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 .
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.
Step 1 of the Subject Matter Eligibility Test entails considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter.
Claims 1-20 are directed to a method (process), a system (machine or manufacture), and a non-transitory medium (manufacture), respectively. As such, the claims are directed to statutory categories of invention.
If the claim recites a statutory category of invention, the claim requires further analysis in Step 2A. Step 2A of the Subject Matter Eligibility Test is a two-prong inquiry. In Prong One, examiners evaluate whether the claim recites a judicial exception.
Claim 1 recites abstract limitations, including: “a stochastic model configured to execute a Monte Carlo algorithm that quantifies uncertainty associated with parameters characterizing a geothermal subsurface reservoir via a plurality of probability distributions; and an economic analyzer configured to estimate an amount of electrical energy associated with the geothermal subsurface reservoir based on the stochastic model.“
Claim 8 recites abstract limitations, including: “executing a Monte Carlo algorithm that quantifies uncertainty associated with parameters characterizing a geothermal subsurface reservoir to generate a stochastic model of the parameters that includes a plurality of probability distributions; and estimating an amount of electrical energy associated with the geothermal subsurface reservoir based on the stochastic model.”
Claim 15 recites abstract limitations, including: “execute a Monte Carlo algorithm that quantifies uncertainty associated with parameters characterizing the geothermal subsurface reservoir to generate a stochastic model of the parameters that includes a plurality of probability distributions; and estimate an amount of electrical energy associated with the geothermal subsurface reservoir based on the stochastic model.”
These limitations, as drafted, are a process that, under its broadest reasonable interpretation, represent mathematical relationships, mathematical formulas or equations, and/or mathematical calculations and are therefore mathematical concepts. The mere recitation of a generic computer does not take the claim out of the mathematical concepts grouping. Thus, the claim recites an abstract idea.
If the claim recites a judicial exception in step 2A Prong One, the claim requires further analysis in step 2A Prong Two. In step 2A Prong Two, examiners evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The claim recites the additional elements of: memory, one or more processors, and a computer readable storage medium.
The memory, one or more processors, and computer readable storage medium are recited at a high-level of generality and are merely invoked as tools to perform the abstract idea (i.e., “apply it”).
Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
If the additional elements do not integrate the exception into a practical application in step 2A Prong Two, then the claim is directed to the recited judicial exception, and requires further analysis under Step 2B to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).
As discussed above, the memory, one or more processors, and computer readable storage medium are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (MPEP 2106.05(f)).
Thus, even when viewed as an ordered combination, nothing in the claims add significantly more (i.e., an inventive concept) to the abstract idea.
Claims 2-4, 9-14, and 16-20 further recite:
the economic analyzer is further configured to determine an amount of avoided carbon dioxide emissions based on an amount of hydrocarbon fuel associated with production of the amount of electrical energy and a carbon dioxide emission rate associated with the hydrocarbon fuel;
an analytical mass and heat model configured to compute an amount of heat energy available from the subsurface reservoir based on the stochastic model, wherein the parameters are user defined inputs;
a reservoir simulator configured to generate a computational model that characterizes the geological and geothermal properties of the subsurface reservoir and outputs one or more of the parameters;
determining an amount of avoided carbon dioxide emissions based on an amount of hydrocarbon fuel associated with production of the amount of electrical energy and a carbon dioxide emission rate associated with the hydrocarbon fuel;
computing, via an analytical mass and heat model, an amount of heat energy available from the subsurface reservoir based on the stochastic model, wherein the parameters are user defined inputs;
generating, via a reservoir simulator, a computational model that characterizes the geological and geothermal properties of the subsurface reservoir and outputs one or more of the parameters;
determining a fluid outflow metric that characterizes a volume, rate, or combination thereof of a fluid extracted from the geothermal subsurface reservoir; and estimating an amount of thermal energy associated with the geothermal subsurface reservoir, wherein the estimating the amount of electrical energy is based on the fluid outflow metric and the estimated amount of thermal energy;
estimating an amount of a targeted mineral comprised within the geothermal subsurface reservoir;
the target mineral is lithium oxide or a lithium carbonate equivalent;
the computer executable instructions cause the one or more processors to: determine an amount of avoided carbon dioxide emissions based on an amount of hydrocarbon fuel associated with production of the amount of electrical energy and a carbon dioxide emission rate associated with the hydrocarbon fuel,
the computer executable instructions cause the one or more processors to: compute, via an analytical mass and heat model, an amount of heat energy available from the subsurface reservoir based on the stochastic model, wherein the parameters are user defined inputs;
the computer executable instructions cause the one or more processors to: generate, via a reservoir simulator, a computational model that characterizes the geological and geothermal properties of the subsurface reservoir and outputs one or more of the parameters;
the computer executable instructions cause the one or more processors to: determine a fluid outflow metric that characterizes a volume, rate, or combination thereof of a fluid extracted from the geothermal subsurface reservoir; and estimate an amount of thermal energy associated with the geothermal subsurface reservoir, wherein the estimating the amount of electrical energy is based on the fluid outflow metric and the estimated amount of thermal energy; and
the computer executable instructions cause the one or more processors to: estimate an amount of a targeted mineral comprised within the geothermal subsurface reservoir which merely narrows the previously recited abstract idea limitations.
Claim 5 recites “an enhanced geothermal system operably coupled to the reservoir simulator and stochastic model, wherein the parameters include: a fluid outflow metric measured by a fluid gathering system of the enhanced geothermal system, and an electric power metric measured by an electrical conversion system of the enhanced geothermal system” which is considered insignificant extra-solution activity. Banting (US 2008/0100436 see ¶ [0023, 0027]) discloses having a geothermal system that includes an electric power metric measured by an electrical conversion system of the geothermal system is well-known, routine, and conventional in the art. Toussaint (US 2022/0178590 see ¶ [0030]) discloses having a geothermal system that includes a fluid outflow metric measured by a fluid gathering system of the geothermal system is well-known, routine, and conventional in the art. Myougan (US 2011/0144947 see ¶ [0009]) discloses having a geothermal system operably coupled to a model is well-known, routine, and conventional in the art.
Claims 6-7 further recite:
the parameters further include a rare earth element concentration measured by a mineral gathering system of the enhanced geothermal system; and
the economic analyzer is further configured to predict an amount of rare earth elements contained within the geothermal subsurface reservoir based on the rare earth element concentration which merely narrows the previously recited abstract idea limitations.
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.
Claims 1, 3-4, 8, 10-12, 15, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Garg (“A reformulation of USGS volumetric ‘heat in place’ resource estimation method) in view of Eliseeva (US 2017/0371984).
With respect to claim 1: Garg discloses a system, comprising:
a stochastic model configured to execute a Monte Carlo algorithm that quantifies uncertainty associated with parameters characterizing a geothermal subsurface reservoir via a plurality of probability distributions (Section 1. Introduction; pgs. 150-151); and
an economic analyzer configured to estimate an amount of electrical energy associated with the geothermal subsurface reservoir based on the stochastic model (Section 5. Electric generating capacity, pgs. 154-157; see specifically pg. 157 and Section 6. Summary and conclusions, pg. 157; Table 3).
Garg does not disclose that the system includes memory to store computer executable instructions; and one or more processors, operatively coupled to the memory, that execute the computer executable instructions to implement the above process.
Eliseeva teaches it is known in the art to have memory to store computer executable instructions (¶ [0039]); and one or more processors, operatively coupled to the memory, that execute the computer executable instructions to implement a modeling/forecasting process (¶ [0039]). It would be obvious to one having ordinary skill in the art before the effective filing date to combine the memory and processor of Eliseeva with the invention of Garg with a reasonable expectation of success since doing so would allow the processor and memory to perform their designed function performing the instructions (i.e., method) (Eliseeva ¶ [0039]).
With respect to claim 3: Garg from the combination of Garg and Eliseeva further teaches an analytical mass and heat model configured to compute an amount of heat energy available from the subsurface reservoir based on the stochastic model, wherein the parameters are user defined inputs (Section 2. Recoverable heat; Section 3. Available work and conversion efficiency; Section 4. Thermal recovery factor; Section 5. Electric generating capacity, pgs. 154-157; see specifically pg. 157; Table 2).
With respect to claim 4: Garg from the combination of Garg and Eliseeva further teaches a reservoir simulator configured to generate a computational model that characterizes the geological and geothermal properties of the subsurface reservoir and outputs one or more of the parameters (Section 5. Electric generating capacity, pgs. 154-157; see specifically pg. 157 and Section 6. Summary and conclusions, pg. 157; Table 3).
With respect to claim 8: Garg discloses a method, comprising:
executing a Monte Carlo algorithm that quantifies uncertainty associated with parameters characterizing a geothermal subsurface reservoir to generate a stochastic model of the parameters that includes a plurality of probability distributions (Section 1. Introduction; pgs. 150-151); and
estimating an amount of electrical energy associated with the geothermal subsurface reservoir based on the stochastic model (Section 5. Electric generating capacity, pgs. 154-157; see specifically pg. 157 and Section 6. Summary and conclusions, pg. 157; Table 3).
Garg does not disclose that the method is a computer implemented method (i.e., includes memory to store computer executable instructions; and one or more processors, operatively coupled to the memory, that execute the computer executable instructions to implement the above process).
Eliseeva teaches it is known in the art to have a computer implemented method (¶ [0039]). It would be obvious to one having ordinary skill in the art before the effective filing date to combine the computer of Eliseeva with the invention of Garg with a reasonable expectation of success since doing so would allow the computer to perform their designed function performing the instructions (i.e., method) (Eliseeva ¶ [0039]).
With respect to claim 10: Garg from the combination of Garg and Eliseeva further teaches computing, via an analytical mass and heat model, an amount of heat energy available from the subsurface reservoir based on the stochastic model, wherein the parameters are user defined inputs (Section 2. Recoverable heat; Section 3. Available work and conversion efficiency; Section 4. Thermal recovery factor; Section 5. Electric generating capacity, pgs. 154-157; see specifically pg. 157; Table 2).
With respect to claim 11: Garg from the combination of Garg and Eliseeva further teaches generating, via a reservoir simulator, a computational model that characterizes the geological and geothermal properties of the subsurface reservoir and outputs one or more of the parameters (Section 5. Electric generating capacity, pgs. 154-157; see specifically pg. 157 and Section 6. Summary and conclusions, pg. 157; Table 3).
With respect to claim 12: Garg from the combination of Garg and Eliseeva further teaches determining a fluid outflow metric that characterizes a volume, rate, or combination thereof of a fluid extracted from the geothermal subsurface reservoir (Section 5.3. Production and injection phase); and estimating an amount of thermal energy associated with the geothermal subsurface reservoir, wherein the estimating the amount of electrical energy is based on the fluid outflow metric and the estimated amount of thermal energy (Section 5. Electric generating capacity, pgs. 154-157; Section 6. Summary and conclusions, pg. 157; Table 3).
With respect to claim 15: Garg discloses a process, comprising:
execute a Monte Carlo algorithm that quantifies uncertainty associated with parameters characterizing the geothermal subsurface reservoir to generate a stochastic model of the parameters that includes a plurality of probability distributions (Section 1. Introduction; pgs. 150-151); and
estimate an amount of electrical energy associated with the geothermal subsurface reservoir based on the stochastic model (Section 5. Electric generating capacity, pgs. 154-157; see specifically pg. 157 and Section 6. Summary and conclusions, pg. 157; Table 3).
Garg does not disclose that the process is on a computer program product for predicting electrical energy production associated with a geothermal subsurface reservoir, the computer program product comprising a computer readable storage medium having computer executable instructions embodied therewith, the computer executable instructions executable by one or more processors to cause the one or more processors to perform the process.
Eliseeva teaches it is known in the art to have a computer program product for modeling/forecasting, the computer program product comprising a computer readable storage medium having computer executable instructions embodied therewith, the computer executable instructions executable by one or more processors to cause the one or more processors to perform the process (¶ [0039]). It would be obvious to one having ordinary skill in the art before the effective filing date to combine the computer program product of Eliseeva with the invention of Garg with a reasonable expectation of success since doing so would allow the computer to perform their designed function performing the instructions (i.e., method) (Eliseeva ¶ [0039]).
With respect to claim 17: Garg from the combination of Garg and Eliseeva further teaches compute, via an analytical mass and heat model, an amount of heat energy available from the subsurface reservoir based on the stochastic model, wherein the parameters are user defined inputs (Section 2. Recoverable heat; Section 3. Available work and conversion efficiency; Section 4. Thermal recovery factor; Section 5. Electric generating capacity, pgs. 154-157; see specifically pg. 157; Table 2).
With respect to claim 18: Garg from the combination of Garg and Eliseeva further teaches generate, via a reservoir simulator, a computational model that characterizes the geological and geothermal properties of the subsurface reservoir and outputs one or more of the parameters (Section 5. Electric generating capacity, pgs. 154-157; see specifically pg. 157 and Section 6. Summary and conclusions, pg. 157; Table 3).
19. The computer program product of claim 15, wherein the computer executable instructions cause the one or more processors to: determine a fluid outflow metric that characterizes a volume, rate, or combination thereof of a fluid extracted from the geothermal subsurface reservoir; and estimate an amount of thermal energy associated with the geothermal subsurface reservoir, wherein the estimating the amount of electrical energy is based on the fluid outflow metric and the estimated amount of thermal energy.
With respect to claim 19: Garg from the combination of Garg and Eliseeva further teaches determine a fluid outflow metric that characterizes a volume, rate, or combination thereof of a fluid extracted from the geothermal subsurface reservoir (Section 5.3. Production and injection phase); and estimate an amount of thermal energy associated with the geothermal subsurface reservoir, wherein the estimating the amount of electrical energy is based on the fluid outflow metric and the estimated amount of thermal energy (Section 5. Electric generating capacity, pgs. 154-157; Section 6. Summary and conclusions, pg. 157; Table 3).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Garg and Eliseeva as applied to claim 4 above, and further in view of Banting (US 2008/0100436).
With respect to claim 5: Garg from the combination of Garg and Eliseeva further teaches an enhanced geothermal system operably coupled to the reservoir simulator and stochastic model (Section 5.3. Production and injection phase), wherein the parameters include: a fluid outflow metric measured by a fluid gathering system of the enhanced geothermal system (Section 5.3. Production and injection phase). The combination of Garg and Eliseeva does not teach the parameters include an electric power metric measured by an electrical conversion system of the enhanced geothermal system. Banting teaches it is known in the art to have an electric power metric measured by an electrical conversion system of the enhanced geothermal system (¶ [0023, 0027]). It would be obvious to one having ordinary skill in the art before the effective filing date to combine the electric power metric measured by the electrical conversion system of Banting with the invention of Garg and Eliseeva with a reasonable expectation of success since doing so would aid in viewing and controlling operations (Banting ¶ [0030]).
Claims 13-14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Garg and Eliseeva as applied to claims 8 and 19 above, and further in view of Smith (US 2023/0125404)
With respect to claims 13 and 20: The combination of Garg and Eliseeva teaches all aspects of the claimed invention except for estimating an amount of a targeted mineral comprised within the geothermal subsurface reservoir. Smith teaches estimating an amount of a targeted mineral within a geothermal subsurface reservoir (¶ [0042, 0055, 0061]). It would be obvious to one having ordinary skill in the art before the effective filing date to combine the estimation of an amount of the targeted mineral of Smith with the invention of Garg and Eliseeva with a reasonable expectation of success since doing so would “aid and enhance current data acquisition methodologies used in industry and research data collected by academia or governmental agencies/organizations. Focusing on adding value to the current global subsurface model and filling the described gaps can augment existing data.” (Smith ¶ [0062]).
With respect to claim 14: The combination of Garg, Eliseeva, and Smith teaches all aspects of the claimed invention except for the target mineral is lithium oxide or a lithium carbonate equivalent.
It would be obvious to one having ordinary skill in the art before the effective filing date to have the target mineral be lithium oxide or a lithium carbonate equivalent, since it has been held to be within the ordinary skill in the art to select a known material on the basis of its suitability for the intended use. Sinclair & Carroll Co. v. Interchemical Corp., 325 U.S. 327, 65 USPQ 297 (1945); See also In re Leshin, 277 F.2d 197, 125 USPQ 416 (CCPA 1960).
Allowable Subject Matter
Claims 2, 6-7, 9, and 16 are objected to as being dependent upon a rejected base claim, but would be allowable over the art if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Examiner notes that claims 1-20 are still rejected under 101 as discussed above.
The following is a statement of reasons for the indication of allowable subject matter:
The art of record does not teach or make obvious the analysis to relate carbon dioxide emissions avoidance related to the amount of electrical energy, and the associated hydrocarbon fuel when modeling geothermal electricity production in combination with the other claim limitations.
The art of record does not teach or make obvious the parameters, that have uncertainties associated with said parameters, that characterize a geothermal reservoir includes a rare earth element concentration measured by a mineral gathering system of the enhanced geothermal system in combination with the other claim limitations.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KRISTYN A HALL whose telephone number is (571)272-8384. The examiner can normally be reached M-F 9:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nicole Coy can be reached at (571) 272-5405. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KRISTYN A HALL/Primary Examiner, Art Unit 3672
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