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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/18/2022 and 07/06/2023 were considered by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention in each of these claims 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 comprising:
obtaining one or more measurements;
performing a measurement normalization on the one or more measurements to form one or more normalized measurements;
forming a material function with the one or more normalized measurements; and
forming a neural operator generated physical response with a neural operator and the material function.”
The claim limitations considered to fall within in the abstract idea are highlighted in bold font above; the remaining features are “additional elements.”
Step 1 of the subject matter eligibility analysis entails determining whether the claimed subject matter falls within one of the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. Claim 1 recites a process and is therefore falls within a statutory category.
Step 2A, Prong One of the analysis entails determining whether the claim recites a judicial exception such as an abstract idea. Under a broadest reasonable interpretation, the highlighted portion of claim 1 comprises process steps that fall within the abstract idea judicial exception. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, the highlighted subject matter falls within the mental processes and/or mathematical concepts category.
Individually and collectively, the steps:
“obtaining one or more measurements”;
“performing a measurement normalization on the one or more measurements to form one or more normalized measurements”;
“forming a material function with the one or more normalized measurements”; and
“forming a neural operator generated physical response with a neural operator and the material function”
may be performed as mental processes and/or mathematical calculation. Obtaining measurements is collecting information, which may be performed by mental processes. Performing measurement normalization is an analysis, which may be performed by mental processes. Forming a material function is a mathematical relationship and/or an analysis, which may be performed by mental processes. Forming neural operator is a mathematical calculation and/or the output of analysis, which may be performed by mental processes. The type of high-level information collecting and analyzing data recited in these elements has been found by the Federal Circuit to constitute patent ineligible matter (see Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind).
Similar limitations comprise the mental processes and/or mathematical concept type abstract idea recited by independent claims 8 and 15.
Step 2A, Prong Two of the analysis entails determining whether a claim includes additional elements that integrate the recited judicial exception (e.g., abstract idea) into a practical application. In view of the various considerations encompassed by the Step 2A, Prong Two analysis, claim 1 does not include additional elements that integrate the recited abstract idea into a practical application. Based on the individual and collective limitations of claim 1, applying a broadest reasonable interpretation, the most significant of such considerations appear to include: improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)); applying the judicial exception with, or by use of, a particular machine (MPEP 2106.05(b)); and effecting a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)).
Regarding improvements to the functioning of a computer or other technology, none of the “additional elements” in any combination appear to integrate the abstract idea to technologically improve any aspect of a system that may be used to implement the highlighted steps such a generic computer.
Regarding application of the judicial exception with, or by use of, a particular machine, none of the additional elements in any combination appear to amount to a particular machine.
Regarding effectuation of a transformation or reduction of a particular article to a different state or thing, the claim includes no such transformation or reduction. Instead, the claim as a whole entails gathering information (“obtaining one or more measurements”), analyzing the information (e.g. “performing a measurement normalization”), and performing mathematical calculations (e.g. “forming a neural operator”).
Independent claim 8 recites additional elements, such as “forming a beamforming map”, which is the display of specific results of analysis. Claim 8 does not recite an improvement to the function of a computer, nor the use of a particular machine. The forming a beamforming map is not a transformation of a particular article. Therefore, independent claim 8 does not integrate the judicial exception into a practical application.
Independent claim 15 recites similar limitations as claim 1, and additional elements including “a non-transitory storage computer-readable medium” and “a processor”. The additional elements amount to a generic computer, and the claim as a whole amounts to mere instruction to implement the process steps in a computer environment (MPEP 2106.05(f)). Therefore, independent claim 15 does not integrate the judicial exception into a practical application.
The above additional elements, considered individually and in combination with the claim elements reciting an abstract idea do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under Step 2B.
Regarding Step 2B, independent claims 1, 8 and 15, do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they are generically recited and are well-understood/conventional in the relevant art as evidenced by the prior art of record as indicated in the rejections under 35 U.S.C. §103.
Independent claims 1, 8 and 15 are therefore not patent eligible.
Dependent claims 2-7, 9-14 and 16-20 provide additional features/steps which are part of an expanded steps that includes the abstract idea of the independent claims (Step 2A, Prong One). Claims 2, 9, and 16 provide further detail on the neural operator. Claims 3, 10, and 17 further detail the obtained measurements. Claims 4-7 and 18-20 recite forming an accuracy index and a comparison, which may be performed as mental processes. Claims 11-14 recite performing a cross correlation, which may be performed as mental processes. None of dependent claims 2-7, 9-14 and 16-20 recite additional elements that integrate the abstract idea into practical application (Step 2A, Prong Two), and all fail the “significantly more” test under the step 2B for the same reasons as discussed with regards to the independent claims.
The dependent claims 2-7, 9-14 and 16-20 therefore are also ineligible subject matter.
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.
Claim(s) 1-2 and 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fouda et al. (US 10996199 B2, provided by applicant, hereafter “Fouda 1”) in view of Goswami (Goswami, Somdatta, et al. "Physics-Informed Deep Neural Operator Networks." arXiv preprint arXiv:2207.05748 (2022).
Regarding claim 1, Fouda 1 teaches A method comprising:
obtaining one or more measurements (EM logging tool 100; col 5 lines 30-35, “The primary electromagnetic fields produce Eddy currents in the concentric pipes. These Eddy currents, in turn, produce secondary electromagnetic fields that may be sensed along with the primary electromagnetic fields by the receivers 104. Characterization of the concentric pipes may be performed by measuring and processing these electromagnetic fields.”);
performing a measurement normalization on the one or more measurements to form one or more normalized measurements (Fig. 5, step 512);
forming a material function with the one or more normalized measurements (col 9 lines 51-55, “The may be performed by normalizing the baseline subtracted signal from step 510, V.sub.bs, with the collar removed signal, V.sub.cr. This may give an output, in step 514, of an artifact indicator.”; e.g. Equations 8 and 9); and
forming (Fig. 8; col 10 lines 42-44, “Step 516 may produce an output in step 518 that is an artifact mask. The artifact mask is a curve versus depth that indicates whether an artifact was detected at a given depth or not.”).
Fouda 1 does not teach the method, comprising:
forming a neural operator generated physical response with a neural operator and the material function
Goswami teaches an analogous method of analysis, comprising:
forming a neural operator (Physics-informed Fourier Neural Operator (PINO) of section 3.3) generated physical response with a neural operator and the material function (Fig. 4 (reproduced below); section 4.2: “v(x) is the loading term which acts as the input function in the solution operator and u(x) is the PDE solution which can be seen as the output function.”)
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It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Fouda 1to include the neural operator of Goswami because it amounts to the application of a known technique to a known method ready for improvement to yield predictable results. Fouda 1 teaches the use of machine learning (col 8 lines 47-49, “ Without limitation, algorithms may be peak detection algorithms, matched filters, machine learning, and/or the like”), and therefore it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the neural operators of Goswami to yield predictable results.
Regarding claim 2, Fouda 1 in view of Goswami teaches The method of claim 1, wherein the neural operator is a Fourier Neural Operator (FNO) or a Physics-Informed Neural Operator (PINO) (Goswami: Physics-informed Fourier Neural Operator (PINO) of section 3.3).
Regarding claim 15, Fouda 1 teaches A non-transitory storage computer-readable medium storing one or more instructions that (col 4 lines 56-59, “systems and methods of the present disclosure may be implemented, at least in part, with non-transitory computer-readable, or machine-readable, media 128.”), when executed by a processor (CPU 122), cause the processor to:
perform a measurement normalization (Fig. 5, step 512) on one or more measurements (EM logging tool 100; col 5 lines 30-35, “The primary electromagnetic fields produce Eddy currents in the concentric pipes. These Eddy currents, in turn, produce secondary electromagnetic fields that may be sensed along with the primary electromagnetic fields by the receivers 104. Characterization of the concentric pipes may be performed by measuring and processing these electromagnetic fields.”) to form one or more normalized measurements;
form a material function with the one or more normalized measurements (col 9 lines 51-55, “The may be performed by normalizing the baseline subtracted signal from step 510, V.sub.bs, with the collar removed signal, V.sub.cr. This may give an output, in step 514, of an artifact indicator.”; e.g. Equations 8 and 9); and
form a (Fig. 8; col 10 lines 42-44, “Step 516 may produce an output in step 518 that is an artifact mask. The artifact mask is a curve versus depth that indicates whether an artifact was detected at a given depth or not.”).
Fouda 1 does not teach the instructions, comprising:
form a neural operator generated physical response with a neural operator and the material function.
Goswami teaches an analogous instructions for analysis, comprising:
form a neural operator (Physics-informed Fourier Neural Operator (PINO) of section 3.3) generated physical response with a neural operator and the material function (Fig. 4 (reproduced below); section 4.2: “v(x) is the loading term which acts as the input function in the solution operator and u(x) is the PDE solution which can be seen as the output function.”).
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It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the instructions of Fouda 1 to include the neural operator of Goswami because it amounts to the application of a known technique to a known method ready for improvement to yield predictable results. Fouda 1 teaches the use of machine learning (col 8 lines 47-49, “ Without limitation, algorithms may be peak detection algorithms, matched filters, machine learning, and/or the like”), and therefore it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the neural operators of Goswami to yield predictable results.
Regarding claim 16, Fouda 1 in view of Goswami teaches The non-transitory storage computer-readable medium of claim 15, wherein the neural operator is a Fourier Neural Operator (FNO) or a Physics-Informed Neural Operator (PINO) (Goswami: Physics-informed Fourier Neural Operator (PINO) of section 3.3).
Claim(s) 4-7 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fouda 1 in view of Goswami as applied to claim 1 above, and further in view of Lyu et al. (CN114168446A)
Regarding claim 4, Fouda 1 in view of Goswami teaches The method of claim 1.
Fouda 1 in view of Goswami does not teach the method, further comprising forming an accuracy index with the neural operator generated physical response and the one or more normalized measurements.
Lyu teaches an analogous method, further comprising forming an accuracy index (“The above performance evaluation index items refer to the performance index items that need to be evaluated for the model to be evaluated, such as but not limited to: Accuracy (correct rate), Precision (precision rate), Recall (recall rate), AUC (curve) related to the accuracy of the model area below), or, memory usage, CPU usage, average inference time, etc. related to model performance.”). The evaluation index is the accuracy index.
with the neural operator generated physical response (“the tensor computing engine may be but not limited to MNN engine, among them, MNN engine is a lightweight deep learning end-side reasoning engine, designed to solve the problem of deep neural network model running in end-side reasoning, including optimization, conversion and reasoning of deep neural network model, with high versatility and high-performance features, supports models of multiple training frameworks, commonly used deep learning operators, multiple systems, and convolution calculation methods for calculation optimization, etc.”). The deep learning operator is the neural operator. and the one or more normalized measurements (“First, define the problem of the application scenarios that need to develop machine learning models, and analyze the requirements based on the defined problems; then, determine which data needs to be collected according to the requirements analysis, and use mobile phones, tablet computers, IOT (Internet of things, Internet of Things, Internet of Things, Internet of Things, Internet of Things, Internet of Things, Internet of Things, Internet of Things, Internet of Things, Internet of Things, Internet of Things ) equipment, wearable equipment, vehicle-mounted equipment and other end-side equipment for data collection; then, the data collected on the end-side is used as training data, and the algorithm design is combined with the training data to determine the model structure of the machine learning model; based on the training data and model structure Perform model training to get the original machine learning model.”; “The preprocessing task is used to preprocess the input data to be input to the machine learning model. For example, in a computer vision scenario, the preprocessing task needs to perform the following data preprocessing: image rotation, image enlargement, image reduction, etc.”).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Fouda 1 in view of Goswami to include the accuracy index of Lyu because it would yield predictable results of indicate the accuracy of the model.
Regarding claim 5, Fouda 1 in view of Goswami and Lyu teaches The method of claim 4, further comprising comparing the accuracy index to a threshold (Lyu: “The performance evaluation index is compared with the reference index corresponding to the performance evaluation index item related to the running performance of the model to determine the evaluation result of the model to be evaluated in the dimension of model running performance” ).
Regarding claim 6, Fouda 1 in view of Goswami and Lyu teaches The method of claim 5, further comprising accepting the neural operator if the accuracy index is greater than the threshold (Lyu: “The performance evaluation result of the model to be evaluated includes, for example, passing the evaluation, failing the evaluation, and the like.”; “In the case that the performance evaluation result of the model to be evaluated indicates that the evaluation has passed, the model to be evaluated is deployed to the real device running the target application”).
Regarding claim 7, Fouda 1 in view of Goswami and Lyu teaches The method of claim 5, further comprising adjusting the material function if the neural operator is below the threshold (Lyu: “when the performance evaluation result of the model to be evaluated indicates that the evaluation fails, the notification information of the failure of the evaluation can be pushed to the algorithm developer responsible for the model to be evaluated for timely treatment by the algorithm developer The evaluation model is fixed. ”).
Regarding claim 18, Fouda 1 in view of Goswami teaches The non-transitory storage computer-readable medium of claim 15.
Fouda 1 in view of Goswami does not teach the non-transitory storage computer-readable medium, wherein the one or more instructions, that when executed by the processor, further cause the processor to: form an accuracy index with the neural operator generated physical response and the one or more normalized measurements; and compare the accuracy index to a threshold.
Lyu teaches analogous non-transitory storage computer-readable medium (“Computer-readable media includes both persistent and non-permanent, removable and non-removable media, and storage of information may be implemented by any method or technology.”; “As defined herein, computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves.”), wherein the one or more instructions, that when executed by the processor (“Embodiments of the present application further provide a computer device, including: a memory and a processor; the memory is used to store a computer program, and the processor is coupled to the memory and used to execute the computer program, so as to realize the simulation of the algorithm model oriented to the operation of the mobile terminal Evaluation method.”), further cause the processor to: form an accuracy index (“The above performance evaluation index items refer to the performance index items that need to be evaluated for the model to be evaluated, such as but not limited to: Accuracy (correct rate), Precision (precision rate), Recall (recall rate), AUC (curve) related to the accuracy of the model area below), or, memory usage, CPU usage, average inference time, etc. related to model performance.”). The evaluation index is the accuracy index.
with the neural operator generated physical response (“the tensor computing engine may be but not limited to MNN engine, among them, MNN engine is a lightweight deep learning end-side reasoning engine, designed to solve the problem of deep neural network model running in end-side reasoning, including optimization, conversion and reasoning of deep neural network model, with high versatility and high-performance features, supports models of multiple training frameworks, commonly used deep learning operators, multiple systems, and convolution calculation methods for calculation optimization, etc.”). The deep learning operator is the neural operator. and the one or more normalized measurements (“First, define the problem of the application scenarios that need to develop machine learning models, and analyze the requirements based on the defined problems; then, determine which data needs to be collected according to the requirements analysis, and use mobile phones, tablet computers, IOT (Internet of things, Internet of Things, Internet of Things, Internet of Things, Internet of Things, Internet of Things, Internet of Things, Internet of Things, Internet of Things, Internet of Things, Internet of Things ) equipment, wearable equipment, vehicle-mounted equipment and other end-side equipment for data collection; then, the data collected on the end-side is used as training data, and the algorithm design is combined with the training data to determine the model structure of the machine learning model; based on the training data and model structure Perform model training to get the original machine learning model.”; “The preprocessing task is used to preprocess the input data to be input to the machine learning model. For example, in a computer vision scenario, the preprocessing task needs to perform the following data preprocessing: image rotation, image enlargement, image reduction, etc.”).; and
compare the accuracy index to a threshold (“The performance evaluation index is compared with the reference index corresponding to the performance evaluation index item related to the running performance of the model to determine the evaluation result of the model to be evaluated in the dimension of model running performance” ).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Fouda 1 in view of Goswami to include the accuracy index of Lyu because it would yield predictable results of indicate the accuracy of the model. Further, it would yield advantageous results, including determining whether the accuracy is acceptable, in comparison to the threshold, thereby enabling a user to disregard inaccurate models.
Regarding claim 19, Fouda 1 in view of Goswami and Lyu teaches The non-transitory storage computer-readable medium of claim 18, wherein the one or more instructions, that when executed by the processor, further cause the processor to accept the neural operator if the accuracy index is greater than the threshold (Lyu: “The performance evaluation result of the model to be evaluated includes, for example, passing the evaluation, failing the evaluation, and the like.”; “In the case that the performance evaluation result of the model to be evaluated indicates that the evaluation has passed, the model to be evaluated is deployed to the real device running the target application”).
Regarding claim 20, Fouda 1 in view of Goswami and Lyu teaches The non-transitory storage computer-readable medium of claim 18, wherein the one or more instructions, that when executed by the processor, further cause the processor to adjust the material function if the neural operator is below the threshold (Lyu: “when the performance evaluation result of the model to be evaluated indicates that the evaluation fails, the notification information of the failure of the evaluation can be pushed to the algorithm developer responsible for the model to be evaluated for timely treatment by the algorithm developer The evaluation model is fixed.”).
Claim(s) 8-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fouda et al. (US 11353617 B1, hereafter “Fouda 2”) in view of Goswami.
Regarding claim 8, Fouda 2 teaches A method comprising:
obtaining one or more measurements (col 6 lines 46-49, “the leak detection tool 202 can take one or more acoustic measurements to determine if the nested tubulars, e.g. the first casing 106 and second casing 108, potentially have a leak.”);
forming a beamforming map with the one or more measurements (col 8 lines 11-14, “Signal fusion can generally be accomplished by array signal processing. Array-signal-processing techniques include various spatial filtering methods (also often referred to as “beamforming” methods).”; col 9 lines 10-17, “each measurement for each sensor at each depth and amplitude can be mapped to a log data point on the log to form the log for each sensor. In another example, each measurement for each sensor array of the receiver, i.e. a fused signal measurement, at each measurement depth and amplitude can be mapped to a log data point on the log to form the log for each sensor array.”); and
forming a neural network leak source location map (col 12 lines 13-16, “The flow likelihood image can include the location of the leaks in the multiple nested tubulars, and thereby can provide a report of the integrity of the multiple nested tubulars.”; col 2 lines 54-56, “ By pre-training a DNN having at least one convolutional layer, the DNN can rapidly provide an accurate flow likelihood map.”) with a neural network and the one or more measurements (Fig. 4, step 408).
Fouda 2 does not teach the method, comprising: forming a neural operator with a neural operator and the material function.
Goswami teaches an analogous method of analysis, comprising:
forming a neural operator (Physics-informed Fourier Neural Operator (PINO) of section 3.3) with a neural operator and the material function (Fig. 4 (reproduced below); section 4.2: “v(x) is the loading term which acts as the input function in the solution operator and u(x) is the PDE solution which can be seen as the output function.”)
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It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Fouda 2 to include the neural operator of Goswami because it amounts to the application of a known technique to a known method ready for improvement to yield predictable results. Fouda 2 teaches the use of machine learning (Fig. 4; DNN and CNN), and therefore it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the neural operators of Goswami to yield predictable results.
Regarding claim 9, Fouda 2 in view of Goswami teaches The method of claim 8, wherein the neural operator is a Fourier Neural Operator (FNO) or a physics-Informed Neural Operator (PINO) (Goswami: Physics-informed Fourier Neural Operator (PINO) of section 3.3).
Regarding claim 10, Fouda 2 in view of Goswami teaches The method of claim 8, wherein the one or more measurements are a reflected acoustic wave (Fouda 2: col 6 lines 46-49, “the leak detection tool 202 can take one or more acoustic measurements to determine if the nested tubulars, e.g. the first casing 106 and second casing 108, potentially have a leak.”; col 8 lines 20-25, “the forward model is adjusted to account for the configuration of the wellbore and surrounding formation (which collectively include various propagation media and boundaries therebetween) and their effect on the wave field (e.g., wave refractions, reflections, and resonances)”).
Regarding claim 11, Fouda 2 in view of Goswami teaches The method of claim 10, further performing a cross correlation with at least the beamforming map and the neural operator leak source location map (Fouda 2: Fig. 7; col 13 lines 3-6, “The CNN 704 outputs a flow likelihood image, and the corresponding true image from the training database 702 is compared at 706 with the flow likelihood image. The comparison 706 is evaluated via an error function 708.”).
Regarding claim 12, Fouda 2 in view of Goswami teaches The method of claim 11, wherein a cross correlation forms a comparison index (Fouda 2: Fig. 7, steps 708 and 710; “The calculated error is fed to a training optimization algorithm 710 which can include a loss function defined as the mean square error for a whole training batch defined”).
Regarding claim 13, Fouda 2 in view of Goswami teaches The method of claim 12, further comprising accepting the neural operator (Fouda 2: Fig. 7; col lines, “the training is complete when the error in the validation data is decreasing, when the CNN 704 performs well on the training data, and when the CNN 704 performs well on the test data”)
Even if Fouda 2 in view of Goswami does not explicitly teach the comparison index is 1, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to select the comparison index to be 1, since it has been held that discovering an optimum value of a result effective variable involves only routine skill in the art. In re Boesch, 617 F.2d 272, 205 USPQ 215 (CCPA 1980).
Regarding claim 14, teaches The method of claim 12, further comprising adjusting the neural operator (Fouda 2: Fig. 7, steps 708 and 710; “The calculated error is fed to a training optimization algorithm 710 which can include a loss function defined as the mean square error for a whole training batch defined”)
Even if Fouda 2 in view of Goswami does not explicitly teach the comparison index is -1 or 0, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to select the comparison index to be -1 or 0, since it has been held that discovering an optimum value of a result effective variable involves only routine skill in the art. In re Boesch, 617 F.2d 272, 205 USPQ 215 (CCPA 1980).
Claim(s) 3 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable Fouda 1 in view of Goswami as applied to claim 1 and 15 respectfully above, and further in view of Nichols et al. (US 20150338541 A1)
Regarding claim 3, Fouda 1 in view of Goswami teaches The method of claim 1, wherein the one or more measurements are electromagnetic (EM) measurements (Fouda: col 2 lines 2-4, “Electromagnetic (EM) sensing may provide continuous in situ measurements of parameters related to the integrity of pipes in cased boreholes.”).
Fouda 1 in view of Goswami does not teach the method, comprising:
the one or more measurements are electromagnetic (EM) measurements from a near field, a transition zone, and a far field.
Nichols teaches an analogous method, comprising:
the one or more measurements are electromagnetic (EM) measurements (Figs. 1 and 3) from a near field (Fig. 4, near field 90), a transition zone (transition zone 88), and a far field (remote field 86).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Fouda 1 in view of Goswami to include the EM measurements of Nichols because it would yield predictable results, such as including measurement data for all measurement sensitivity domains of the measuring device.
Regarding claim 17, teaches The non-transitory storage computer-readable medium of claim 15, wherein the one or more measurements are electromagnetic (EM) (Fouda: col 2 lines 2-4, “Electromagnetic (EM) sensing may provide continuous in situ measurements of parameters related to the integrity of pipes in cased boreholes.”).
Fouda 1 in view of Goswami does not teach the method, comprising:
the one or more measurements are electromagnetic (EM) measurements from a near field, a transition zone, and a far field.
Nichols teaches an analogous computer readable medium (memory 24), comprising:
the one or more measurements are electromagnetic (EM) measurements (Figs. 1 and 3) from a near field (Fig. 4, near field 90), a transition zone (transition zone 88), and a far field (remote field 86).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the computer-readable medium of Fouda 1 in view of Goswami to include the EM measurements of Nichols because it would yield predictable results, such as including measurement data for all measurement sensitivity domains of the measuring device.
Conclusion
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/B.B.G./Examiner, Art Unit
2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857