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 .
DETAILED ACTION
This communication is a non-final action in response to application filed on 03/20/2024. Claims 1-20 are pending.
Information Disclosure Statement
The IDS filed on 04/05/2024 has been considered.
Claim Objection
Claim 3 is objected because the words “consisting of” appears to be missing from the Markush grouping to ensure it’s a closed list. This appears to be a typographical error. See MPEP 2117 I and 2173.05(h) regarding closed grouping for Markush claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-7, 10-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mehta (US 20220283575) in view of Willmott (US 20190032947)
As per claim 1, Mehta discloses a computer-implemented method of operating a hydrogen production facility to meet carbon intensity (CI) requirements, the method being executed by at least one hardware processor and comprising:
receiving, using a computer system, operational parameter data from the hydrogen production facility, the operational parameter data being representative of measured and/or determined time-dependent values of one or more operational parameters of the hydrogen production facility (see at least Mehta, 0124, hydrogen production plant control system 112 monitors generation by measurement. See also 0204 plant operation module uses time-dependent data inputs);
;
generating, from the one or more linear terms, control system CI values representative of the CI of hydrogen produced by the hydrogen production facility (see at least Mehta, 0128-0131, real-time optimization module 156 derives plant operation policy strategy based on 154, which users machine learning model. Results are fed to 110 to control processes. See also 0123-0126 where control systems 110 that comprises 112 to control parameters of plant.);
generating, using a computer system and based on a function of the control system CI values, control variables for controlling one or more operational parameters of the hydrogen production facility (see at least Mehta, 0128-0131, real-time optimization module 156 derives plant operation policy strategy based on 154, which users machine learning model. Results are fed to 110 to control processes. See also 0123-0126 where control systems 110 that comprises 112 to control parameters of plant.); and
controlling the hydrogen production facility in accordance with the determined control variables (see at least Mehta, 0128-0131, real-time optimization module 156 derives plant operation policy strategy based on 154, which users machine learning model. Results are fed to 110 to control processes. See also 0123-0126 where control systems 110 that comprises 112 to control parameters of plant.).
Mehta only discloses non-linear relationship of modeling. However, Willmott teaches processing non-linear relationship’s operational parameter data to define one or more linear terms, wherein the linear terms are linear with respect to one or more models (see at least Willmott, 0121-0123 regarding identifying reduced subset states through linear propagation that can be calculated using linear solver).
Therefore, it would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine Willmott’s linear solver that can be used to solve reduced states of non-linear problem to Mehta’s non-linear modeling for the purpose of reducing reliance on complicated non-linear solver (Willmott: 0006).
As per claim 2, Mehta further discloses the computer-implemented method of claim 1, wherein the operational parameter data comprises measured and/or determined time-dependent values for one or more operational parameters relating to materials and/or energy input to the hydrogen production facility and materials and/or energy output from the hydrogen production facility (0204-0205).
As per claim 3, Mehta further discloses the computer-implemented method of claim 2, wherein the one or more operational parameters are selected from the group of: a quantity of hydrogen produced; a quantity of electricity consumed; a quantity of steam produced; a quantity of syngas produced; a quantity of carbon monoxide produced; and a quantity of electricity produced (see at least Mehta, 0204-0205 regarding gas output and hydrogen production).
As per claim 4, Mehta does not but Willmott teaches the computer-implemented method of claim 2, wherein the step of processing comprises applying one or more non-linear transforms to the operational parameter data for one or more operational parameters to define the one or more linear terms (see at least Willmott, 0121-0123 regarding identifying reduced subset states through linear propagation that can be calculated using linear solver).
The rationale to combine would persist.
As per claim 5, Mehta further discloses the computer-implemented method of claim 4, wherein one or more of the
Mehta does not explicitly disclose linear relationship but Willmott does (0121-0123).
The rationale to combine would persist.
As per claim 6, Mehta the computer-implemented method of claim 5, wherein one or more of the
i) a total mass flow rate of hydrogen and coproducts produced at the hydrogen production facility (see at least 0124, output can be measured by direct flow measurement of elctrolyzer. See also Fig. 7 and 0259 regarding input being energy flow to electrolyser);
ii) a total molar flow rate of hydrogen and coproducts produced at the hydrogen production facility;
iii) a total economic value of hydrogen and coproducts produced at the hydrogen production facility
Mehta does not explicitly disclose linear relationship but Willmott does (0121-0123).
The rationale to combine would persist.
As per claim 7, Mehta further discloses the computer-implemented method of claim 5, wherein one or more of the
Mehta does not explicitly disclose linear relationship but Willmott does (0121-0123).
The rationale to combine would persist.
As per claim 10, Mehta does not but Willmott teaches the computer-implemented method of claim 1, wherein the step of generating control system CI values comprises determining a sum of the product of each of the one or more linear terms with a corresponding linear coefficient, the linear coefficients being derived from one or more of the CI reference models (see at least Willmott, 0121-0123 regarding identifying reduced subset states through linear propagation that can be calculated using linear solver).
The rationale to combine would persist.
As per claim 11, Mehta discloses the computer-implemented method of claim 10, further comprising the step of updating
Mehta does not explicitly disclose updating for linear relationship but Willmott teaches a linear solver for modeling (0121-0123).
The rationale to combine would persist.
As per claim 12, Mehta further discloses the computer-implemented method of claim 1, wherein the steps of generating control variables and controlling the hydrogen production facility utilize model predictive control (see at least Mehta, 0274-0275 regarding using prediction to control plants).
Claims 13-17, 20 contains limitations substantially similar to those discussed above and are rejected under similar rationale set forth above.
Claim(s) 8-9, 18-19, is/are rejected under 35 U.S.C. 103 as being unpatentable over Mehta (US 20220283575) in view of Willmott (US 20190032947), further in view Gibson (US 20070119718)
As per claim 8, Mehta further discloses the computer-implemented method of claim 7, wherein one or more coproducts comprises a gas (see at least Mehta, 0092, Oxygen produced).
Mehta/Willmott does not but Gibson teaches the total energy rate is determined as the product of the flow rate of the gas and its lower heating value (0044, equation 3).
Therefore, it would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to apply Gibson’s relationship between lower heating value and flow rate to Mehta’s efficiency KPI for the purpose of using power production as measurement for efficiency.
As per claim 9, Mehta further disclsoes the computer-implemented method of claim 8, wherein a coproduct comprises electricity (0116).
Mehta does not but Gibson teaches the energy rate is the generated electric power (0044, equation 3).
Therefore, it would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to apply Gibson’s relationship between lower heating value and flow rate to Mehta’s efficiency KPI for the purpose of using power production as measurement for efficiency.
Claims 18-19 contains limitations substantially similar to claims 8-9 and are rejected under similar rationale set forth above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GEORGE CHEN whose telephone number is (571)270-5499. The examiner can normally be reached Monday-Friday, 8:30 AM -5:00 PM Eastern.
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GEORGE CHEN
Primary Examiner
Art Unit 3628
/GEORGE CHEN/Primary Examiner, Art Unit 3628