupled to a control system for controlling at least a portion of a fluid production network”.).
Regarding Claim 4, combination of Hauge and Sadovnychiy, teaches the system of claim 1,
Hauge further teaches facilitating (Hauge, Figure 14) the one or more operations at the fluid facility based on the likelihoods of the multiple fluid leak probability levels at the location includes generating an alert based on a determination that the location includes a fluid leak. (Hauge, Figure 8, step 850, [0188], block 830 for checking probability and threshold(s), a probability block 840 for determining a total
probability of one or more pairs, an issuance block 850 for issuing a warning and/or an alarm”).
Regarding Claim 5, combination of Hauge and Sadovnychiy, teaches the system of claim 1,
Hauge further teaches wherein facilitating (Hauge, Figure 14) the one or more operations at the fluid facility based on the likelihoods of the multiple fluid leak probability levels at the location includes determining whether the location includes a fluid leak based on highest of the likelihoods of multiple fluid leak probability levels at the location. Hauge, [0284] “FIG. 15 shows the GUI 1500 being associated with the location tab of the GUI 1400 such that the rendered pane switches from leak information to location information. As shown, a plot is rendered in the GUI 1500 that graphically indicates where a leak location is estimated to be located. In the GUis 1400 and 1500, information may be color coded, for example, where red indicates a leak and where green indicates no leak. As an example, one or more thresholds may be set as to probabilities of a leak that may trigger a transition from green to red or, for example, from red to green after mitigation action(s) (e.g., based on data from the field, etc”.).
Regarding Claim 6, combination of Hauge and Sadovnychiy, teaches the system of
claim 5,
Hauge further teaches, wherein the determination of whether the location includes the fluid leak based on the highest of the likelihoods of multiple fluid leak probability levels at the location is confirmed or invalidated based on observation made by one or more other sensors different from the multiple sensors. (Hauge, [0272]” As an example, a method can include implementing one or more strategies for detecting and locating leaks. For example, consider a method that includes using equipment data from equipment such as one or more pumps and/or compressors and/or using one or more field temperature measurements. [0273] As to pumps and/or compressors, for a fluid production network including pumps or compressors, data such as speed and power may be used in order to detect and locate leaks. These data can be used in detection pairs”).
Regarding Claim 7, combination of Hauge and Sadovnychiy, teaches the system of claim 1,
Hauge is silent on wherein facilitating the one or more operations at the fluid facility based on the likelihoods of the multiple fluid leak probability levels at the location includes determining of a hole size for the fluid leak
However, Sadovnychiy wherein facilitation of the one or more operations at the fluid facility based on the likelihoods of the multiple fluid leak probability levels at the location includes determination of a hole size for the fluid leak (Sadovnychiy, [0075], The systems and sensors used can withstand the operating pressure and temperature of the pipeline. [0076] It has a sensitivity to detect product leaks of 1 % of the duct flow rate or product losses through holes with an area equivalent to a 6 mm diameter hole”) based on sound captured at the location and pressure of equipment at the location. (Sadovnychiy [0071] In the development of the application, various events were obtained where disturbances or noise detected
by the hydrophones were recorded, one of which is shown in FIG. 17, where the response of hydrophone 1 (from the Remote Pipeline Monitoring Terminal 1, RPMT 1) and hydrophone 2 (from the Remote Pipeline Monitoring Terminal
2, RPMT 2). As can be seen in the figure, the sensors show an inherent noise, in which there is no direct correlation between the signals recorded by station one and two, but in the period of 21 hours it is observed that both sensors detected an event that exceeds the 2 dB peak and flt is less than the maximum time of the window between both terminals, for which it was reported as an event “).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Hauge multiple monitoring sensor system to include with the surveillance system with camera and acoustic sensors as taught by Sadovnychiy with the benefit of monitoring pipeline system leaks non-intrusively, allowing the operation of the pipeline without any alteration of the flow and operate with night vision and works 24/7, preventing risk of the system and allowing an optimal operation of pipelines that transport hydrocarbons.( Sadovnychiy,[0022],[0051], [0044]-[0045]).
Regarding Claim 8, combination of Hauge and Sadovnychiy, teaches the system of claim 1,
Hauge further teaches wherein the separate fluid leak probability levels detected at the location by the multiple sensors include a low fluid leak probability level, a medium fluid leak probability level, and a high fluid leak probability level. Hauge, Figure 8, [0190] “In such an approach, each of a plurality of probability calculations may be combined in a weighted average for a total probability calculation that can be used for raising leak warnings and/or alarms. The combined approach, as in a method that includes combining the probability calculation from each of a plurality of detection pairs (e.g., N total pairs), can provide robustness and reduce the amount of false alarm”).
Regarding Claim 9, combination of Hauge and Sadovnychiy, teaches the system of claim 8,
Hauge further teaches wherein for the given combination of the separate fluid leak probability levels detected at the location by the multiple sensors of different types, the wherein the Bayesian model probability table defines separate likelihoods of the low fluid leak probability level, the medium fluid leak probability level, and the high fluid leak probability level at the location of the fluid facility. (Hauge, Figure 8, [0190], the method 800 of FIG. 8. As an example, a detection pair can be sent through to a Bayesian Changepoint Analysis (BCA) routine where the output from the routine is a probability calculation for a breach of one or more defined detection thresholds. In such an approach, each of a plurality of probability calculations may be combined in a weighted average for a total probability calculation that can be used for raising leak warnings and/or alarms. The combined approach, as in a method that includes combining the probability calculation from each of a plurality of detection pairs (e.g., N total pairs), can provide robustness and reduce the amount of false alarms. As an example, a user may adjust one or more weights, optionally during runtime of a leak detection fran1ework in order to strike an appropriate balance between detection sensitivity and robustness based on field performance experience”).
Regarding Claim 10, combination of Hauge and Sadovnychiy, teaches the system of claim 1,
Hauge further teaches wherein a fluid leak includes a gas leak or a liquid leak. (Hauge, [0135]” As an example, an LDS may provide for detection of leaks as to one or more types of pipelines (e.g., consider a scenario of an Oil Pipeline and a Gas Pipeline from Station X to a Resource Processing Facility (RPF) and a Fuel Gas Pipeline from the RPF to Station X)”)
Regarding Claim 11, Hauge teaches
A method for detecting fluid leaks, (Hauge, Figure 8), the method comprising:
obtaining multi-sensor information, Hauge, Figure 3, [0059] “The oilfield network 302 may also include one or more surface units (e.g., a surface unit 1 316, a surface unit 318, etc.), for example, a surface unit for each wellsite. Such surface units may include functionality to collect data from sensors”) the multi-sensor information characterizing separate fluid leak probability levels detected at a location by multiple sensors of different types for a fluid facility, (Hauge, Figure 8, a probability block 840, [0178], a method includes performing a probability analysis for fluid leak probability”) (Hauge, Figure 6, [0133] “An LDS (leak detection system) can help to provide operational support by detecting a pipeline leak and estimating the location of a leak.” [ 0158] “As shown in FIG. 6, the LDS 622 can receive measured flow (FM) values and simulated flow (FS) values as well as measured pressure (PM) values and simulated pressure (PS) values and optionally measured temperature (TM) values and simulated temperature (TS) values. As shown, along a length of the simulator online pipeline 610”),
reconciling the different fluid leak probability levels detected by different ones of the multiple sensors (Hauge, Figure 7. [0189], “. A detection pair can have a corresponding location value. model-calculated value and a corresponding field-measured value may be identical in location in that the model-calculated value corresponds identically with a sensor or sensors in a fluid production network, Figure 8, [0190] “a plurality of probability calculations may be combined in a weighted average for a total probability calculation that can be used for raising leak warnings and/or alarm”); using a Bayesian model probability table, wherein the Bayesian model probability table defines likelihoods of multiple fluid leak probability levels at the location for different combinations of the separate fluid leak probability levels detected at the location by the multiple sensors of different types, (the method 800 of FIG. 8. Step 840, As an example, a detection pair can be sent through to a Bayesian Changepoint Analysis (BCA) routine. [0188]” FIG. 8 shows an example of a method 800 that includes performing a Bayesian changepoint analysis (BCA). FIG. 8. Step 840, “Total Probability of Pairs”, and step 860 “ Estimate I Output Leak Location(s) 860”) NOTE: “each pair” of data represent different sensor measurement at different locations.)
wherein the Bayesian model probability table defines a first likelihood of a first fluid leak probability level and a second likelihood of a second fluid leak probability level for a given combination of the separate fluid leak probability levels detected at the location by the multiple sensors of different types; (Hauge, Figure 8, [0190], the method 800 of FIG. 8. As an example, a detection pair can be sent through to a Bayesian Changepoint Analysis (BCA) routine where the output from the routine is a probability calculation for a breach of one or more defined detection thresholds. In such an approach, each of a plurality of probability calculations may be combined in a weighted average for a total probability calculation that can be used for raising leak warnings and/or alarms. The combined approach, as in a method that includes combining the probability calculation from each of a plurality of detection pairs (e.g., N total pairs), can provide robustness and reduce the amount of false alarms. As an example, a user may adjust one or more weights, optionally during runtime of a leak detection fran1ework in order to strike an appropriate balance between detection sensitivity and robustness based on field performance experience”)
and facilitating one or more operations at the fluid facility based on the likelihoods of the multiple fluid leak probability levels at the location, (Hauge, [0182]” As an example, a method can include rendering at least one graphical user interface to a display that includes a graphic of leak-related information” [0195] “leak location estimation during flowing conditions” [0290] As an example, an LDS may analyze a plurality of field measurements from a plurality of locations of a production network where such an analysis may aim to verify whether a leak exists or does not exist”.) wherein facilitating the one or more operations at the fluid facility based on the likelihoods of the multiple fluid leak probability levels at the location includes presenting the likelihoods of the multiple fluid leak probability levels at the location within a graphical user interface on a display for use in operating the fluid facility (Hauge, [0282] In FIG. 14, the GUI 1400 shows a gas pipeline overview where a leak alarm can be transferred to a SCADA system or other suitable system. As shown, probability information may be rendered to the display, as to a probability of a leak at one or more locations. As m1 example, an
LDS may generate a leak rate estimate and trm1sfer such information to, for example, a SCADA system. As an example, an LDS may provide for land and/or sea operation monitoring”).
Hauge is silent on the multiple sensors of the different types including an image sensor and a sound sensor,
the muti-sensor information characterizing an image sensor fluid leak probability level detected at the location by the image sensor and a sound sensor fluid leak probability level detected at the location by the sound sensor, wherein the image sensor fluid leak probability level detected by the image sensor is different from the sound sensor fluid leak probability level detected by the sound sensor;
However, Sadovnychiy teaches multiple sensors of the different types including an image sensor (Sadovnychiy, Figure 2, perimeter surveillance module 6, [0022] , “[0022] One more object of the present disclosure is to provide a pipeline monitoring system, which is complemented by a perimeter surveillance equipment, integrated by motion sensors and a camera, with the option of night vision, with which an alarm can be activated, providing the ability to view the site remotely.”) and a sound sensor, (Sadovnychiy [0051] The location of the hydrophones 9, 10 according to FIG. 2, can record acoustic phenomena upstream and downstream, so it can be installed at intermediate points along the pipe”);
the muti-sensor information characterizing a first an image sensor (Sadovnychiy, Figure 2, perimeter surveillance module 6, Figure 3, 7 see [0053] a perimeter surveillance camera 7,) and fluid leak probability level detected at the location (Hauge teaches, method 800 of FIG. 8. Step 840) by the image sensor of the first type and a second sound sensor fluid leak probability level detected at the location by the second sound sensor, (Sadovnychiy, Figure 2, perimeter surveillance module 6, The location of the hydrophones 9, 10 ) wherein the image sensor fluid leak probability level detected by the image sensor is different from the sound sensor fluid leak probability level (Hauge teaches probability estimation for each sensor data and use Bayesian model to integrate all the sensor information to obtain total probability see figure 8, 13-14. And Hauge [0188]-[0190].)”detected by the sound sensor; of the second type;
It would have been obvious to a person of ordinary skill before the effective filing date to modify Hauge multiple monitoring sensor system to include with the surveillance system with camera and acoustic sensors as taught by Sadovnychiy with the benefit of monitoring pipeline system leaks non-intrusively, allowing the operation of the pipeline without any alteration of the flow and operate with night vision and works 24/7, preventing risk of the system and allowing an optimal operation of pipelines that transport hydrocarbons.( Sadovnychiy,[0022],[0051], [0044]-[0045]).
Regarding Claim 12, combination of Hauge and Sadovnychiy, teaches the method of claim 11,
Hauge further teaches wherein the likelihoods of the multiple fluid leak probability levels at the location are presented on the display for use in fixing a fluid leak at the fluid facility. (Hauge, Figure 2, Display 226, figure 14, (Hauge, [0282] In FIG. 14, the GUI 1400 shows a gas pipeline overview where a leak alarm can be transferred to a SCADA system or other suitable system. As shown, probability information may be rendered to the display, as to a probability of a leak at one or more locations);
Regarding Claim 13, combination of Hauge and Sadovnychiy, teaches the method of claim 11,
Hauge further teaches wherein facilitating the one or more operations at the fluid facility based on the likelihoods of the multiple fluid leak probability levels at the location includes automatically stopping or changing the one or more operations at the fluid facility to stop or change generating, processing, storing, and/or transporting fluid at the fluid facility. (Hague, [0130] upon detection of a leak or some probability of a leak, one or more actions may be taken to mitigate leakage of fluid or [0183] As an example, a method can include transmitting leak-related information to a data transmission system that is operatively coupled to a control system for controlling at least a portion of a fluid production network”.).
Regarding Claim 14, combination of Hauge and Sadovnychiy, teaches the method of claim 11,
Hauge further teaches wherein facilitating (Hauge, Figure 14) the one or more operations at the fluid facility based on the likelihoods of the multiple fluid leak probability levels at the location includes generating an alert based on a determination that the location includes a fluid leak. (Hauge, Figure 8, step 850, [0188], block 830 for checking probability and threshold(s), a probability block 840 for determining a total probability of one or more pairs, an issuance block 850 for issuing a warning and/or an alarm”).
Regarding Claim 15, combination of Hauge and Sadovnychiy, teaches the method of claim 11,
Hauge further teaches wherein facilitating (Hauge, Figure 14) the one or more operations at the fluid facility based on the likelihoods of the multiple fluid leak probability levels at the location includes determining whether the location includes a fluid leak based on highest of the likelihoods of multiple fluid leak probability levels at the location. Hauge, [0284] “FIG. 15 shows the GUI 1500 being associated with the location tab of the GUI 1400 such that the rendered pane switches from leak information to location information. As shown, a plot is rendered in the GUI 1500 that graphically indicates where a leak location is estimated to be located. In the GUis 1400 and 1500, information may be color coded, for example, where red indicates a leak and where green indicates no leak. As an example, one or more thresholds may be set as to probabilities of a leak that may trigger a transition from green to red or, for example, from red to green after mitigation action(s) (e.g., based on data from the field, etc”).
Regarding Claim 16, combination of Hauge and Sadovnychiy, teaches the method of claim 15,
Hauge further teaches, wherein the determination of whether the location includes the fluid leak based on the highest of the likelihoods of multiple fluid leak probability levels at the location is confirmed or invalidated based on observation made by one or more other sensors different from the multiple sensors. (Hauge, [0272]” As an example, a method can include implementing one or more strategies for detecting and locating leaks. For example, consider a method that includes using equipment data from equipment such as one or more pumps and/or compressors and/or using one or more field temperature measurements. [0273] As to pumps and/or compressors, for a fluid production network including pumps or compressors, data such as speed and power may be used in order to detect and locate leaks. These data can be used in detection pairs”).
Regarding Claim 17, combination of Hauge and Sadovnychiy, teaches the method of claim 11,
Hauge is silent on wherein facilitating the one or more operations at the fluid facility based on the likelihoods of the multiple fluid leak probability levels at the location includes determining of a hole size for the fluid leak
However, Sadovnychiy wherein facilitation of the one or more operations at the fluid facility based on the likelihoods of the multiple fluid leak probability levels at the location includes determination of a hole size for the fluid leak (Sadovnychiy , [0075], The systems and sensors used can withstand the operating pressure and temperature of the pipeline. [0076] It has a sensitivity to detect product leaks of 1 % of the duct flow rate or product losses through holes with an area equivalent to a 6 mm diameter hole”) based on sound captured at the location and pressure of equipment at the location. (Sadovnychiy [0071] In the development of the application, various events were obtained where disturbances or noise detected
by the hydrophones were recorded, one of which is shown in FIG. 17, where the response of hydrophone 1 (from the Remote Pipeline Monitoring Terminal 1, RPMT 1) and hydrophone 2 (from the Remote Pipeline Monitoring Terminal
2, RPMT 2). As can be seen in the figure, the sensors show an inherent noise, in which there is no direct correlation between the signals recorded by station one and two, but in the period of 21 hours it is observed that both sensors detected an event that exceeds the 2 dB peak and flt is less than the maximum time of the window between both terminals, for which it was reported as an event “).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Hauge multiple monitoring sensor system to include with the surveillance system with camera and acoustic sensors as taught by Sadovnychiy with the benefit of monitoring pipeline system leaks non-intrusively, allowing the operation of the pipeline without any alteration of the flow and operate with night vision and works 24/7, preventing risk of the system and allowing an optimal operation of pipelines that transport hydrocarbons.( Sadovnychiy,[0022],[0051], [0044]-[0045]).
Regarding Claim 18, combination of Hauge and Sadovnychiy, teaches the method of claim 11,
Hauge further teaches wherein the separate fluid leak probability levels detected at the location by the multiple sensors include a low fluid leak probability level, a medium fluid leak probability level, and a high fluid leak probability level. Hauge, Figure 8, [0190] “In such an approach, each of a plurality of probability calculations may be combined in a weighted average for a total probability calculation that can be used for raising leak warnings and/or alarms. The combined approach, as in a method that includes combining the probability calculation from each of a plurality of detection pairs (e.g., N total pairs), can provide robustness and reduce the amount of false alarm”).
Regarding Claim 19, combination of Hauge and Sadovnychiy, teaches the method of claim 18,
Hauge further teaches wherein for the given combination of the separate fluid leak probability levels detected at the location by the multiple sensors of different types, the wherein the Bayesian model probability table defines separate likelihoods of the low fluid leak probability level, the medium fluid leak probability level, and the high fluid leak probability level at the location of the fluid facility. (Hauge, Figure 8, [0190], the method 800 of FIG. 8. As an example, a detection pair can be sent through to a Bayesian Changepoint Analysis (BCA) routine where the output from the routine is a probability calculation for a breach of one or more defined detection thresholds. In such an approach, each of a plurality of probability calculations may be combined in a weighted average for a total probability calculation that can be used for raising leak warnings and/or alarms. The combined approach, as in a method that includes combining the probability calculation from each of a plurality of detection pairs (e.g., N total pairs), can provide robustness and reduce the amount of false alarms. As an example, a user may adjust one or more weights, optionally during runtime of a leak detection fran1ework in order to strike an appropriate balance between detection sensitivity and robustness based on field performance experience”).
Regarding Claim 20, combination of Hauge and Sadovnychiy, teaches the method of claim 11,
Hauge further teaches wherein a fluid leak includes a gas leak or a liquid leak. (Hauge, [0135]” As an example, an LDS may provide for detection of leaks as to one or more types of pipelines (e.g., consider a scenario of an Oil Pipeline and a Gas Pipeline from Station X to a Resource Processing Facility (RPF) and a Fuel Gas Pipeline from the RPF to Station X)”)
Conclusion
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hornacek et al. (US 2021/0356350 A1) recites “A method for identifying the occurrence of a defect in a pipeline by estimation, wherein at least one first indicator is identified by a first detector assigned to a first detection location, from which a first estimation value regarding the occurrence of the defect in the pipeline is determined, and at least one second indicator is identified by at least one second detector assigned to a second detection location, from which
at least one second estimation value regarding the occurrence of the defect in the pipeline is determined. An overall estimation value is determined from the first estimation value and the at least one second estimation value by an
overall estimation function by taking into account the respective positions of the first detection location and the second detection location, from which overall estimation value the occurrence of the defect is estimated” (Abstract).
Guerriero et al. (US20170076563A1) discloses “Provided are an apparatus and method for detecting an anomaly in a plant pipe using multiple meta-learning. When a multi-sensor data stream about a plant pipe is received, each of a plurality of meta-learning modules for processing different packet section ranges, extracts one or more preset types of features from sensor data of packet section ranges set according to trend from an arbitrary reception time point, generates 2D image features of the features according to multi-sensor-specific times, generates 3D volume features by accumulating the 2D image features in a depth direction according to multiple sensors, and learns the 3D volume features in parallel through multi-sensor-specific learning modules. Results of the learning of the meta-learning modules are aggregated, and it is determined whether there is an anomaly in a plant pipe according to a learning result selected based on an optimal combination of multiple features, multiple sensors, and multiple packet sections”.(abstract)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/DILARA SULTANA/Examiner, Art Unit 2858
08/25/2026
/EMAN A ALKAFAWI/Supervisory Patent Examiner, Art Unit 2858 9/3/2026
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