DETAILED ACTION
This Office Action is in response to the RCE filed on 05/19/2026.
Claims 1-6, 9-16, 19, and 20 are currently amended.
Claims 1-20 are pending in this application and have been examined.
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 .
Allowable Subject Matter
Claims 7-10 and 17-20 objected to as being dependent upon a rejected base claim, but would be allowable over the prior art if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
In reference to Applicant’s arguments on page(s) 8-13 regarding rejections made under 35 U.S.C. 101:
At least in view of these amendments, Applicant respectfully requests that the § 101 rejection to the claims be withdrawn. Moreover, Applicant submits that claims 1-20 are patent eligible at least for the reasons discussed below.
Applicant respectfully submits that claims 1 and 11 are directed to training a set of deep learning models for geo-exploration, drilling, and production characterization and optimization of production rates of wells under various operating conditions as indicated in the following cited paragraphs of the Application as filed, which describes: “… Recent development in distributed and robotized sensors enable the acquisition of high-resolution characterization datasets of mechanical, chemical, and electromagnetic properties. Examples of such sensors or probes may include: impulse hammer geomechanical probe, hyperspectral and Fourier transform spectrometers (e.g. FTIR spectroscopy, X-Ray fluorescence (XRF), Fluorescence, Raman), Nuclear Magnetic Resonance (NMR), and acoustic transducers. These scanning tools can measure, for example, unconstrained sonic velocities and near-surface gas permeability.”
As discussed in further detail herein, amended claims 1 and 11 are patent-eligible in view of the following. Applicant submits that the independent claims 1 and 11, as amended are directed to patent eligible subject matter under at least the guidance provided under the Subject Matter Eligibility Examples provided by USPTO in January 2019. For example, Example 39 from the USPTO's 2019 Subject Matter Eligibility Examples demonstrates that claims directed to training neural networks for specific technical applications are patent eligible under 35 U.S.C. § 101.
Under MPEP § 2106.04(a)(2) and the USPTO's 2024 Guidance on Patent Subject Matter Eligibility for Artificial Intelligence (AI), claims do not recite a mental process when they contain limitations that "cannot practically be performed in the human mind." The human mind cannot mentally process high-dimensional spectroscopic IR data through layers of a deep learning model, iteratively adjusting network weights across hidden layers, to output geological formation properties. Because the claimed computational operations cannot practically be performed in the human mind, the independent claims 1 and 11 do not recite a mental process.
As claimed, the independent claims 1 and 11 describe a specific technique, by which the deep learning model can be trained for subsequent geo-exploration, drilling, and production characterization and optimization of production rates of wells under various operating conditions, thereby achieving an improved technological result. Consequently, the claims are not merely directed to "mathematical concepts", nor do they merely cover "mental processes," as alleged in the Office Action. Thus, for similar reasons as described in Example 39, the claimed invention here is directed to patent eligible subject matter.
In the present application, the claims recite additional elements that apply or use the alleged judicial exception in a meaningful way. For example, the independent claims 1 and 11 recite a specific improvement over prior art systems at least by "predicting, using the set of trained deep learning models, the one or more geological formation properties at a second geo- exploration, drilling, and production site based on, at least in part, the second set of geo- exploration data, the second geo-exploration, drilling, and production site being different from the first geo-exploration, drilling, and production site." As described in the preceding paragraphs, the specific improvement of the additional elements also allow for machine operations for enhancing well drilling operations based on geological formation properties at the second geo- exploration, drilling, and production site.
At least for the above reasons, Applicant submits that the independent claims 1 and 11, as amended and when considered as a whole, specifically integrate any purported abstract idea into a practical application and provides significant technical advances and inventive concepts over prior solutions. Accordingly, Applicant submits that the independent claims 1 and 11 are directed to patent eligible subject matter under § 101 and respectfully requests withdrawal of the § 101 rejections with respect to the independent claims 1 and 11 (and its respective dependent claims).
Examiner’s response:
Applicant’s arguments have been fully considered but are found to be not persuasive.
Applicant argues that the instant invention is patent eligible because the independent claims are drawn to training a set of models that are an improvement over the state of the art since there exists newly created and refined sensors and probes that allow for more granular and high resolution data capture. The claims do not reflect the use of one or more of these sensors and probes. It is the recommendation of the Examiner that, if Applicant wants to pursue this line of argumentation, the specific probes and/or sensors that allow for enhanced data capture be included in the claims.
Applicant argues that the instant invention is similar to that of Example 39 from the USPTO's 2019 Subject Matter Eligibility Examples. Examiner disagrees. Example 39 was found to be patent eligible because the claim does not recite any mathematical calculations or relationships, nor does it recite any abstract ideas of mental processes. That is not the case with the instant application because the instant application recites actions of making predictions based on processed data, which is an action that can be reasonably performed in the human mind as making informed predictions is a mental process.
Applicant argues that the instant invention provides a specific technique that culminates in an improved technological result. Examiner disagrees. The independent claims of the instant invention do not specify any unique techniques used in making predictions about the geological data. Applicant specifies that the deep learning models consist of convolutional blocks that are followed by either a softmax layer or regressor layer. This combination of layers of deep learning models is not a unique organization of components and does not recite a unique way of training the models, therefore there is no technological improvement presented. Furthermore, a technological improvement cannot arise from an abstract idea, in this case predicting geological properties based on a geo-exploration site.
Applicant argues that the instant invention provides a meaningful application of the alleged judicial exception(s). Examiner disagrees. Simply applying the trained models at a second drill site does not provide any meaningful application of the invention because the training process is not changed from site to site, the same models are used to make similar predictions; this is very similar to training a model on dedicated training data and then testing the trained model on a test dataset, a process that is well known in the art.
In light of the amendments made on the claims, the rejections made under 35 U.S.C. 101 are maintained and updated below.
In reference to Applicant’s arguments on page(s) 13-14 regarding rejections made under 35 U.S.C. 103:
Claims 1, 3, 4, 6, 9-11, 13, 14, 16, 19, and 20 are rejected under 35 U.S.C. § 103(a) as being unpatentable over U.S. Publication No. 20240077642 ("Fuchey") in view of U.S. Publication No. 20220207079 ("Shebl").
Applicant respectfully traverses this rejection and the assertions and holdings therein, at least because Fuchey and Shebl have not been shown to teach or to suggest at least the above- identified features of amended claims 1 and 11.
For example, the Office Action acknowledges that Fuchey does not distinctly disclose processing a "second set of geo-exploration data," and relies on Shebl to cure this deficiency. This reliance is respectfully submitted to be misplaced. The Office Action cites Shebl at paragraph [0128] for disclosing a "second subset of the labelled image training database." However, Shebl's "second subset" refers strictly to partitioning a training database for supervised learning (i.e., creating a validation or test split during the model training phase), which is different than "predicting, using the set of trained deep learning models, the one or more geological formation properties at a second geo-exploration, drilling, and production site based on, at least in part, the second set of geo-exploration data, the second geo-exploration, drilling, and production site being different from the first geo-exploration, drilling, and production site," as recited in amended claims 1 and 11. Consequently, Shebl fails to cure the deficiencies of Fuchey. For at least these reasons, claims 1 and 11 and their respective dependents are patentable over combinations of Fuchey and Shebl. In view of the foregoing, reconsideration and withdrawal of the rejections are respectfully requested.
Examiner’s response:
Applicant’s arguments have been fully considered but are found to be not persuasive.
Applicant argues that the applied prior art references of Fuchey and Shebl do not teach the use of a second geo-exploration site different from the first site. Examiner agrees. A new search was conducted in order to find appropriate prior art to teach the newly amended limitation.
In light of the amendments made on the claims, the rejections made under 35 U.S.C. 103 are maintained and updated below.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-20 rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.
Step 1 analysis: Independent Claim 1 recites, in part, a computer implemented method, therefore falling into the statutory category of process. Independent Claim 11 recites, in part, a computer system comprising one or more processors configured to perform operations, therefore falling into the statutory category of machine.
Regarding Claim 1:
Step 2A: Prong 1 analysis:
Claim 1 recites in part:
“predicting the one or more geological formation properties at a second geo-exploration. drilling, and production site based on, at least in part, the second set of geo-exploration data, the second geo-exploration. drilling. and production site being different from the first geo-exploration. drilling, and production site”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses making a prediction based on gathered data.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“accessing a first set of geo-exploration data from a first geo-exploration, drilling, and production site” This additional element is recited at a high level of generality and amounts to extra-solution activity of gathering data i.e. pre-solution activity of gathering data for use in the claimed process.
“wherein the first set of geo-exploration data comprises spectroscopic infra-red (IR) data, wherein at least portions of the first set of geo-exploration data are based on measurements of core samples taken from the first geo-exploration, drilling, and production site”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (spectroscopy) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
“based on, at least in part, the first set of geo-exploration data comprising the spectroscopic IR data, training a set of deep learning models, each deep learning model comprising a plurality of layers and configured to predict one or more geological formation properties”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (deep learning model) (See MPEP 2106.05(f)).
“wherein the set of deep learning models each comprises a layer of one or more convolutional neural network (CNN) blocks, wherein the layer of one or more CNN blocks is followed by a softmax layer or a regressor layer, wherein the softmax layer is configured to generate a classification as a geological formation property. and wherein the regressor layer is configured to quantify a value of a geological formation property”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (convolutional neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
“processing, using the set of trained deep learning models, a second set of geo-exploration data that also comprises spectroscopic IR data”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (deep learning models) (See MPEP 2106.05(f)).
“using the set of trained deep learning models”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (deep learning models) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “accessing a first set of geo-exploration data from a first geo-exploration, drilling, and production site” is/are recited at a high level of generality and amount(s) to extra-solution activity of receiving data i.e., pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The additional element(s) of “wherein the first set of geo-exploration data comprises spectroscopic infra-red (IR) data, wherein at least portions of the first set of geo-exploration data are based on measurements of core samples taken from the first geo-exploration, drilling, and production site” and “wherein the set of deep learning models each comprises a layer of one or more convolutional neural network (CNN) blocks, wherein the layer of one or more CNN blocks is followed by a softmax layer or a regressor layer, wherein the softmax layer is configured to generate a classification as a geological formation property. and wherein the regressor layer is configured to quantify a value of a geological formation property” is/are directed to particular field(s) of use (spectroscopy and convolutional neural networks ) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
As discussed above, the additional element(s) of “based on, at least in part, the first set of geo-exploration data comprising the spectroscopic IR data, training a set of deep learning models, each deep learning model comprising a plurality of layers and configured to predict one or more geological formation properties”, “processing, using the set of trained deep learning models, a second set of geo-exploration data that also comprises spectroscopic IR data” and “using the set of trained deep learning models” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 2:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the spectroscopic IR data includes Fourier Transform Infrared Spectroscopy (FTIR) data of core samples at the second geo-exploration, drilling, and production site”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (spectroscopy) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein the spectroscopic IR data includes Fourier Transform Infrared Spectroscopy (FTIR) data of core samples at the second geo-exploration, drilling, and production site” is/are directed to particular field(s) of use (spectroscopy and drill site data) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 3:
Step 2A: Prong 2 analysis:The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the set of deep learning models include a first deep learning model configured to predict a rock type of the core samples”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (deep learning models) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
“wherein training the first deep learning model includes training based on, at least in part, the FTIR data of the core samples from the first geo-exploration, drilling, and production site”. This additional element is recited at a high level of generality such that the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein the set of deep learning models include a first deep learning model configured to predict a rock type of the core samples” is/are directed to particular field(s) of use (deep learning models) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
As discussed above, the additional element(s) of “wherein training the first deep learning model includes training based on, at least in part, the FTIR data of the core samples from the first geo-exploration, drilling, and production site” is/are recited at a high-level of generality such that the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 4:
Step 2A: Prong 2 analysis:The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the set of deep learning models include a second deep learning model configured to predict a geomechanical property of the core samples”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (deep learning models) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
“wherein training the second deep learning model includes training based on, at least in part, the FTIR data of the core samples at the first geo-exploration, drilling, and production site”. This additional element is recited at a high level of generality such that the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein the set of deep learning models include a second deep learning model configured to predict a geomechanical property of the core samples” is/are directed to particular field(s) of use (deep learning models) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
As discussed above, the additional element(s) of “wherein training the second deep learning model includes training based on, at least in part, the FTIR data of the core samples at the first geo-exploration, drilling, and production site” is/are recited at a high-level of generality such that the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 5:
Step 2A: Prong 2 analysis:The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the set of deep learning models include a third deep learning model configured to predict a sonic velocity of the core samples”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (deep learning models) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
“wherein training the third deep learning model includes training based on, at least in part, the FTIR data of the core samples at the first geo-exploration, drilling, and production site”. This additional element is recited at a high level of generality such that the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein the set of deep learning models include a third deep learning model configured to predict a sonic velocity of the core samples” is/are directed to particular field(s) of use (deep learning models) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
As discussed above, the additional element(s) of “wherein training the third deep learning model includes training based on, at least in part, the FTIR data of the core samples at the first geo-exploration, drilling, and production site” is/are recited at a high-level of generality such that the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 6:
Step 2A: Prong 2 analysis:The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the set of deep learning models include a fourth deep learning model configured to predict a permeability of the core samples”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (deep learning models) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
“wherein training the fourth deep learning model includes training based on, at least in part, the FTIR data of the core samples at the first geo-exploration, drilling, and production site”. This additional element is recited at a high level of generality such that the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein the set of deep learning models include a fourth deep learning model configured to predict a permeability of the core samples” is/are directed to particular field(s) of use (deep learning models) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
As discussed above, the additional element(s) of “wherein training the fourth deep learning model includes training based on, at least in part, the FTIR data of the core samples at the first geo-exploration, drilling, and production site” is/are recited at a high-level of generality such that the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 7:
Step 2A: Prong 1 analysis:Claim 7 recites in part:
“validating the set of deep learning models by cross correlating predicted values of the one or more geological formation properties with measured values of the one or more geological formation properties”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses making sure that the measure values match the predicted values.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 8:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein at least one deep learning model from the set of deep learning models is trained to predict a geological formation property with a spatial resolution that is higher than well logs in the first of geo-exploration data”. This additional element is recited at a high level of generality such that the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “wherein at least one deep learning model from the set of deep learning models is trained to predict a geological formation property with a spatial resolution that is higher than well logs in the first of geo-exploration data” is/are recited at a high-level of generality such that the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 9:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the CNN blocks comprise down-sampling blocks extracting high-level features from the first set of geo-exploration data and up-sampling blocks outputting the one or more geological formation properties”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (convolutional neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein the CNN blocks comprise down-sampling blocks extracting high-level features from the first set of geo-exploration data and up-sampling blocks outputting the one or more geological formation properties” is/are directed to particular field(s) of use (convolutional neural networks) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 10:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the softmax layer is configured to generate the classification as the geological formation property by mapping the one or more geological formation properties from the up-sampling blocks into a value between 0 and 1”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (softmax layer) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “wherein the softmax layer is configured to generate the classification as the geological formation property by mapping the one or more geological formation properties from the up-sampling blocks into a value between 0 and 1” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 11:
Due to claim language similar to that of Claim 1, Claim 11 is rejected for the same reasons as presented above in the rejection of Claim 1.
Regarding Claim 12:
Due to claim language similar to that of Claim 2, Claim 12 is rejected for the same reasons as presented above in the rejection of Claim 2.
Regarding Claim 13:
Due to claim language similar to that of Claim 3, Claim 13 is rejected for the same reasons as presented above in the rejection of Claim 3.
Regarding Claim 14:
Due to claim language similar to that of Claim 4, Claim 14 is rejected for the same reasons as presented above in the rejection of Claim 4.
Regarding Claim 15:
Due to claim language similar to that of Claim 5, Claim 15 is rejected for the same reasons as presented above in the rejection of Claim 5.
Regarding Claim 16:
Due to claim language similar to that of Claim 6, Claim 16 is rejected for the same reasons as presented above in the rejection of Claim 6.
Regarding Claim 17:
Due to claim language similar to that of Claim 7, Claim 17 is rejected for the same reasons as presented above in the rejection of Claim 7.
Regarding Claim 18:
Due to claim language similar to that of Claim 8, Claim 18 is rejected for the same reasons as presented above in the rejection of Claim 8.
Regarding Claim 19:
Due to claim language similar to that of Claim 9, Claim 19 is rejected for the same reasons as presented above in the rejection of Claim 9.
Regarding Claim 20:
Due to claim language similar to that of Claim 10, Claim 20 is rejected for the same reasons as presented above in the rejection of Claim 10.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-6 and 11-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fuchey et al (US 20240077642 A1, hereinafter Fuchey) in view of Wang et al (US 20210325558 A1, hereinafter Wang).
Regarding Claim 1:
Fuchey teaches
accessing a first set of geo-exploration data from a first geo-exploration, drilling, and production site, wherein the first set of geo-exploration data comprises spectroscopic infra-red (IR) data, wherein at least portions of the first set of geo-exploration data are based on measurements of core samples taken from the first geo-exploration, drilling, and production site (Fuchey [0057]: "As an example, data can include geochemical data. For example, consider data acquired using X-ray fluorescence (XRF) technology, Fourier transform infrared spectroscopy (FTIR) technology and/or wireline geochemical technology."; [0082]: "As an example, such an interpolation may be constrained by interpretations from log and core data, and by prior geological knowledge"; [0187]: "a framework may be implemented at a field site where imagery, etc., may be acquired at the field site for purposes of searching, adding to a dataset, adding to a database, adjusting a data structure, determining one or more virtual distances, etc."; (EN): it is noted that the "log and core data" is obtained from well sites as depicted in Fig. 4);
based on, at least in part, the first set of geo-exploration data comprising the spectroscopic JR data, training a set of deep learning models, each deep learning model comprising a plurality of layers and configured to predict one or more geological formation properties (Fuchey [0086]: "As to the various applications of the applications block 340, the well prognosis application 342 may include predicting type and characteristics of geological formations that may be encountered by a drill-bit, and location where such rocks may be encountered (e.g., before a well is drilled)"; [0149]: "As shown, the block 1220 can process data in the data structure 1250 using machine learning (ML). For example, consider a machine learning model that can cluster fields using data of the data structure 1250 via unsupervised machine learning. As shown, the block 1220 can generate, augment, update, etc., the data structure 1260, which can include, for example, field asset clusters. The block 1230 can provide for graph analysis of the fields and assets, for example, via rendering one or more graphs such as the example graph 1280 to a display."; [0176]: "As an example, a machine learning model can be a deep learning model (e.g., deep Boltzmann machine, deep belief network, convolutional neural network, stacked auto-encoder, etc.)")
processing, using the set of trained deep learning models, a second set of geo-exploration data that also comprises spectroscopic IR data (Fuchey [0057]: "As an example, data can include geochemical data. For example, consider data acquired using X-ray fluorescence (XRF) technology, Fourier transform infrared spectroscopy (FTIR) technology and/or wireline geochemical technology."; [0082]: "As an example, such an interpolation may be constrained by interpretations from log and core data, and by prior geological knowledge"; [0187]: "a framework may be implemented at a field site where imagery, etc., may be acquired at the field site for purposes of searching, adding to a dataset, adding to a database, adjusting a data structure, determining one or more virtual distances, etc.");
predicting, using the set of trained deep learning models, the one or more geological formation properties at a second geo-exploration. drilling, and production site based on, at least in part, the second set of geo-exploration data, the second geo-exploration. drilling. and production site being different from the first geo-exploration. drilling, and production site (Fuchey [0086]: "As to the various applications of the applications block 340, the well prognosis application 342 may include predicting type and characteristics of geological formations that may be encountered by a drill-bit, and location where such rocks may be encountered (e.g., before a well is drilled)"; [0088]: "FIG. 4 shows an example of a geologic environment 400 as including various types of equipment and features. As shown, the geologic environment 400 includes a plurality of wellsites 402, which may be operatively connected to a processing facility."; [0149]: "As shown, the block 1220 can process data in the data structure 1250 using machine learning (ML). For example, consider a machine learning model that can cluster fields using data of the data structure 1250 via unsupervised machine learning. As shown, the block 1220 can generate, augment, update, etc., the data structure 1260, which can include, for example, field asset clusters. The block 1230 can provide for graph analysis of the fields and assets, for example, via rendering one or more graphs such as the example graph 1280 to a display."; [0176]: "As an example, a machine learning model can be a deep learning model (e.g., deep Boltzmann machine, deep belief network, convolutional neural network, stacked auto-encoder, etc.)"; (EN): the geologic environment including a plurality of wellsites is analogous to collecting data from a second geo-exploration site).
Fuchey does not distinctly disclose
wherein the set of deep learning models each comprises a layer of one or more convolutional neural network (CNN) blocks, wherein the layer of one or more CNN blocks is followed by a softmax layer or a regressor layer, wherein the softmax layer is configured to generate a classification as a geological formation property. and wherein the regressor layer is configured to quantify a value of a geological formation property;
However, Wang teaches
wherein the set of deep learning models each comprises a layer of one or more convolutional neural network (CNN) blocks, wherein the layer of one or more CNN blocks is followed by a softmax layer or a regressor layer, wherein the softmax layer is configured to generate a classification as a geological formation property, and wherein the regressor layer is configured to quantify a value of a geological formation property (Wang [0057]: "Further, CNN 1400 may include a fully connected layer 1410 preceding a Softmax function 1412 which produces an output value for classifiers 1414 to produce estimated formation properties 1416 downstream.");
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the system and methods for geological classification of Fuchey with the method(s) for monitoring and predicting borehole characteristics throughout drilling of Wang in order to develop convolutional neural networks that include a softmax layer for geological classification(s) (Wang [0057]: "Further, CNN 1400 may include a fully connected layer 1410 preceding a Softmax function 1412 which produces an output value for classifiers 1414 to produce estimated formation properties 1416 downstream.")
Regarding Claim 2:
Fuchey teaches
The computer-implemented method of claim 1, wherein the spectroscopic JR data includes Fourier Transform Infrared Spectroscopy (FTIR) data of core samples at the second geo-exploration, drilling, and production site (Fuchey [0057]: "As an example, data can include geochemical data. For example, consider data acquired using X-ray fluorescence (XRF) technology, Fourier transform infrared spectroscopy (FTIR) technology and/or wireline geochemical technology."; [0088]: "FIG. 4 shows an example of a geologic environment 400 as including various types of equipment and features. As shown, the geologic environment 400 includes a plurality of wellsites 402, which may be operatively connected to a processing facility."; (EN): it can be reasonably inferred that the FTIR data can be collected at each of the plurality of wellsites).
Regarding Claim 3:
Fuchey teaches
The computer-implemented method of claim 2, wherein the set of deep learning models include a first deep learning model configured to predict a rock type of the core samples (Fuchey [0082]: "As to the facies and petrophysical property interpolation 353, it may include an assessment of type of rocks and of their petrophysical properties (e.g. porosity, permeability)")
wherein training the first deep learning model includes training based on, at least in part, the FTIR data of the core samples from the first geo-exploration, drilling, and production site (Fuchey [0057]: "As an example, data can include geochemical data. For example, consider data acquired using X-ray fluorescence (XRF) technology, Fourier transform infrared spectroscopy (FTIR) technology and/or wireline geochemical technology."; [0088]: "FIG. 4 shows an example of a geologic environment 400 as including various types of equipment and features. As shown, the geologic environment 400 includes a plurality of wellsites 402, which may be operatively connected to a processing facility."; [0187]: "a framework may be implemented at a field site where imagery, etc., may be acquired at the field site for purposes of searching, adding to a dataset, adding to a database, adjusting a data structure, determining one or more virtual distances, etc."; (EN): “field site” and “wellsite” as mentioned in the reference reads as analogous to “drilling site”).
Regarding Claim 4:
Fuchey teaches
The computer-implemented method of claim 2, wherein the set of deep learning models include a second deep learning model configured to predict a geomechanical property of the core samples (Fuchey [0084]: "As an example a geomechanical simulation may be used for a variety of purposes such as, for example, prediction of fracturing, reconstruction of the paleo-geometries of the reservoir as they were prior to tectonic deformations, etc.")
wherein training the second deep learning model includes training based on, at least in part, the FTIR data of the core samples at the first geo-exploration, drilling, and production site (Fuchey [0057]: "As an example, data can include geochemical data. For example, consider data acquired using X-ray fluorescence (XRF) technology, Fourier transform infrared spectroscopy (FTIR) technology and/or wireline geochemical technology."; [0088]: "FIG. 4 shows an example of a geologic environment 400 as including various types of equipment and features. As shown, the geologic environment 400 includes a plurality of wellsites 402, which may be operatively connected to a processing facility."; [0187]: "a framework may be implemented at a field site where imagery, etc., may be acquired at the field site for purposes of searching, adding to a dataset, adding to a database, adjusting a data structure, determining one or more virtual distances, etc."; (EN): while Fuchey does not specify the use of a second, third, fourth, etc. model to perform these actions, it can be seen in the reference that the plurality of models perform the same actions and can be easily applied to individual models).
Regarding Claim 5:
Fuchey teaches
wherein training the third deep learning model includes training based on, at least in part, the FTIR data of the core samples at the first geo-exploration, drilling, and production site (Fuchey [0057]: "As an example, data can include geochemical data. For example, consider data acquired using X-ray fluorescence (XRF) technology, Fourier transform infrared spectroscopy (FTIR) technology and/or wireline geochemical technology."; [0088]: "FIG. 4 shows an example of a geologic environment 400 as including various types of equipment and features. As shown, the geologic environment 400 includes a plurality of wellsites 402, which may be operatively connected to a processing facility."; [0187]: "a framework may be implemented at a field site where imagery, etc., may be acquired at the field site for purposes of searching, adding to a dataset, adding to a database, adjusting a data structure, determining one or more virtual distances, etc."; (EN): while Fuchey does not specify the use of a second, third, fourth, etc. model to perform these actions, it can be seen in the reference that the plurality of models perform the same actions and can be easily applied to individual models)
Fuchey does not distinctly disclose
The computer-implemented method of claim 2, wherein the set of deep learning models include a third deep learning model configured to predict a sonic velocity of the core samples
However, Wang teaches
The computer-implemented method of claim 2, wherein the set of deep learning models include a third deep learning model configured to predict a sonic velocity of the core samples (Wang [0030]: “Borehole sonic wave modes are often dispersive (e.g., will separate into component frequencies due to passing through a medium) and can include, for example and without imputing limitation, leaky-P waves, flexural waves, screw waves, and pseudo-Rayleigh waves. Information, such as low frequency asymptotes and cut-off frequencies, about the propagation medium can be obtained from analyzing the wave modes in order to determine formation body wave slowness (e.g., compressional slowness, shear slowness, and the like).”)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the system and methods for geological classification of Fuchey with the method(s) for monitoring and predicting borehole characteristics throughout drilling of Wang in order to develop convolutional neural networks that include a softmax layer for geological classification(s) (Wang [0057]: "Further, CNN 1400 may include a fully connected layer 1410 preceding a Softmax function 1412 which produces an output value for classifiers 1414 to produce estimated formation properties 1416 downstream.")
Regarding Claim 6:
Fuchey teaches
The computer-implemented method of claim 2, wherein the set of deep learning models include a fourth deep learning model configured to predict a permeability of the core samples (Fuchey [0082]: "As to the facies and petrophysical property interpolation 353, it may include an assessment of type of rocks and of their petrophysical properties (e.g. porosity, permeability)")
wherein training the fourth deep learning includes training based on, at least in part, the FTIR data of the core samples at the first geo-exploration, drilling, and production site (Fuchey [0057]: "As an example, data can include geochemical data. For example, consider data acquired using X-ray fluorescence (XRF) technology, Fourier transform infrared spectroscopy (FTIR) technology and/or wireline geochemical technology."; [0088]: "FIG. 4 shows an example of a geologic environment 400 as including various types of equipment and features. As shown, the geologic environment 400 includes a plurality of wellsites 402, which may be operatively connected to a processing facility."; [0187]: "a framework may be implemented at a field site where imagery, etc., may be acquired at the field site for purposes of searching, adding to a dataset, adding to a database, adjusting a data structure, determining one or more virtual distances, etc."; (EN): while Fuchey does not specify the use of a second, third, fourth, etc. model to perform these actions, it can be seen in the reference that the plurality of models perform the same actions and can be easily applied to individual models).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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US 11883166 B2 – An apparatus for estimating a component of an analyte
US 20230266241 A1 – the disclosure concerns methods of identifying and quantifying constituent phases in rock samples based on spectroscopic measurements
US 20220275719 A1 – a system and process to predict the petrophysical properties of unclean rock samples using Medical-CT scanned three-dimensional (3D) images at both low and high resolutions.
US 20220179121 A1 – Methods and systems to learn and apply a mapping function from data representing concentrations of atomic elements in a geological formation (or other data corresponding thereto) to mineral component concentrations in the geological formation
US 20220114302 A1 – a reservoir performance system that measures its porosity and fluid permeability.
US 20210319257 A1 – Apparatus and methods for ascribing one of multiple predetermined sub-classes to multiple pixels of an image of an unknown rock sample retrieved from a geological formation.
US 20090288881 A1 – relates generally to measuring formation fluids and, more particularly, to methods and apparatus to form a well.
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/COREY SACKALOSKY/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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