DETAILED ACTION
This office action is responsive to the above identified application filed 10/26/2023. The application contains claims 1-9, all examined and rejected.
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
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 2 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 symbols are not clear and S13 formula is not complete. Examiner noticed similar issue in the specification. Examiner advice to point to the equivalent formulas in the parent case to avoid new matter rejection. Examiner tried to guess the missing terms in the mathematical equation.
Claim 3 symbols are not clear and S21, S22, S28 formula is not complete. Examiner noticed similar issue in the specification. Examiner advice to point to the equivalent formulas in the parent case to avoid new matter rejection. Examiner tried to guess the missing terms in the mathematical equation.
Claim 3 does not disclose the term
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.. Examiner considered
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as the spatial position vector for examination purposes.
Claim 3 does not disclose the terms of step S23 equation.
Claim 3 does not fully disclose the terms of step S24 equation.
The term “acceptable range” in claim 3 is a relative term which renders the claim indefinite. The term “acceptable range” 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. What is considered acceptable for one can be unacceptable for another, also what is acceptable in a single task can be unacceptable in different task.
Claim 4 does not disclose the term L in step (1).
Claim 4 recites the limitation "the obtained
z
c
o
k
is S22" in S33. There is insufficient antecedent basis for this limitation in the claim.
Claim 4 recites the limitation " the original learning sample". There is insufficient antecedent basis for this limitation in the claim.
Claim 8 recites the limitation " the processor". There is insufficient antecedent basis for this limitation in the 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.
Claim 6 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. Independent claim n recites a “system,” which the specification states may be that it could be a software (Fig. 9, paragraph 69 and 71). This allows the claim to encompass software per se, which is not a “process,” a “machine,” a “manufacture,” or a “composition of matter” as defined in 35 U.S.C. § 101. Examiner suggests adding a recitation of a “processor.”
Claim 8 is rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. During examination, the claims must be interpreted as broadly as their terms reasonably allow. In re American Academy of Science Tech Center, 367 F.3d 1359, 1369, 70 U.S.P.Q.2d 1827, 1834 (Fed. Cir. 2004). Independent claim 8 recites a “computer readable storage medium,” which is not comprehensively defined by the specification. The broadest reasonable interpretation of a claim drawn to a computer readable medium covers forms of transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. Transitory propagating signals are non-statutory subject matter. In re Nuijten, 500 F.3d 1346, 1356-57, 84 U.S.P.Q.2d 1495, 1502 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter). See also Subject Matter Eligibility of Computer Readable Media, 1351 Off. Gaz. Pat. Office 212 (Feb. 23, 2010). Examiner suggests adding the word “non-transitory.”
Claim 9 is rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. During examination, the claims must be interpreted as broadly as their terms reasonably allow. In re American Academy of Science Tech Center, 367 F.3d 1359, 1369, 70 U.S.P.Q.2d 1827, 1834 (Fed. Cir. 2004). Independent claim 9 recites a “An information data processing terminal,” which is not comprehensively defined by the specification. The broadest reasonable interpretation of a claim drawn to an information data processing terminal covers software per se in view of the ordinary and customary meaning of system, particularly when the specification is silent. Software per se is not a “process,” a “machine,” a “manufacture,” or a “composition of matter” as defined in 35 U.S.C. § 101. Examiner suggests adding a recitation of a “processor.”
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 1 is rejected under 35 USC 101 because the claimed inventions are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
While independent claims 1, 7 are each directed to a statutory category, and claims 6, 8, and 9 are not directed to a statutory category. The claims recites a series of steps which appears to be directed to an abstract idea (mental process, mathematical concept).
Claims 1-9 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
When considering subject matter eligibility under 35 U.S.C. 101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include certain methods of organizing human activities; a mental processes; and mathematical concepts, (2019 PEG)
STEP 1.
Per Step 1, the claims are determined to include process, manufacture, and machine as in independent Claim 1, 7, and in the therefrom dependent claims. Therefore, the claims are directed to a statutory eligibility category. Claim 6, 8, and 9 are not directed to a statutory eligibility category.
At step 2A, prong 1, The invention is directed to steps which is akin to Mental Process (see Alice) and mathematical concepts, As such, the claims include an abstract idea. When considering the limitations individually and as a whole the limitations directed to the abstract idea are:
Claim 1
“data
“interpolation of a prediction target based on collocated Co-Kriging”, “calculation of sample weights based on sequential Gaussian simulation and construction of a loss function based on spatial information constraints”, “optimization of the loss function and data-driven prediction based on a
The claim recites additional elements as
“data acquisition” (insignificant extra-solution activity, MPEP 2106.05(g));
“deep fully connected neural network” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)).
This judicial exception is not integrated into a practical application. The elements are recited at a high level of generality, i.e. a generic computing system performing generic functions including generic processing of data. Accordingly the additional elements do not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore the claims are directed to an abstract idea. (2019 Revised Patent Subject Matter Eligibility Guidance ("2019 PEG"). Thus, under Step 2A of the Mayo framework, the Examiner holds that the claims are directed to concepts identified as abstract.
STEP 2B.
Because the claims include one or more abstract ideas, the examiner now proceeds to Step 2B of the analysis, in which the examiner considers if the claims include individually or as an ordered combination limitations that are "significantly more" than the abstract idea itself. This includes analysis as to whether there is an improvement to either the "computer itself," "another technology," the "technical field," or significantly more than what is "well-understood, routine, or conventional" (WURC) in the related arts.
The instant application includes in Claim 1 additional steps to those deemed to be abstract idea(s).
When taken the steps individually, these steps are:
“data acquisition” (well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i));
“deep fully connected neural network” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f)).
In the instant case, Claim 1 is directed to above mentioned abstract idea. Technical functions such as receiving, and extracting are common and basic functions in computer technology. The individual limitations are recited at a high level and do not provide any specific technology or techniques to perform the functions claimed.
In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps does not add "significantly more" by virtue of considering the steps as a whole, as an ordered combination. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments using what is well-understood, routine, and conventional in the related arts. The steps are still a combination made to the abstract idea. The additional steps only add to those abstract ideas using well understood and conventional functions, and the claims do not show improved ways of, for example, an unconventional non-routine functions for analyzing model operations or updating the model that could then be pointed to as being "significantly more" than the abstract ideas themselves.
Moreover, Examiner was not able to identify any "unconventional" steps, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is well-understood, routine, and conventional (WURC) in the related arts.
Further, note that the limitations, in the instant claims, are done by the generically
recited computing devices. The limitations are merely instructions to implement the abstract idea on a computing device that is recited in an abstract level and require no more than a generic computing devices to perform generic functions.
Claim 7 recites a computer device comprising “a processor”, and “a memory” configured to perform the same method as set forth in claim 1, the added element of “a processor”, and “a memory” do not transform the judicial exception into a practical application because they are tantamount to a mere instruction to apply the judicial exception to a generic computer. The additional elements are also not sufficient to amount to significantly more than the judicial exception because the action of implementing the method on a general purpose computer with at least one processor and at least one memory is tantamount to a mere instruction to apply the judicial exception to a computer.
Claim 7 is therefore rejected according to the same findings and rationale as provided above.
Claim 8 recites “A computer-readable storage medium “ and “processor”, configured to perform the same method as set forth in claim 1, the added element of “A computer-readable storage medium “ and “processor” do not transform the judicial exception into a practical application because they are tantamount to a mere instruction to apply the judicial exception to a generic computer. The additional elements are also not sufficient to amount to significantly more than the judicial exception because the action of implementing the method on a general purpose computer with at least one processor and at least one memory is tantamount to a mere instruction to apply the judicial exception to a computer.
Claim 8 is therefore rejected according to the same findings and rationale as provided above.
Claim 9 recites “An information data processing terminal”, used to perform the same method as set forth in claim 1, the added element of “An information data processing terminal” do not transform the judicial exception into a practical application because they are tantamount to a mere instruction to apply the judicial exception to a generic computer. The additional elements are also not sufficient to amount to significantly more than the judicial exception because the action of implementing the method on a general purpose computer with at least one processor and at least one memory is tantamount to a mere instruction to apply the judicial exception to a computer.
Claim 9 is therefore rejected according to the same findings and rationale as provided above.
Independent claims 6-9 are the same analogy and rejected using similar analysis as claim 1.
CONCLUSION
It is therefore determined that the instant application not only represents an abstract idea identified as such based on criteria defined by the Courts and on USPTO examination guidelines, but also lacks the capability to bring about "Improvements to another technology or technical field" (Alice), bring about "Improvements to the functioning of the computer itself" (Alice), "Apply the judicial exception with, or by use of, a particular machine" (Bilski), "Effect a transformation or reduction of a particular article to a different state or thing" (Diehr), "Add a specific limitation other than what is well-understood, routine and conventional in the field" (Mayo), "Add unconventional steps that confine the claim to a particular useful application" (Mayo), or contain "Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment" (Alice), transformed a traditionally subjective process performed by humans into a mathematically automated process executed on computers (McRO), or limitations directed to improvements in computer related technology, including claims directed to software (Enfish).
The dependent claims, when considered individually and as a whole, likewise do not provide "significantly more" than the abstract idea for similar reasons as the independent claim.
claims 2 disclose
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(data collection is insignificant extra-solution activity, MPEP 2106.05(g) that is well-understood, routine, or conventional activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i), and data format is data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h));
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(mental process, mathematical concept), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 3 disclose “
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”(mental process, mathematical concept), “
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(mental process, mathematical concept), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 4 disclose
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(mathematical concept). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 5 disclose The method for data-driven prediction based on spatial information constraints according to claim 1, wherein, the optimization of the loss function and the data-driven prediction based on the deep fully connected neural network in the S4 comprise the following steps: S41, the samples at some sampling points are reserved to form a test set, and the remaining samples form a training set for training the deep fully connected neural network (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)); S42, optimizing the loss function by adopting a batch random gradient descent algorithm (mental process , mathematical concept); and updating the parameters of the deep fully connected neural network training a system which is a high-generic computer software process of training data. This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)); and S43, the trained network model is used to predict the test set(data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea.
The dependent claims which impose additional limitations also fail to claim patent eligible subject matter because the limitations cannot be considered statutory. The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1 ; where all claims are directed to the same abstract idea, "addressing each claim of the asserted patents [is] unnecessary." Content Extraction &. Transmission LLC v, Wells Fargo Bank, Natl Ass'n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter. Claims for the other statutory classes are similarly analyzed.
For at least these reasons, the claimed inventions of each of dependent claims 2-5,are directed or indirect to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more and are rejected under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 5, 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Sequential Gaussian simulation for geosystems modeling: A machine learning approach Published 2021 [hereinafter D1] in view of “Integrating seismic Data in Reservoir Modeling: The Collocated Cokriging Alternative“ Published 1992 [hereinafter D2].
With regard to Claim 1,
D1 teach a method for data-driven prediction based on spatial information constraints, (D1, Abstract, “a physics-informed machine learning (PIML) model is proposed to improve the computational efficiency of the SGSIM. To this end, only a small amount of data produced by SGSIM are used as the training dataset based on which the model can dis-cover the spatial correlations between available data and unsampled points. To achieve this, the govern-ing equations of the SGSIM algorithm are incorporated into our proposed network”, P. 2, Col. 2, “The prior information such as governing equations and physical constraints are added into the loss function as a regularization term”) comprising:
S1: data acquisition and preprocessing (D1,P. 4,” (1)Transform the hard data into a standard normal distribution based on the normal score transformation”)
S3: calculation of sample weights based on sequential Gaussian simulation (D1, Abstract, “Sequential Gaussian Simulation (SGSIM) as a stochastic method has been developed to avoid the smoothing effect produced in deterministic methods by generating various stochastic realizations”, P. 4-5, “In each iteration, the weights are obtained by solving the OK system and the simulated value is sampled from its corresponding normal distribution. The input variables include the covariance the values of selected data are used to guide the prediction of the values simulated by SGSIM. Moreover, they will be used to calculate the loss function) and construction of a loss function based on spatial information constraints (D1, P. 8,Col. 2, ¶2, “The model is optimized with the Adam optimization algorithm based on the loss function given in Eqs.(16–21)”, P. 5-6, “the loss function for the DNN is defined with multiple components, such as: MSE = MSEz +MSEw + MSEconstraint + MSEmean + MSEvariance”, P. 7, “the linear constraint, the law of maintaining the residual mean as zero, and minimizing the variance are also included”); and
S4: optimization of the loss function and data-driven prediction based on a deep fully connected neural network (D1, P. 8,Col. 2, ¶2, “The model is optimized with the Adam optimization algorithm based on the loss function given in Eqs.(16–21)”, P. 2, “deep neural networks (DNN) usually have more than one hidden layer. Compared to the neural network with a shallow architecture, each hidden layer of DNN can extract the inherent features from its corresponding input, and a hierarchical relationship is constructed through the deep architecture“, P. 4, 2.3, “DNN is a general functional approximator which can learn the complex nonlinear relationships between input and output. Generally, the input layer, hidden layer, and output layer are fully connected and parameterized by the weight and bias parameters“, “Many optimization algorithms have been developed to train the DNN, such as stochastic gradient descent(SGD)(Bottou,2010), and adaptive moment estimation(Adam)”, P. 8, “The DNN model has 4 hidden layers with 30 neurons in each”).
D1 does not explicitly disclose S2: interpolation of a prediction target based on collocated Co-Kriging.
D2 teach S1: data acquisition and preprocessing (P. 4, Col. 2, “Gaussian-based simulations: the variable to be simulated is the normal score transform of the primary variable ZI (u));
S2: interpolation of a prediction target based on collocated Co-Kriging (P. 3, The collocated cokriging model , “A solution to this problem consists simply of retaining at each location u to be estimated only the collocated secondary datum Z2(u), thus making n2 = 1 … Eq(7)”).
D1 and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of geostatistical spatial prediction using gaussian simulation. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1 resulting in resolutions as disclosed by D2 with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1 as described above to and adopt D2 collocated co-Kriging as it could be easily adapted to handle the single additional equation required by collocated cokriging and it perform better than ordinary kriging (D2, P. 7, “Presently available code for Gaussian sequential simulation, such as sgsim of GSLIB [10], can be easily adapted to handle the single additional equation required by collocated cokriging”, “the scattergram of the 25 depth estimates provided by kriging with external drift (using only 20 well data) vs. the actual values at these locations. Figure 8h gives the scattergram with, now, the 25 estimates provided by collocated cokriging (again using only 20 well data), Collocated cokriging is seen to perform slightly better.”). This simply combining prior art elements according to known methods to yield predictable results, use of known technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
With regard to Claim 5,
D1-D2 teach the method for data-driven prediction based on spatial information constraints according to claim 1, wherein, the optimization of the loss function and the data-driven prediction based on the deep fully connected neural network in the S4 comprise the following steps:
S41, the samples at some sampling points are reserved to form a test set, and the remaining samples form a training set for training the deep fully connected neural network (D1, P. 7, “30%of the training data set is used as validation set to evaluate the generalizability of the model, while the rest of them is used to train the model”, P. 8, “Fig.3b shows two realizations generated by SGSIM, which are used to construct the training data set, which includes19,880 training examples in total. 5964 examples of this dataset are used as the validation set.”);
S42, optimizing the loss function by adopting a batch random gradient descent algorithm, and updating the parameters of the deep fully connected neural network (D1, P. 4, 2.3, “Many optimization algorithms have been developed to train the DNN, such as stochastic gradient descent (SGD) (Bottou,2010), and adaptive moment estimation (Adam)”, P. 8, “The model is optimized with the Adam optimization algorithm”); and
S43, the trained network model is used to predict the test set (D1, P.9, “For the results produced with the direct prediction approach, these errors are related to the generalization capability of the trained model, i.e., the ability to produce good predictions when the trained model is tested on new examples. In our case, the trained PIML model works well on most of the new input data”). The same motivation to combine for claim 1 equally applies for current claim.
Claim 7 is similar to claim 1 therefore it is rejected under similar rationale. D1-D2 further teach a computer device which comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps (D1, “All the computations are performed on a desktop computer with an Intel i7 3.2GHz central processing unit”, D2, “AU runs were produced with the GSLIB software”).
Claim 8 is similar to claim 1 therefore it is rejected under similar rationale. D1-D2 further teach a computer-readable storage medium storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method according to claim 1 (D1, “All the computations are performed on a desktop computer with an Intel i7 3.2GHz central processing unit”, D2, “AU runs were produced with the GSLIB software”).
Claims 2, 6, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Sequential Gaussian simulation for geosystems modeling: A machine learning approach Published 2021 [hereinafter D1] in view of “Integrating seismic Data in Reservoir Modeling: The Collocated Cokriging Alternative“ Published 1992 [hereinafter D2] further in view of Luan et al. [US 2024/0038324 A1, hereinafter Luan].
With regard to Claim 2,
D1-D2 teach
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(D2, P. 2, Notations, “The goal of integration is to produce one or several maps for the distribution of ZI(u) over field A utilizing both hard data {Z. (U α), α = 1, ....n1} and soft data {Z2(U~ α), α= 1,.., n2}.”, P. 5, A case study, “depths to the top of the reservoir as measured from 20 wells; these are considered as hard data z1(uα), α =1,.., n1 = 20. the two-way travel times from 3D seismic, recorded at 15,753 CDP locations; these are considered as soft data z2(u α) α = 1, ..., n2 = 15,753 informing”, Abstract, “While, the well data provide the most accurate measurements of depths there are rarely enough wells to permit an accurate appraisal from well data alone. On the other hand, the seismic data are generally less precise but more abundant. Two geostatistical methods, “external drift” and “collocated cokriging”, are proposed to integrate the two sources of information.”, P. 1, Introduction, “The well data provide accurate local information that can be considered aa ‘hard”, The numerous seismic locations provide a quasi exhaustive coverage”),
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(D1, P. 4,” (1)Transform the hard data into a standard normal distribution based on the normal score transformation”, (5) Back transform all the sampled data and simulated values to the original space”, D2, P. 5, “the normal score transforms … of the primary and secondary variables, Z1(u) and Z2(u)”, Examiner notes that the limitation is a contingent limitations that is not mandatory as it depends on a limitation may never occur See MPEP 2111.03). The same motivation to combine for claim 1 equally applies for current claim.
D1-D2 does not explicitly teach
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(¶75,”where a method for filling a missing value in the process data of the fermentation stage is selected from average filling, median filling”¶74, “eliminating an outlier in the process data”)
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(¶76, normalization, Examiner notes that the limitation is a contingent limitations that is not mandatory as it may never occur See MPEP 2111.03).
D1-D2 and Luan are analogous art to the claimed invention because they are from a similar field of endeavor of integration for different sourced data. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1-D2 resulting in resolutions as disclosed by Luan with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1-D2 as described above to preserve sample size, robust to outliers as median remains unaffected by extremely large or small values, and have high efficiency as calculating the median is computationally lightweight, easy to implement using standard tools, and maintains Central Tendency as by substituting the midpoint value for missing entries, you preserve the overall center of your distribution without introducing arbitrary constants. This simply combining prior art elements according to known methods to yield predictable results, use of known technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
With regard to Claim 6,
D1 teach a system for data-driven prediction based on spatial information constraints according to the method for data-driven prediction based on spatial information constraints according to claim 1, which comprises (D1, “All the computations are performed on a desktop computer with an Intel i7 3.2GHz central processing unit”):
a sample weight calculating module, which is connected with the prediction target interpolation module and is used for calculating the sample weight based on sequential Gaussian simulation (Abstract, “Sequential Gaussian Simulation (SGSIM) as a stochastic method has been developed to avoid the smoothing effect produced in deterministic methods by generating various stochastic realizations”, 3.1, P. 10, “The conditional variance map is also used to assess the uncertainty of the estimations.”);
a loss function construction module, which is connected with the sample weight calculating module and is used for constructing the loss function based on the spatial information constraint (P. 5-6, “the loss function for the DNN is defined with multiple components, such as: MSE = MSEz +MSEw + MSEconstraint + MSEmean + MSEvariance”, P. 7, “the linear constraint, the law of maintaining the residual mean as zero, and minimizing the variance are also included”, P. 2, Col. 2, “The prior information such as governing equations and physical constraints are added into the loss function as a regularization term”);
a loss function optimization module, which is connected with the loss function construction module and is used for optimizing the loss function by using a batch random gradient descent algorithm (P. 4, 2.3, “Many optimization algorithms have been developed to train the DNN, such as stochastic gradient descent (SGD) (Bottou,2010), and adaptive moment estimation (Adam)”, P. 8, “The model is optimized with the Adam optimization algorithm”); and
a data-driven prediction module, which is connected with the loss function optimization module and is used for data-driven prediction based on the deep fully connected neural network (D1, P. 4, 2.3, “DNN is a general functional approximator which can learn the complex nonlinear relationships between input and output. Generally, the input layer, hidden layer, and output layer are fully connected and parameterized by the weight and bias parameters“, “Many optimization algorithms have been developed to train the DNN, such as stochastic gradient descent(SGD)(Bottou,2010), and adaptive moment estimation(Adam)”, P. 8, “The DNN model has 4 hidden layers with 30 neurons in each”).
D1 does not explicitly teach a data acquisition module, which is used for acquiring various observation data with different acquisition modes aiming at the specific prediction target of the research object.
D2 teach a data acquisition module, which is used for acquiring various observation data with different acquisition modes aiming at the specific prediction target of the research object (P. 2, Notations, “The goal of integration is to produce one or several maps for the distribution of ZI(u) over field A utilizing both hard data {Z. (U α), α = 1, ....n1} and soft data {Z2(U~ α), α= 1,.., n2}.”, P. 5, A case study, “depths to the top of the reservoir as measured from 20 wells; these are considered as hard data z1(uα), α =1,.., n1 = 20. the two-way travel times from 3D seismic, recorded at 15,753 CDP locations; these are considered as soft data z2(u α) α = 1, ..., n2 = 15,753 informing”, Abstract, “While, the well data provide the most accurate measurements of depths there are rarely enough wells to permit an accurate appraisal from well data alone. On the other hand, the seismic data are generally less precise but more abundant. Two geostatistical methods, “external drift” and “collocated cokriging”, are proposed to integrate the two sources of information.”, P. 1, Introduction, “The well data provide accurate local information that can be considered aa ‘hard”, The numerous seismic locations provide a quasi exhaustive coverage”).
D1 and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of geostatistical spatial prediction using gaussian simulation. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1 resulting in resolutions as disclosed by D2 with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1 as described above to and adopt D2 collocated co-Kriging as it could be easily adapted to handle the single additional equation required by collocated cokriging and it perform better than ordinary kriging specially that D2 allow integration of data from multiple sources acquired by different acquisition methods which provide predictable improvement than data from single source or share similar acquisition method (D2, P. 7, “Presently available code for Gaussian sequential simulation, such as sgsim of GSLIB [10], can be easily adapted to handle the single additional equation required by collocated cokriging.”, “the scattergram of the 25 depth estimates provided by kriging with external drift (using only 20 well data) vs. the actual values at these locations. Figure 8h gives the scattergram with, now, the 25 estimates provided by collocated cokriging (again using only 20 well data), Collocated cokriging is seen to perform slightly better.”). This simply combining prior art elements according to known methods to yield predictable results, use of known technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
D1-D2 does not explicitly teach a data preprocessing module, which is connected with the data acquisition module and is used for processing the abnormal value of median filling on the observation data; if the dimensions of the observed data are quite different, they will be further normalized.
Luan teach a data preprocessing module, which is connected with the data acquisition module and is used for processing the abnormal value of median filling on the observation data (¶75,”where a method for filling a missing value in the process data of the fermentation stage is selected from average filling, median filling”¶74, “eliminating an outlier in the process data”);
if the dimensions of the observed data are quite different, they will be further normalized (¶76, normalization, Examiner notes that the limitation is a contingent limitations that is not mandatory as it may never occur See MPEP 2111.03).
D1-D2 and Luan are analogous art to the claimed invention because they are from a similar field of endeavor of integration for different sourced data. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1-D2 resulting in resolutions as disclosed by Luan with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1-D2 as described above to preserve sample size, robust to outliers as median remains unaffected by extremely large or small values, and have high efficiency as calculating the median is computationally lightweight, easy to implement using standard tools, and maintains central tendency as by substituting the midpoint value for missing entries, you preserve the overall center of your distribution without introducing arbitrary constants. This simply combining prior art elements according to known methods to yield predictable results, use of known technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
Claim 9 is similar to claim 6 therefore it is rejected under similar rationale. D1-D2 further teach An information data processing terminal, which is used for implementing the system for data-driven prediction (D1, “All the computations are performed on a desktop computer with an Intel i7 3.2GHz central processing unit”, D2, “AU runs were produced with the GSLIB software”).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Sequential Gaussian simulation for geosystems modeling: A machine learning approach Published 2021 [hereinafter D1] in view of “Integrating seismic Data in Reservoir Modeling: The Collocated Cokriging Alternative“ Published 1992 [hereinafter D2] in view of “Kriging and Semivariogram Deconvolution in the Presence of Irregular Geographical Units“ Published 2006 [hereinafter D3] further in view of Brunner et al. [US 20250062040 A1, hereinafter Brunner].
With regard to Claim 3,
D1-D2 teach
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(D2, The collocated cokriging model, “retaining at each location u to be estimated only the collocated secondary datum z2(u)”
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(D2, P. 4, Collocated cokriging under a Markov model , “the major advantage of the collocated cokriging model is that it relies on a calibration (tuning) parameter: the correlation coefficient P12(0)”, “P12(0) being the traditional (collocated) coefficient of correlation between Z1 (u) and Z2(u)”, P. 6, “The correlation coefficient P12(0) used for the Markov model (9) is that based on the seven wells intersecting the dome structure i,e., P12 (0) = -0,6”),
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(D2,
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D2, P.3, “
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, P. 2,
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”)
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(D2, P. 3, Eq. 9, P. 8, Eq. (18), P. 3-4, “Then it can be shown, see Appendix, that the cross covariance C12(h) = C21(h) takes the congenial form: … (9) or equivalently,
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)
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(D2, P. 4, “Collocated cokriging under a Markov model With the Markov model, the collocated cokriging estimate and system (7) are rewritten in their standardized form … EQ (10)”)
S27, interpolating the principal variable Z, based on S22 (D2, P. 6, Kriging with external drift, “The resulting kriging map is shown on. Figure 3a, with the corresponding histogram of 15,753 kriging estimates shown on Figure 3b.”); and
S28, based on the interpolated Y (represented by Y') (D2, P. 6, “The resulting cokriging map is shown on Figure 4b, with the corresponding histogram of 15,753 estimated values”, P. 4,
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,
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”),
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(D1, P. 4-5, “the values of selected data are used to guide the prediction of the values simulated by SGSIM. Moreover, they will be used to calculate the loss function.”). The same motivation to combine for claim 1 equally applies for current claim.
D1-D2 does not explicitly teach
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D3 teach
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(P. 8, 3.1 “To compute the kriging weights λi(uβ), the Lagrange multiplier μ(uβ), and kriging variance σ2 OK(uβ) one needs to know the point support covariance C(h), or equivalently the semivariogram γ(h) = C(0) − C(h) which can be estimated using an expression of type (1).”, P. 4, Eq. 1).
D1-D2 and D3 are analogous art to the claimed invention because they are from a similar field of endeavor of kriging based spatial interpolation of variables from limited samples. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1-D2 resulting in resolutions as disclosed by D3 with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1-D2 as described above to improve accuracy and reliability of interpolated estimates. This simply combining prior art elements according to known methods to yield predictable results, use of known technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
D1-D2-D3 does not explicitly teach
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Brunner teach
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(¶420 “observational data can originate from disparate electronic sources (e.g., different databases). For example, data in a first source can be obtained utilizing a first data generating technique and/or can be collected at a first time. In turn, data in a second source can be obtained utilizing a second data generating technique”, ¶421-423, [0488] subsampling: k=100 iterations of subsampling of the larger group (between control and drug treated samples) to match sample size n of the smallest group).
D1-D2-D3 and Brunner are analogous art to the claimed invention because they are from a similar field of endeavor of extracting features to train machine learning models. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1-D2-D3 resulting in resolutions as disclosed by Brunner with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1-D2-D3 as described above to prevents models from becoming biased toward overrepresented groups. This simply combining prior art elements according to known methods to yield predictable results, use of known technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
Examiner notes
None of the reference found by the examiner disclose claim 4 specially S34 and S35; therefore claim 4 is not rejected under art rejection. However, further consideration/search will be provided based on the applicant’s response.
Conclusion
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
“GEOSTATISTICAL METHODS FOR PREDICTION OF SPATIAL VARIABILITY OF RAINFALL IN A MOUNTAINOUS REGION’ that disclose interpolation of a prediction target based on collocated Co-Kriging See at least Abstract, “The ordinary co-kriging (OCK) and collocated co-kriging (CCK) methods of interpolation were applied for the standardized rainfall depths associated with elevation, as the primary variate, and the surface elevation values as the secondary variate.”, P. 5, Eq(11).
Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED ABOU EL SEOUD whose telephone number is (303)297-4285. The examiner can normally be reached Monday-Thursday 9:00am-6:00pm MT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle Bechtold can be reached at (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148
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