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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 11/22/2024 and 05/17/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Rejections- 35 USC §101
U.S.C. §101 reads as follows:
Whoever invents or discovers any new and useful process, machine,
manufacture, or composition of matter, or any new and useful improvement thereof,
may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C.§101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The following analysis is based on claim 1,
Regarding claim 1,
A system for detecting fluid leaks, the system comprising:
one or more physical processors configured by machine-readable instructions to: obtain multi-sensor information, the multi-sensor information characterizing separate fluid leak probability levels detected at a location by multiple sensors of different types for a fluid facility, the multiple sensors of the different types including a first sensor of a first type and a second sensor of a second type different from the first type,
the muti-sensor information characterizing a first fluid leak probability level detected at the location by the first sensor of the first type and a second fluid leak probability level detected at the location by the second sensor of the second type;
reconcile different fluid leak probability levels detected by different ones of the multiple sensors using a Bayesian model,
wherein the Bayesian model determines likelihoods of multiple fluid leak probability levels at the location based on the separate fluid leak probability levels detected at the location by the multiple sensors; and
facilitate one or more operations at the fluid facility based on the likelihoods of the multiple fluid leak probability levels at the location.
The claim limitations underlined above is abstract idea, and the remaining limitations are “additional elements”.
Step 1 (Statutory Category): Yes. we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (a mathematical manipulation). Therefore, it is directed to a statutory category, i.e., a mathematical manipulation.
Step 2 A, Prong-1 (the claim is evaluated to determine whether it is directed to a judicial-exception/abstract-idea): Yes.
In the above claim 1, the underlined portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exception. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation that covers mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations, a mathematical manipulation).
For example, steps of “characterizing a first fluid leak probability level” “second fluid leak probability level”, The steps of “reconcile different fluid leak probability levels detected by different ones of the multiple sensors using a Bayesian model,” and “wherein the Bayesian model determines likelihoods of multiple fluid leak probability levels at the location based on the separate fluid leak probability levels” represents the mathematical manipulations/ using “Bayesian model” to calculate the “leak probability level” based on multiple sensor detected data. The Bayesian model is a statistical model. These steps represent a process (a mathematical manipulation) as disclosed in the instant application (Specification [0040]-[0045] ). The highlighted steps above under its broadest reasonable interpretation, are considered as abstract idea and encompasses a mathematical manipulation of sensor acquired data.
Step 2A, Prong-2 (the claim is evaluated to determine whether the judicial exception/abstract-idea is integrated into a Practical Application): No.
Claim 1 recites additional elements
“detected at a location by multiple sensors of different types for a fluid facility, the multiple sensors of the different types including a first sensor of a first type and a second sensor of a second type different from the first type”; “detected at the location by the first sensor of the first type”, “detected at the location by the second sensor of the second type” are data gathering steps for the particular technological environment or field of use. Multiple sensors and of different types at different location are additional element for data acquisition is a routine data gathering steps and only add an insignificant extra-solution activity to the judicial exception. The above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application.
The other additional element " one or more physical processors configured by machine-readable instructions to: obtain multi-sensor information” this step merely represents insignificant solution activity. Furthermore, nothing in the claim reasonably indicates that anything other than a generic computer (i.e., "input interface" and "one or more processors") needs to be used to carry out the abstract idea.
The additional element “facilitate one or more operations at the fluid facility based on the likelihoods of the multiple fluid leak probability levels at the location”.
this step merely represents post-solution activity. However, this does not disclose how the calculated result of “leak probability level” is presented to facilitate one of more operation. The claim limitation does not disclose a display or an interface used to present result to a person or operator to implement in a practical application.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
Step 2B (the claim is evaluated to determine whether recites additional elements that amount to an inventive concept, or also, the additional elements are significantly more than the recited the judicial-exception/abstract-idea): No. the additional element(s) are just insignificant extra-solution activity which are simply routine and conventional steps previously known to the pertinent industry that includes acquiring data from external factors such as pipe environmental and dimensional data. Therefore, the claim does not include additional element(s) significantly more, and/or, does not amount to more than the judicial-exception/abstract-idea itself and the claim is not patent eligible.
claims 2-10 are rejected under 35 U.S.C. 101 because claims depend on claim 1, therefore, has the abstract idea of claim 1 and also has the routine and conventional structure above of claim 1. In addition, claims 2-10 further recite the elements which are simply more standard computational, mathematical-calculation to data gathering /generate data and/ or a model, and. Furthermore, claims 2-10 do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 11,
A method for detecting fluid leaks, the method comprising:
obtaining multi-sensor information, the multi-sensor information characterizing separate fluid leak probability levels detected at a location by multiple sensors of different types for a fluid facility, the multiple sensors of the different types including a first sensor of a first type and a second sensor of a second type different from the first type, the muti-sensor information characterizing a first fluid leak probability level detected at the location by the first sensor of the first type and a second fluid leak probability level detected at the location by the second sensor of the second type;
reconciling different fluid leak probability levels detected by different ones of the multiple sensors using a Bayesian model,
wherein the Bayesian model determines likelihoods of multiple fluid leak probability levels at the location based on the separate fluid leak probability levels detected at the location by the multiple sensors; and
facilitating one or more operations at the fluid facility based on the likelihoods of the multiple fluid leak probability levels at the location.
The claim limitations underlined above is abstract idea, and the remaining limitations are “additional elements”.
Step 1 (Statutory Category): Yes. we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (a mathematical manipulation). Therefore, it is directed to a statutory category, i.e., a mathematical manipulation.
Step 2 A, Prong-1 (the claim is evaluated to determine whether it is directed to a judicial-exception/abstract-idea): Yes.
In the above claim 11, the underlined portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exception. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation that covers mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations, a mathematical manipulation).
For example, steps of “characterizing separate fluid leak probability levels” “characterizing a first fluid leak probability level”, and “second fluid leak probability level”, The steps of “reconcile different fluid leak probability levels detected by different ones of the multiple sensors using a Bayesian model,” and “wherein the Bayesian model determines likelihoods of multiple fluid leak probability levels at the location based on the separate fluid leak probability levels” represents the mathematical manipulations /using “Bayesian model” to calculate the “leak probability level” based on multiple sensor detected data. The Bayesian model is a statistical model. These steps represent a process (a mathematical manipulation) as disclosed in the instant application (Specification [0040]-[0045] ). The highlighted steps above under its broadest reasonable interpretation, are considered as abstract idea and encompasses a mathematical manipulation of sensor acquired data.
Step 2A, Prong-2 (the claim is evaluated to determine whether the judicial exception/abstract-idea is integrated into a Practical Application): No.
Claim 11 recites additional elements
“detected at a location by multiple sensors of different types for a fluid facility, the multiple sensors of the different types including a first sensor of a first type and a second sensor of a second type different from the first type”; “detected at the location by the first sensor of the first type”, “detected at the location by the second sensor of the second type” are data gathering steps for the particular technological environment or field of use. Multiple sensors and of different types at different location are additional element for data acquisition is a routine data gathering steps and only add an insignificant extra-solution activity to the judicial exception. The above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application.
The other additional element " one or more physical processors configured by machine-readable instructions to: obtain multi-sensor information” this step merely represents insignificant solution activity. Furthermore, nothing in the claim reasonably indicates that anything other than a generic computer (i.e., "input interface" and "one or more processors") needs to be used to carry out the abstract idea.
The additional element “facilitate one or more operations at the fluid facility based on the likelihoods of the multiple fluid leak probability levels at the location”. this step merely represents post-solution activity. However, this does not disclose how the calculated result of “leak probability level” is presented to facilitate one of more operation. The claim limitation does not disclose a display or an interface used to present result to a person or operator to implement in a practical application.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
Step 2B (the claim is evaluated to determine whether recites additional elements that amount to an inventive concept, or also, the additional elements are significantly more than the recited the judicial-exception/abstract-idea): No. the additional element(s) are just insignificant extra-solution activity which are simply routine and conventional steps previously known to the pertinent industry that includes acquiring data from external factors such as pipe environmental and dimensional data. Therefore, the claim does not include additional element(s) significantly more, and/or, does not amount to more than the judicial-exception/abstract-idea itself and the claim is not patent eligible.
claims 12-20 are rejected under 35 U.S.C. 101 because claims depend on claim 11, therefore, has the abstract idea of claim 1 and also has the routine and conventional structure above of claim 11. In addition, claims 12-20 further recite the elements which are simply more standard computational, mathematical-calculation to data gathering /generate data and/ or a model, and. Furthermore, claims 12-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 102
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 4-6, 8-11, 14-16, and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hauge et al. (US 2019/0169982 A1, hereinafter Hauge, IDS ref.)
Regarding Claim 1, Hauge teaches,
A system for detecting fluid leaks (Hauge, Figure 6, 622, leak detection system), the system comprising:
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 (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. Such sensors may include sensors for measuring flow rate, water cut, gas lift rate, pressure, and/or other such variables related to measuring and monitoring hydrocarbon production”), the multi-sensor information characterizing separate fluid leak probability levels (Hauge, Figure 8, a probability block 840, [0178], a method includes performing a probability analysis for fluid leak probability”) detected at a location by multiple sensors of different types for a fluid facility (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”),
the multiple sensors of the different types including a first sensor of a first type and a second sensor of a second type different from the first type (Hauge, [0177] As an example, a fluid production network can be characterized at least in part by one or more profiles, which can include, for example, a pressure profile, a temperature profile, etc. Profile information can facilitate leak detection and/or leak location determination. As an example, profile information may facilitate diagnosing a leak that has been detected, for example, to characterize one or more aspects of the leak (e.g., a leak into a pipeline, a leak out of a pipeline, a stable leak, an unstable leak, etc.).” NOTE: a pressure profile is obtained by a pressure sensor and a temperature profile is obtained by a temperature sensor. Temperature sensor and pressure sensors read on first type of sensor and second type of sensors),
the muti-sensor information characterizing a first fluid leak probability level detected at the location by the first sensor of the first type and a second fluid leak probability level detected at the location by the second sensor of the second type (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”) ;
reconcile different fluid leak probability levels detected by different ones of the multiple sensors using a Bayesian model, wherein the Bayesian model determines likelihoods of multiple fluid leak probability levels at the location based on the separate fluid leak probability levels detected at the location by the multiple sensors (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”. NOTE: the LDS 622 system (fig. 6) obtain different types of data (pressure, temperature etc.) to determine probability of leaks at various location. See [0158]); 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, Figure 8, Step 850-860, [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 alarms [0191], “As an example, a framework may provide for one or more automatic shutdown actions. For example, a framework may be configured in the field as part of a Process Control System (PCS) that can respond at least in part to leak alarms generated by one or more LDSs. As an example, when a leak has been detected, an LDS algorithm of a framework can estimate a leak rate and a volume that is lost (e.g., at various points in time, optionally cumulative”).
Regarding Claim 4, Hauge teaches the system of claim 1,
Hauge further teaches wherein the 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 generation of an alert based on a determination that the location includes the 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, Hauge teaches the system of claim 1,
Hauge further teaches 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 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, Hauge 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 8, Hauge 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 first fluid leak probability level and a second 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, Hauge teaches the system of claim 1,
Hauge further teaches wherein the multiple fluid leak probability levels for which the Bayesian model determines the likelihoods include a first fluid leak probability level and a second fluid leak probability level. (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”. NOTE: the LDS 622 system (fig. 6) obtain different types of data (pressure, temperature etc.) to determine probability of leaks at various location. See [0158]);
Regarding Claim 10, Hauge teaches the system of claim 1,
Hauge further teaches wherein the 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:
obtain 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. Such sensors may include sensors for measuring flow rate, water cut, gas lift rate, pressure, and/or other such variables related to measuring and monitoring hydrocarbon production”), the multi-sensor information characterizing separate fluid leak probability levels (Hauge, Figure 8, a probability block 840, [0178], a method includes performing a probability analysis for fluid leak probability”) detected at a location by multiple sensors of different types for a fluid facility (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”),
the multiple sensors of the different types including a first sensor of a first type and a second sensor of a second type different from the first type (Hauge, [0177] As an example, a fluid production network can be characterized at least in part by one or more profiles, which can include, for example, a pressure profile, a temperature profile, etc. Profile information can facilitate leak detection and/or leak location determination. As an example, profile information may facilitate diagnosing a leak that has been detected, for example, to characterize one or more aspects of the leak (e.g., a leak into a pipeline, a leak out of a pipeline, a stable leak, an unstable leak, etc.).” NOTE: a pressure profile is obtained by a pressure sensor and a temperature profile is obtained by a temperature sensor. Temperature sensor and pressure sensors read on first type of sensor and second type of sensors),
the muti-sensor information characterizing a first fluid leak probability level detected at the location by the first sensor of the first type and a second fluid leak probability level detected at the location by the second sensor of the second type (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”) ;
reconcile different fluid leak probability levels detected by different ones of the multiple sensors using a Bayesian model, wherein the Bayesian model determines likelihoods of multiple fluid leak probability levels at the location based on the separate fluid leak probability levels detected at the location by the multiple sensors (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”. NOTE: the LDS 622 system (fig. 6) obtain different types of data (pressure, temperature etc.) to determine probability of leaks at various location. See [0158]); 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, Figure 8, Step 850-860, [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 alarms [0191], “As an example, a framework may provide for one or more automatic shutdown actions. For example, a framework may be configured in the field as part of a Process Control System (PCS) that can respond at least in part to leak alarms generated by one or more LDSs. As an example, when a leak has been detected, an LDS algorithm of a framework can estimate a leak rate and a volume that is lost (e.g., at various points in time, optionally cumulative”).
Regarding Claim 14, Hauge teaches the method of claim 11,
Hauge further teaches wherein the 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 generation of an alert based on a determination that the location includes the 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, Hauge teaches the method of claim 11,
Hauge further teaches 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 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, Hauge 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 18, Hauge 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 first fluid leak probability level and a second 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, Hauge teaches the method of claim 11,
Hauge further teaches wherein the multiple fluid leak probability levels for which the Bayesian model determines the likelihoods include a first fluid leak probability level and a second fluid leak probability level. (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”. NOTE: the LDS 622 system (fig. 6) obtain different types of data (pressure, temperature etc.) to determine probability of leaks at various location. See [0158]);
Regarding Claim 20, Hauge teaches the method of claim 11, wherein the 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)”)
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 2-3, 7, 12-13, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Hauge and in view of Sadovnychiy et al. (US 2022/0276116 A1, hereinafter Sadovnychiy).
Regarding Claim 2, Hauge teaches the system of claim 1,
Hauge teaches multiple different types of sensors (Hauge, (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. Such sensors may include sensors for measuring flow rate, water cut, gas lift rate, pressure, and/or other such variables related to measuring and monitoring hydrocarbon production”),
Hauge is silent on wherein the different types include an infrared image sensor and a sound sensor.
However, Sadovnychiy teaches wherein the different types include an infrared image sensor and a sound sensor (Sadovnychiy, Abstract, sensor system collect information on the physical variables of the fluid. This system may be made up of acoustic monitoring techniques, detection of negative pressure waves, as well as methods of mass and energy balance, integrated by multiple sensors, which are managed and controlled by electronic systems that process the information to determine the point of occurrence of a leak in digital maps (…) also can be remotely linked with SCADA systems (Supervisory Control and Data Acquisition) in real time. Additionally, it also has the technological integration of a motion sensor and a video and/or infrared camera together with a satellite modem whereby it sends images and video to the monitoring center, at the time of an unauthorized intrusion occurred on the pipeline in the area protected by a remote pipeline monitoring terminal (RPMT)”[0044], The comprehensive pipeline monitoring system detects hydrocarbon leaks and identifies the point where the damage occurs through the configuration of three integrated modules of an Acoustic System made up of hydrophones and temperature
sensors).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Hauge multiple sensor system to include with the infrared/ acoustic sensor systems 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, [0044]-[0045])
Regarding Claim 3, combination of Hauge and Sadovnychiy, teaches the system of claim 2,
Hauge is silent on wherein the multiple sensors of the different types further include multiple infrared image sensors of different types.
However, Sadovnychiy teaches wherein the multiple sensors of the different types further include multiple infrared image sensors of different types (Sadovnychiy, Abstract, sensor system collects information on the physical variables of the fluid. This system may be made up of acoustic monitoring techniques, detection of negative pressure waves, as well as methods of mass and energy balance, integrated by multiple sensors, which are managed and controlled by electronic systems that process the information to determine the point of occurrence of a leak in digital maps.(…) also can be remotely linked with SCADA systems (Supervisory Control and Data Acquisition) in real time. Additionally, it also has the technological integration of a motion sensor and a video and/or infrared camera together with a satellite modem whereby it sends images and video to the monitoring center, at the time of an unauthorized intrusion occurred on the pipeline in the area protected by a remote pipeline monitoring terminal (RPMT)”[0044], The comprehensive pipeline monitoring system detects hydrocarbon leaks and identifies the point where the damage occurs through the configuration of three integrated modules of an Acoustic System made up of hydrophones and temperature sensors, ).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Hauge multiple sensor system to include with the infrared/ acoustic sensor systems 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, [0044]-[0045]).
Regarding Claim 7, Hauge teaches the system of claim 1,
Hauge is silent on 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
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 sensor system to include with the infrared/ acoustic sensor systems 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, [0044]-[0045]).
Regarding Claim 12, Hauge teaches the method of claim 11,
Hauge teaches multiple different types of sensors (Hauge, (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. Such sensors may include sensors for measuring flow rate, water cut, gas lift rate, pressure, and/or other such variables related to measuring and monitoring hydrocarbon production”),
Hauge is silent on wherein the different types include an infrared image sensor and a sound sensor.
However, Sadovnychiy teaches wherein the different types include an infrared image sensor and a sound sensor (Sadovnychiy, Abstract, sensor system collect information on the physical variables of the fluid. This system may be made up of acoustic monitoring techniques, detection of negative pressure waves, as well as methods of mass and energy balance, integrated by multiple sensors, which are managed and controlled by electronic systems that process the information to determine the point of occurrence of a leak in digital maps (…) also can be remotely linked with SCADA systems (Supervisory Control and Data Acquisition) in real time. Additionally, it also has the technological integration of a motion sensor and a video and/or infrared camera together with a satellite modem whereby it sends images and video to the monitoring center, at the time of an unauthorized intrusion occurred on the pipeline in the area protected by a remote pipeline monitoring terminal (RPMT)”[0044], The comprehensive pipeline monitoring system detects hydrocarbon leaks and identifies the point where the damage occurs through the configuration of three integrated modules of an Acoustic System made up of hydrophones and temperature sensors” ).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Hauge multiple sensor system to include with the infrared/ acoustic sensor systems 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, [0044]-[0045])
Regarding Claim 13, combination of Hauge and Sadovnychiy, teaches the method of claim 12,
Hauge is silent on wherein the multiple sensors of the different types further include multiple infrared image sensors of different types.
However, Sadovnychiy teaches wherein the multiple sensors of the different types further include multiple infrared image sensors of different types (Sadovnychiy, Abstract, sensor system collects information on the physical variables of the fluid. This system may be made up of acoustic monitoring techniques, detection of negative pressure waves, as well as methods of mass and energy balance, integrated by multiple sensors, which are managed and controlled by electronic systems that process the information to determine the point of occurrence of a leak in digital maps (…) also can be remotely linked with SCADA systems (Supervisory Control and Data Acquisition) in real time. Additionally, it also has the technological integration of a motion sensor and a video and/or infrared camera together with a satellite modem whereby it sends images and video to the monitoring center, at the time of an unauthorized intrusion occurred on the pipeline in the area protected by a remote pipeline monitoring terminal (RPMT)”[0044], The comprehensive pipeline monitoring system detects hydrocarbon leaks and identifies the point where the damage occurs through the configuration of three integrated modules of an Acoustic System made up of hydrophones and temperature sensors, ).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Hauge multiple sensor system to include with the infrared/ acoustic sensor systems 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, [0044]-[0045])
Regarding Claim 17, combination of Hauge and Sadovnychiy, teaches the method of claim 11,
Hauge is silent on 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
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 sensor system to include with the infrared/ acoustic sensor systems 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, [0044]-[0045]).
Conclusion
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhu et al (US11274797 B1) recites “ A pipeline leak detection and location range determination system includes a set of monitoring devices installed at ends of segments of a pipeline, and server computer system communicating with the set of monitoring devices over the Internet. The server computer system is also adapted to communicate with client computer systems over the Internet. Each monitoring device within the set includes a set of sensors for reading time-varying signals, including pressure, of the pipeline, and communicates the time-varying signals and corresponding timestamps to the server computer system. The server computer system analyzes such data using regression to derive a range of possible locations of a leak in the pipeline. A probability distribution corresponding to subranges within the range is also determined. The server computer system communicates the range and probability distribution to client computer systems for being presented to pipeline maintenance professionals”. (abstract)
David Rossi (US 2020/0133251 A1) discloses “Techniques for detecting and correcting for discrepancy events in a fluid pipeline are presented. The techniques can include obtaining a plurality of empirical temperature and pressure measurements at a plurality of locations within the pipeline; simulating, using a pipeline model, a plurality of simulated temperature and pressure measurements for the plurality of locations within the pipeline; detecting, by a discrepancy event detector, at least one discrepancy event representing a discrepancy between the empirical temperature and pressure measurements and the simulated temperature and pressure measurements; outputting to t a user an indication that the at least one discrepancy event has been detected; accounting for the at least one discrepancy; determining, after the accounting and using an estimator applied to the pipeline model, a corrected branch flow rate for the pipeline; and outputting the corrected branch flow rate for the pipeline to the user” (Abstract).
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/DILARA SULTANA/Examiner, Art Unit 2858
/EMAN A ALKAFAWI/Supervisory Patent Examiner, Art Unit 2858
3/10/2026