DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Examiner notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below 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 that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea without significantly more.
Claim 1
A method for accelerating numerical solution of a differential equation representing fluid flow in porous media associated with hydrocarbon well environments, the method comprising:
obtaining input data associated with a previous timestep of a numerical solver operating on the differential equation;
predicting, by a machine learning model, a current timestep size for the numerical solver from the previous timestep to a current timestep immediately following the previous timestep;
and executing the numerical solver using the current timestep size on the differential equation to generate a simulation output for the current timestep.
Step 1 – The claim is directed towards a method, one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards an abstract idea. The claim recites the following limitations: (Highlighted portions of the claim in bold above constitute an abstract idea; the remaining limitations are "additional elements")
The predicting limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP § 2106.04(a)(2)(III). The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. This limitation could be performed in the human mind or with the aid of pen and paper.
The executing a solver limitation is directed towards the abstract concept of mathematical calculations. (See MPEP § 2106.04(a)(2)(C)).
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
Obtaining input data is directed towards an insignificant extra-solution activity of mere data gathering, or outputting in particular section
(iv) Obtaining information about. . . (See MPEP §2106.05(g)(3)(iv)).
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
Obtaining input data is directed towards an insignificant extra-solution activity of mere data gathering, or outputting in particular section
(iv) Obtaining information about. . . (See MPEP §2106.05(g)(3)(iv)).
Claim 2.
The method of claim 1, wherein the machine learning model is an artificial neural network (ANN).
Step 1 – The claim is directed towards a method, one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards the abstract idea of claim 1.
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
The machine learning model being an artificial neural network is recited at a high level of generality, it does not bring this out of the realm of insignificant extra solution activity.
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
The machine learning model being an artificial neural network is recited at a high level of generality, it does not bring this out of the realm of insignificant extra solution activity.
Claim 3.
The method of claim 2, wherein the ANN makes the prediction of the current timestep size based on at least one selected from a group consisting of a previous timestep size, pressure changes, and residual errors.
Step 1 – The claim is directed towards a method, one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards an abstract idea. The claim recites the following limitations: (Highlighted portions of the claim in bold above constitute an abstract idea; the remaining limitations are "additional elements")
Wherein the ANN makes the prediction limitation is directed towards the abstract concept of mathematical calculations. (See MPEP § 2106.04(a)(2)(C)). This is a calculation performed by an ANN
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
Claim 4.
The method of claim 2, further comprising training the ANN.
Step 1 – The claim is directed towards a method, one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards the abstract idea of claim 1.
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
The machine learning model being trained is recited at a high level of generality, it does not bring this out of the realm of insignificant extra solution activity.
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
The machine learning model being trained is recited at a high level of generality, it does not bring this out of the realm of insignificant extra solution activity.
Claim 5.
The method of claim 4, wherein the training is specific to one hydrocarbon field, using training data associated with the one hydrocarbon field only.
Step 1 – The claim is directed towards a method, one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards the abstract idea of claim 1.
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
The machine learning model being trained is recited at a high level of generality, it does not bring this out of the realm of insignificant extra solution activity.
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
The machine learning model being trained is recited at a high level of generality, it does not bring this out of the realm of insignificant extra solution activity.
Claim 6.
The method of claim 4, wherein the training is performed using training data for a feature set, and wherein the training further comprises reducing the feature set to features relevant to the prediction of the current timestep size.
Step 1 – The claim is directed towards a method, one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards the abstract idea of claim 1.
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
The machine learning model being trained is recited at a high level of generality, it does not bring this out of the realm of insignificant extra solution activity.
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
The machine learning model being trained is recited at a high level of generality, it does not bring this out of the realm of insignificant extra solution activity.
Claim 7.
The method of claim 4, wherein the training further comprises serializing the machine learning model.
Step 1 – The claim is directed towards a method, one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards the abstract idea of claim 1.
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
The machine learning model being trained is recited at a high level of generality, it does not bring this out of the realm of insignificant extra solution activity.
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
The machine learning model being trained is recited at a high level of generality, it does not bring this out of the realm of insignificant extra solution activity.
Claim 8.
The method of claim 1, wherein the numerical solver uses Newton’s method.
Step 1 – The claim is directed towards a method, one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards an abstract idea. The claim recites the following limitations: (Highlighted portions of the claim in bold above constitute an abstract idea; the remaining limitations are "additional elements")
This limitation is directed towards the abstract concept of mathematical calculations. (See MPEP § 2106.04(a)(2)(C)).
The solver using Newtons method does not bring this out of the realm of mathematical caluclations.
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
Claim 9.
The method of claim 1, further comprising a preprocessing of the input data, the preprocessing comprising at least one selected from a group consisting of data smoothing and data scaling.
Step 1 – The claim is directed towards a method, one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards the abstract idea of claim 1.
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
Preprocessing input data is directed towards an insignificant extra-solution activity of mere data gathering, or outputting in particular section (iv) Obtaining information about. . . (See MPEP §2106.05(g)(3)(iv)).
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
Preprocessing input data is directed towards an insignificant extra-solution activity of mere data gathering, or outputting in particular section (iv) Obtaining information about. . . (See MPEP §2106.05(g)(3)(iv)).
Claim 10
Claim 10 contains similar limitations as claim 1, albeit for a system. It is rejected for the same reasons claim 1 was rejected. A system is a machine and one of the four statutory categories.
Claim 11
The system of claim 10, wherein a first of the plurality of computing systems is a Flask server, and wherein a second of the plurality of computing systems is a Flask client.
Step 1 – The claim is directed towards a method, one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards the abstract idea of claim 10.
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
The Computing system being a flask server and client is directed towards an insignificant extra-solution activity of whether the limitation is significant. (2106.05(g)(2)).
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
The Computing system being a flask server and client is directed towards an insignificant extra-solution activity of whether the limitation is significant. (2106.05(g)(2)).
Claim 12
The system of claim 11, wherein the Flask server forwards a request for the current timestep size from the numerical solver to the Flask client.
Step 1 – The claim is directed towards a method, one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards the abstract idea of claim 10.
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
The Computing system where flask server forwards a request is directed towards an insignificant extra-solution activity of whether the limitation is significant. (2106.05(g)(2)).
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
The Computing system where flask server forwards a request is directed towards an insignificant extra-solution activity of whether the limitation is significant. (2106.05(g)(2)).
Claim 13
The system of claim 11, wherein the Flask client performs the prediction of the current timestep size.
Step 1 – The claim is directed towards a method, one of the four statutory categories.
Step 2A Prong 1 – The claim is directed towards an abstract idea. The claim recites the following limitations: (Highlighted portions of the claim in bold above constitute an abstract idea; the remaining limitations are "additional elements")
This limitation is directed towards the abstract concept of mathematical calculations. (See MPEP § 2106.04(a)(2)(C)).
Step 2A Prong 2 – The claim does not recite any additional elements which integrate the abstract idea into a practical application.
Step 2B – The claims as a whole do not amount to significantly more than the judicial exception.
Claim 14
Claim 14 contains similar limitations as claim 8, albeit for a system. It is rejected for the same reasons claim 8 was rejected. A system is a machine and one of the four statutory categories.
Claim 15
Claim 15 contains similar limitations as claim 4, albeit for a system. It is rejected for the same reasons claim 4 was rejected. A system is a machine and one of the four statutory categories.
Claim 16
Claim 16 contains similar limitations as claim 3, albeit for a system. It is rejected for the same reasons claim 3 was rejected. A system is a machine and one of the four statutory categories.
Claim 17
Claim 17 contains similar limitations as claim 2, albeit for a system. It is rejected for the same reasons claim 2 was rejected. A system is a machine and one of the four statutory categories.
Claim 18
Claim 18 contains similar limitations as claim 5, albeit for a system. It is rejected for the same reasons claim 5 was rejected. A system is a machine and one of the four statutory categories.
Claim 19
Claim 19 contains similar limitations as claim 6, albeit for a system. It is rejected for the same reasons claim 6 was rejected. A system is a machine and one of the four statutory categories.
Claim 20
Claim 20 contains similar limitations as claim 7, albeit for a system. It is rejected for the same reasons claim 7 was rejected. A system is a machine and one of the four statutory categories.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 1-10 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Usadi et al., US 2013/0096900 A1 (Usadi) in view of Zandbergen, Predicting the optimal CFL number for pseudo time-stepping with machine learning in the COMSOL CFD module (Zandbergen).
Claim 1.
Usadi teaches A method for accelerating numerical solution of a differential equation representing fluid flow in porous media associated with hydrocarbon well environments, the method comprising: (Usadi [0079) “The equations that describe the evolution of state variables such as pressure and composition for each sub region 502 may be represented by a matrix structure characterized by a set of physical, geometrical, or numerical parameters based on the geological characteristics of the matrix structure, such as rock porosity, phase permeability, and the like. A first region may include an injector well 504, and an nth region may include a producer well”
obtaining input data associated with a previous timestep of a numerical solver operating on the differential equation; (Usadi [0097]) “In the above formula, Kv,effective(tn ) equals the coarse scale approximation of the coarse grid cell at a time step, n. The term K, n 700 v equals the discretized phase permeability at each fine grid cell, and the term Sv, equals the phase saturation at each fine grid cell. For two dimensional or three-dimensional models, the effective phase permeability can be written as a tensor as shown in Eqn. 8.” {Examiners note: Conditions a learned function on one prior time step (t^(n-1) See equation 8.}
Usadi does not explicitly teach, but Zandbergen teaches predicting, by a machine learning model, a current timestep size for the numerical solver from the previous timestep to a current timestep immediately following the previous timestep; (Zandbergen pg 9 Paragraph 1) “A neural network is used to predict local CFL numbers for pseudo time-stepping” {EXAMINERS NOTE: Pseudo time stepping is using a previous timestep to calculate the next step.}
and executing the numerical solver using the current timestep size on the differential equation to generate a simulation output for the current timestep. (Zandbergen pg 16 Paragraph 2) “In this error estimate, 𝑢 and 𝑣 are the solutions after one pseudo time-step iteration computed with the predicted CFL number from the network” {Examiners note: Running a solver with the predicted step control value to produce the next step result.}
Usadi and Zandbergen are analogous to the claimed invention because they are from the same field of endeavor of learning based simulation.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Usadi and Zandbergen before him or her, to modify the numerical time step solver of Usadi with the prediction methods of Zandbergen to help with the reusability and flexibility of allowing pseudo functions to reach their potential as suggest in Usadi 0009.
Claim 2.
Modified Usadi teaches The method of claim 1, wherein the machine learning model is an artificial neural network (ANN). (Usadi 0012) “A reservoir simulator was used to generate training sets for the Neural Networks. And for these cases, the authors were able to reproduce the narrowly modeled behavior response via the ANN.”
Claim 3.
Modified Usadi with Zandbergen teaches The method of claim 2, wherein the ANN makes the prediction of the current timestep size based on at least one selected from a group consisting of a previous timestep size, pressure changes, and residual errors. (Zandbergen Abstract) “The local data consist of the velocities, pressure and residuals, as well as the cell Reynolds number and the element edge lengths”
Claim 4.
Modified Usadi teaches The method of claim 2, further comprising training the ANN. (Usadi 0011) “Artificial neural networks (ANNs) were trained to predict peak injection volumes and volumes of produced oil and gas at three and seven years after the commencement of injection”
Claim 5.
Modified Usadi teaches The method of claim 4, wherein the training is specific to one hydrocarbon field, using training data associated with the one hydrocarbon field only. (Usadi 0011) “Artificial neural networks (ANNs) were trained to predict peak injection volumes and volumes of produced oil and gas at three and seven years after the commencement of injection” (0087) “In some embodiments, the boundary condition values may be specified based on known conditions of an actual reservoir.”
Claim 6.
Modified Usadi teaches The method of claim 4, wherein the training is performed using training data for a feature set, and wherein the training further comprises reducing the feature set to features relevant to the prediction of the current timestep size. (Usasi 0082) “Examples of parameters that may serve as lookup keys for sub-regions and their surrogate model solutions include, but are not limited to row sum or column sum vectors, diagonal vectors, L1, L2, LN norms of these vectors, and so on.” {Examiners note: Constructs a “feature set” (vectors/norms/physical parameters) used as reduced descriptors.}
Claim 7.
Modified Usadi teaches The method of claim 4, wherein the training further comprises serializing the machine learning model. (Usadi 0089) “At block 620, the solution surrogate may be stored to a database of solution surrogates. Each solution surrogate in the database may be paired with the corresponding physical, geometrical, or numerical parameters used to generate the training set 416. In this way, the solution surrogate may be reused for future reservoir simulations based on a degree of similarity between the physical, geometrical, or numerical parameters used to the generate the solution surrogate and the physical, geometrical, or numerical parameters of subsequent sub regions 102 used for future reservoir simulations” {EXAMINERS NOTE: Serializing is being view as storing the trained model in a persistent form for later reuse.}
Claim 8.
Modified Usadi with Zandbergen teaches The method of claim 1, wherein the numerical solver uses Newton’s method. (Zandbergen Pg 1 Paragraph 2) “And there are a lot of nonlinear solver methods, such as Automatic Newton and Newton Constant.”
Claim 9.
Modified Usadi teaches The method of claim 1, further comprising a preprocessing of the input data, the preprocessing comprising at least one selected from a group consisting of data smoothing and data scaling. (Usadi 0119) “In some embodiments, the constitutive relationship used for training may be that of the fine grid solution after it has been averaged or smoothed.” (0009) “This is handled by both scaling up the absolute permeability and assuming that relative permeability scales uniformly in the volume of the coarse grid cell,”
Claim 10.
Usadi teaches A system, comprising: a plurality of computing systems configured to perform operations comprising: obtaining input data associated with a previous timestep of a numerical solver operating on the differential equation; (Usadi [0097]) “In the above formula, Kv,effective(tn ) equals the coarse scale approximation of the coarse grid cell at a time step, n. The term K, n 700 v equals the discretized phase permeability at each fine grid cell, and the term Sv, equals the phase saturation at each fine grid cell. For two dimensional or three-dimensional models, the effective phase permeability can be written as a tensor as shown in Eqn. 8.” {Examiners note: Conditions a learned function on one prior time step (t^(n-1) See equation 8.}
Usadi does not explicitly teach, but Zandbergen teaches predicting, by a machine learning model, a current timestep size for the numerical solver from the previous timestep to a current timestep immediately following the previous timestep; (Zandbergen pg 9 Paragraph 1) “A neural network is used to predict local CFL numbers for pseudo time-stepping”. {EXAMINERS NOTE: Pseudo time stepping is using a previous timestep to calculate the next step.}
and executing the numerical solver using the current timestep size on the differential equation to generate a simulation output for the current timestep. (Zandbergen pg 16 Paragraph 2) “In this error estimate, 𝑢 and 𝑣 are the solutions after one pseudo time-step iteration computed with the predicted CFL number from the network” {Examiners note: Running a solver with the predicted step control value to produce the next step result.}
Usadi and Zandbergen are analogous to the claimed invention because they are from the same field of endeavor of learning-based simulation.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Usadi and Zandbergen before him or her, to modify the numerical time step solver of Usadi with the prediction methods of Zandbergen to help with the reusability and flexibility of allowing pseudo functions to reach their potential as suggest in Usadi 0009.
Claim 15.
Claim 15 is rejected as being substantially similar to claim 2 albeit for a system. It is rejected under the same rational.
Claim 16.
Claim 16 is rejected as being substantially similar to claim 3 albeit for a system. It is rejected under the same rational.
Claim 17.
Claim 17 is rejected as being substantially similar to claim 4 albeit for a system. It is rejected under the same rational.
Claim 18.
Claim 18 is rejected as being substantially similar to claim 5 albeit for a system. It is rejected under the same rational.
Claim 19.
Claim 19 is rejected as being substantially similar to claim 6 albeit for a system. It is rejected under the same rational.
Claim 20.
Claim 20 is rejected as being substantially similar to claim 7 albeit for a system. It is rejected under the same rational.
Claims 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over Usadi et al., US 2013/0096900 A1 (Usadi) in view of Zandbergen, Predicting the optimal CFL number for pseudo time-stepping with machine learning in the COMSOL CFD module (Zandbergen) in further view of Rosebrock Building a simple Keras deep learning rest api. (Rosebrock).
Claim 11.
Modified Usadi does not explicitly teach, but Rosebrock teaches The system of claim 10, wherein a first of the plurality of computing systems is a Flask server, and wherein a second of the plurality of computing systems is a Flask client. (Rosebrock pg 1) “How to use the Flask web framework to create an endpoint for our API. How to make predictions using our model, JSON-ify them, and return the results to the client” {Examiners note: Flask hosted endpoint (server) and a separate caller (client). Under BRI flask client can read on a client process configured to invoke flask endpoints.}
Usadi and Zandbergen are analogous to the claimed invention because they are from the same field of endeavor of learning-based simulation. Rosebrock is analogous to the claimed invention because they are from the same field of endeavor of flask api.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Usadi, Zandbergen, and Rosebrock before him or her, to modify the numerical time step solver of Usadi with the prediction methods of Zandbergen, and the flask server and client of Rosebrock to help with the reusability and flexibility of allowing pseudo functions to reach their potential as suggest in Usadi 0009.
Claim 12.
Modified Usadi with Rosebrock teaches The system of claim 11, wherein the Flask server forwards a request for the (Rosebrock pg 1) “How to use the Flask web framework to create an endpoint for our API. How to make predictions using our model, JSON-ify them, and return the results to the client” {Examiners note: While Rosebrock does not teach the flask server request being the current timestep size from the numerical solver, it would have been obvious to combine with Zandbergen to forward the timesteps of Zandbergen from a flask server to flask client.}
Claim 13.
Modified Usadi with Zandbergen and Rosebrock teaches The system of claim 11, wherein the(Zandbergen pg 9 Paragraph 1) “A neural network is used to predict local CFL numbers for pseudo time-stepping” {Examiners note: While Zandbergen does not explicitly use a flask client, it would have been obvious to combine with Rosebrock to have the flask client of Rosebrock perform the prediction of Zandbergen}
Claim 14.
Claim 14 is rejected as being substantially similar to claim 8 albeit for a system and the numerical solver utilizing Newton’s method. It is rejected under the same rational.
Conclusion
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/JOHN DAVID HAGLER/ Examiner, Art Unit 2189
/REHANA PERVEEN/ Supervisory Patent Examiner, Art Unit 2189