DETAILED ACTION
Claims 1-20 are presented for examination. Claims 1, 3, 8, 10, 15, and 17 stand currently amended.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12 November 2025 has been entered.
Response to Arguments
Applicant's remarks filed 12 November 2025 have been fully considered and Examiner’s response is as follows:
Applicant remarks page 11 argues:
The features of amended claim 1 reflect the asserted improvement to the "machine learning model" and "[pertain] to an improvement to the functioning of a computer.
The machine learning model is a mathematical construct. An improvement to mathematical subject matter corresponds with an allegation of an improvement to the identified abstract idea itself. An abstract idea, even an improved abstract idea, is not subject matter eligible under §101.
An allegation that a claim represents an improvement to the functioning of a computer requires the claim to be directed towards the functioning of a computer. The discussed features from Specification paragraphs 4 and 7 are regarding the DNN model. A DNN model is not a computer nor is a DNN model a computer component or computer operation. MPEP §2106.05(a)(II) states:
To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology.
The argued statements from the Specification (e.g. “reduces and amount computing resources used” and “the data processing system”) does not describe any such details as articulated in MPEP §2106.05(a)(II).
Furthermore, claim 1 does not even recite any computer or computer components. It is unpersuasive to argue the claims pertains to an alleged improvement to functioning of a computer when the claim does not even recite any computer.
Applicant remarks pages 11-12 further argues:
The performance threshold can be satisfied because the "DNN ... [enables] an integrated machine learning model network for performing both phase identification and phase split determination," as described in the Specification. …. The second sub-network processes both the first output data and the input data for "generating, by the second sub-network, second output data including fractional values for equilibrium of the individual phase components, vapor fraction, vapor compressibility, and liquid compressibility for the hydrocarbon sample ... [that] satisfy the performance threshold," as recited in amended claim 1. ….
These steps reflect the improvement to the machine learning model itself as claimed in claim 1.
Applicant admits these steps reflect an improvement to the machine learning model itself. Examiner has identified the machine learning model as a mathematical construct and corresponding with the identified abstract idea itself. An improved abstract idea is not subject matter eligible under §101.
Applicant remarks page 12 further argues:
The claimed invention reflects this improvement in the technical field of reservoir simulation.
This argument is unpersuasive. “[G]enerally linking the use of a judicial exception to a particular technological environment or field of use” does not make a claim subject matter eligible. See MPEP §2106.05(h).
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 to an abstract idea without significantly more.
To determine if a claim is directed to patent ineligible subject matter, the Court has guided the Office to apply the Alice/Mayo test, which requires:
1. Determining if the claim falls within a statutory category;
2A. Determining if the claim is directed to a patent ineligible judicial exception consisting of a law of nature, a natural phenomenon, or abstract idea; and
2B. If the claim is directed to a judicial exception, determining if the claim recites limitations or elements that amount to significantly more than the judicial exception.
See MPEP §2106.
Step 2A is a two prong inquiry. MPEP §2106.04(II)(A). Under 2A(i), the first prong, examiners evaluate whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Abstract ideas include mathematical concepts, certain methods of organizing human activity, and mental processes. MPEP §2106.04(a)(2). Under 2A(ii), the second prong, examiners determine whether any additional limitations integrates the judicial exception into a practical application. MPEP §2106.04(d).
Claim 1 step 2A(i):
The claim(s) recite:
1. A method for hydrocarbon phase behavior modeling for compositional reservoir simulation, the method comprising:
estimating phase properties of a hydrocarbon sample based on a mole-fraction weighted mixing rule;
determining contributions of individual phase components to the mole-fraction weighted phase properties;
… a machine learning model including a first sub-network including a first number of layers each including respective neurons, and a second sub-network including a second number of layers each including respective neurons …;
wherein the first sub-network and the second sub-network are trained in parallel to enable the machine learning model to satisfy a performance threshold that indicates a recall threshold and a precision threshold;
generating, based on processing the input data using the neurons of the first number of layers of the first sub-network of the machine learning model, first output data including probability values for each potential phase state of the hydrocarbon sample, the probability values including a first value representing a liquid state for a grid block, a second value for a vapor state for the grid block, a third value for a two-phase state of the grid block, and a fourth value representing a critical state of the grid block;
processing the first output data including the probability values and the input data including the contributions from the individual phase components to the phase properties by the second sub-network of the machine learning model; and
generating, by the second sub-network, second output data including fractional values for equilibrium of the individual phase components, vapor fraction, vapor compressibility, and liquid compressibility for the hydrocarbon sample;
….
Behavior modeling for compositional reservoir simulation corresponds with mathematical modeling.
Estimating phase properties based on a fraction weighted mixing rule, is reciting the mathematical relationship of the fraction weighted mixing rule.
Determining contributions of phase contributions of phase components to the mole-fraction weighted phase properties is a broad recitation encompassing mathematical and mental process determinations of the calculated values.
The structure of the machine learning model as a first and second sub-network with respective numbers of layers is a mathematical description of the mathematical structure of the machine learning model.
Training a first and second network corresponds with performing respective mathematical calculations. The performance, recall, and precision thresholds correspond with respective mathematical conditions. These mathematical calculations correspond with mathematical subject matter.
Generating, based on the machine learning model, phase state probability values correspond with performing the corresponding calculations of the machine learning model and/or performing the mathematical calculations for training the machine learning model. The first and second sub-networks neurons are descriptions of the mathematical structure.
Processing probability values by the machine learning model is performing the corresponding mathematical computations of the machine learning model. Processing the probability values by the second sub-network is corresponding mathematical calculations of the second sub-network.
Generating second output data of the sub-network is performing corresponding calculations of the machine learning model and of the sub-network thereof.
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 1 step 2A(ii):
This judicial exception is not integrated into a practical application because:
The claim(s) recite:
generating input data for …, the input data including the contributions from the individual phase components to the phase properties;
…; and
outputting, for the grid block of a compositional reservoir simulation, the second output data, wherein the first output data and the second output data satisfy the performance threshold.
Generating input data is a generic recitation of data gathering. Insignificant extra solution activity in the form of data gathering necessary for performing an abstract idea fails to integrate the abstract idea into a practical application. See MPEP §2106.05(g).
Outputting the result of the abstract idea is insignificant extra solution activity. See MPEP §2106.05(g) (“(3) Whether the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968.”).
Claim 1 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
The claim(s) recite:
generating input data for …, the input data including the contributions from the individual phase components to the phase properties;
…; and
outputting, for the grid block of a compositional reservoir simulation, the second output data, wherein the first output data and the second output data satisfy the performance threshold.
MPEP §2106.05(d) provides two examples of data gathering and data output:
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)
Both of these examples are Berkheimer evidence for the data gathering recited at a high level of generality. Transmitting and storing data correspond with respective generic recitations of ‘outputting’ the output data.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claims 2, 9, and 16 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
2. The method of claim 1, further comprising:
…
determining a categorical cross-entropy error from the first sub-network;
generating a probabilities vector based on the probability values and the categorical cross-entropy error;
processing the probability vector by the second sub-network;
determining, based on the processing, a mean-squared error (MSE) between predicted output values and output values of the output data; and
training the first sub-network and the second sub-network simultaneously by minimizing the MSE value over a plurality of training epochs.
Determining a categorical cross-entropy error is performing corresponding mathematical calculations.
Generating a probability vector based on the probability values is performing a mathematical construction of the vector using corresponding values.
Processing the probability vector by the second sub-network is performing corresponding mathematical calculations.
Determining the mean-squared error (MSE) is performing the mathematical calculations to calculate the MSE.
Training the sub-networks is performing corresponding mathematical calculations. See further specific example of claim 3.
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claims 2, 9, and 16 step 2A(ii):
This judicial exception is not integrated into a practical application because:
The claim(s) recite:
receiving training data comprising phase properties values;
Generating input data is a generic recitation of data gathering. Insignificant extra solution activity in the form of data gathering necessary for performing an abstract idea fails to integrate the abstract idea into a practical application. See MPEP §2106.05(g).
Claims 2, 9, and 16 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
The claim(s) recite:
receiving training data comprising phase properties values;
MPEP §2106.05(d) provides two examples of data gathering:
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)
Both of these examples are Berkheimer evidence for the data gathering recited at a high level of generality.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claims 3, 10, and 17 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
3. The method of claim 2, further comprising generating the training data comprising the phase properties values by performing operations comprising:
selecting a grid block for a simulated reservoir;
for the selected grid block:
generating input data of mole fractions based on a uniform distribution for pressure at a specified reservoir temperature;
determining a stability value and a split-phase value for the generated mole-fraction phase properties at each specified temperature and pressure;
determine a phase state value based on the stability value and the split-phase value; and
generating one or more of a vapor fraction value, a vapor compressibility value, a liquid compressibility value, and liquid fraction value based on the phase state value.
A uniform distribution for pressure is a mathematical relationship for the pressure for calculating the respective mole fractions.
Determining a stability value is performing corresponding mathematical calculations.
Determining the phase state values is performing corresponding mathematical calculations.
Generating the corresponding output values is performing corresponding mathematical calculations.
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claims 3, 10, and 17 step 2A(ii):
This judicial exception is not integrated into a practical application because:
Claim(s) do not recite any “additional” limitations.
Claims 3, 10, and 17 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Claim(s) do not recite any “additional” limitations.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claims 4, 11, and 18 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claims 4, 11, and 18 step 2A(ii):
This judicial exception is not integrated into a practical application because:
The claim(s) recite:
4. The method of claim 1, wherein the input data further comprises a grid-block temperature, a grid-block pressure, and mole fractions data.
The input data comprising blocks of temperature, pressure, and mole-fraction data is generally linking the mathematical calculations to a particular field of use. See MPEP §2106.05(h).
Claims 4, 11, and 18 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Field of use limitations under MPEP §2106.05(h) are analyzed the same under step 2B as under step 2A(ii) above.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claims 5, 12, and 19 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claims 5, 12, and 19 step 2A(ii):
This judicial exception is not integrated into a practical application because:
The claim(s) recite:
5. The method of claim 1, wherein the phase properties include a critical temperature, a critical pressure, a critical volume, an acentric factor, and a molecular weight value.
The phase properties include a critical temperature a critical pressure, a critical volume, an acentric factor, a molecular weight value is generally linking the mathematical calculations to a particular field of use. See MPEP §2106.05(h).
Claims 5, 12, and 19 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Field of use limitations under MPEP §2106.05(h) are analyzed the same under step 2B as under step 2A(ii) above.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claims 6, 13, and 20 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claims 6, 13, and 20 step 2A(ii):
This judicial exception is not integrated into a practical application because:
The claim(s) recite:
6. The method of claim 1, wherein the hydrocarbon sample represents one of a five component sample, a seven component sample, or a nine component sample.
The number of components in the sample is identification of corresponding field of use (which samples to analyze). See MPEP §2106.05(h).
Claims 6, 13, and 20 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Field of use limitations under MPEP §2106.05(h) are analyzed the same under step 2B as under step 2A(ii) above.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claims 7 and 14 step 2A(i):
Dependent claims recite at least the identified judicially excepted subject matter of their parent claim(s).
The claim(s) recite:
7. The method of claim 1, wherein the machine learning model comprises a deep neural network (DNN) having at least three hidden layers and at least one output layer.
The type of machine learning model being a DNN affects the mathematical structure of the machine learning model. The calculations corresponding with a DNN are mathematical calculations.
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claims 7 and 14 step 2A(ii):
This judicial exception is not integrated into a practical application because:
Claim(s) do not recite any “additional” limitations.
Claims 7 and 14 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
Claim(s) do not recite any “additional” limitations.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 8 step 2A(i):
The claim(s) recite:
8. A data processing system for hydrocarbon phase behavior modeling for compositional reservoir simulation, the data processing system comprising:
…
estimating phase properties of a hydrocarbon sample based on a mole-fraction weighted mixing rule;
determining contributions of individual phase components to the mole-fraction weighted phase properties;
… a machine learning model including a first sub-network including a first number of layers each including respective neurons, and a second sub-network including a second number of layers each including respective neurons, …;
wherein the first sub-network and the second sub-network are trained in parallel to enable the machine learning model to satisfy a performance threshold that indicates a recall threshold and a precision threshold;
generating, based on processing the input data using the neurons of the first number of layers of the first sub-network of the machine learning model, first output data including probability values for each potential phase state of the hydrocarbon sample, the probability values including a first value representing a liquid state for a grid block, a second value for a vapor state for the grid block, a third value for a two-phase state of the grid block, and a fourth value representing a critical state of the grid block;
processing the first output data including the probability values and the input data including the contributions from the individual phase components to the phase properties by the neurons of the second number of layers of the second sub-network of the machine learning model;
generating, by the second sub-network, second output data including fractional values for equilibrium of the individual phase components, vapor fraction, vapor compressibility, and liquid compressibility for the hydrocarbon sample; and
….
Behavior modeling for compositional reservoir simulation corresponds with mathematical modeling.
Estimating phase properties based on a fraction weighted mixing rule, is reciting the mathematical relationship of the fraction weighted mixing rule.
Determining contributions of phase contributions of phase components to the mole-fraction weighted phase properties is a broad recitation encompassing mathematical and mental process determinations of the calculated values.
The structure of the machine learning model as a first and second sub-network with respective numbers of layers is a mathematical description of the mathematical structure of the machine learning model.
Training a first and second network corresponds with performing respective mathematical calculations. The performance, recall, and precision thresholds correspond with respective mathematical conditions. These mathematical calculations correspond with mathematical subject matter.
Generating, based on the machine learning model, phase state probability values correspond with performing the corresponding calculations of the machine learning model and/or performing the mathematical calculations for training the machine learning model. The first and second sub-networks neurons are descriptions of the mathematical structure.
Processing probability values by the machine learning model is performing the corresponding mathematical computations of the machine learning model. Processing the probability values by the second sub-network is corresponding mathematical calculations of the second sub-network.
Generating second output data of the sub-network is performing corresponding calculations of the machine learning model and of the sub-network thereof.
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 8 step 2A(ii):
This judicial exception is not integrated into a practical application because:
The claim(s) recite:
at least one processor; and
a memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
…
generating input data for …, the input data including the contributions from the individual phase components to the phase properties;
…; and
outputting, for the grid block of a compositional reservoir simulation, the second output data, wherein the first output data and the second output data satisfy the performance threshold.
The processor and memory are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.05(b) (“Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014).”).
Generating input data is a generic recitation of data gathering. Insignificant extra solution activity in the form of data gathering necessary for performing an abstract idea fails to integrate the abstract idea into a practical application. See MPEP §2106.05(g).
Outputting the result of the abstract idea is insignificant extra solution activity. See MPEP §2106.05(g) (“(3) Whether the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968.”).
Claim 8 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
The claim(s) recite:
at least one processor; and
a memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
…
generating input data for …, the input data including the contributions from the individual phase components to the phase properties;
…; and
outputting, for the grid block of a compositional reservoir simulation, the second output data, wherein the first output data and the second output data satisfy the performance threshold.
Generic computer implementation (see MPEP §2106.05(b)) is analyzed the same here under step 2B as under step 2A(ii) above.
MPEP §2106.05(d) provides two examples of data gathering:
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)
Both of these examples are Berkheimer evidence for the data gathering recited at a high level of generality. Transmitting and storing data correspond with respective generic recitations of ‘outputting’ the output data.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Claim 15 step 2A(i):
The claim(s) recite:
15. … storing instructions for hydrocarbon phase behavior modeling for compositional reservoir simulation, the instructions, when executed by at least one processor, being configured to cause the at least one processor to perform operations comprising:
estimating phase properties of a hydrocarbon sample based on a mole-fraction weighted mixing rule;
determining contributions of individual phase components to the mole-fraction weighted phase properties;
… a machine learning model including a first sub-network including a first number of layers each including respective neurons, and a second sub-network including a second number of layers each including respective neurons, …
wherein the first sub-network and the second sub-network are trained in parallel to enable the machine learning model to satisfy a performance threshold that indicates a recall threshold and a precision threshold;
generating, based on processing the input data using the neurons of the first number of layers of the first sub-network of the machine learning model, first output data including probability values for each potential phase state of the hydrocarbon sample, the probability values including a first value representing a liquid state for a grid block, a second value for a vapor state for the grid block, a third value for a two-phase state of the grid block, and a fourth value representing a critical state of the grid block;
processing the first output data including the probability values and the input data including the contributions from the individual phase components to the phase properties by the second sub-network of the machine learning model; and
generating, by the second sub-network, second output data including fractional values for equilibrium of the individual phase components, vapor fraction, vapor compressibility, and liquid compressibility for the hydrocarbon sample;
Behavior modeling for compositional reservoir simulation corresponds with mathematical modeling.
Estimating phase properties based on a fraction weighted mixing rule, is reciting the mathematical relationship of the fraction weighted mixing rule.
Determining contributions of phase contributions of phase components to the mole-fraction weighted phase properties is a broad recitation encompassing mathematical and mental process determinations of the calculated values.
The structure of the machine learning model as a first and second sub-network with respective numbers of layers is a mathematical description of the mathematical structure of the machine learning model.
Training a first and second network corresponds with performing respective mathematical calculations. The performance, recall, and precision thresholds correspond with respective mathematical conditions. These mathematical calculations correspond with mathematical subject matter.
Generating, based on the machine learning model, phase state probability values correspond with performing the corresponding calculations of the machine learning model and/or performing the mathematical calculations for training the machine learning model. The first and second sub-networks neurons are descriptions of the mathematical structure.
Processing probability values by the machine learning model is performing the corresponding mathematical computations of the machine learning model. Processing the probability values by the second sub-network is corresponding mathematical calculations of the second sub-network.
Generating second output data of the sub-network is performing corresponding calculations of the machine learning model and of the sub-network thereof.
This falls within the mathematical concepts grouping of abstract ideas. See MPEP §2106.04(a)(2).
Claim 15 step 2A(ii):
This judicial exception is not integrated into a practical application because:
The claim(s) recite:
One or more non-transitory computer readable media …
generating input data for …, the input data including the contributions from the phase properties;
…; and
outputting, for the grid block of a compositional reservoir simulation, the second output data, wherein the first output data and the second output data satisfy the performance threshold.
The non-transitory computer readable media is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.05(b) (“Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014).”).
Generating input data is a generic recitation of data gathering. Insignificant extra solution activity in the form of data gathering necessary for performing an abstract idea fails to integrate the abstract idea into a practical application. See MPEP §2106.05(g).
Outputting the result of the abstract idea is insignificant extra solution activity. See MPEP §2106.05(g) (“(3) Whether the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968.”).
Claim 15 step 2B:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and in combination, because:
The claim(s) recite:
One or more non-transitory computer readable media …
generating input data for …, the input data including the contributions from the phase properties;
…; and
outputting, for the grid block of a compositional reservoir simulation, the second output data, wherein the first output data and the second output data satisfy the performance threshold.
Generic computer implementation (see MPEP §2106.05(b)) is analyzed the same here under step 2B as under step 2A(ii) above.
MPEP §2106.05(d) provides two examples of data gathering:
i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information);
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)
Both of these examples are Berkheimer evidence for the data gathering recited at a high level of generality. Transmitting and storing data correspond with respective generic recitations of ‘outputting’ the output data.
When further considering the claims as a whole and as an ordered combination the claims fail to amount to significantly more than the judicially excepted abstract idea.
Allowable Subject Matter
Claims 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. §101, set forth in this Office action.
Examiner previously presented reasons for indication of allowable subject matter in the office action dated 19 August 2025.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jay B Hann whose telephone number is (571)272-3330. The examiner can normally be reached M-F 10am-7pm EDT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Renee Chavez can be reached at (571) 270-1104. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Jay Hann/Primary Examiner, Art Unit 2186 25 November 2025