DETAILED ACTION
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 .
Election/Restrictions
Applicant’s election without traverse of Invention I, claims 1-7 and 15-20 in the reply filed on 12/4/2025 is acknowledged.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4 and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 4 recites the integrity criterion comprises a predetermined threshold for cracks, dents, and deformations in the first pipe component, wherein the integrity criterion comprises a predetermined limit for flow change in the first pipe component, and wherein the predetermined threshold is based on severity. However, the examiner is unsure how a single threshold value represents three different types of defects, i.e. cracks, dents and deformations. Does the threshold merely indicate that these defects could exist or are these recites defects the only types of defects capable of being determined? How does one threshold represent three different defects? Further, the examiner is unsure how the integrity criterion comprises a predetermined limit for flow change. How does a criterion comprise a limit and when is a flow change detected such that the detected change can be compared to that limit?
Claim 18 is rejected similarly.
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claim(s) 1, 4, 15 and 19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al. (CN 112580182A).
With respect to claim 1, Wang et al. teaches a method for a flowline corrosion manager, comprising: obtaining (as Wang et al. teaches in step 102, damage events, internal and external factors are obtained; [00056]), by a computer processor (i.e. a processor; 401), gas flowline data regarding a first pipe (i.e. a first pipe of a process pipeline) component in a gas production network (i.e. oil and gas process pipeline; [00041]), wherein gas flowline data (i.e. obtained damage events, internal and external factors; [00056]) comprises operating sensor data (i.e. as Wang et al. teaches using collected flow rate of a medium seen in [00082]), cathodic protection status updates (as Wang et al. teaches the data obtained includes corrosion layer status; [00059]), and corrosion inhibitor compliance data over the gas production network (as Wang teaches in [00059] the information includes anti-corrosion coating age; thereby reading on the claimed “corrosion inhibitor compliance data” ); determining, by the computer processor (401) and based on the gas flowline data (as obtained at step 102), internal corrosion assessment data (i.e. calculated corrosion rates and correction factors, as read in the Abstract and [00061], based on the obtained data; [00059]) for the first pipe component (i.e. the first pipe of the process pipeline network), wherein internal corrosion assessment data (as calculated by Wang using the obtained data) comprises an internal report identifying internal corrosion presence in the gas production network (as Wang et al. teaches using the obtained data to calculate corrosion rates based on the obtained data, these rates thereby read on the claimed “report” as the calculated internal corrosion rates report the internal corrosion status of the first pipe; see step 103 and as read in [00081]); determining, by the computer processor (401) and based on the gas flowline data (obtained at step 102), external corrosion assessment data for the first pipe component, wherein external corrosion assessment data comprises an external report identifying external corrosion presence in the gas production network (as Wang et al. teaches using the obtained data to calculate corrosion rates based on the obtained data, these calculated rates thereby read on the claimed “report” as the calculated internal corrosion rates report the external corrosion of the first pipe; at step 103 and as read in [00089]); determining, by the computer processor (401), whether the internal corrosion assessment data and the external corrosion assessment data (as calculated in [00081 and 00089]) satisfy an integrity criterion for the first pipe component (as Wang et al. teaches the calculated corrosion data is used to determine when maintenance is to occur; [00099]); and transmitting (via a communication interface 403), by the computer processor (401) in response to the internal corrosion assessment data and the external corrosion assessment data failing to satisfy the integrity criterion (i.e. as Wang et al. teaches communicating the calculated corrosion data which indicates when maintenance is to occur based on either the internal or external corrosion data not satisfying the integrity criterion), a command that implements a remediation operation (i.e. maintenance) for the first pipe component, wherein the remediation operation (i.e. maintenance) comprises a procedure that adjusts the gas production network to mitigate internal corrosion and minimize external corrosion (as Wang et al. teaches the maintenance as being a remediation operation which is a procedure that ensures the safe and effective operation of the process pipeline in the station based on the determined data; [0005], insofar as how both “the remediation operation” and “procedure” are structurally recited).
With respect to claim 15, Wang et al. teaches a non-transitory computer-readable medium comprising computer-executable instructions stored thereon [000136] that, when executed on a processor (401), cause the processor (401) to perform the rejected steps of method claim 1.
With respect to claims 4 and 18, Wang et al. teaches the method wherein the integrity criterion comprises a predetermined threshold for cracks, dents, and deformations (as Wang et al. teaches the corrosion data is related to aging of pipes in the system, which is capable of equating to a variety of issues, like cracks, dents and deformation, in light of the 112(b) rejection seen above) in the first pipe component [0099], wherein the integrity criterion comprises a predetermined limit for flow change in the first pipe component, and wherein the predetermined threshold is based on severity (insofar as what is structurally defined, based on the determined corrosion data as it relates to flow changes during operation, as the threshold defining when maintenance should occur is based on the severity of that determined corrosion; thereby reading on the claimed limitations, insofar as how flow change is determined).
With respect to claim 19, Wang et al. teaches the non-transitory computer-readable medium wherein the flow manager comprises a machine-learning model (as Wang et al. teaches in [00015-00018] using a model for matching correction factors and then using a relationship model for determining corrosion rates; as these portions define several inputs and using these inputs to produce a desired output through structured mathematical algorithms; further the use of correction factors teaches the model has been developed using historical data).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 2 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (CN 112580182A) in view of Pittalwala et al. (2003/0171879).
With respect to claims 2 and 16, Wang et al. teaches all that is claimed in the above rejection of claims 1 and 15, but remains silent regarding the method further comprising: transmitting, by the computer processor, integrity assessment data in an integrity assessment report to a user device, wherein the user device is coupled to the flowline corrosion manager, and wherein the user device is configured to obtain a user selection within a graphical user interface regarding the remediation operation among a plurality of remediation operations in response to a presentation of the integrity assessment report on a display device.
Pittalwala et al. teaches a similar method that includes transmitting, by a computer processor (i.e. a processor; [0046]), integrity assessment data (i.e. digital data related to calculated assessment data; [0057]) in an integrity assessment report to a user device (as calculated and determined time for replacement is taught to be sent to a user display; [0046]), wherein the user device is coupled to the flowline corrosion manager (as display is coupled to the processor which is part of a flowline corrosion manager), and wherein the user device (i.e. display) is configured to obtain a user selection within a graphical user interface (as Pittalwala teaches the user interacting with the display using a Palm Pilot RTM running versions of Windows for supporting a graphical user interface with key fields; [0047-0048]) regarding a remediation operation (i.e. to repair or replace; as seen in Fig. 3) among a plurality of remediation operations (i.e. repair, replacement, monitoring, or inspection) in response to a presentation of the integrity assessment report on a display device (i.e. the display).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the processor of Wang et al. to include the control logic of transmitting assessment report data to a user device, as taught in Pittalwala et al. because Pittalwala et al. teaches such a modification improves management actions, thereby improving cost over a longer period of time [0039].
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (CN 112580182A) in view of Older et al. (2016/0362956).
With respect to claim 3, Wang et al. teaches all that is claimed in the above rejection of claim 1 including the flowline corrosion manager is a server [000136], but remains silent regarding the flowline corrosion manager is coupled to a gas plant and a gathering system having a plurality of remote headers configured for controlling streams from a first plurality of gas wells.
Older et al. teaches a similar method that includes a flowline corrosion manager (62) is coupled to a gas plant (as seen in Fig. 3) and a gathering system (i.e. a variety of sensors found within the manifold; [0055]) having a plurality of remote headers (i.e. manifolds found within a clustering of well [0010], Fig. 3 shows an example of at least one of those manifolds 14) configured for controlling streams from a first plurality of gas wells (as Older et al. teaches using control signals from the manager 62 to control the manifolds of the wells; [0045]).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the server and system of Wang et al. to include the taught control manager and well clustering of Older et al. such that, based on determined corrosion data, the well clusters are controlled through their respective manifolds to aid in corrosion prevention; [0060], thereby improving the overall operation of Wang et al.
Claim(s) 5-6 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (CN 112580182A) in view of Wada et al. (CN 2022131956A).
With respect to claim 5, Wang et al. teaches all that is claimed in the above rejection of claim 1 including: predicting the internal corrosion assessment data using a machine-learning model (as Wang et al. teaches in [00015-00018] using a model for matching correction factors and then using a relationship model for determining corrosion rates; as these portions define several inputs and using these inputs to produce a desired output through structured mathematical algorithms; further the use of correction factors teaches the model has been developed using historical data), wherein the flowline corrosion manager (i.e. processor) comprises the machine-learning model [0015], however, remains silent regarding the machine-learning model is a neural network.
Wada et al. teaches a similar method related to corrosion prediction that includes the machine learning model being a neural network (Wada et al. teaches a learning model being a neural network used to predicted corrosion in a pipe network).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the prediction model of Wang et al. such that the model is a neural network, as taught by Wada et al., because Wada et al. teaches such a modification allows for improved accuracy, as the use of a predictive neural network allows for faster and more reliable predictions of corrosion.
With respect to claim 6, Wang et al. teaches all that is claimed in the above rejection of claim 1 including: predicting the external corrosion assessment data using a machine-learning model (as Wang et al. teaches in [00015-00018] using a model for matching correction factors and then using a relationship model for determining corrosion rates; as these portions define several inputs and using these inputs to produce a desired output through structured mathematical algorithms; further the use of correction factors teaches the model has been developed using historical data), wherein the flowline corrosion manager (i.e. processor) comprises the machine-learning model [0015], however, remains silent regarding the machine-learning model is a neural network.
Wada et al. teaches a similar method related to corrosion prediction that includes the machine learning model being a neural network (Wada et al. teaches a learning model being a neural network used to predicted corrosion in a pipe network).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the prediction model of Wang et al. such that the model is a neural network, as taught by Wada et al., because Wada et al. teaches such a modification allows for improved accuracy, as the use of a predictive neural network allows for faster and more reliable predictions of corrosion.
With respect to claim 20, Wang et al. teaches all that is claimed in the above rejection of claim 19 including: predicting the internal corrosion assessment data using a machine-learning model (as Wang et al. teaches in [00015-00018] using a model for matching correction factors and then using a relationship model for determining corrosion rates; as these portions define several inputs and using these inputs to produce a desired output through structured mathematical algorithms; further the use of correction factors teaches the model has been developed using historical data), predicting the external corrosion assessment data using a machine-learning model (as Wang et al. teaches in [00015-00018] using a model for matching correction factors and then using a relationship model for determining corrosion rates; as these portions define several inputs and using these inputs to produce a desired output through structured mathematical algorithms; further the use of correction factors teaches the model has been developed using historical data), predicting the integrity assessment data (i.e. the disclosed digital data related to the calculated assessment data; [0057]) using a machine-learning model (as Wang et al. teaches in [00015-00018] using a model for matching correction factors and then using a relationship model for determining corrosion rates; as these portions define several inputs and using these inputs to produce a desired output through structured mathematical algorithms; further the use of correction factors teaches the model has been developed using historical data), However, Wang et al. remains silent regarding the machine-learning model is a neural network.
Wada et al. teaches a similar method related to corrosion prediction that includes the machine learning model being a neural network (Wada et al. teaches a learning model being a neural network used to predicted corrosion in a pipe network).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the prediction model of Wang et al. such that the model is a neural network, as taught by Wada et al., because Wada et al. teaches such a modification allows for improved accuracy, as the use of a predictive neural network allows for faster and more reliable predictions of corrosion.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (CN 112580182A) in view of Pittalwala et al. (2003/0171879), as applied to claim 2, further in view of Wada et al. (CN 2022131956A).
With respect to claim 7, Wang et al. as modified teaches all that is claimed in the above rejection of claim 1 including: predicting the integrity assessment data (digital data related to calculated assessment data; [0057]) using a machine-learning model (as Wang et al. teaches in [00015-00018] using a model for matching correction factors and then using a relationship model for determining corrosion rates; as these portions define several inputs and using these inputs to produce a desired output through structured mathematical algorithms; further the use of correction factors teaches the model has been developed using historical data), wherein the flowline corrosion manager (i.e. processor) comprises the machine-learning model [0015], however, remains silent regarding the machine-learning model is a neural network.
Wada et al. teaches a similar method related to corrosion prediction that includes the machine learning model being a neural network (Wada et al. teaches a learning model being a neural network used to predicted corrosion in a pipe network).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the prediction model of Wang et al. such that the model is a neural network, as taught by Wada et al., because Wada et al. teaches such a modification allows for improved accuracy, as the use of a predictive neural network allows for faster and more reliable predictions of corrosion.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (CN 112580182A) in view of Pittalwala et al. (2003/0171879), as applied to claim 16, further in view of Older et al. (2016/0362956).
With respect to claim 17, Wang et al. teaches all that is claimed in the above rejection of claim 16 including the flowline corrosion manager is a server [000136], but remains silent regarding the flowline corrosion manager is coupled to a gas plant and a gathering system having a plurality of remote headers configured for controlling streams from a first plurality of gas wells.
Older et al. teaches a similar method that includes a flowline corrosion manager (62) is coupled to a gas plant (as seen in Fig. 3) and a gathering system (i.e. a variety of sensors found within the manifold; [0055]) having a plurality of remote headers (i.e. manifolds found within a clustering of well [0010], Fig. 3 shows an example of at least one of those manifolds 14) configured for controlling streams from a first plurality of gas wells (as Older et al. teaches using control signals from the manager 62 to control the manifolds of the wells; [0045]).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the server and system of Wang et al. to include the taught control manager and well clustering of Older et al. such that, based on determined corrosion data, the well clusters are controlled through their respective manifolds to aid in corrosion prevention; [0060], thereby improving the overall operation of Wang et al.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hernandez et al. (2010/0185401) which teaches using models for predicting corrosion in a pipe.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW G MARINI whose telephone number is (571)272-2676. The examiner can normally be reached Monday-Friday 8am-5pm.
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/MATTHEW G MARINI/ Primary Examiner, Art Unit 2853