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 .
Priority
Current application, US Application No. 18/309,489, is filed on filed on 04/27/2023.
DETAILED ACTION
This office action is responsive to the application filed on 04/27/2023. Claims 1-20 are currently pending.
Claim Objections
Claims 7 and 14 are objected to because of the following informalities: As per claims 7 and 14, the limitation “human/animal” in “a human/animal body” should be replaced with “a human or animal body” as disclosed in the specification for clarity (see specification – a human or animal body [0024]) because the limitation “human/animal” can mean “human” or “animal” or “human and animal”, and one of interpreted phrase, e.g. “a human and animal body”, would not make sense.
Appropriate correction is required.
Claim Interpretation – 35 USC 112(f)
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
The current application includes limitations in claim 8 that do not use the word “means,” but are nonetheless interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because of the following reasons:
Claim 8 includes a limitation/element that use a generic placeholder “analyzer” that are coupled with functional language, configured to “perform”, “without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
The physical structure of the “a pipe anomaly analyzer” is interpreted as an analysis system including a general computer and analysis software (see specification – the pipe anomaly analyzer (160) may include hardware and/or software with functionality for facilitating operations of the well system [0023], the pipe anomaly analyzer (160) may include a computer system that is similar to the computing system (400) described below with regard to FIG. 4 [0027-0029, 0031-0036, 0040, Fig. 1A and 1B160, Fig. 4 400]).
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation to avoid it being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation recites sufficient structure to perform the claimed function so as to avoid it being interpreted under 35 U.S.C. 112(f).
Claim Rejections - 35 USC § 101
35 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 1-2, 7-9 and 14-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to nonstatutory subject matter. The claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative claim 1 recites:
“A method to perform a maintenance operation of an equipment structure, (1.A) comprising:
collecting monitoring data of the equipment structure disposed in a region of interest; (1.B)
processing the monitoring data to generate normalized monitoring data of the equipment structure; (1.C)
training, based on a first historical portion of the normalized monitoring data as training data and using a machine learning algorithm, an artificial neural network (ANN) model; (1.D)
validating, based on a second historical portion of the normalized monitoring data as validation data, the ANN model to generate a validated ANN model; (1.E)
detecting, using a real time portion of the normalized monitoring data as input to the validated ANN model, an anomaly of the equipment structure; (1.F)
and performing, in response to detecting the anomaly, the maintenance operation of the equipment structure. (1.G)”.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, 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 (Process - Method).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted 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 mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations), and mental processes (concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion).
For example, highlighted limitations/steps (1.C), (1.E) and (1.F) are treated by the Examiner as belonging to Mathematical Concept grouping or a combination of Mathematical Concept and Mental Processing groupings as the limitations include Mathematical Calculations or show Mathematical Relationship with optional Mental observation or judgement. (see specification - the collected pipeline monitoring data are … normalized to ensure that all variables are on the same scale [0043], regularization techniques (e.g., LI and L2 regularization) and cross-validation techniques (e.g., k-fold cross-validation) are used [0036], showing use of mathematical algorithm, the trained ANN models are evaluated on a set of validation data to assess performance and accuracy. Accordingly, the ANN models are fine-tuned based on the evaluation results to generate validated ANN models [0045], showing mathematical calculation and relationships, The real time monitoring data is used as input to the ANN models to detect pipe anomalies [0044], showing mathematical relationship).
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The above claims comprise the following additional elements: (Side Note: duplicated elements are not repeated)
In Claim 1: “A method to perform a maintenance operation of an equipment structure”, “collecting monitoring data of the equipment structure disposed in a region of interest”, “training, based on a first historical portion of the normalized monitoring data as training data and using a machine learning algorithm, an artificial neural network (ANN) model” and “performing, in response to detecting the anomaly, the maintenance operation of the equipment structure”;
In Claim 8: “A pipe anomaly analyzer for performing a maintenance operation of an equipment structure” and “a computer processor; and memory storing instructions, when executed by the computer processor comprising functionality”;
In Claim 15: “A system” and “an equipment structure disposed in a region of interest”;
As per claim 1, the additional element in the preamble “A method to perform a maintenance operation of an equipment structure” is not a meaningful limitation because the preamble simply links the method with an intended purpose, i.e. perform a maintenance operation of an equipment structure. The limitation/step “collecting monitoring data of the equipment structure disposed in a region of interest” represents a standard data collection step in the art and only adds insignificant extra solution to the judicial exception. The limitation/step “training, based on a first historical portion of the normalized monitoring data as training data and using a machine learning algorithm, an artificial neural network (ANN) model” represents a standard model training step reciting the step in a high level of generality in the art and only adds insignificant extra solution to the judicial exception. The limitation/step “performing, in response to detecting the anomaly, the maintenance operation of the equipment structure” represent a standard maintenance step reciting the step in a high level of generality and only adds insignificant extra solution to the judicial exception.
As per claim 8: the additional elements “A pipe anomaly analyzer for performing a maintenance operation of an equipment structure” is not a meaningful limitation because the preamble simply links the analyzer, i.e. a general computing system consists of computer hardware/software, with an intended purpose, i.e. performing a maintenance operation of an equipment structure. The limitations/elements “a computer processor; and memory storing instructions, when executed by the computer processor comprising functionality” represents sub components of a general computer and they are not particular in the art.
As per claim 15, the additional element in the preamble “A system” is not qualified as a meaningful limitation because the preamble even fails to link the system with a particular operation or field of use. The limitation/element “an equipment structure disposed in a region of interest” is a broadly recited standard element in the art (see specification – the term "equipment structure" refers to mechanical structures of equipment and piping network.[0020], pipeline system [0046]), which is not particular.
In conclusion, the above additional elements considered individually and in combination with the other claim elements as a whole do not reflect an improvement to the computer technology or other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. No particular machine or real-world transformation are claimed. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
Under Step 2B analysis, the above claims fail to include additional elements that are sufficient to amount to significantly more than the judicial exception as shown in the prior art of record.
The limitations/elements listed as additional elements above are well understood, routine and conventional steps/elements in the art according to the prior art of record. (See Grae, Kadem, Heid, Ree, Dinta, Chung and others in the list of prior art of record)
Claims 1-2, 7-9 and 14-16, therefore, are not patent eligible.
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 1-2, 7-9 and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Graeme (GB 2628475 A), hereinafter ‘Grae” in view of Kadem
(Kadem, Mohammad et al., "Automated Subsea Pipeline Leak Detection Using Real-Time Downhole Gauges"; 4 Proceedings of the Offshore Technology Conference; Paper Number: OTC-32132-MS; pp. 1-12; April 25, 2022), hereinafter ‘Kadem’ and Heidari (US 12442284 B2), hereinafter ‘Heid’.
As per claim 1, Grae discloses
A method to perform a maintenance operation of an equipment structure, (computer implemented method [title, pg. 1 1-6], control maintenance apparatus, determine a maintenance program [abs], tubulars [pg. 1 line 9-12], maintaining the structure [pg. 2 line 14-17], maintenance activity [pg. 4 line 15-20], side note” see the definition of “equipment structure” in the specification - the term "equipment structure" refers to mechanical structures of equipment and piping network [0020]) comprising:
collecting monitoring data of the equipment structure disposed in a region of interest; (input data … collected while surveying the structure [pg. 4 line 1-3], collecting the input data associated with the structure [pg. 12 line 14-20])
Grae further discloses data may be pre-processed (training data may be pro-processed [pg. 6 line 34-35]), but is silent regarding processing the monitoring data to generate normalized monitoring data of the equipment structure.
Kadem discloses use of normalized data for leak detection model (estimation of the leak location, mathematical model, GAP network model, normalized GAP model pressure drop, normalized actual pressure drop [pg. 9 par. 1, Fig, 5]).
Kadem is in the same art as Grae in gas and oil industry researching for ways to detect leaks in the pipeline.
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of Grae in view of Kadem to process the monitoring data to generate normalized monitoring data of the equipment structure with a rationale that the normalized data can be used to train a model for an accurate detection of defects in the equipment structure (Grae - training a model to predict corrosion rate … provide more accurate prediction of corrosion, surface of entire structure [pg. 4 line 5-13]).
Grae in view of Kadem further discloses
training, based on a first historical portion of the normalized monitoring data as training data and using a machine learning algorithm, an artificial neural network (ANN) model; (model is trained, historical data [pg. 2 line 2-5], training, via the machine learning algorithm, the … model [pg. 3 1-3], machine learning algorithm may comprise a neural network [pg. 7 line 28 – pg. 8 line 2])
However, Grae is silent regarding validating, based on a second historical portion of the normalized monitoring data as validation data, the ANN model to generate a validated ANN model.
Heid discloses validating, based on a historical portion of the monitoring data set as validation data, the ANN model to generate a validated ANN model. (historical … dataset, validation portion can be used to validate the training model [col 5 line 49 – 59], neural networks [abs, col 9 line 9-11, col 13 line 49-55]).
Heid is in the same seismic operations art using well site equipment for a well like the combined prior art.
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of the combined prior art in view of Heid to validate, based on a second historical portion of the normalized monitoring data as validation data, the ANN model to generate a validated ANN model for an accurate detection of defects in the equipment structure.
Grae in view of Kadem and Heid further discloses
detecting, using a real time portion of the normalized monitoring data as input to the validated ANN model, an anomaly of the equipment structure; (the input data may … collected while surveying the structure [pg. 4 line 1-2], failure information, anomaly … in the structure [pg. 4 line 24 – 30], machine learning model further receives the UT data as sensor data in step 140. The machine learning model may receive 145 additional external inspection data … may assist in generated a predicted failure, input ‘UT’ data [pg. 30 line 1-10, Fig. 1 140 145])
and performing, in response to detecting the anomaly, the maintenance operation of the equipment structure (failure data may be used to determine a maintenance program for the structure, maintaining the structure [pg. 2 line 14-17], maintenance activity [pg. 4 line 15-20]).
As per claim 8, Grae discloses
A pipe anomaly analyzer for performing a maintenance operation of an equipment structure, (computer [title, pg. 1 1-6], computer programs [pg. 1 line 31-33], maintenance program [pg. 1 line 14-19], control maintenance apparatus [abs], tubulars [pg. 1 line 9-12], maintaining the structure [pg. 2 line 14-17], maintenance activity [pg. 4 line 15-20]) comprising:
a computer processor; (one or more processors [pg. 20 line 28])
and memory storing instructions, (programs stores on … computer readable medium [pg. 1 line 32-33]), when executed by the computer processor comprising functionality for: (one or more computer-readable storage media storing instructions that when executed by one or more processors cause the one or more processors to perform any of the described methods [pg. 20 line 33-35])
Grae in view of Kadam and Heid discloses the remaining limitations as shown in claim 1 above.
As per claim 15, Grae discloses
A system (system [pg. 20 line 27]) comprising:
an equipment structure disposed in a region of interest; (tubulars [pg. 1 line 9-12], maintaining the structure [pg. 2 line 14-17], structure … rigs, oil and gas well, wellbores, wind turbine or other offshore structures [pg. 3 line 23-24])
Grae in view of Kadam and Heid discloses the remaining limitations as shown in claim 8 set forth above.
As per claims 2, 9 and 16, Grae, Kadem and Heid disclose claims 1, 8 and 15 set forth above.
Grae further discloses the region of interest comprises at least a portion of a field, and the field comprises a plurality of well sites, a plurality of processing plants, and a plurality of pipeline networks. Grae (structure may comprise rigs, oil and gas well, wellbores, wind turbine or other offshore structures [pg. 3 line 23-24], representing an oil and gas field or electric generation plants, tubular, casing, drilling site [pg. 34 line 23-pg. 35 line 17])
As per claims 7 and 14, Grae, Kadem and Heid disclose claims 1, 8 and 15 set forth above.
Grae further discloses the region of interest corresponds to an oil and gas field. (structure may comprise rigs, oil and gas well, wellbores, wind turbine or other offshore structures [pg. 3 line 23-24], representing an oil and gas or electric generation, i.e. plant, field).
Claims 3, 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Grae, Kadem and Heid in view of Reece (US 20210216852 A1), hereinafter ‘Ree’ and Dintakkurti (US 20130041601 A1), hereinafter ‘Dinta’.
As per claims 3, 10 and 17, Grae, Kadem and Heid disclose claims 1, 8 and 15 set forth above.
The set forth combined prior art is silent regarding the ANN model comprises a leak detection model, wherein training the ANN model comprises using a fuzzy logic algorithm to train the leak detection model, wherein detecting the anomaly of the equipment structure comprises using the leak detection model to detect a leak in a pipe of the equipment structure.
Ree discloses the ANN model comprises a leak detection model, wherein training the ANN model comprises using a fuzzy logic algorithm to train the leak detection model, wherein detecting the anomaly of the equipment structure comprises using the leak detection model to detect a leak in a pipe of the equipment structure. (leak detection models [0004-0005], artificial neural network ‘ANN’ [0006], machine learning, training, fuzzy logic [0006], detecting leaks, including for pipelines , and including for pipeline that transport oil, natural gas, or water [0009], AI or Deep-Learning, use deep learning [0010], deep learning model, neural network [0013-0014])
Ree is in the same art as the combined prior art dealing with leak detection of structures using artificial intelligence technology.
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of the combined prior art in view of Ree to use the ANN model comprising a leak detection model, wherein training the ANN model comprises using a fuzzy logic algorithm to train the leak detection model, wherein detecting the anomaly of the equipment structure comprises using the leak detection model to detect a leak in a pipe of the equipment structure for an accurate detection of defects in the equipment structure.
However, the combined prior art is silent regrading performing the maintenance operation comprises replacing the pipe.
Dinta discloses maintaining pipes by replacing leaky pipes (maintenance … to repair or replace leaky pipes [0047]).
Dinta is in the same art as the combined prior art dealing with leak detection of the pipes.
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of the combined prior art in view of Dinta to replace leaky pipes as a maintenance operation for the safety of the equipment structure.
Claims 4, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Grae, Kadem and Heid in view of Hsu (US 20170032281 A1), hereinafter ‘Hsu’ and Dinta.
As per claims 4, 11 and 18, Grae, Kadem and Heid disclose claims 1, 8 and 15 set forth above.
The set forth combined prior art is silent regarding wherein the ANN model comprises a pipe weld defect classification model, wherein training the ANN model comprises using a support vector machine algorithm to train the pipe weld defect classification model, wherein detecting the anomaly of the equipment structure comprises using the pipe weld defect classification model to detect and classify a pipe weld defect in a pipe of the equipment structure.
Hsu discloses uses machine learning algorithms including SVM and ANN to classify the pipe weld defects (machine learning algorithm, support vector machine ‘SVM’ [0006], artificial neural networks; support vector machines [0025, claims 14 and 30], fault classification, weld cable fault, quality classification, a missing weld, misaligned weld, weld is too large or too small, porosity, undercut, burn thru, lack of fusion, failed bend test, failed tensile test, failed fatigue test, failed Charpy test, distortion out of spec, [0073], pipe welds [0081], analytic computing platform, pipe welding in oil & gas industry [0117, Fig. 6]).
Hsu is in the same art as the combined prior art finding ways to detect pipe leaks using artificial intelligence techniques.
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of the combined prior art in view of Hsu to use the ANN model comprises a pipe weld defect classification model, wherein training the ANN model comprises using a support vector machine algorithm to train the pipe weld defect classification model, wherein detecting the anomaly of the equipment structure comprises using the pipe weld defect classification model to detect and classify a pipe weld defect in a pipe of the equipment structure for an accurate detection of defects in the equipment structure.
However, the combined prior art is silent regrading performing the maintenance operation comprises repairing the pipe weld defect based on a classification generated by the pipe weld defect classification model.
Dinta discloses maintaining pipes by repairing leaky pipes (maintenance … to repair or replace leaky pipes [0047]).
Dinta is in the same art as the combined prior art dealing with leak detection of the pipes.
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of the combined prior art in view of Dinta to repair leaky pipes as a maintenance operation based on a classification generated by the pipe weld defect classification model for the safety of the equipment structure.
Claims 5, 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Grae, Kadem and Heid in view of Li (CN 113268883 A), hereinafter “Li’ and Chung (US 20240310004 A1), hereinafter ‘Chung’.
As per claims 5, 12 and 19, Grae, Kadem and Heid disclose claims 1, 8 and 15 set forth above.
The set forth combined prior art is silent regarding wherein the ANN model comprises a pipe failure prediction model, wherein training the ANN model comprises using an artificial bee colony algorithm to train the pipe failure prediction model, wherein detecting the anomaly of the equipment structure comprises using the pipe failure prediction model to generate a failure prediction of a pipe of the equipment structure.
Li discloses artificial intelligent algorithms and models predicting the pipe failure including artificial bee colony algorithm (pipeline corrosion … prediction model, pipeline corrosion failure risk pre-warning [abs], prediction … pipeline corrosion defect [pg. 2 line 12-24], artificial bee colony algorithm, ABC algorithm [pg. 4 line 13-17]).
Li is in the same art as the combined prior art with respect to detecting the defects in the pipeline.
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of the combined prior art in view of Li to use the ANN model comprising a pipe failure prediction model, wherein training the ANN model comprises using an artificial bee colony algorithm to train the pipe failure prediction model, wherein detecting the anomaly of the equipment structure comprises using the pipe failure prediction model to generate a failure prediction of a pipe of the equipment structure for an accurate detection of defects in the equipment structure.
However, the combined prior art is silent regrading performing the maintenance operation comprises a preventive maintenance of the pipe based on the failure prediction.
Chung discloses performing a preventive maintenance for the expected leakage and damage of the pipeline (pipeline monitoring system, detecting leaks [0012], preventive maintenance and remediation before leakage and the damage resulting [0027], analysis using artificial intelligence-based (AI) for enabling effective and efficient preventative and timely maintenance and repair [0039]).
Chung is in the same art as the combined prior art dealing with leak detection of the pipes using AI techniques.
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of the combined prior art in view of Chung to perform preventive maintenance of the pipe based on the failure prediction for the safety of the equipment structure.
Claims 6, 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Grae, Kadem and Heid in view of Zabihi (Zabihi, Reza, and et al. "Artificial intelligence approach to predict drag reduction in crude oil pipelines." Journal of Petroleum Science and Engineering 178 (2019): 586-593), hereinafter ‘Zabi’ and Cavness (US 20220140609 A1), hereinafter ‘Cav’.
As per claims 6, 13 and 20, Grae, Kadem and Heid disclose claims 1, 8 and 15 set forth above.
The set forth combined prior art is silent regarding wherein the ANN model comprises a crude oil pipeline drag reduction model, wherein detecting the anomaly of the equipment structure comprises detecting a drag reduction in a crude oil pipeline of the equipment structure, and wherein performing the maintenance operation comprises optimizing transport of the crude oil pipeline.
Zabi discloses wherein the ANN model comprises a crude oil pipeline drag reduction model (drag reducing … pipeline, models, oil pipeline [abs], ANN … powerful modeling tool [pg. 587 right col par2 from the bottom – pg. 588 right col par. 3])
wherein detecting the anomaly of the equipment structure comprises detecting a drag reduction in a crude oil pipeline of the equipment structure, (new ANN models for prediction of drag reduction (DR) in crude oil pipelines due to drag reducing agents, determining the optimal ANN models, performance of the optimal models has been evaluated with new experimental data [pg. 587 right col par. 1 from the bottom – pg. 588 left col par. 1])
and wherein performing the maintenance operation comprises optimizing transport of the crude oil pipeline. (addition of a small amount of drag reducing agents (DRAs) to a flowing fluid in a pipeline causes reduction in pressure drop through the pipeline [abs], petroleum industries, the effect of drag reduction additives in … flow [pg. 586 left col par. 1 – pg. 587 right col par. 2])
Zabi is also in the same oil and gas transport art as the combined prior art.
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of the combined prior art in view of Zabi to use the ANN model comprising a crude oil pipeline drag reduction model, wherein detecting the anomaly of the equipment structure comprises detecting a drag reduction in a crude oil pipeline of the equipment structure, and wherein performing the maintenance operation comprises optimizing transport of the crude oil pipeline for efficient production of the oil and gas through well operation.
However, the combined prior art is silent regrading training the ANN model comprises using an independent component analysis algorithm to train the crude oil pipeline drag reduction model.
Cav discloses training ML models using ICA algorithm (training computationally intensive machine learning … models, independent component analysis) and reducing the safety and environment risks by controlling the oil and gas production operations (Flaring is the controlled burning of natural gas produced in association with oil in the course of routine oil and gas production operations, and is designed to minimize the safety and environmental risks associated with venting uncombusted natural gas [0004], mitigates the risk of damage to pipes and process equipment from blocked flow and corrosion [0088], to meet pipeline quality specifications [0093]).
Cav is also in the same art of oil and gas production operation as the combined prior art.
Therefore, it would have been obvious to one of ordinary skill in the art at the time when invention is filed before the effective filing date of the current application to modify the teachings of the combined prior art in view of to use an independent component analysis algorithm to train the crude oil pipeline drag reduction model for the safety of well production operation.
Notes with regard to Prior Art
The prior arts made of record are provided as additional references relevant to the current claims.
Peng (CN 109858707 A) discloses using an artificial bee colony algorithm as intelligent diagnosis of furnace tube (a fusion artificial bee ‘utopia Bee Colony ABC’ algorithm, an adaptive nerve fuzzy reasoning system ‘Adaptive cranial Fuzzy Inference System “ANFIS”’ and coking time factor ‘Coking Time Factor CTF’ of ethylene cracking furnace tube intelligent diagnosis method of coking [abs[)
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DOUGLAS KAY, whose telephone number is (408) 918-7569. The examiner can normally be reached on M, Th & F 8-5, T 2-7, and W 8-1.
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/DOUGLAS KAY/
Primary Examiner, Art Unit 2857