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 .
Detailed Action
This action is in response to the claims filed 8/13/2024:
Claims 1 – 20 are pending.
Claims 1 is independent.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, "providing a training geophysical data set with a plurality of input channels, the training geophysical data set" is grammatically indefinite. It's unclear what ", the training geophysical data set" is modifying. In the interest of further examination the claim limitation is interpreted as simply "providing a training geophysical data set with a plurality of input channels."
Regarding claim 6, "wherein the dimension of the mathematical operations is less than or equal to the training geophysical data set." is indefinite. The claim limitation compares two unlike things (a dimension and a training geophysical data set). Secondly, "the dimension of the mathematical operation" lacks antecedent basis. In the interest of further examination the limitation is interpreted as "wherein a dimension of the mathematical operations is less than or equal to a dimension of the training geophysical data set."
Regarding claims 11 and 12, "the input dimension" lacks antecedent basis. "An input dimension" is recommended.
Regarding claim 18, "conventionally computed technique" is indefinite. One of ordinary skill in the art could not readily determine the bounds of a "conventionally computed technique." In the interest of further examination the claim limitation is interpreted simply as "a computed technique."
The remaining claims are rejected with respect to their dependence on the rejected claims.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 20 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Regarding claim 20, claim 20 does not limit the claim from which it depends. The Markush group in claim 20 contains the entire universe of possible options and is therefore non-limiting. The instant specification further supports this interpretation and does not provide any additional subset that is not comprised by the Markush group of claim 20. It is therefore an improper dependent claim.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 101
101 Rejection
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 USC § 101 because the claimed invention is directed to non-statutory subject matter.
Regarding Claim 1: Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Claim 1 under its broadest reasonable interpretation is a series of mental processes. For example, but for the generic computer components language, the above limitations in the context of this claim encompass machine learning processing, including the following:
capturing long-range dependencies in geophysical data sets (observation, evaluation, and judgement),
dependency-training a first backpropagation-enabled process to compute spatial relationships for each of the plurality of input channels, thereby producing a dependency-trained backpropagation-enabled process, which outputs featurized input channels for each of the plurality of input channels (observation, evaluation, and judgement)
interdependency-training the dependency-trained backpropagation-enabled process to compute inter-feature and spatial relationships between each of the featurized input channels, thereby producing an interdependency-trained backpropagation-enabled process, which outputs conditional featurized input channels (observation, evaluation, and judgement)
fusing the conditional featurized input channels to produce a combined representation of the conditional featurized input channels (observation, evaluation, and judgement)
inputting the combined representation to a second backpropagation-enabled process to compute a prediction selected from the group consisting of a geologic feature occurrence, a geophysical property occurrence, a fluid occurrence, an attribute of subsurface data, and combinations thereof (observation, evaluation, and judgement)
Therefore, claim 1 recites an abstract idea which is a judicial exception.
Step 2A Prong Two Analysis: Claim 1 recites additional elements “A non-transitory computer-readable recording medium having stored therein a program that causes a computer to execute a process”. However, these additional features are computer components recited at a high-level of generality, such that they amount to no more than mere instructions to apply the judicial exception using a generic computer component. An additional element that merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, does not integrate the judicial exception into a practical application (See MPEP 2106.05(f)). Claim 1 also recites additional elements “providing a training geophysical data set with a plurality of input channels, the training geophysical data set” which amounts to gathering and outputting data which is insignificant extra-solution activity (See MPEP 2106.05(g)). Therefore, claim 1 is directed to a judicial exception.
Step 2B Analysis: Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the lack of integration of the abstract idea into a practical application, the additional elements recited in claim 1 amount to no more than mere instructions to apply the judicial exception using a generic computer component and insignificant extra-solution activity. The gathering and outputting data is considered well-understood, routine, and conventional in the art (See MPEP 2106.05(d)(II)(i)).
For the reasons above, claim 1 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to dependent claims 2-20. The additional limitations of the dependent claims are addressed briefly below:
Dependent claim 2 recites additional observation, evaluation, and judgement “wherein the dependency-training and interdependency-training steps are repeated”
Dependent claim 3 recites additional mathematical calculations “the training geophysical data set has a set of associated training labels and step (e) further comprises the step of comparing the prediction to a label in the set of associated labels.”
Dependent claim 4 recites additional insignificant extra-solution activity “label-training the interdependency-trained backpropagation-enabled process using the training geophysical data set and the associated training labels to compute a prediction selected from an occurrence, a value of an attribute, and combinations thereof, thereby producing a label-trained backpropagation-enabled process” and “using the label-trained backpropagation-enabled process to capture long-range dependencies in a non-training geophysical data set by computing a prediction selected from the group consisting of a geologic feature occurrence, a geophysical property occurrence, a hydrocarbon occurrence, an attribute of subsurface data, and combinations thereof”
Dependent claim 5 recites additional insignificant extra-solution activity “the dependency-training step computes spatial relationships by applying self-attention weights to the training geophysical data set”.
Dependent claim 6 recites additional observation, evaluation, and judgement based on mathematical calculations and relationships “the dependency-training step comprises the steps of: i. preparing a square self-attention matrix using the training geophysical data set; ii. populating at least a portion of the square self-attention matrix with values defining the spatial relationships between any two elements in the square self-attention matrix iii. defining an updated training geophysical data set by performing a linear transformation of the populated square self-attention matrix with the training geophysical data set; and iv. executing one or more mathematical operation on the updated training geophysical data, wherein the dimension of the mathematical operations is less than or equal to the training geophysical data set”
Dependent claim 7 recites additional observation, evaluation, and judgement “further comprising the step of repeating steps i-iii”
Dependent claim 8 recites additional observation, evaluation, and judgement “the step of repeating steps i-iv”
Dependent claim 9 recites additional mathematical calculations and relationships “wherein the linear transformation is selected from the group consisting of convolution, pooling, softmax, Fourier, and combinations thereof”
Dependent claim 10 recites additional observation, evaluation, and judgement “wherein the mathematical operation is selected from the group consisting of multiplying, adding, and combinations thereof”
Dependent claim 11 recites additional observation, evaluation, and judgement based on mathematical calculations and relationships “the prediction is a regression prediction computed by computing a predicted value of the attribute, wherein the predicted value has a prediction dimension of at least 1 and is at least 1 dimension less than the input dimension”
Dependent claim 12 recites additional observation, evaluation, and judgement based on mathematical calculations and relationships “the prediction is a segmentation prediction computed by computing a prediction of the occurrence of one or more of a geologic feature, a geophysical property and a hydrocarbon, wherein the prediction has a prediction dimension of at least 1 and is at least 1 dimension less than the input dimension”
Dependent claim 13 recites additional observation, evaluation, and judgement “the geologic feature occurrence is selected from the group consisting of occurrences of a boundary layer variation, an overlapping bed, a river, a channel, a tributary, a salt dome, a basin, an indicator of tectonic deformation, an indicator of erosion, an indicator of infilling, a geologic environment in which rocks were deposited, a source rock, a migration pathway, a reservoir rock, a seal, a trapping element, and combinations thereof”
Dependent claim 14 recites additional observation, evaluation, and judgement “the geophysical property occurrence is selected from the group consisting of occurrences of an elastic parameter, a P-wave velocity, an S-wave velocity, a porosity, an impedance, a reservoir thickness, and combinations thereof”
Dependent claim 15 recites additional observation, evaluation, and judgement “the fluid occurrence is selected from the group consisting of occurrences of oil, gas, brine, and combinations thereof”
Dependent claim 16 recites additional observation, evaluation, and judgement “the attribute of subsurface data is selected from the group consisting of quantities of spectral content, energy associated with changes in a frequency band, a signal associated with a filter, an acoustic impedance, a reflectivity, a semblance, a loop-based property, an envelope, a phase, a dip, an azimuth, a curvature, and combinations thereof”
Dependent claim 17 recites additional observation, evaluation, and judgement “one or both of the first backpropagation-enabled process and the second backpropagation-enabled process is a deep learning process”
Dependent claim 18 recites additional observation, evaluation, and judgement based on mathematical calculations and relationships “one or both of the first backpropagation-enabled process and the second backpropagation-enabled process is a supervised regression process, comprising the step of comparing attributes computed in a conventionally computed technique with the ones from a supervised regression technique”
Dependent claim 19 recites additional observation, evaluation, and judgement “the first backpropagation-enabled process and the second backpropagation-enabled process are each independently selected from the group consisting of supervised, semi-supervised, unsupervised processes and combinations thereof”
Dependent claim 20 recites additional observation, evaluation, and judgement “the training geophysical data set is comprised of geophysical data selected from the group consisting of real geophysical data, synthetically generated geophysical data, augmented geophysical data, and combinations thereof”
Therefore, when considering the elements separately and in combination, they do not add significantly more to the inventive concept. Accordingly, claims 1-20 are rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5, 11-14, and 16-20 are rejected under U.S.C. §102(a)(1) as being anticipated by Dou (“Attention-Based 3D Seismic Fault Segmentation Training by a Few 2D Slice Labels”, 2021).
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FIG. 3 of Dou
Regarding claim 1, Dou teaches A method for capturing long-range dependencies in geophysical data sets, comprising the steps of: ([p. 5] "the input and output are both 3D, and the model fully extracts the three-dimensional features of seismic data" [p. 11] "CNN provide large receptive fields")
a) providing a training geophysical data set ([p. 1] "our study focuses on the use of seismic data for fault detection" [p. 6] "the number of training sets is 5000")
with a plurality of input channels, the training geophysical data set;([p. 6] "the train set as shown in 6 and divide it into 5000×64×64×64 samples" [p. 4] "The prediction layer is one convolution layer. Use γl,l ∈ [1,C1] to represent the convolution kernel of this layer, then the shape of γl is (C1,k,k,k,C0), where C1 is the number of channels in the upper layer, k = 1, is the size of the convolution kernel, C0 = 1, is the number of convolution kernels")
b) dependency-training a first backpropagation-enabled process([p. 1] "we presented λ-BCE and λ-smooth L1 loss to effectively train 3D-CNN by some slices from 3D seismic volume label" the instant specification defines a backpropagation-enabled process as a convolutional neural network [¶0027] "A preferred embodiment of a backpropagation-enabled process is a deep learning process, including, but not limited to a convolutional neural network.")
to compute spatial relationships for each of the plurality of input channels, ([p. 4] "the model is trained by our proposed λ loss function, which is labeled with a few 2D slices, it can fully extract 3D spatial information from the limited number of slice labels" [p. 6] "we randomly rotate the data during training, which also enables the convolution kernel to fully learn the spatial characteristics of the data")
thereby producing a dependency-trained backpropagation-enabled process, ([p. 2] "this module can be treated as intermediate supervision to provide more effective gradients for training, thereby increasing the sensitivity to the foreground in the back propagation to improve the performance of the model")
which outputs featurized input channels for each of the plurality of input channels;([p. 3] "This module obtains the linear projections ωlFl and ωhFh from the low-level detail feature Fl and the high-level semantic feature Fh through 1 × 1 convolution respectively, and then combines them into a single channel" Dou's trained CNN takes seismic information through successive convolutions and outputs feature maps/channels encoding detail and semantic information. This is exactly "featurized" channels in ordinary CNN terminology. See also FIG. 3)
c) interdependency-training the dependency-trained backpropagation-enabled process([p. 3] "we propose an attention module that can be trained through active supervision […] our attention maps are generated through supervised training" [p. 3] "AAM is an intermediate supervision mechanism that provides an additional effective gradient in the back propagation process")
to compute inter-feature and spatial relationships between each of the featurized input channels, thereby producing an interdependency-trained backpropagation-enabled process, ([p. 3] "This module obtains the linear projections ωlFl and ωhFh from the low-level detail feature Fl and the high-level semantic feature Fh through 1 × 1 convolution respectively, and then combines them into a single channel" AAM explicitly receives two different feature levels, projects each by convolution, adds them, and interprets the resulting feature in terms of the relationship/difference between those features)
which outputs conditional featurized input channels;([p. 3] "multiplying the attention map with low-level features can improve the sensitivity to fault areas while suppressing factors such as noise in non-fault areas" See also FIG. 2 and FIG. 3)
d) fusing the conditional featurized input channels to produce a combined representation of the conditional featurized input channels; and([p. 3] "The widely used segmentation models all merge with high level and low-level features […] The main reason is that low-level features contain richer edges and detailed features, while high-level features have more semantic features [26], and the fusion of the two is conducive to finer pixel classification [...] Before fusion, multiplying the attention map with low-level features" [p. 4] "AAM filters the background feature during the feature fusion process of the model" The low-level feature is first attention-conditioned. Only then does Dou fuse it in the U-Net decoder with higher-level information. See also FIG. 2 and FIG. 3)
e) inputting the combined representation to a second backpropagation-enabled process to compute a prediction([p. 4] "U-Net can be divided into two parts, backbone and prediction layer. The backbone part is used to extract features. The prediction layer is one convolution layer" [p. 5] "the gradient generated by each voxel Is η∂Lbce" [p. 4] "the weight slides on the last feature map to obtain the final result of the prediction P, which can be expressed as equation (7)." Instant specification discloses ([¶0034] "The first and second backpropagation-enabled processes may be the same or different"). The fused U-Net representation is supplied downstream to the prediction portion of the same CNN. The prediction layer itself is a CNN and participates directly in backpropagation)
selected from the group consisting of a geologic feature occurrence, a geophysical property occurrence, a fluid occurrence, an attribute of subsurface data, and combinations thereof.([p. 1] "Seismic fault detection").
Regarding claim 2, Dou teaches The method of claim 1, wherein the dependency-training and interdependency-training steps are repeated.(Dou [p. 6] "the training epoch is 35 and the batch size is 32" [p. 7] "During training, an average of 3.06s elapsed per Adam optimization" epoch interpreted as repeat of steps).
Regarding claim 3, Dou teaches The method of claim 1, wherein the training geophysical data set has a set of associated training labels (Dou [p. 5] "ground truth as yi" [p. 6] "The positive samples are labeled as 1, the negative samples are labeled as 0")
and step (e) further comprises the step of comparing the prediction to a label in the set of associated labels.(Dou [p. 5] "Denote […] ground truth as yi, and use the BCE loss to calculate the cost" See Eqn. 9 which shows prediction being directly compared to prediction ^yi).
Regarding claim 4, Dou teaches The method of claim 3, further comprising the steps of: f) label-training the interdependency-trained backpropagation-enabled process using the training geophysical data set and the associated training labels (Dou [p. 2] "the 3D segmentation model can be trained by a few 2D slice labels [...] we propose an attention module that can be actively supervised and trained (Active Attention Module, AAM) based on the characteristics of seismic faults, its supervision information is generated by the combined simulation of ground truth and Gaussian function")
to compute a prediction selected from an occurrence, a value of an attribute, and combinations thereof, thereby producing a label-trained backpropagation-enabled process; and(Dou [p. 1] "our study focuses on the use of seismic data for fault detection" [p. 6] "the number of training sets is 5000")
g) using the label-trained backpropagation-enabled process to capture long-range dependencies in a non-training geophysical data set (Dou [p. 6] "We have data from six work areas (A,B,C,D,E,F), where A is used as the train set and the others as the test set" Dou trains on work area A and then applies that trained network to separate seismic work areas, where the other work areas are interpreted as non-training geophysical data sets)
by computing a prediction selected from the group consisting of a geologic feature occurrence, a geophysical property occurrence, a hydrocarbon occurrence, an attribute of subsurface data, and combinations thereof.(Dou [p. 11] "The model detects most of the faults, indicating that our method performs good generalization in data with similar faults.").
Regarding claim 5, Dou teaches The method of claim 1, wherein the dependency-training step computes spatial relationships by applying self-attention weights to the training geophysical data set.(Dou [p. 3] "Before fusion, multiplying the attention map with low-level features can improve the sensitivity to fault areas while suppressing factors such as noise in non-fault areas").
Regarding claim 11, Dou teaches The process of claim 1, wherein the prediction is a regression prediction computed by computing a predicted value of the attribute, wherein the predicted value has a prediction dimension of at least 1 and is at least 1 dimension less than the input dimension.(Dou [p. 4] "the last feature map shares a set of convolution weights γl, and the weight slides on the last feature map to obtain the final result of the prediction P, which can be expressed as equation (7). C1 P =sigmoid({ where, l=1 C1 γla1 l, l=1 sigmoid(x) = C1 γla2 l, ... 1 e−x +1 l=1 γlawhd l }) (7) (8)" [p. 5] "Denote sigmoid(C1 l=1γlai l)asˆyi" The final sigmoid layer in Dou for probability calculation is mathematically a logistic-regression).
Regarding claim 12, Dou teaches The process of claim 1, wherein the prediction is a segmentation prediction computed by computing a prediction of the occurrence of one or more of a geologic feature, a geophysical property and a hydrocarbon, wherein the prediction has a prediction dimension of at least 1 and is at least 1 dimension less than the input dimension.(Dou [p. 1] "an image segmentation task" [p. 4] "the last feature map shares a set of convolution weights γl, and the weight slides on the last feature map to obtain the final result of the prediction P, which can be expressed as equation (7). C1 P =sigmoid({ where, l=1 C1 γla1 l, l=1 sigmoid(x) = C1 γla2 l, ... 1 e−x +1 l=1 γlawhd l }) (7) (8)").
Regarding claim 13, Dou teaches The method of claim 1, wherein the geologic feature occurrence is selected from the group consisting of occurrences of a boundary layer variation, an overlapping bed, a river, a channel, a tributary, a salt dome, a basin, an indicator of tectonic deformation, an indicator of erosion, an indicator of infilling, a geologic environment in which rocks were deposited, a source rock, a migration pathway, a reservoir rock, a seal, a trapping element, and combinations thereof.(Dou [p. 6] "the 3D segmentation results still show a clear and smooth fault plane" Dou repeatedly predicts fault planes which are structural discontinuities indicating deformation of rock (tectonic deformation).).
Regarding claim 14, Dou teaches The method of claim 1, wherein the geophysical property occurrence is selected from the group consisting of occurrences of an elastic parameter, a P-wave velocity, an S-wave velocity, a porosity, an impedance, a reservoir thickness, and combinations thereof.(Dou [p. 1] "the fault parameters [6], but the anisotropy as a basic property of the fault" anisotropy interpreted as an elastic parameter).
Regarding claim 16, Dou teaches The method of claim 1, wherein the attribute of subsurface data is selected from the group consisting of quantities of spectral content, energy associated with changes in a frequency band, a signal associated with a filter, an acoustic impedance, a reflectivity, a semblance, a loop-based property, an envelope, a phase, a dip, an azimuth, a curvature, and combinations thereof.(Dou [P. 3] "Fs shows the signal response of ground truth […] AAM improves model performance in two ways. First, when propagating forward, by generating ˆ Θ and then multiplying with Fl, the low-level noise features are suppressed, which is equivalent to selecting the approximate area to be segmented in advance, helping the final classification layer to filter the area in advance" Dou explicitly discloses a signal associated with a filter as an attribute of subsurface data).
Regarding claim 17, Dou teaches The method of claim 1, wherein one or both of the first backpropagation-enabled process and the second backpropagation-enabled process is a deep learning process.(Dou [p. 2] "this module can be treated as intermediate supervision to provide more effective gradients for training, thereby increasing the sensitivity to the foreground in the back propagation to improve the performance of the model").
Regarding claim 18, Dou teaches The method of claim 1, wherein one or both of the first backpropagation-enabled process and the second backpropagation-enabled process is a supervised regression process, comprising the step of comparing attributes computed in a conventionally computed technique with the ones from a supervised regression technique.(Dou [p. 2] "the attention map of our proposed attention module is generated by active supervised learning" [p. 4] "the last feature map shares a set of convolution weights γl, and the weight slides on the last feature map to obtain the final result of the prediction P, which can be expressed as equation (7). C1 P =sigmoid({ where, l=1 C1 γla1 l, l=1 sigmoid(x) = C1 γla2 l, ... 1 e−x +1 l=1 γlawhd l }) (7) (8)" [p. 5] "Denote sigmoid(C1 l=1γlai l)asˆyi" The final sigmoid layer in Dou for probability calculation is mathematically a logistic-regression).
Regarding claim 19, Dou teaches The method of claim 1, wherein the first backpropagation-enabled process and the second backpropagation-enabled process are each independently selected from the group consisting of supervised, semi-supervised, unsupervised processes and combinations thereof.(Dou [p. 2] "this module can be treated as intermediate supervision to provide more effective gradients for training, thereby increasing the sensitivity to the foreground in the back propagation to improve the performance of the model").
Regarding claim 20, Dou teaches The method of claim 1, wherein the training geophysical data set is comprised of geophysical data selected from the group consisting of real geophysical data, synthetically generated geophysical data, augmented geophysical data, and combinations thereof.(Dou [p. 5] "the input and output are both 3D, and the model fully extracts the three-dimensional features of seismic data" [p. 11] "CNN provide large receptive fields").
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 6-10 are rejected under U.S.C. §103 as being unpatentable over the combination of Dou and Fu (“Dual Attention Network for Scene Segmentation”, 2019).
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Regarding claim 6, Dou teaches The method of claim 1, […] using the training geophysical data set;(Dou [p. 1] "our study focuses on the use of seismic data for fault detection" [p. 6] "the number of training sets is 5000").
However, Dou doesn't explicitly teach wherein the dependency-training step comprises the steps of: i. preparing a square self-attention matrix
ii. populating at least a portion of the square self-attention matrix with values defining the spatial relationships between any two elements in the square self-attention matrix;
iii. defining an updated training [geophysical] data set by performing a linear transformation of the populated square self-attention matrix with the training [geophysical] data set; and
iv. executing one or more mathematical operation on the updated training geophysical data,
wherein the dimension of the mathematical operations is less than or equal to the training geophysical data set.
Fu, in the same field of endeavor, teaches The method of claim 1, wherein the dependency-training step comprises the steps of: i. preparing a square self-attention matrix ([p. 4] "the spatial attention map S ∈ RN×N")
ii. populating at least a portion of the square self-attention matrix with values defining the spatial relationships between any two elements in the square self-attention matrix;([p. 4] "sji measures the ith position’s impact on jth position" See also Eqn. 1. Each Sji is explicitly a learned relationship between two spatial position)
iii. defining an updated training [geophysical] data set by performing a linear transformation of the populated square self-attention matrix with the training [geophysical] data set; and([p. 4] "we feed feature A into a convolution layer to generate a new feature map D ∈ RC×H×W and reshape it to RC×N. Then we perform a matrix multiplication between D and the transpose of S and reshape the result to RC×H×W")
iv. executing one or more mathematical operation on the updated training geophysical data, ([p. 4] "Finally, we multiply it by a scale parameter α and perform a element-wise sum operation with the features Ato obtain the final output E ∈ RC×H×W")
wherein the dimension of the mathematical operations is less than or equal to the training geophysical data set.([p. 4] "A ∈ RC×H×W […] final output E ∈ RC×H×W").
Dou as well as Fu are directed towards convolutional neural networks with attention for spatial segmentation. Therefore, Dou as well as Fu are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to use the geophysical dataset in Dou with the attention model in Fu. Fu provides as additional motivation for combination ([p. 3] “Our method could adaptively aggregate long range contextual information, thus improving feature representation for scene segmentation”). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 7, the combination of Dou, and Fu teaches The method of claim 6, further comprising the step of repeating steps i-iii.(Fu [p. 5] "we set training time to 180 epochs for COCO Stuff and 240 epochs for other datasets").
Regarding claim 8, the combination of Dou, and Fu teaches The method of claim 6, further comprising the step of repeating steps i-iv.(Fu [p. 5] "we set training time to 180 epochs for COCO Stuff and 240 epochs for other datasets").
Regarding claim 9, the combination of Dou, and Fu teaches The method of claim 6, wherein the linear transformation is selected from the group consisting of convolution, pooling, softmax, Fourier, and combinations thereof.(Fu [p. 4] "we perform a matrix multiplication between the transpose of C and B, and apply a softmax layer to calculate the spatial attention map S ∈ RN×N [...] we feed feature A into a convolution layer to generate a new feature map D ∈ RC×H×W and reshape it to RC×N. Then we perform a matrix multiplication between D and the transpose of S and reshape the result to RC×H×W […] we apply a softmax layer to obtain the channel attention map" See FIG. 3).
Regarding claim 10, the combination of Dou, and Fu teaches The method of claim 6, wherein the mathematical operation is selected from the group consisting of multiplying, adding, and combinations thereof.(Fu [p. 4] "we feed feature A into a convolution layer to generate a new feature map D ∈ RC×H×W and reshape it to RC×N. Then we perform a matrix multiplication between D and the transpose of S and reshape the result to RC×H×W").
Claim 15 is rejected under U.S.C. §103 as being unpatentable over the combination of Dou and Kulkarni (US20210089892A1).
Regarding claim 15, Dou teaches The method of claim 1.
However, Dou doesn't explicitly teach, wherein the fluid occurrence is selected from the group consisting of occurrences of oil, gas, brine, and combinations thereof.
Kulkarni, in the same field of endeavor, teaches wherein the fluid occurrence is selected from the group consisting of occurrences of oil, gas, brine, and combinations thereof. ([¶0017] "The rock formations may include at least one source rock, for example the shale layer (106-4), which may act as a hydrocarbon generation source. The geologic sedimentary basin (106) may further contain hydrocarbon or other fluids accumulations associated with certain features of the subsurface formations. For example, accumulations (108-2), (108-5), and (108-7) associated with structural high areas of the reservoir layer (106-5) and containing gas, oil, water or any combination of these fluids.").
Dou as well as Kulkarni are directed towards convolutional neural networks with attention for segmentation. Therefore, Dou as well as Kulkarni are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Dou with the teachings of Kulkarni by applying the model to gas, oil, or water fluid analysis. Kulkarni provides as additional motivation for combination ([¶0017] “The rock formations may include at least one source rock, for example the shale layer (106-4), which may act as a hydrocarbon generation source. The geologic sedimentary basin (106) may further contain hydrocarbon or other fluids accumulations associated with certain features of the subsurface formations. For example, accumulations (108-2), (108-5), and (108-7) associated with structural high areas of the reservoir layer (106-5) and containing gas, oil, water or any combination of these fluids.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Bi (“Deep Relative Geologic Time: A Deep Learning Method for Simultaneously Interpreting 3-D Seismic Horizons and Faults”, 2021) is directed towards a convolutional neural network with attention for geospatial segmentation.
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/SIDNEY VINCENT BOSTWICK/Examiner, Art Unit 2124
