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 .
This communication is responsive to amended application filed on 05/26/2026.
Claim 15 is canceled.
Claim 21 is added.
Claims 1-14, and 16-21 are presented for examination.
Response to Arguments
Applicant's arguments filed 05/26/2026 have been fully considered but they are not persuasive.
Applicants argued:
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Examiner respectfully disagrees. Each and every step falls under mathematical concept. Any purported improvement to a technology or technical field as direct consequence of the “mathematical concept” grouping of abstract ideas. “An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself” (MPEP 2106.05(I)).
Applicants argued:
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Examiner respectfully disagrees. Examiner consulted the specification whether the disclosed invention improves technology and to ensure the claim itself reflects the improvement in technology. However, after carefully examined the claimed solution to a problem recited in applicant argument, the claims do not reflect to cover a particular solution to a problem. Rather, the focus of the claims here is the improved mathematical algorithm to predict flux values.
Regarding claim 16, the claim recites substantially similar limitations to claim 1, respectively, but are directed to a computer system rather than a method. Therefore, the claim is ineligible under 35 U.S.C 101 for the same reasons above.
Applicant’s arguments, see Remarks pgs. 10-12, filed 05/26/2026, with respect to claim 9 have been fully considered and are persuasive. The rejection of 35 USC 1018 has been withdrawn.
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 1-8, and 16-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (Does this claim fall within at least one statutory category?):
Claims 1-8 and 21 are directed to a method.
Claims 16-20 are directed to a system.
Therefore, claims 1-8, and 16-21 fall into at least one of the four statutory categories.
Step 2A, Prong 1: ((a) identify the specific limitation(s) in the claim that recites an abstract idea: and (b) determine whether the identified limitation(s) falls within at least one of the groups of abstract ideas enumerates in MPEP 2106.04(a)(2)):
Claim 1:
A method implemented on a computer system for executing a plurality of elements of a model of a physical system, the method comprising:
calculating, by a particular processing unit in a given time step, estimating a particular flux value representing a flux between a portion neighboring element and a particular element of the plurality of elements based on state data of the neighboring element received in a communication from another processing unit [mathematical concepts];
calculating, by the particular processing unit in a subsequent time step, a predicted flux value representing the flux between the neighboring element and the particular element of the plurality of elements based, at least in part, on the particular flux value and evaluation of a communication skipping condition [mathematical concepts]; and
calculating, by the particular processing unit, state data for the particular element based, at least in part, on the predicted flux from state data value [mathematical concepts].
Step 2A, Prong 2 (1. Identifying whether there are any additional elements recited in the claim beyond the judicial exception; and 2. Evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application): The claim is directed to the judicial exception.
Claim 1 recites additional element of “processing unit”. The component recited at a high level of generality (e.g. a generic computer element for performing a generic computer functions) such that it amounts to no more than mere application of the judicial exception using generic computer component(s). Accordingly, the additional element(s) of each of these claims do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B: (Does the claim recite additional elements that amount to significantly more than the judicial exception? No): As discussed above with respect to the integration of the abstract into a practical application, the additional element of “processing unit” amounts to no more than mere instructions to apply the judicial exception using generic computer component(s). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
As per claim 2, the claim falls into [“mental process i.e. concepts performed in the human mind or with pen and paper (including an observation, evaluation judgement, opinion)].
As per claims 3-8, the claims fall into [mathematical concepts].
As per Claims 16-21, claims 16-21 recite limitations analogous in scope to those of claims 1-4 and 8, and as such are similar rejected.
Allowable Subject Matter
Claims 1-8, and 16-21 are allowable over prior art.
The following is a statement of reasons for the indication of allowable subject matter:
Usadi et al. (US Publication No. 2013/0118736 A1) discloses the method also includes simulating the plurality of fine grid models using a training simulation to obtain a set of training parameters, including a potential at each coarse grid cell surrounding the flux interface and a flux across the flux interface. A machine learning algorithm is used to generate a constitutive relationship that provides a solution to fluid flow through the flux interface. The method also includes simulating the hydrocarbon reservoir using the constitutive relationship and generating a data representation of a physical hydrocarbon reservoir in a non-transitory, computer-readable medium based on the results of the simulation (Abstract); the plurality of fine grid models can be simulated using a training simulation to obtain a set of training parameters comprising a potential at each coarse grid cell surrounding the flux interface and a flux across the flux interface. A machine learning algorithm can be used to generate a constitutive relationship that provides a solution to fluid flow through the flux interface. The method also includes simulating the hydrocarbon reservoir using the constitutive relationship and generating a data representation of a physical hydrocarbon reservoir in a non-transitory, computer-readable medium based, at least in part, on the results of the simulation par [0015]; To compute the matrix equation solution for the sub region 502, the sub region's boundaries may be partitioned into representative sets by type, for example, flux boundaries and pressure boundaries par [0080]; At block 1120, fine grid simulations computed for each of the different mesh scales generated at block 1114 and 1116 may be evaluated to determine an uncertainty estimate for the coarse grid constitutive relationship. This may be done through numerical experiment using a variety of different fine scale parameter distributions. The uncertainty estimate is a measure of the accuracy of the constitutive relationships computed at different coarse scales. The uncertainty estimate may be used to determine an estimated level of geologic feature detail that will provide suitable accuracy during the generation of the training set used to train the neural net par [0119]; The constitutive relationship between flux and pressure for each coarse grid cell 1010 may be computed using machine learning techniques, for example, using a neural net such as the neural net described in relation to FIG. 4. For example, in accordance with Eqn. 11, the input to the machine learning method such as the neural net may be .PHI..sub.i, geometry, K.sub.v(S.sub.v(t)), and params and the output may be the flux, F, on the boundary of the cell par [0111]; FIG. 20 is a block diagram of an exemplary cluster computing system 2000 that may be used in exemplary embodiments of the present techniques. The cluster computing system 2000 illustrated has four computing units 2002, each of which may perform calculations for part of the simulation model. However, one of ordinary skill in the art will recognize that the present techniques are not limited to this configuration, as any number of computing configurations may be selected. For example, a small simulation model may be run on a single computing unit 2002, such as a workstation, while a large simulation model may be run on a cluster computing system 2000 having 10, 100, 1000, or even more computing units 2002. In an exemplary embodiment, each of the computing units 2002 will run the simulation for a single subdomain or group of computational cells par [0141].
Krishnamurthy et al (US Publication No. 2020/0394277 A1) discloses The computed modified flux is a computed modified heat flux and the spatially averaged gradient is a spatially averaged temperature gradient. Computing the modified heat flux further includes an applied flux; and computing a balance flux. For a given one of the voxels, the computed applied flux is used to calculate a temperature evolution for the given one of the voxels. The balance flux is used in the calculation of the temperature evolution depending on the size of the voxel. The balance flux is used in the temperature evolution when the size of the voxel is large enough to as to satisfy the constraint. The aspect further includes transmitting by the computer system the balance flux to one or more neighboring voxels in a direction of the flux par [0015]; The disclosed techniques introduce modifications to heat flux calculations between two neighboring elements when at least one of the elements violates a constraint, e.g., the Courant-Friedrichs-Lewy (CFL) constraint. These modifications to heat flux calculations are dependent on the material and geometric properties of the two elements as well as the existing state of quantity of interest in the immediate vicinity of the elements, and help stabilize the numerical solution irrespective of the size of the two elements and ensure spatio-temporal accuracy. When the two neighboring elements are large (and therefore satisfy the CFL constraint) the new proposed flux calculation reduces to an explicit scheme implementation implying that this novel approach is consistent with the explicit approach, and yet overcomes the above mentioned deficiencies in the explicit approach par [0017].
However, none of the cited prior art references of record fully anticipate or render obvious the independent claims in particular the limitation of: “calculating, by a particular processing unit in a given time step, estimating a particular flux value representing a flux between a portion neighboring element and a particular element of the plurality of elements; communicating based on state data between the portion of the plurality of elements in response to uncertainty in the model of the physical system of the neighboring element received in a communication from another processing unit; calculating, by the particular processing unit in a subsequent time step, a predicted flux value representing the flux between the neighboring element and the particular element of the plurality of elements based, at least in part, on the particular flux value and evaluation of a communication skipping condition;” as recited in claims 1, and 16.
Claims 9-14 are allowed.
The following is an examiner’s statement of reasons for allowance:
Usadi et al. (US Publication No. 2013/0118736 A1) discloses the method also includes simulating the plurality of fine grid models using a training simulation to obtain a set of training parameters, including a potential at each coarse grid cell surrounding the flux interface and a flux across the flux interface. A machine learning algorithm is used to generate a constitutive relationship that provides a solution to fluid flow through the flux interface. The method also includes simulating the hydrocarbon reservoir using the constitutive relationship and generating a data representation of a physical hydrocarbon reservoir in a non-transitory, computer-readable medium based on the results of the simulation (Abstract); the plurality of fine grid models can be simulated using a training simulation to obtain a set of training parameters comprising a potential at each coarse grid cell surrounding the flux interface and a flux across the flux interface. A machine learning algorithm can be used to generate a constitutive relationship that provides a solution to fluid flow through the flux interface. The method also includes simulating the hydrocarbon reservoir using the constitutive relationship and generating a data representation of a physical hydrocarbon reservoir in a non-transitory, computer-readable medium based, at least in part, on the results of the simulation par [0015]; To compute the matrix equation solution for the sub region 502, the sub region's boundaries may be partitioned into representative sets by type, for example, flux boundaries and pressure boundaries par [0080]; At block 1120, fine grid simulations computed for each of the different mesh scales generated at block 1114 and 1116 may be evaluated to determine an uncertainty estimate for the coarse grid constitutive relationship. This may be done through numerical experiment using a variety of different fine scale parameter distributions. The uncertainty estimate is a measure of the accuracy of the constitutive relationships computed at different coarse scales. The uncertainty estimate may be used to determine an estimated level of geologic feature detail that will provide suitable accuracy during the generation of the training set used to train the neural net par [0119]; The constitutive relationship between flux and pressure for each coarse grid cell 1010 may be computed using machine learning techniques, for example, using a neural net such as the neural net described in relation to FIG. 4. For example, in accordance with Eqn. 11, the input to the machine learning method such as the neural net may be .PHI..sub.i, geometry, K.sub.v(S.sub.v(t)), and params and the output may be the flux, F, on the boundary of the cell par [0111]; FIG. 20 is a block diagram of an exemplary cluster computing system 2000 that may be used in exemplary embodiments of the present techniques. The cluster computing system 2000 illustrated has four computing units 2002, each of which may perform calculations for part of the simulation model. However, one of ordinary skill in the art will recognize that the present techniques are not limited to this configuration, as any number of computing configurations may be selected. For example, a small simulation model may be run on a single computing unit 2002, such as a workstation, while a large simulation model may be run on a cluster computing system 2000 having 10, 100, 1000, or even more computing units 2002. In an exemplary embodiment, each of the computing units 2002 will run the simulation for a single subdomain or group of computational cells par [0141].
Krishnamurthy et al (US Publication No. 2020/0394277 A1) discloses The computed modified flux is a computed modified heat flux and the spatially averaged gradient is a spatially averaged temperature gradient. Computing the modified heat flux further includes an applied flux; and computing a balance flux. For a given one of the voxels, the computed applied flux is used to calculate a temperature evolution for the given one of the voxels. The balance flux is used in the calculation of the temperature evolution depending on the size of the voxel. The balance flux is used in the temperature evolution when the size of the voxel is large enough to as to satisfy the constraint. The aspect further includes transmitting by the computer system the balance flux to one or more neighboring voxels in a direction of the flux par [0015]; The disclosed techniques introduce modifications to heat flux calculations between two neighboring elements when at least one of the elements violates a constraint, e.g., the Courant-Friedrichs-Lewy (CFL) constraint. These modifications to heat flux calculations are dependent on the material and geometric properties of the two elements as well as the existing state of quantity of interest in the immediate vicinity of the elements, and help stabilize the numerical solution irrespective of the size of the two elements and ensure spatio-temporal accuracy. When the two neighboring elements are large (and therefore satisfy the CFL constraint) the new proposed flux calculation reduces to an explicit scheme implementation implying that this novel approach is consistent with the explicit approach, and yet overcomes the above mentioned deficiencies in the explicit approach par [0017].
However, none of the cited prior art references of record fully anticipate or render obvious the independent claims in particular the limitation of: “executing, on a computer system, a model of a physical system including a plurality of elements each having one or more state variables, the plurality of elements divided into a plurality of partitions; and for each element of the plurality of elements that is on an edge of a first partition of the plurality of partitions that is adjacent a second partition of the plurality of partitions: for each first time step of a plurality of first time steps of a plurality of time steps, communicating first state data to each element from the second partition and updating a state of each element according to the state of each element and a first flux value calculated from the state data; and for each second time step of a plurality of second time steps of the plurality of time steps, estimating an uncertainty in the model of the physical system, determining that the uncertainty in the model of the physical system does not meet a threshold condition, and in response to determining that the uncertainty in the model of the physical system does not meet the threshold condition, estimating a second flux value for each element based on the state of each element and a preceding flux value from a preceding time step of the plurality of time steps and updating the state of each element according to the state of each element and the second flux value” as recited in claim 9.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KIBROM K GEBRESILASSIE whose telephone number is (571)272-8571. The examiner can normally be reached M-F 9:00 AM-5:30 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rehana Perveen can be reached at 571 272 3676. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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KIBROM K. GEBRESILASSIE
Primary Examiner
Art Unit 2189
/KIBROM K GEBRESILASSIE/ Primary Examiner, Art Unit 2189 07/14/2026
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