DETAILED ACTION
This Office action is in reply to application no. 18/243,332, filed 7 September 2023. Claims 1-20 are pending and are considered below.
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 .
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. The claim(s) recite(s) data gathering, performing a regression in no particular manner, determining a residual and model in no particular manner, performing a second regression in no particular manner, and updating a model in no particular manner but simply based on the available data.
First, regression is explicitly a mathematical technique. It is quite routine that mathematics textbooks at the collegiate level include entire chapters on the topic. So the claims recite mathematics. Second, there are many simply regression techniques that can be performed mentally and with pen and paper – the students who study from the aforementioned textbooks do this quite routinely. None of this presents any practical difficulty and none requires any technology beyond a pen and paper. So the claims recite human mental work.
This judicial exception is not integrated into a practical application because aside from the bare inclusion of a generic computer, discussed below, nothing is done beyond what was set forth above, which does not go beyond using a generic computer as a tool to implement the abstract idea. See MPEP § 2106.05(f).
As the claims only manipulate data regarding regressions, they do not improve the “functioning of a computer” or of “any other technology or technical field”. See MPEP § 2106.05(a). They do not apply the abstract idea “with, or by use of a particular machine”, MPEP § 2106.05(b), as the below-cited Guidance is clear that a generic computer is not the particular machine envisioned.
They do not effect a “transformation or reduction of a particular article to a different state or thing”, MPEP § 2106.05(c). First, such data, being intangible, are not a particular article at all. Second, the claimed manipulation is neither transformative nor reductive; as the courts have pointed out, in the end, data are still data.
They do not apply the abstract idea “in some other meaningful way beyond generally linking [it] to a particular technological environment”, MPEP § 2106.05(e), as the lack of technical and algorithmic detail in the claims is so as not to go beyond such a general linkage.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional claim limitations, considered individually and as an ordered combination, are insufficient to elevate an otherwise-ineligible claim.
Taking claims 1 and 20 together, they include a processor and a memory storing instructions for the processor to execute. These elements are recited at a high degree of generality and the specification is clear, ¶ 72, that nothing more than a “general purpose processor” is required, which encompasses a generic computer.
It only performs generic computer functions of nondescriptly manipulating data and sharing data with persons and/or other devices. Generic computers performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea.
The type of information being manipulated does not impose meaningful limitations or render the idea less abstract. The claim elements when considered in ordered combination – a generic computer performing a chronological sequence of abstract steps – do nothing more than when analyzed individually. The other independent claims are simply different embodiments but are likewise directed to a generic computer performing, essentially, the same process.
The dependent claims further do not amount to significantly more than the abstract idea: claims 2, 5, 12 and 15 consist entirely of a mere duplication of parts, of no patentable significance and which in any case does nothing to make the invention less abstract. Claims 3, 4, 6, 8-10, 13, 14, 16, 18 and 19 are simply further descriptive of the type of information being manipulated, and claims 7 and 17 simply recite further, abstract manipulation of data.
The claims are not patent eligible. For further guidance please see MPEP § 2106.03 – 2106.07(c) (formerly referred to as the “2019 Revised Patent Subject Matter Eligibility Guidance”, 84 Fed. Reg. 50, 55 (7 January 2019, revised October 2019)).
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.
Claim(s) 1-3, 6-13 and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (U.S. Publication No. 2019/0339407) in view of Kim et al. (U.S. Publication No. 2022/0065618).
In-line citations are to Chen.
With regard to Claim 1:
Chen teaches: A system comprising:
a memory; and
one or more processors coupled to the memory, [0012; a computer includes “one or more processors and memory”; the memory stores instructions for the processor to execute] the one or more processors being configured to:
receive training data for modeling at least one of a petrophysical parameter and a fluid property based on reservoir formation data; [0044; “fluid saturation” is a factor in the computation of acoustic impedance and so must have been obtained; 0006; the data may be from possible “hydrocarbon reservoirs in reservoir rock”; referring to data as “training data” is considered mere labeling and given no patentable weight]
perform symbolic regression using the training data to obtain a first set of symbolic regression models… [0053; a “regression function” is used and updated]
perform symbolic regression… to obtain a second set of symbolic regression models; [id.] and
update the first symbolic regression model based on a second symbolic regression model from the second set of symbolic regression models to yield a first revised symbolic regression model. [Sheet 1, Fig. 1; note the entire process iterates]
Chen does not explicitly teach determine a first residual based on the training data and a first symbolic regression model from the first set of symbolic regression models, or using the first residual, but it is known in the art. Kim teaches a process control method [title] that uses and updates a “regression analysis model”. [0006] It may make updates to a model “using a residual error in a previously trained model”; [0090] the residual error may be calculated. [0091] It may include a “difference between an output value of a model” and a desired output value. [0077] A “high-performance regression analysis model” may be used by substituting one model for another. [0086] Kim and Chen are analogous art as each is directed to electronic means for making and using updated regression models.
It would have been obvious to one of ordinary skill in the art just prior to the filing of the claimed invention to combine the teaching of Kim with that of Chen in order to save time, as taught by Kim; [abstract] further, it is simply a substitution of one known part for another with predictable results, simply using Kim’s error datum in a calculation in place of, or in addition to, the data of Chen; the substitution produces no new and unexpected result.
With regard to Claim 2:
The system of claim 1, wherein the one or more processors are further configured to:
determine a second residual based on the training data and the second symbolic regression model;
perform symbolic regression using the second residual to obtain a third set of symbolic regression models; and
update the first revised symbolic regression model based on a third symbolic regression model from the third set of symbolic regression models to yield a second revised symbolic regression model.
This claim is not patentably distinct from claim 1 as it consists entirely of a mere duplication of parts, simply repeating an already-claimed sequence of steps an additional time; this is of no patentable significance as no new and unexpected result is inherent or disclosed. See MPEP § 2144.04(VI)(B).
With regard to Claim 3:
The system of claim 2, wherein the one or more processors are further configured to:
determine a performance delta between the first revised symbolic regression model and the second revised symbolic regression model. [Kim, 0077 as cited above in regard to claim 1; the difference is which number is used for the comparison, which is an obvious substitution of one known datum for another with predictable results]
With regard to Claim 6:
The system of claim 1, wherein the one or more processors are further configured to:
select the first symbolic regression model from the first set of symbolic regression models based on a threshold performance parameter. [Kim, 0086 as cited above in regard to claim 1]
With regard to Claim 7:
The system of claim 1, wherein the one or more processors are further configured to:
receive at least one logging sensor measurement associated with a reservoir formation; [0005; seismic sensors are used to provide data to the system] and
estimate, based on the first revised symbolic regression model, at least one of the petrophysical parameter and the fluid property for the reservoir formation. [0044 as cited above in regard to claim 1]
With regard to Claim 8:
The system of claim 1, wherein the at least one of the petrophysical parameter and the fluid property include at least one of permeability, porosity, saturation, capillary pressure, bound fluid volume, shale volume, rock saturation, productivity index, relative permeability, effective permeability, hydrocarbon properties, formation salinity and gas-oil ratio. [0038; it includes “hydrocarbon saturation level”]
This claim is not patentably distinct from claim 1 as it consists entirely of nonfunctional, descriptive language, disclosing at most human interpretation of data but which imparts neither structure nor functionality to the claimed system and so is considered but given no patentable weight. The reference is provided for the purpose of compact prosecution.
With regard to Claim 9:
The system of claim 1, wherein the reservoir formation data include at least one of nuclear magnetic resonance (NMR) data, resistivity data, induction data, acoustic data, density data, photoelectric (PE) data, spontaneous potential (SP) data, natural gamma ray data, neutron data, volume data, temperature data, and pressure data. [0038; acoustic data may be used]
This claim is not patentably distinct from claim 1 as it consists entirely of nonfunctional, descriptive language, disclosing at most human interpretation of data but which imparts neither structure nor functionality to the claimed system and so is considered but given no patentable weight. The reference is provided for the purpose of compact prosecution.
With regard to Claim 10:
The system of claim 1, wherein the training data includes customized reservoir formation data obtained from a reservoir formation surrounding a wellbore drilled within the reservoir formation. [0007]
This claim is not patentably distinct from claim 1 as it consists entirely of nonfunctional, descriptive language, disclosing at most human interpretation of data but which imparts neither structure nor functionality to the claimed system and so is considered but given no patentable weight. The reference is provided for the purpose of compact prosecution.
With regard to Claim 11:
Chen teaches: A computer-implemented method [abstract; “the method may be executed by a computer system”] comprising:
receiving training data for modeling at least one of a petrophysical parameter and a fluid property based on reservoir formation data; [0044; “fluid saturation” is a factor in the computation of acoustic impedance and so must have been obtained; 0006; the data may be from possible “hydrocarbon reservoirs in reservoir rock”; referring to data as “training data” is considered mere labeling and given no patentable weight]
performing symbolic regression using the training data to obtain a first set of symbolic regression models… [0053; a “regression function” is used and updated]
performing symbolic regression… to obtain a second set of symbolic regression models; [id.] and
updating the first symbolic regression model based on a second symbolic regression model from the second set of symbolic regression models to yield a first revised symbolic regression model. [Sheet 1, Fig. 1; note the entire process iterates]
Chen does not explicitly teach determining a first residual based on the training data and a first symbolic regression model from the first set of symbolic regression models, or using the first residual, but it is known in the art. Kim teaches a process control method [title] that uses and updates a “regression analysis model”. [0006] It may make updates to a model “using a residual error in a previously trained model”; [0090] the residual error may be calculated. [0091] It may include a “difference between an output value of a model” and a desired output value. [0077] A “high-performance regression analysis model” may be used by substituting one model for another. [0086] Kim and Chen are analogous art as each is directed to electronic means for making and using updated regression models.
It would have been obvious to one of ordinary skill in the art just prior to the filing of the claimed invention to combine the teaching of Kim with that of Chen in order to save time, as taught by Kim; [abstract] further, it is simply a substitution of one known part for another with predictable results, simply using Kim’s error datum in a calculation in place of, or in addition to, the data of Chen; the substitution produces no new and unexpected result.
With regard to Claim 12:
The computer-implemented method of claim 11, further comprising:
determining a second residual based on the training data and the second symbolic regression model;
performing symbolic regression using the second residual to obtain a third set of symbolic regression models; and
updating the first revised symbolic regression model based on a third symbolic regression model from the third set of symbolic regression models to yield a second revised symbolic regression model.
This claim is not patentably distinct from claim 11 as it consists entirely of a mere duplication of parts, simply repeating an already-claimed sequence of steps an additional time; this is of no patentable significance as no new and unexpected result is inherent or disclosed. See MPEP § 2144.04(VI)(B).
With regard to Claim 13:
The computer-implemented method of claim 12, further comprising:
determining a performance delta between the first revised symbolic regression model and the second revised symbolic regression model. [Kim, 0077 as cited above in regard to claim 11; the difference is which number is used for the comparison, which is an obvious substitution of one known datum for another with predictable results]
With regard to Claim 16:
The computer-implemented method of claim 11, further comprising:
selecting the first symbolic regression model from the first set of symbolic regression models based on a threshold performance parameter. [Kim, 0086 as cited above in regard to claim 11]
With regard to Claim 17:
The computer-implemented method of claim 11, further comprising:
receiving at least one logging sensor measurement associated with a reservoir formation; [0005; seismic sensors are used to provide data to the system] and
estimating, based on the first revised symbolic regression model, at least one of the petrophysical parameter and the fluid property for the reservoir formation. [0044 as cited above in regard to claim 1]
With regard to Claim 18:
The computer-implemented method of claim 11, wherein the at least one of the petrophysical parameter and the fluid property include at least one of permeability, porosity, saturation, capillary pressure, bound fluid volume, shale volume, rock saturation, productivity index, relative permeability, effective permeability, hydrocarbon properties, formation salinity, and gas-oil ratio, [0038; it includes “hydrocarbon saturation level”] and wherein the reservoir formation data include at least one of nuclear magnetic resonance (NMR) data, resistivity data, induction data, acoustic data, density data, photoelectric (PE) data, spontaneous potential (SP) data, natural gamma ray data, neutron data, volume data, temperature data, and pressure data. [0038; it includes “hydrocarbon saturation level”]
This claim is not patentably distinct from claim 11 as it consists entirely of nonfunctional, descriptive language, disclosing at most human interpretation of data but which imparts neither structure nor functionality to the claimed system and so is considered but given no patentable weight. The reference is provided for the purpose of compact prosecution.
With regard to Claim 19:
The computer-implemented method of claim 11, wherein the training data includes customized reservoir formation data obtained from a reservoir formation surrounding a wellbore drilled within the reservoir formation. [0007]
This claim is not patentably distinct from claim 11 as it consists entirely of nonfunctional, descriptive language, disclosing at most human interpretation of data but which imparts neither structure nor functionality to the claimed system and so is considered but given no patentable weight. The reference is provided for the purpose of compact prosecution.
With regard to Claim 20:
Chen teaches: A non-transitory computer-readable medium having instructions stored thereon which, when executed by a computer or processor, [0012; a computer includes “one or more processors and memory”; the memory stores instructions for the processor to execute] cause the computer or the processor to:
receive training data for modeling at least one of a petrophysical parameter and a fluid property based on reservoir formation data; [0044; “fluid saturation” is a factor in the computation of acoustic impedance and so must have been obtained; 0006; the data may be from possible “hydrocarbon reservoirs in reservoir rock”; referring to data as “training data” is considered mere labeling and given no patentable weight]
perform symbolic regression using the training data to obtain a first set of symbolic regression models… [0053; a “regression function” is used and updated]
perform symbolic regression… to obtain a second set of symbolic regression models; [id.] and
update the first symbolic regression model based on a second symbolic regression model from the second set of symbolic regression models to yield a first revised symbolic regression model. [Sheet 1, Fig. 1; note the entire process iterates]
Chen does not explicitly teach determine a first residual based on the training data and a first symbolic regression model from the first set of symbolic regression models, or using the first residual, but it is known in the art. Kim teaches a process control method [title] that uses and updates a “regression analysis model”. [0006] It may make updates to a model “using a residual error in a previously trained model”; [0090] the residual error may be calculated. [0091] It may include a “difference between an output value of a model” and a desired output value. [0077] A “high-performance regression analysis model” may be used by substituting one model for another. [0086] Kim and Chen are analogous art as each is directed to electronic means for making and using updated regression models.
It would have been obvious to one of ordinary skill in the art just prior to the filing of the claimed invention to combine the teaching of Kim with that of Chen in order to save time, as taught by Kim; [abstract] further, it is simply a substitution of one known part for another with predictable results, simply using Kim’s error datum in a calculation in place of, or in addition to, the data of Chen; the substitution produces no new and unexpected result.
Claim(s) 4, 5, 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. in view of Kim et al. further in view of Polcari et al. (Canada Patent Pub. No. 3 179 983).
Claims 4 and 14 are similar so are analyzed together.
With regard to Claim 4:
The system of claim 3, wherein the one or more processors are further configured to:
select the second revised symbolic regression model as a final symbolic regression model in response to the performance delta being less than a threshold improvement value.
With regard to Claim 14:
The computer-implemented method of claim 13, further comprising:
selecting the second revised symbolic regression model as a final symbolic regression model in response to the performance delta being less than a threshold improvement value.
Chen and Kim teach the system of claim 3 and method of claim 13, including selection of models, but do not explicitly teach basing this on a value being less than a threshold value, but it is known in the art. Polcari teaches a risk modeling system [title] which performs “regression” and in which selection of models may be based on “performance degradation below specified thresholds”. [0167] It may also use a model that results in the “greatest information gain”. [0121] Polcari and Chen are analogous art as each is directed to electronic means for using regression modeling.
It would have been obvious to one of ordinary skill in the art just prior to the filing of the claimed invention to combine the teaching of Polcari with that of Chen and Kim in order to improve the efficiency of a training process, as taught by Polcari; [0107] further, it is simply a substitution of one known part for another with predictable results, simply choosing a model on Polcari’s basis rather than, or in addition to, that of Chen; the substitution produces no new and unexpected result.
With regard to Claim 5:
The system of claim 3, wherein the one or more processors are further configured to:
determine that the performance delta is greater than a threshold improvement value [Polcari, 0121 as cited above in regard to claim 4] and in response:
determine a third residual based on the training data and the third symbolic regression model;
perform symbolic regression using the third residual to obtain a fourth set of symbolic regression models; and
update the second revised symbolic regression model based on a fourth symbolic regression model from the fourth set of symbolic regression models to yield a third revised symbolic regression model.
The last three steps of this claim consist of a mere duplication of parts, simply repeating an already-claimed sequence of steps an additional time; this is of no patentable significance as no new and unexpected result is inherent or disclosed. See MPEP § 2144.04(VI)(B).
With regard to Claim 15:
The computer-implemented method of claim 13, further comprising:
in response to determining that the performance delta is greater than a threshold improvement value: [Polcari, 0121 as cited above in regard to claim 4]
determining a third residual based on the training data and the third symbolic regression model;
performing symbolic regression using the third residual to obtain a fourth set of symbolic regression models; and
updating the second revised symbolic regression model based on a fourth symbolic regression model from the fourth set of symbolic regression models to yield a third revised symbolic regression model.
The three steps of this claim consist of a mere duplication of parts, simply repeating an already-claimed sequence of steps an additional time; this is of no patentable significance as no new and unexpected result is inherent or disclosed. See MPEP § 2144.04(VI)(B).
Conclusion
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/SCOTT C ANDERSON/Primary Examiner, Art Unit 3694
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