DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The amendment filed 11/06/2025 has been received and considered. Claims 1-4, 6-11, 13-18, and 20 are currently pending.
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 1-4, 6-11, 13-18, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claim 1, Step 1: a method (process = 2019 PEG Step 1 = yes)
Independent claim 1, Step 2A, Prong One: claim recites:
predictive modeling comprising: determining at least one processing time for performing a simulation through a reservoir simulation model; determining a grid cell count (mathematical concepts)
Claim 1 is substantially drawn to mathematical concepts: relationships, formulas or equations, calculations. These limitations, under their broadest reasonable interpretations, are mathematical concepts. As to the limitations “determining at least one processing time", see for example in the Specification (underline emphasis added):
“[0032]… determine a processing time for each time step received as an input. Therefore, when both minimum and maximum time steps are provided as input, processing unit 202 can generate a processing time for the minimum time step and a processing time for a maximum time step. Such processing time can also be referred to as a CPU time per iteration, which can be determined as follows.
[0033] Processing unit 202 determines a number of iterations for simulation. Processing unit 202 can determine a minimum number of iterations for a maximum time step received at S300 and a maximum number of iterations for a minimum time step received at S300. In one example, a minimum number of iterations is given by a ratio of the production time received at S300 and the maximum time step per:
minimum number of iterations = production time/maximum time step (1)
[0034] Furthermore, a maximum number of iterations is given by a ration of the production time to the minimum time step per:
maximum number of iterations = production time/minimum time step (2)
[0035] Based on equations (1) and (2), processing unit 202 can determine a minimum and maximum processing time (CPU time) per iteration”.
If a claim limitation, under its broadest reasonable interpretation, covers mathematical concepts, then it falls within the "(a) Mathematical concepts" grouping of abstract ideas (2019 PEG Step 2A, Prong One: Abstract Idea Grouping? = Yes, (a) Mathematical concepts).
Independent claim 1 Step 2A, Prong Two: As to the limitations “for creating a geocellular grid for the simulation based on both the at least one processing time for performing the simulation and a number of processors available to the reservoir simulation model for performing the simulation" and "creating the geocellular grid using the grid cell count”, these limitations appear to be just “apply it” limitations, because the limitations invoke computers or other machinery merely as a tool to perform an existing process.
As to the limitations “inputting the at least one processing time and the number of processors into a machine learning model" and "receiving an output of the machine learning model as the grid cell count”, these limitations describe the concept of “mere data gathering”, which corresponds to the concepts identified as abstract ideas by the courts. Data gathering, including when limited to particular content does not change its character as information, is also within the realm of abstract ideas. Data gathering has not been held by the courts to be enough to qualify as “significantly more”. See Electric Power Group1 (Electric Power hereinafter). See also MPEP § 2106.05(g).
As to the limitations "that is trained based on previously run reservoir simulations including cell grid counts used during the previously run simulations", "generating the reservoir simulation model for the simulation using the geocellular grid", and "applying the reservoir simulation model to generate, for a survey region, a three-dimensional array of data values that is rendered to form a three dimensional image of the survey region”, they represent no more than just “apply it” limitations, because 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.
This judicial exception is not integrated into a practical application (2019 PEG Step 2A, Prong Two: Additional elements that integrate the Judicial exception/Abstract idea into a practical application? = NO).
Independent claim 1, Step 2B: As discussed with respect to Step 2A, Prong two, limitations invoking computers or other machinery merely as a tool to perform an existing process are just “apply it” limitations. See MPEP 2106.05(f)(2). See for example in the Specification (underline emphasis added) "[0053]… generate (create) a geocellular grid for the earth model (e.g., data volume 170) by running the simulation on either cloud platform 204 and/or desktop platform”.
As discussed with respect to Step 2A, Prong two, claim 1 recites data gathering, these limitations are recited at a high level of generality; and therefore, remain insignificant extra-solution activity even upon reconsideration.
As discussed with respect to Step 2A, Prong two, limitations reciting only the idea of a solution or outcome are just “apply it” limitations, because they fail to recite details of how a solution to a problem is accomplished. See MPEP 2106.05(f)(1).
As to the limitations "that is trained based on previously run reservoir simulations including cell grid counts used during the previously run simulations", the Specification merely reads:
'[0040] Each neural network model (cloud neural network or desktop neural network) may be trained using data collected from simulations running on corresponding cloud or desktop platforms. As more and more simulations are executed and data therefrom are collected, such neural networks are better trained and accuracy of their predictions improves. The data collected from simulations, with which neural networks can be trained, include but are not limited to, cell grid counts (number of cells) used initially and adjustments made thereto (e.g., upscaling or downscaling the grid count) during the simulation process, whether created earth models (based on such grid counts) resulted in acceptable simulations or not, etc.
[0047]… In an example in which neural network 412 is used to identify objects in images, neural network 412 can be trained using training data of past instances of execution of reservoir simulation models using various collected data, number of processors used, CPU processing times, etc.'
As to the limitations "generating the reservoir simulation model for the simulation using the geocellular grid", they are not elaborated but merely repeated in the Application description.
As to the limitations "applying the reservoir simulation model to generate, for a survey region, a three-dimensional array of data values that is rendered to form a three dimensional image of the survey region", see in the Specification (underline emphasis added):
'[0021] Various computer modeling techniques exist by way a reservoir such as reservoir 154 and behavior thereof can be modeled. These modeling techniques can provide a three dimensional array of data values… The volumetric data format readily lends itself to computational analysis and visual rendering, and for this reason, data volume 170 may be termed a "three-dimensional image" of the survey region (e.g. oilfield 100).
[0022] In one example, in order to generate data volume 170, implemented computer reservoir modeling programs require a grid cell count for the geocellular reservoir model to be generated or for a gridless reservoir model to be rendered onto a grid with the requisite purpose of numerical flow simulation'.
Thus, taken alone the individual additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the additional elements taken individually. There is no indication that their combination improves the functioning of a computer itself or improves any other technology (underline emphasis added). Therefore, the claim does not amount to significantly more than the abstract idea itself (2019 PEG Step 2B: NO).
Claims 8 and 15 recite substantially the same elements as claim 1 and are rejected for the same reasons above. Further, the additional elements of these claims are rejected below:
Independent claims 8 and 15, Step 2A, Prong Two and Step 2B: the claims recite the additional elements: a device comprising one or more memories and one or more processors and computer-readable media. They are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. Their collective functions merely provide conventional computer implementation, which is described in the specification (underline emphasis added): "[0057]… processor 510 can include any general purpose processor". The use of a computer to implement the abstract idea of a mathematical algorithm has not been held by the courts to be enough to qualify as “significantly more”.
Dependent claims, Step 2A, Prong One: The claim limitations further the mathematical concepts of their independent claims. (See Independent claim 1, Step 2A, Prong One above). If a claim limitation, under its broadest reasonable interpretation, covers mathematical concepts, then it falls within the "(a) Mathematical concepts" grouping of abstract ideas (2019 PEG Step 2A, Prong One: Abstract Idea Grouping? = Yes, (a) Mathematical concepts).
Dependent claims, Step 2A Prong two:
As to the limitations “2/9/16… receiving a first input, a second input and at least one third input, the first input specifying a simulation time for using a simulation platform to create the reservoir simulation model, the second input specifying a duration of time over which an underlying object is to be simulated, the at least one third input identifying a time step for the simulation” and “3/10/17… wherein the at least one third input includes a minimum time step and a maximum time step”, these limitations describe the concept of “mere data gathering”, which corresponds to the concepts identified as abstract ideas by the courts. Data gathering, including when limited to particular content does not change its character as information, is also within the realm of abstract ideas. Data gathering has not been held by the courts to be enough to qualify as “significantly more”. See Electric Power. See also MPEP § 2106.05(g).
As to the limitations "6/13/20… wherein the machine learning model is a neural network model that is one of a first model for cloud based simulation or a second model for desktop, workstation or laptop machine based simulation", they are no more than intended use.
As to the limitations "7/14… wherein the reservoir simulation model is an earth, geomechanical, petro-elastic model for examining natural resource availability within a target reservoir; and the reservoir simulation model is used to generate a reservoir simulation model for the target reservoir”, they represent no more than just “apply it” limitations, because the claims recite only the idea of a solution or outcome.
This judicial exception is not integrated into a practical application of the exception (2019 PEG Step 2A, Prong Two: Additional elements that integrate the Judicial exception/Abstract idea into a practical application? = NO).
Dependent claims Step 2B:
As discussed with respect to Step 2A, claims recite data gathering. These limitations are recited at a high level of generality; and therefore, remain insignificant extra-solution activity even upon reconsideration.
As discussed with respect to Step 2A, Prong two, the limitations "6/13/20… wherein the machine learning model is a neural network model that is one of a first model for cloud based simulation or a second model for desktop, workstation or laptop machine based simulation" are no more than intended use, because no actual simulation is ever performed in the body of the claim.
As discussed with respect to Step 2A, Prong two, limitations reciting only the idea of a solution or outcome are just “apply it” limitations, because they fail to recite details of how a solution to a problem is accomplished. See MPEP 2106.05(f)(1). As to the limitations "wherein the reservoir simulation model is an earth, geomechanical or petro-elastic model for examining natural resource availability within a target reservoir", see in the Specification (underline emphasis added):
'BACKGROUND
[0002] During various phases of natural resource exploration and production, it may be necessary to characterize and model a target reservoir to determine availability and potential of natural resources production in the target reservoir…
[0012] Analysis of a target reservoir for production of natural resources such as oil, gas, etc., involves studying various petrophysical properties and a large amount of seismic data. An earth model, a geomechanical model and/or a petro-elastic model is an integral part of such analysis to understand the target reservoir and is used in simulating the reservoir…
[0013]… optimizing a determination of a number of grid cells to be used in creating an earth model (and/or alternatively a geomechanical model and/or a petro-elastic model) for reservoir simulation'.
Therefore, the claims do not amount to significantly more than the abstract idea itself (2019 PEG Step 2B: NO).
Allowable Subject Matter
Claims 1-4, 6-11, 13-18, and 20 contain allowable subject matter.
The following is a statement of reasons for the indication of allowable subject matter:
No reference cited taken either alone or in combination and with the prior art of record discloses
Claims 1, 8, and 15, "… determining a grid cell count… based on both the at least one processing time… and a number of processors available to the reservoir simulation model for performing the simulation by: inputting the at least one processing time and the number of processors into a machine learning model, that is trained based on previously run reservoir simulations including cell grid counts used during the previously run simulations; and receiving an output of the machine learning model as the grid cell count; creating the geocellular grid using the grid cell count; generating the reservoir simulation model for the simulation using the geocellular grid…",
in combination with the remaining steps, elements, and features of the claimed invention. Also, there is no motivation to combine any references to meet these limitations. It is for these reasons that Applicant's invention defines over the prior art of record.
As allowable subject matter has been indicated, applicant's reply must either comply with all formal requirements or specifically traverse each requirement not complied with. See 37 CFR 1.111(b) and MPEP § 707.07(a).
Response to Arguments
Regarding the claim 112 objections, the amendment corrected all deficiencies, and those rejections are withdrawn.
Regarding the rejections under 101, Applicant's arguments have been considered, but they are not persuasive. Applicant argues, (see page 7, 4th paragraph to page 9, 3rd paragraph):
‘… A claim does not constitute a mental process when it contains limitations that cannot practically be performed in the human mind. The currently pending claims include limitations that cannot practically be performed in the human mind.
For example, claim 1 includes the limitations of "determining a grid cell count for creating a geocellular grid for the simulation based on both the at least one processing time for performing the simulation and a number of processors available to the reservoir simulation model for performing the simulation by: inputting the at least one processing time and the number of processors into a machine learning model, that is trained based on previously run reservoir simulations including cell grid counts used during the previously run simulations; and receiving an output of the machine learning model as the grid cell count." It is impractical to practice the foregoing limitations in a human mind since the mind is incapable of implementing a complex machine learning model, let alone a complex machine learning model that is trained on previously run reservoir simulations.
Claim 1 also includes the limitations of "generating the reservoir simulation model for the simulation using the geocellular grid; and applying the reservoir simulation model to generate, for a survey region, a three-dimensional array of data values that is rendered to form a three dimensional image of the survey region." Similar to as discussed with the previous limitations it is impractical to practice the foregoing limitations in the human mind since the human mind is incapable of implementing a complex reservoir simulation model, let alone a complex model to generate a three dimensional array of data values for rendering a three dimensional image of a survey region. Further, generating a reservoir simulation model is complex process that can be performed by training the model with a large number of variables. These models are trained and implemented "using high performance cloud based computation resources and/or stationary desktop resources." As-Filed Specification para. [0012]. The process of training the reservoir simulation model is therefore impractical to implement in the human mind…
… the limitations merely involve a judicial exception. Specifically, the limitations do not describe any specific calculations, relationships, formulas, or equations. More Specifically, the limitations merely state:
"determining a grid cell count ... by: inputting the at least one processing time and the number of processors into a machine learning model, that is trained based on previously run reservoir simulations including cell grid counts used during the previously run simulations; and receiving an output of the machine learning model as the grid cell count."
Further, the limitations state "generating the reservoir simulation model for the simulation using the geocellular grid; and applying the reservoir simulation model to generate, for a survey region, a three-dimensional array of data values that is rendered to form a three dimensional image of the survey region."
These limitations are silent as to any specific calculations, relationships, formulas, or equations that are used in training and implementing the models.’
The MPEP reads (underline emphasis added):
‘2106.04… II… A… 2. Prong Two asks does the claim recite additional elements that integrate the judicial exception into a practical application?… If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B… For a claim reciting a judicial exception to be eligible, the additional elements (if any) in the claim must "transform the nature of the claim" into a patent-eligible application of the judicial exception, Alice… either at Prong Two or in Step 2B’
‘2106.05(f) Mere Instructions To Apply An Exception [R-10.2019]… In addition to the abstract idea, the claims also recited the additional element of…’.
‘2106.07(a)… II… After identifying the judicial exception in the rejection, identify any additional elements (features/limitations/steps) recited in the claim beyond the judicial exception and explain why they do not integrate the judicial exception into a practical application and do not add significantly more to the exception’
About "additional elements", BASCOM2, (BASCOM hereinafter) reads:
“the ‘elements of each claim both individually and ‘as an ordered combination’ to determine whether the additional elements [beyond those that recite the abstract idea”.
MPEP 2106.04(a)(2) Abstract Idea Groupings [R-07.2022] reads (underline emphasis added):
'C. Mathematical calculations A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation. Examples of mathematical calculations recited in a claim include… v. using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps'.
Examiner's response: Applicant’s argument is not persuasive, because Applicant’s arguments conflate judicial exception(s) or abstract idea(s) (Step 2A, Prong One) with additional elements (Step 2A, Prong Two or Step 2B). Throughout the prosecution of this application, in accordance with the guidance set forth in MPEP and in several decisions, BASCOM (supra) for example, the Examiner does not conflate judicial exception(s) or abstract idea(s) (Step 2A, Prong One) with additional elements (Step 2A, Prong Two or Step 2B).
Applicant argues 'claim 1 includes the limitations… inputting the at least one processing time and the number of processors into a machine learning model, that is trained based on previously run reservoir simulations including cell grid counts used during the previously run simulations; and receiving an output of the machine learning model as the grid cell count."', but these limitations were addressed in Examiner's rejection Step 2A, Prong Two and/or Step 2B. Applicant's arguments do not address these limitations as additional elements, as pointed out by the Examiner. (See rejection Independent claim 1, Step 2B). In sum, Applicant's arguments address these claim limitations as not being directed to abstract ideas and do not address these claim limitations as additional elements as pointed out by the Examiner.
If a claim limitation, under its broadest reasonable interpretation – see MPEP 2106.04(a)(2) supra, covers mathematical concepts, then it falls within the "(a) Mathematical concepts" grouping of abstract ideas. (See Independent claim 1, Step 2A, Prong One above).
Applicant further argues, (see page 9, 4th to last paragraph):
‘… Claim 1 is related to determining a geocellular count for generating a reservoir simulation model through application of a machine learning model. This practical application corresponds to an improvement to a technical field. Specifically, the practical application relates to improving how a reservoir simulation model is generated. More specifically, the processing time and available processors in implementing a reservoir simulation model with a specific grid cell count are accounted for through application of a machine learning model to determine a suitable grid cell count and subsequently create the reservoir simulation model. As described in the Specification, "higher cell counts, generated data volume 170 can model greater detail of the assumed behavior of reservoir 154 more accurately at a cost of significant computational resource consumption and time." As-Filed Specification, para. [0022]. Conversely, "the lower the cell count, with lower cell counts, generated data volume 170 can model the assumed behavior of reservoir 154 less accurately at a lower cost of computational resource consumption and time." Id. Therefore, optimization of the number of grid cells to be used for generating data volume 170 can be of significant value to end users and relevant businesses."…’
As pointed out by Applicant, the application description reads (underline emphasis added):
'[0021] Various computer modeling techniques exist by way a reservoir such as reservoir 154 and behavior thereof can be modeled. These modeling techniques can provide a three dimensional array of data values. Such data values may correspond to collected survey data, scaling data, simulation data, and/or other values. Collected survey data, scaling data, and/or simulation data is of little use when maintained in a raw data format. Hence collected data, scaling data, and/or simulation data is sometimes processed to create a data volume, i.e., a three dimensional array of data values such as the data volume 170 of FIG. 1C. Data volume 170 represents a distribution of formation characteristics throughout the survey region.… The volumetric data format readily lends itself to computational analysis and visual rendering, and for this reason, data volume 170 may be termed a "three-dimensional image" of the survey region (e.g. oilfield 100).
[0022] In one example, in order to generate data volume 170, implemented computer reservoir modeling programs require a grid cell count for the geocellular reservoir model to be generated or for a gridless reservoir model to be rendered onto a grid with the requisite purpose of numerical flow simulation. With higher cell counts, generated data volume 170 can model greater detail of the assumed behavior of reservoir 154 more accurately at a cost of significant computational resource consumption and time. On the other hand, the lower the cell count, with lower cell counts, generated data volume 170 can model the assumed behavior of reservoir 154 less accurately at a lower cost of computational resource consumption and time. Accordingly, optimization of the number of grid cells to be used for generating data volume 170 can be of significant value to end users and relevant businesses.'
The MPEP reads (underline emphasis added):
'2106.04(d)(1) Evaluating Improvements in the Functioning of a Computer, or an Improvement to Any Other Technology or Technical Field in Step 2A Prong Two [R-10.2019]... the "improvements" analysis in Step 2A determines whether the claim pertains to an improvement to the functioning of a computer or to another technology… invention may integrate the judicial exception into a practical application by demonstrating that it improves the relevant existing technology although it may not be an improvement over well-understood, routine, conventional activity… the word "improvements" in the context of this consideration is limited to improvements to the functioning of a computer or any other technology/technical field, whether in Step 2A Prong Two or in Step 2B...'.
Examiner's response: Applicant's argument is not persuasive, because the claims may provide an improved abstract idea but do not provide limitations such that an improvement to the functioning of a computer itself or to any other technology is realized. An improved abstract idea is an abstract idea. An improved abstract idea is a species of the genus abstract idea. As to the argued 'generated data volume 170 can model', see supra Specification paragraphs [0021,0022], information and/or data also fall within the realm of abstract ideas as information and data are intangible. See Electric Power: “Information… is an intangible”. This idea is similar to the basic concept of manipulating information using mathematical relationships, e.g., converting numerical representation in Gottschalk v. Benson3 (underline emphasis added) (holding that an algorithm that merely transforms data from one form to another is not patent-eligible).
Therefore, the rejections are maintained.
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.
Examiner would like to point out that any reference to specific figures, columns and lines should not be considered limiting in any way, the entire reference is considered to provide disclosure relating to the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUAN CARLOS OCHOA whose telephone number is (571)272-2625. The examiner can normally be reached Mondays, Tuesdays, Thursdays, and Fridays 9:30AM 7:00 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, Renee Chavez can be reached on 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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/JUAN C OCHOA/Primary Examiner, Art Unit 2186
1 Electric Power Group, LLC v. Alstom S.A., 119 USPQ2d 1739 Fed. Cir. 2016
2 BASCOM Global Internet Services, Inc. v. AT&T Mobility LLC, U.S. Court of Appeals for the Federal Circuit, No. 2015-1763 (June 27, 2016)
3 Gottschalk v. Benson, 409 U.S. 63, 67 (1972)