DETAILED ACTION
Claims 1-9 are presented for examination. Claim 1 stands currently amended.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Finality of Office Action
The following is a brief summary description of new ground(s) of rejection (if any) and the reason why those new ground(s) are made necessary by this amendment:
New §112(b) rejections are made as necessitated by the amended claim language.
Response to Arguments
Applicant's remarks filed 2 June 2026 have been fully considered and Examiner’s response is as follows:
Applicant remarks page 8 argues:
First, the claimed invention is inextricably tied to physical drilling hardware. Claim 1 step 1 explicitly recites that the monitoring-while-drilling parameters are "13 core parameters collected from on-site physical drilling monitoring hardware, including conventional drilling parameter monitoring equipment and sand discharge pipeline return gas component monitoring equipment." These are not generic data sources-they are specific, physical components of a drilling operation. The claimed method is not a free-floating algorithm; it is a method executed on and integrated with these physical systems.
This argument is unpersuasive.
The claim does not actively recite collecting the on-site physical drilling parameter monitoring hardware. The claim uses passive voice saying “13 core parameters collected from” and does not actively recite any collection as a part of the method. Claim 1 first clause is thus separated from any data collection and data collection hardware.
Accordingly, Examiner is unpersuaded that the claim is tied to physical drilling hardware.
Applicant remarks page 8 argues:
Second, the claimed invention produces a direct, tangible change in the physical drilling operation. Claim 1 step 5 recites outputting the early warning signal to a "drilling site on-site operation control system" and then "adjusting physical drilling construction parameters or executing a standard risk disposal physical operation flow." This is not merely "outputting the calculated result" as the Office Action asserts. Rather, the warning signal directly drives physical actions: the adjustment of drilling parameters (e.g., altering rotation speed, mud flow rate, or weight on bit) or the execution of physical risk disposal operations (e.g., activating blowout preventers, adjusting mud weight, or initiating well control procedures). This forms a complete closed-loop control system from physical data collection, through algorithmic processing, to physical actuation.
This argument is unpersuasive.
First, outputting a warning signal does not effect any physical change to a drilling operation.
Second, “adjusting the physical drilling construction parameters or executing a standard risk disposal physical operation flow” is not recited as a specific transformation. MPEP §2106.05(f) outlines the factor to consider including the particularity or generality of the application of the judicial exception. Here, the claim recites changing unspecified drilling parameters in unspecified ways with no particular relationship to the result of the abstract idea. Furthermore, the claim is written in the alternative and does not even require any change to any drilling parameter because it could instead “executing a standard risk disposal physical operation flow.” The risk disposal operation being standard is directly teaching away from the risk disposal operation being particularized according to the early warning. Accordingly, considering the particularity or the generality of the application of the judicial exception weighs against a finding of eligibility. See MPEP §2106.05(f).
Applicant remarks page 8 argues:
Third, field deployment data confirms that the claimed invention is a practical industrial application, not an abstract idea. The system has been integrated into the physical drilling monitoring infrastructure at these sites, running on-site data acquisition servers, data integration servers, risk recognition servers, and control systems. The system processes real-time data at rates of 1-2 seconds per data point and outputs risk warnings that directly drive on-site risk mitigation actions.
This argument is unpersuasive. Applicant is arguing factors typically considered as secondary considerations of non-obviousness. See MPEP §716.03. In contrast, Applicant has argued the “system has been integrated into the physical drilling monitoring infrastructure” and not that the claimed invention is the physical drilling monitoring infrastructure. As Examiner has discussed above, the physical infrastructure is not actively claimed. Examiner’s §101 analysis is based on what is actually claimed and not external factors outside of what is actually claimed.
Applicant remarks page 9 argues:
The Examiner's Reliance on Kang and In re Meyer Is Inapposite
…. Kang does not involve outputting a warning signal to a specialized drilling site operation control system, nor does it involve the subsequent adjustment of physical drilling parameters or execution of physical risk disposal operations.
This argument is unpersuasive. Kang is relied upon for respective Berkheimer evidence which is required by current §101 analysis. In re Meyer is a precedential circuit court decision regarding §101 and thus relevant at least as a persuasive authority.
The signal being output corresponds with the result of the abstract idea. Kang is not relied upon for teaching the abstract idea nor does Examiner’s §101 rejection require such a finding.
Kang is not cited regarding the subsequent adjustment to drilling parameters. Limitations analyzed under MPEP §2106.05(f) are analyzed the same under step 2B as under step 2A(ii) above. The adjusting of drilling parameters was analyzed under MPEP §2106.05(f) and thus is not based on the subsequent citation of Kang in step 2B which is only cited regarding the outputting limitation.
Applicant remarks page 9 argues:
Regarding In re Meyer, 688 F.2d 789, 795 (CCPA 1982), the Office Action draws an analogy between the claimed invention and replacing the thinking processes of a neurologist with a computer. This analogy is misplaced. Unlike the claims in Meyer, the present claims are not directed to substituting a human mental process with a computer. The claimed invention performs complex mathematical operations-convolution calculations on multi-dimensional time-series data, PCA dimensionality reduction, SMOTE data enhancement, and multi-network concurrent training-that cannot practically be performed in the human mind. As the USPTO's August 4, 2025 Kim Memo reiterates, the "mental process" grouping of abstract ideas "is not without limits" and Examiners are not to expand this grouping to encompass claim limitations that "cannot practically be performed in the human mind." Many machine learning algorithms, including the convolutional neural network operations claimed here, process volumes of data and perform calculations that cannot practically be performed in the human mind.
Examiner’s rejection has identified claim 1 as reciting a mathematical concept. Here, Applicant admits the claim “performs complex mathematical operations” which seems to be in agreement with Examiner’s assessment of the claims.
Examiner further finds that the existence of some differences between the claims in Meyer and the instant application does not necessarily mean all of the reasoning in Meyer is irrelevant. Regardless, Examiner’s rejection is based upon the current §101 guidance for subject matter eligibility.
Applicant remarks page 10 argues:
(i) Drilling-specific dual convolutional layer CNN architecture. Claim 1 step 3 recites a first convolutional layer …. This acknowledgment underscores that the claimed architecture is not well-understood, routine, or conventional in the drilling industry.
Examiner has found claim 1 step 3 as being itself part of the identified abstract idea in the form of mathematical concept. Thus, claim 1 step 3 is not an ‘additional’ limitation.
Furthermore, Examiner has not asserted that claim 1 step 3 is well-understood, routine, or conventional and Examiner’s rejection does not require such a finding.
Applicant remarks page 10 argues:
(ii) Three-time-span, three-network concurrent training scheme. While not every technical detail is explicitly recited in the claims, the specification and field deployment data confirm that this feature contributes to the claimed invention's improved performance, achieving faster recognition times than conventional methods;
An improved abstract idea remains ineligible subject matter under §101.
Applicant remarks page 10 argues:
(iii) Drilling-specific small-sample learning and data enhancement pipeline. The claimed method incorporates scaling, cropping, interpolation, and SMOTE algorithms specifically tailored for the drilling data environment, addressing the unique industry challenge of scarce while-drilling risk samples.
Explicitly or implicitly recited mathematical algorithms are mathematical subject matter properly identified as mathematical concept.
Applicant remarks page 11 argues:
Under Berkheimer v. HP Inc., 881 F.3d 1360, 1368 (Fed. Cir. 2018), whether a claim limitation is well-understood, routine, or conventional is a question of fact requiring substantial evidence. The Examiner's conclusion that the claimed limitations are "well-understood, routine, and conventional" lacks evidentiary support beyond a single citation to Kang (which is not analogous). The Examiner has not identified any evidence establishing that the specific combination of the dual one-dimensional convolutional kernel CNN architecture, the drilling-specific data collection from 13 core parameters, and the output of warnings to a drilling site control system is routine or conventional in the drilling industry. See also MPEP §2106.05(d)(I) and the USPTO's post-Berkheimer guidance, which require examiners to provide factual support for such findings.
This argument is unpersuasive. Berkheimer evidence is only required for ‘additional’ limitations at step 2B. Furthermore, limitations analyzed under MPEP §2106.05(g) in step 2A prong 2 require a further Berkheimer finding at step 2B.
Applicant’s argument here implies Berkheimer evidence is alleged require for every limitation when it is not. Only the output limitation requires Berkheimer evidence and Kang has been properly cited for such evidence as required.
Applicant remarks pages 11-12 argue:
Consistent with Desjardins, the claimed invention is directed to an improvement in the technology of drilling safety risk recognition. The field deployment data-showing recognition up to 56 seconds faster than conventional methods-demonstrates that the claimed invention provides a concrete, measurable improvement to how drilling safety risks are identified and addressed in real time. This is precisely the type of technical improvement that the USPTO now recognizes as patent-eligible under § 101.
This argument is unpersuasive.
Ex parte Desjardins stands for the relatively narrow proposition that improvements to a training method can represent an improvement to computer technology similar to Enfish. Improvements to mathematical calculation remains ineligible. See Ex parte Desjardins at page 9 (“We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation.”).
Claim Rejections - 35 USC § 112
Claim 1 has been appropriately corrected. Accordingly, Examiner's rejection of claims 1-9 under § 112 is withdrawn. However, a new rejection is made as follows:
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 1-9 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 pre-AIA the applicant regards as the invention.
Claim 1 first clause recites “including logging data such as ….” The phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Claim 1 second clause recites “the sample data mentioned above.” There is inconsistent antecedent basis for this limitation as more than one sample data is mentioned above.
Claim 1 second clause recites “comparative experiment is made.” It is unclear what the scope of “comparative experiment” is.
Claim 1 second clause recites “reduce the system delay as much as possible.” The term “as much as possible” is a relative term which renders the claim indefinite. The term “as much as possible” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim 1 second clause recites “contain most of the features of the while-drilling safety risks.” The term “most” is a relative term which renders the claim indefinite. The term “most” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Dependent claims 2-9 are rejected for depending on a rejected claim.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
To determine if a claim is directed to patent ineligible subject matter, the Court has guided the Office to apply the Alice/Mayo test, which requires:
1. Determining if the claim falls within a statutory category;
2A. Determining if the claim is directed to a patent ineligible judicial exception consisting of a law of nature, a natural phenomenon, or abstract idea; and
2B. If the claim is directed to a judicial exception, determining if the claim recites limitations or elements that amount to significantly more than the judicial exception.
See MPEP §2106.
Step 2A is a two prong inquiry. MPEP §2106.04(II)(A). Under 2A(i), the first prong, examiners evaluate whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Abstract ideas include mathematical concepts, certain methods of organizing human activity, and mental processes. MPEP §2106.04(a)(2). Under 2A(ii), the second prong, examiners determine whether any additional limitations integrates the judicial exception into a practical application. MPEP §2106.04(d).
Claim 1 step 2A(i):
The claim(s) recite:
1. An intelligent recognition method for while-drilling safety risks based on a convolutional neural network, comprising the following steps:
1: processing while-drilling safety risk parameter features and data, and establishing a correlation analysis model for monitoring-while-drilling parameters by using a Pearson coefficient correlation analysis method, wherein the monitoring-while-drilling parameters are 13 core parameters collected from on-site physical drilling monitoring hardware, including conventional drilling parameter monitoring equipment and sand discharge pipeline return gas component monitoring equipment, the data mentioned above are collected through monitoring instruments, including logging data such as hook load, top drive speed, riser pressure, and excluding invalid or erroneous data caused by factors such as collection instruments and signal transmission;
2: processing while-drilling safety monitoring data, analyzing a time span of each sample, constructing training sample data and test sample data, and preprocessing the samples, step 2 further comprising for each type of while-drilling safety risk including formation gas production, formation water production, sticking and picking up stands, constructing sample data with three risk-specific different time spans, and performing while-drilling safety risk recognition training by using three networks corresponding to the three time spans at the same time, the sample data mentioned above with three different time spans are established for each while-drilling safety risk, and while-drilling safety risk recognition training is performed by using three networks at the same time, …;
3: designing a while-drilling safety risk recognition network structure, and training a network model, wherein the network structure comprises an input layer, a first convolutional layer, a second convolutional layer, a hidden layer and an output layer; the first convolutional layer uses a one-dimensional longitudinal convolution kernel of m1 to perform separate convolution calculations on each monitoring parameter respectively, so as to extract the time-dependent change trend of each single parameter; the second convolutional layer uses a one-dimensional transverse convolution kernel of 1n to perform separate feature extraction on each row of the parameter matrix, so as to extract the associated change relationship between different monitoring parameters;
4: recognizing the while-drilling safety risks by the trained safety risk recognition network; and
Processing parameters and data to establish a correlation analysis model by using a Pearson coefficient correlation analysis method is explicit recitation of mathematical subject matter. Establishing a correlation analysis is mathematical. The Pearson coefficient correlation analysis method is a mathematical method. The source of the monitoring-while drilling parameters does not change these parameters as they still encompass the numerical values. The monitoring equipment is not actively recited, merely passively referenced as a data source for the collection which previously took place outside what is actively claimed. Alternatively, a general recitation of data gathering is insignificant extra solution activity. See MPEP §2106.05(g).
Processing monitoring data, analyzing sample data, and preprocessing the sample data encompasses mathematical subject matter at an extremely high level of generality. Identifying respective data samples for training, testing, and performing training is performing respective mathematical calculations of training the neural networks.
Designing a network structure and training the network model is recitation of performing mathematical calculations of the network model training. The layers of the network structure in respective dimensions denote the dimensions of the respective vectors and matrices. Matrices and vectors are mathematical entities.
Recognizing safety risks using the trained recognition network is recitation of mental process evaluation, judgment, or opinion enacted by performing the mathematical calculations of the recognition network model.
This falls within the mathematical concept grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 1 step 2A(ii):
This judicial exception is not integrated into a practical application because:
Claim 1 recites:
comparative experiment is made to ensure that the networks can not only contain most of the features of the while-drilling safety risks, but also reduce the system delay as much as possible;
…
5: outputting an early warning signal of the recognized while-drilling safety risks to a drilling site on-site operation control system, and adjusting physical drilling construction parameters or executing a standard risk disposal physical operation flow based on the early warning signal.
Performing a comparative experiment as recited here is extra solution activity. See MPEP §2106.05(g). No steps are recited dependent upon any particular results of the comparative experiment. Accordingly, the comparative experiment is tangential to the recited intelligent recognition method.
Outputting an early warning signal of the calculated risk is insignificant extra solution activity in the form of outputting the calculated result of the abstract idea. See MPEP §2106.05(g).
Executing a standard risk disposal physical operation flow based on the abstract idea result amounts to mere instruction to apply the result of the abstract idea. See MPEP §2106.05(f). Adjusting physical drilling construction parameters is recited in the alternative and thus not required for infringement of the claim. However, because of the generality of the “adjusting” the adjustment would also be considered mere instruction to apply the result of the abstract idea. See MPEP §2106.05(f).
Claim 1 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Limitations analyzed under MPEP §2106.05(f) are analyzed the same under step 2B as under step 2A(ii) above.
Claim 1 recites:
5: outputting an early warning signal of the recognized while-drilling safety risks to a drilling site on-site operation control system, ….
Outputting the result of an abstract idea is well understood, routine, and conventional. US patent 5,850,560 Kang [herein “Kang”] teaches “It is of course conventional to display information and results of an operation performed by the computer as an image on a video monitor.” This is sufficient Berkheimer evidence for a generic recitation of “outputting” a calculated result of an abstract idea.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 2 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
2. The intelligent recognition method for the while-drilling safety risks based on the convolutional neural network according to claim 1, wherein the step 1 specifically comprises the following sub-steps:
…, initially screening out monitoring parameters that can reflect the changes in working conditions during the drilling process in a timely manner, and removing invalid or incorrect data;
102: further selecting a plurality of core parameters based on the importance of parameters in the monitoring-while-drilling process, to reduce the amount of subsequent data processing;
103: further classifying data sets in respective stages according to different stages of the drilling process; and
104: forming a macro law of changes in monitoring data corresponding to various safety risks by using a while-drilling safety risk theoretical model, and determining the composition of respective parameters in the most refined sample that characterizes various safety risk conditions in conjunction with Pearson parameter correlation analysis results.
Screening and removing monitoring parameters that somehow reflect the changes in working conditions during the drilling process corresponds with mental process evaluation, judgment, or opinion. Combining mathematical concepts with mental process decisions about the result is a combination of abstract idea which itself is an abstract idea.
Selecting core parameters based on an “importance” of parameters to reduce an amount of subsequent data processing is further recitation of a step in a mathematical algorithm. Alternatively, the selecting comprises mental process in the form of evaluation, judgment, and/or opinion.
Classifying data sets has a broad claim scope which encompasses both a mathematical classification and/or mental process determination.
Forming a “macro law” corresponds with a mathematical construction of a mathematical modeling in conjunction with Pearson parameter correlation analysis results. The theoretical model corresponds with a mathematical model.
This falls within the mathematical concept grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 2 step 2A(ii):
This judicial exception is not integrated into a practical application because:
The claim(s) recite:
101: acquiring historical data of monitoring-while-drilling in multiple wells, …
A step of “acquiring” data is a high level recitation of data gathering. Data gathering, recited at a high level of generality, is insignificant extra solution activity. See MPEP §2106.05(g).
Claim 2 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
MPEP §2106.05(d) provides examples of data gathering:
i. Receiving or transmitting data over a network, …
iv. Storing and retrieving information in memory
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 3 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
3. The intelligent recognition method for the while-drilling safety risks based on the convolutional neural network according to claim 1, wherein the step 2 specifically comprises the following sub-steps:
201: performing a comparative experiment to ensure that the networks can not only contain most of the features of the while-drilling safety risks, but also reduce the system delay; and meanwhile, performing offline analysis on drilling monitoring data, and constructing the training sample data and the test sample data;
202: preprocessing sample data by using few sample learning, processing the samples by using scaling, cropping, interpolation and Synthetic Minority Over-sampling Technique (SMOTE) algorithms in data enhancement, and transferring a weight in a trained similar network by using a transfer learning algorithm to a new network with a certain correlation for training; and
203: normalizing a part of data that has too a difference greater than Y in numerical value in the samples, wherein the preset threshold Y is determined according to the full scale range of the monitoring parameter.
Performing a comparative experiment to ensure the machine learning network can contain features of the safety risks and reduce system delay corresponds with mathematical operations tuning of training the machine learning model accordingly. Comparison to evaluate is further a mental process in the form of evaluation, judgment, or opinion.
Preprocessing data using few sample learning, scaling, cropping, interpolation, and Synthetic Minority Over-sampling Technique (SMOTE) algorithms is performing corresponding mathematical operations. Similarly, the transfer learning algorithm is a mathematical algorithm.
Normalizing data is a mathematical operation on the data.
This falls within the mathematical concept grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 3 step 2A(ii):
This judicial exception is not integrated into a practical application because:
Claim(s) do not recite any “additional” limitations.
Claim 3 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Claim(s) do not recite any “additional” limitations.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 4 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
4. The intelligent recognition method for the while-drilling safety risks based on the convolutional neural network according to claim 3, wherein said processing the samples by using scaling, cropping, interpolation and SMOTE algorithms in data enhancement is specifically as follows: for a part of historical parameters with an increase of more than 30% of full scale within a sample period, a part of the data in the changing process can be extracted and expanded to the same time span by using data scaling and cropping to form a new training sample, and then the scaled data is filled to make it the same as an original sample by using a piecewise interpolation method; and after the data scaling and interpolation, fewer samples are analyzed by using a SMOTE algorithm, and a new sample is artificially synthesized based on the fewer samples and added to a data set, wherein the data enhancement processing is only performed on the historical parameters of while-drilling safety risks with an increase of more than 30% of full scale within a sample period.
Using scaling, cropping, interpolation and SMOTE algorithms is using mathematical algorithms on corresponding data.
The piecewise interpolation method is further recitation of mathematical algorithm.
The so-called “synthesis” of a new sample corresponds with calculating data with corresponding mathematical calculations.
This falls within the mathematical concept grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 4 step 2A(ii):
This judicial exception is not integrated into a practical application because:
Claim(s) do not recite any “additional” limitations.
Claim 4 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Claim(s) do not recite any “additional” limitations.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 5 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
5. The intelligent recognition method for the while-drilling safety risks based on the convolutional neural network according to claim 1, wherein the step 3 specifically comprises the following sub-steps: 301:
performing feature extraction, including pre-learning, on the sample data by using a convolutional layer, and then optimizing all network parameters by using a back-propagation algorithm; and
302: designing a network structure, which comprises an input layer, a convolutional layer 1, a convolutional layer 2, a hidden layer and an output layer; and performing a dimension reduction process on data before being inputted to a fully connected layer by using a principal component analysis method and by taking an elu function as an activation function.
Using a convolution layer and back-propagation algorithm is performing corresponding mathematical calculations.
Using PCA to perform a dimension reduction is performing corresponding mathematical calculations of the mathematical algorithm.
The elu function is another mathematical function.
This falls within the mathematical concept grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 5 step 2A(ii):
This judicial exception is not integrated into a practical application because:
Claim(s) do not recite any “additional” limitations.
Claim 5 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Claim(s) do not recite any “additional” limitations.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 6 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
6. The intelligent recognition method for the while-drilling safety risks based on the convolutional neural network according to claim 5, wherein the convolutional layer 1 is used to extract the changing trend of each parameter, and a one-dimensional longitudinal convolution kernel of m*1 is used to perform separate convolution calculations on n parameters respectively.
The convolution layer is a mathematical structure of the CNN. The dimensional size of the convolution kernel is a further mathematical structure.
This falls within the mathematical concept grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 6 step 2A(ii):
This judicial exception is not integrated into a practical application because:
Claim(s) do not recite any “additional” limitations.
Claim 6 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Claim(s) do not recite any “additional” limitations.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 7 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
7. The intelligent recognition method for the while-drilling safety risks based on the convolutional neural network according to claim 5, wherein the convolutional layer 2 is used to extract a change relationship between parameters, and a one-dimensional transverse convolution kernel of 1*n is used to perform separate feature extraction on each row of a matrix.
The convolution layer is a mathematical structure of the CNN. The dimensional size of the convolution kernel is a further mathematical structure.
This falls within the mathematical concept grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 7 step 2A(ii):
This judicial exception is not integrated into a practical application because:
Claim(s) do not recite any “additional” limitations.
Claim 7 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Claim(s) do not recite any “additional” limitations.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 8 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
8. The intelligent recognition method for the while-drilling safety risks based on the convolutional neural network according to claim 5, wherein the principal component analysis method aims to reduce a set of N-dimensional vectors to K-dimensional vectors, where
0
<
K
<
N
, and the calculation process includes the following steps:
3021: normalizing each row of a variable matrix of a p*n order to form a new matrix X according to columns;
3022: solving a covariance matrix of the m-order matrix X;
3023: calculating feature values and corresponding feature vectors of the covariance matrix C;
3024: arranging the feature vectors from top to bottom in rows according to magnitudes of the corresponding feature values to form a matrix, and then taking their corresponding k feature vectors as column vectors respectively to form a feature vector matrix P; and
3025: multiplying the matrix X and the matrix P to acquire data after reduction to k dimension.
Normalizing is a mathematical operation on the data.
Solving the covariance matrices is further mathematical operations.
Calculating feature values of the vectors is further mathematical calculation.
Arranging the feature vectors to form a matrix is further mathematical calculation and mathematical construction
Lastly, multiplying the matrices is further explicit mathematical calculation.
This falls within the mathematical concept grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 8 step 2A(ii):
This judicial exception is not integrated into a practical application because:
Claim(s) do not recite any “additional” limitations.
Claim 8 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Claim(s) do not recite any “additional” limitations.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 9 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
9. The intelligent recognition method for the while-drilling safety risks based on the convolutional neural network according to claim 5, wherein the number of nodes in the hidden layer is
S
=
2
x
+
1
, where x is the number of nodes in the input layer; and the number of nodes in the hidden layer is
S
<
N
-
1
, where N is the number of network training samples.
The layers of the CNN are the mathematical structure of the CNN. The number of hidden layers and nodes correspond with the mathematical structure of this neural network.
This falls within the mathematical concept grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 9 step 2A(ii):
This judicial exception is not integrated into a practical application because:
Claim(s) do not recite any “additional” limitations.
Claim 9 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Claim(s) do not recite any “additional” limitations.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Allowable Subject Matter
Claims 1-9 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. §101, and under 35 U.S.C. §112(b) or 35 U.S.C. §112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter:
Qodirov, S. & Shestakov, A. “Development of Artificial Neural Network for Predicting Drill Pipe Sticking in Real-Time Well Drilling Process” IEEE, Global Smart Industry Conf., GloSIC (2020) [herein “Qodirov”] teaches predicting drill pipe sticking using ANN. Qodirov page 144 table 1 “Neural Network Configuration” teaches seven layers with respective numbers of neurons and activation functions. Qodirov fails to teach two different one-dimensional convolutional layers as claimed.
Siruvuri, C., et al. “Stuck Pipe Prediction and Avoidance: A Convolutional Neural Network Approach” IADC/SPE Drilling Conf. (2006) [herein “Siruvuri”] stuck pipe prediction using CNN. Siruvuri page 4 figure 4 “Neural Network Architecture” where “The output layer is fully connected to all the units in the hidden layers as shown in Fig. 4.” Siruvuri fails to teach two different one-dimensional convolutional layers as claimed.
US patent 11,989,657 B2 Chavoshi, et al. [herein “Chavoshi”] teaches technology background on machine learning for timeseries data. Chavoshi does not teach drilling. Chavoshi fails to teach two different one-dimensional convolutional layers as claimed.
US 2022/0390633 A1 Laigle, et al. [herein “Laigle”] teaches deep learning of parameters in the subsurface. Laigle paragraph 28 teaches “a two-layer fully-connected dense neural network.” Laigle 23 teaches PCA for dimensionality reduction. Laigle fails to teach two different one-dimensional convolutional layers as claimed.
US patent 11,796,714 B2 Smith, et al. [herein “Smith”] figure 5 teaches “example layering, filter size, output shape, and a number of parameters.” Smith column 8 lines 20-24 teaches “The use of stacked 1D dilated convolutions enable the TCN to build a large receptive field (the size of the input that affects a particular feature or output) using only a few layers.” Stacked 1D convolutions, plural, are at least two convolutional layers. However, Smith fails to teach the network structure claimed as these stacked 1D convolutional layers are not specifically
m
×
1
and
1
×
n
as claimed.
None of the references taken either alone or in combination with the prior art of record disclose “wherein the network structure comprises an input layer, a [first] convolutional layer, a [second] convolutional layer, a hidden layer and an output layer; the [first] convolutional layer uses a one-dimensional longitudinal convolution kernel of m1 to perform separate convolution calculations on each monitoring parameter respectively, …, the [second] convolutional layer uses a one-dimensional transverse convolution kernel of 1n to perform separate feature extraction on each row of the parameter matrix” in combination with the remaining elements and features of the claimed invention.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Jay Hann/Primary Examiner, Art Unit 2186 15 June 2026
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