DETAILED ACTION
This Office Action is in response to the claims filed on 5/24/2023.
Claims 1-7 are pending.
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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. CN 202210613754.9, filed on 5/31/2022.
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the
references as applied to the claims below for the convenience of the applicant. Although
the specified citations are representative of the teachings in the art and are applied to
the specific limitations within the individual claim, other passages and figures may apply
as well. Examiner may also include cited interpretations encompassed within parenthesis, e.g. (Examiner’s interpretation), for clarity. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
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 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.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/24/2023 was filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
Claims 1-4 do not fall within at least one of the four categories of patent eligible subject matter (i.e. processes, machines, manufactures and compositions of matter). For purposes of compact prosecution, the claims will be interpreted as a method / process.
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “runoff production calculation module” in claim 1, step S(2).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 1-7 are rejected as failing to define the invention in the manner required by 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
The claim(s) are narrative in form and replete with indefinite language. The structure which goes to make up the device must be clearly and positively specified. The structure must be organized and correlated in such a manner as to present a complete operative device. The claim(s) must be in one sentence form only. Note the format of the claims in the patent(s) cited.
Claim 1 step S(3) recites “assuming that differences between runoff yields”. Step S(4) recites “so as to consider a river channel”.
Claim 5 Ln.5 recites “considers the uncertainty of a surface runoff”, Ln.9 recites “considers the uncertainty of an interflow runoff”, and Ln.15 recites “considers the uncertainty of the base flow”.
A claim requires positive recitation of claimed steps/structure not assumptions or considerations.
Claims 1-7 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 1 step S(3) recites “assuming that differences between runoff yields”. Per MPEP 2111.04, the broadest reasonable interpretation of a method (or process) claim having contingent steps that are not required to be performed because the condition(s) precedent are not met. Step S(3) is contingent upon differences in runoff yields following a normal distribution. Thus, step S(3) fails to further limit the claimed subject matter.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
The dependent claims included in the statement of rejection but not specifically addressed in the body of the rejection have inherited the deficiencies of their parent claim and have not resolved the deficiencies. Therefore, they are rejected based on the same rationale as applied to their parent claims above.
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 1-7 are rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception, is directed to that judicial exception (an abstract idea), as it has not been integrated into a practical application and the claim(s) further do/does not recite significantly more than the judicial exception. Examiner has evaluated the claim(s) under the framework provided in MPEP 2106 and has provided such analysis below.
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:
Step 1. Determining if the claim falls within a statutory category of a Process, Machine, Manufacture, or a Composition of Matter (see MPEP 2106.03);
Step 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 (MPEP 2106.04);
Step 2A is a two-prong inquiry. MPEP 2106.04(II)(A).
Under 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).
The second prong is an inquiry into whether the claim integrates a judicial exception into a practical application. MPEP 2106.04(d).
Step 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 1:
Claims 1-4 are directed to non-statutory subject matter and are rejected as not patent eligible. The claims do not fall within at least one of the four categories of patent eligible subject matter (i.e. processes, machines, manufactures and compositions of matter). For purposes of compact prosecution, the claims will be interpreted as a method/process.
Claims 5-7 are directed to a method, as such these claims fall within the statutory category of process.
Step 2A, Prong 1 (claim 1):
The examiner submits that the foregoing claim limitations constitute abstract ideas, as the claims cover Mental Processes and/or Mathematical Concepts, given the broadest reasonable interpretation.
In order to apply Step 2A, a recitation of claims is copied below. The limitations of those claims which describe an abstract idea are bolded.
As per claim 1, the claim recites the limitations of:
step S(2) calculating runoff production: establishing a runoff production
calculation module based on a structure of a SIMHYD model, and mainly including calculating four parts including evaporation loss, soil infiltration, water storage and runoff production; (As drafted and under its broadest reasonable interpretation, this limitation amounts to Mental Processes (MPEP 2106.04(a)(2)(III)) which are defined as concepts that can practically be performed in the human mind (e.g. observations, evaluations, judgments, opinions), or by a human using pen and paper as a physical aid; and/or Mathematical Concepts (MPEP 2106.04(a)(2)(1)) which is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. For instance, a person can reasonably calculate (via formulas disclosed in Specification [P.0008-0025]) evaporation loss, soil infiltration, water storage, and runoff production with/without the aid of pen and paper.)
step S(3) dealing with the uncertainty of the runoff production structure: assuming that differences between runoff yields of different runoff components of the SIMHYD model and real runoff yields follow a normal distribution, that is, a product of multiplying a runoff yield of each of three runoff components of the SIMHYD model with a random number following the normal distribution is equal to a corresponding real runoff yield, the three runoff components including surface runoff, interflow and base flow; for the surface runoff, the random number following the normal distribution is quantitatively expressed as a random number for the surface runoff, the random number following the normal distribution is quantitatively expressed as a random number of normal distribution with a mean of mIRUN and a variance of
δ
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; for the interflow, the random number following the normal distribution is quantitatively expressed as a random number of normal distribution with a mean of mSRUN and a variance of
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; for the base flow, a random number following the normal distribution is quantitatively expressed as a random number of normal distribution with a mean of mBAS and a variance of
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; variances
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,
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, and
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respectively represent uncertainties of runoff production structures including a surface runoff structure, an interflow structure and a base flow structure; (As drafted and under its broadest reasonable interpretation, this limitation amounts to Mental Processes (MPEP 2106.04(a)(2)(III)) and/or Mathematical Concepts (MPEP 2106.04(a)(2)(1)). For instance, a person can reasonably deal with the uncertainty of a runoff production structure (via an optimization algorithm and functions as those disclosed in Specification [P.0027-0032]).)
step S(4) calculating a confluence: using a lag-and-route method to adjust a total outflow process of the SIMHYD model considering the uncertainty of the runoff production structure, so as to consider a river channel confluence; (As drafted and under its broadest reasonable interpretation, this limitation amounts to Mental Processes (MPEP 2106.04(a)(2)(III)) and/or Mathematical Concepts (MPEP 2106.04(a)(2)(1)). For instance, a person can reasonably calculate a confluence via formula disclosed in Specification [P.0035-0038])
step S(5) optimizing parameters: selecting a runoff sequence observed by the hydrometeorological stations at an outlet of the watershed in a continuous year, defining a calibration period and validation period, using a NSE coefficient as an objective function, taking a maximization of an NSE coefficient as an optimization objective, using a Shuffled Complex Evolution (SCE-UA) algorithm as a global optimization algorithm, inputting an average areal precipitation in the watershed in the calibration period, average areal evaporation from water surface and runoff observed at the outlet of the watershed, setting upper and lower boundary values for parameters to be optimized, and optimizing the parameters of the hydrological model; (As drafted and under its broadest reasonable interpretation, this limitation amounts to Mental Processes (MPEP 2106.04(a)(2)(III)) and/or Mathematical Concepts (MPEP 2106.04(a)(2)(1)). For instance, a person can reasonably optimize parameters via Applicant’s disclosure in Specification [P.0039-0044])
Step 2A, Prong 2 (claim 1):
As per claim 1, this judicial exception is not integrated into a practical application because the additional claim limitations outside the abstract idea only present Insignificant Extra-solution Activity and/or Mere Instructions to Apply an Exception. In particular, the claim recites the additional limitations:
step S(1) collecting data: collecting an observation sequence of
hydrometeorological stations in a watershed, including data of precipitation, water surface evaporation and runoff observed by the hydrometeorological stations in the watershed; (The additional element amounts to Insignificant Extra-solution Activity (mere data gathering, pre-solution activity) per MPEP 2106.05(g). The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process.)
step S(6) substituting the optimized parameter values into the validation
period to calculate and obtain values of simulated runoffs in the calibration period and the validation period. (The additional element amounts to Mere Instructions to Apply an Exception per MPEP 2106.05(f). Specifically, this limitation is fails to recite details of how values of simulated runoffs in the calibration/validation periods are calculated and obtained. i.e. How does substituting optimized parameters into the validation period result in simulated runoff values? The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".)
Accordingly, 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 when considered as an ordered combination and as a whole.
Step 2B (claim 1):
For step 2B of the analysis, the Examiner must consider whether each claim limitation individually or as an ordered combination amounts to significantly more than the abstract idea. This analysis includes determining whether an inventive concept is furnished by an element or a combination of elements that are beyond the judicial exception. For limitations that were categorized as “apply it” or generally linking the use of the abstract idea to a particular technological environment or field of use, the analysis is the same.
The additional elements as described in Step 2A Prong 2 are not sufficient to amount to significantly more than the judicial exception. As disclosed above, per MPEP 2106.05(f), “[t]he recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it"”. Additionally, per MPEP 2106.05(g), “[a]nother consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. [ ] As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional.” Per MPEP 2106.05(d), “[t]he courts have recognized the following [applicable] computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, ii. Performing repetitive calculations, iii. Electronic recordkeeping, iv. Storing and retrieving information in memory.”
For the foregoing reasons, claim 1 is directed to an abstract idea without significantly more and is rejected as not patent eligible under 35 U.S.C. 101.
Step 2A, Prong 1 (claim 5):
The examiner submits that the foregoing claim limitations constitute abstract ideas, as the claims cover Mathematical Concepts, given the broadest reasonable interpretation.
In order to apply Step 2A, a recitation of claims is copied below. The limitations of those claims which describe an abstract idea are bolded.
As per independent claim 5, the claim recites the limitations of:
wherein random Monte Carlo sampling is used to simulate an impact of runoff production structure uncertainty on the surface-subsurface hydrological process; for a surface runoff simulation that considers the uncertainty of a surface runoff production structure, after parameter optimization, an estimated value
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t of the surface runoff is randomly generated from the normal distribution
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with a mean of IRUNt and a variance of
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, and cyclic sampling is performed N times to obtain a quantitative estimate of the impact of the surface runoff production structure uncertainty on the surface runoff; for an interflow simulation that considers the uncertainty of an interflow runoff production structure, after parameter optimization, an estimated value
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t of the interflow is randomly generated from the normal distribution
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with a mean of SRUNt and a variance of
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, and cyclic sampling is performed N times to obtain a quantitative estimate of the impact of the uncertainty of the interflow runoff production structure on the interflow; and for a base flow simulation that considers a uncertainty of the base flow production structure, after parameter optimization, an estimated value
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t of the base flow is randomly generated from the normal distribution
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with a mean of BASt and a variance of
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, and cyclic sampling is performed N times to obtain a quantitative estimate of the impact of the uncertainty of the base flow production structure on the base flow. (As drafted and under its broadest reasonable interpretation, claim 5 amounts to Mathematical Concepts (MPEP 2106.04(a)(2)) which are defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols. A claim that recites a numerical formula or equation will be considered as falling within the "mathematical concepts" grouping. In addition, there are instances where a formula or equation is written in text format that should also be considered as falling within this grouping. 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 [ ] a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation.)
Step 2A, Prong 2 and Step 2B (claim 5):
There are no additional elements, additional to the abstract idea itself, and therefore no additional elements which could integrate the abstract idea into a practical application (in Step 2A Prong 2) nor provide significantly more than the abstract idea itself (in Step 2B).
Therefore, claim 5 is directed to an abstract idea without significantly more and is rejected as not patent eligible under 35 U.S.C. 101.
Independent claim 2 recites wherein in the step S(1), the precipitation and evaporation from water surface observed by the hydrometeorological stations in the watershed are further converted to the average areal precipitation in the watershed and the average areal evaporation from water surface, and a multi-site arithmetic averaging method is adopted as a conversion method:
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where PEi,t represents the precipitation or evaporation from water surface observed by a station i at time t, n represents a total number of stations in the watershed, and
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represents the average areal precipitation in the watershed or the evaporation from water surface at time t. (The additional elements further amount to Mathematical Concepts per MPEP 2106.04(a)(2)(I). Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101.)
Claim 3 recites wherein a formula for the step S(4) calculating the confluence is as follows:
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where Qt is a flow rate at the outlet of the watershed at time t, m³/s; CR is a coefficient of extinction for channel storage,
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t represents an actual surface runoff at time t,
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t represents an actual interflow at time t, and
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t represents an actual base flow at time t. (The additional elements further amount to Mathematical Concepts per MPEP 2106.04(a)(2)(I). Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101.)
Claim 4 recites wherein in the step S(5), the parameters of the hydrological model comprise intercepted precipitation storage capacity, maximum infiltration loss, soil moisture storage capacity, interflow outflow coefficient, infiltration loss index, subsurface water replenishment coefficient, subsurface runoff coefficient, mean random multiplier of the surface runoff, mean random multiplier of the interflow, mean random multiplier of the base flow, variance of a random multiplier of the surface runoff, variance of a random multiplier of the interflow, variance of a random multiplier of the base flow, and coefficient of extinction for channel storage. (The additional elements further amount to Mathematical Concepts per MPEP 2106.04(a)(2)(I). Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101.)
Claim 6, the method of claim 5, recites wherein the three runoff components, in a same sampling scenario, are superimposed to obtain a runoff yield of the whole watershed, and then according to a watershed confluence formula and an optimized confluence parameters CR, the flow rates at the outlet of the watershed under different random sampling scenarios are calculated to obtain N kinds of processes of flow at the outlet of the watershed, which represents the impact of the uncertainty of the runoff production structure on a simulation of the flow rate at the outlet of the watershed. (The additional elements further amount to Mathematical Concepts per MPEP 2106.04(a)(2)(I). Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101.)
Claim 7, the method of claim 6, recites wherein N is set to 1000, that is, cyclic sampling is performed for 1000 times. The additional element elaborates on the number of times cyclic sampling is performed, thus further amounts to Mathematical Concepts per MPEP 2106.04(a)(2)(I). Therefore, the claim is rejected as not patent eligible under 35 U.S.C. 101.
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.
The factual inquiries set forth in Graham V. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) 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.
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 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.
Claims 1-4 are rejected under 35 U.S.C. 103 as being unpatentable over Guan, Xiaoxiang, et al. "The capacity of the hydrological modeling for water resource assessment under the changing environment in semi-arid river basins in China." Water 11.7 (2019): 1328 (hereinafter referred to as “Guan”) in view of Ajami, Newsha K., Qingyun Duan, and Soroosh Sorooshian. "An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction." Water resources research 43.1 (2007) (hereinafter referred to as “Ajami”).
Regarding claim 1, Guan discloses A hydrological model considering an uncertainty of a runoff production structure, comprising the following steps:
step S(1) collecting data: collecting an observation sequence of hydrometeorological stations in a watershed, including data of precipitation, water surface evaporation and runoff observed by the hydrometeorological stations in the watershed (“Hydrological data were collected [ ] Daily precipitation,
daily average, and maximum and minimum air temperature over 1951–2013 were collected” Guan [Pg.3 P.6], “SIMHYD models simulated the daily discharge (i.e. runoff) and results were aggregated to monthly discharge volumes at the hydrometric stations, while monthly rainfall, temperature, and potential evaporation data series were necessary as model inputs” Guan [Pg.10 3.4.1]);
step S(2) calculating runoff production: establishing a runoff production calculation module based on a structure of a SIMHYD model, and mainly including calculating four parts including evaporation loss, soil infiltration, water storage and runoff production (see Guan Fig.3 SIMHYD);
step S(3) dealing with the uncertainty of the runoff production structure: assuming that differences between runoff yields of different runoff components of the SIMHYD model and real runoff yields follow a normal distribution, that is, a product of multiplying a runoff yield of each of three runoff components of the SIMHYD model with a random number following the normal distribution is equal to a corresponding real runoff yield (“NSE is a normalized (i.e. follows a normal distribution) statistic. Normalization facilitates the easier comparison of hydrological model performance for disparate catchments. It is desirable to have a good fit between the observed and simulated runoff time series, but also to minimize overall bias in the simulation. Therefore, the NSE and the relative error (RE) between simulated and observed runoff were both employed as objective functions in calibrating the models” Guan [Pg.7 2.5]), the three runoff components including surface runoff, interflow and base flow (“Components of runoff simulated with the SIMHYD model consist of surface flow (SRUN), interflow (INF), and base flow (BAS)” Guan [Pg.6 2.4.2]);
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S
;
step S(4) calculating a confluence: using a lag-and-route method to adjust a total outflow process of the SIMHYD model considering the uncertainty of the runoff production structure, so as to consider a river channel confluence (“The model is mainly composed of four parts, namely evapotranspiration being represented by a model of three soil layers, runoff yield component which separates runoff into surface flow, interflow and ground water, and a flow-routing component (i.e. lag-and-route).” Guan [Pg.5 2.4.1]);
step S(5) optimizing parameters: selecting a runoff sequence observed by the hydrometeorological stations at an outlet of the watershed in a continuous year (“the average values of two sequences before and after the base year” Guan [Pg.5 2.3]), defining a calibration period and validation period (“Appropriate periods of data were applied to calibrate and validate” Guan [Pg.16 P.3]), using a NSE coefficient as an objective function (“the NSE and the relative error (RE) between
simulated and observed runoff were both employed as objective functions in calibrating the models” Guan [Pg.8 P.1]), taking a maximization of an NSE coefficient as an optimization objective (“a good simulation result will have NSEs approaching to 1 and REs close to 0.” Guan [Pg.8 P.1]), ,
inputting an average areal precipitation in the watershed in the calibration period, average areal evaporation from water surface and runoff observed at the outlet of the watershed (“areal average precipitation (sum of rainfall and snowfall) and potential evapotranspiration data series are required to drive the model” Guan [Pg.6 P.]), setting upper and lower boundary values for parameters to be optimized, and optimizing the parameters of the hydrological model (“the default value ranges of model parameters are estimated, then different parameter sets are selected to drive the model and calculate the objective functions which represent the quality of simulation results and is used for later parameter modification until optimum is obtained.” Guan [Pg.7 2.5]); and
step S(6) substituting the optimized parameter values into the validation period to calculate and obtain values of simulated runoffs in the calibration period and the validation period (“the monthly simulation results for calibration and verification periods are presented in Tables 6 and 7 (see tables Pg.13)” Guan [Pg.12 3.4.2]).
Guan fails to specifically disclose for the surface runoff, the random number following the normal distribution is quantitatively expressed as a random number for the surface runoff, the random number following the normal distribution is quantitatively expressed as a random number of normal distribution with a mean of mIRUN and a variance of
δ
2
I
R
U
N
, for the interflow, the random number following the normal distribution is quantitatively expressed as a random number of normal distribution with a mean of mSRUN and a variance of
δ
2
S
R
U
N
, for the base flow, a random number following the normal distribution is quantitatively expressed as a random number of normal distribution with a mean of mBAS and a variance of
δ
2
B
A
S
, variances
δ
2
I
R
U
N
,
δ
2
S
R
U
N
, and
δ
2
B
A
S
respectively represent uncertainties of runoff production structures including a surface runoff structure, an interflow structure and a base flow structure, and using a Shuffled Complex Evolution (SCE-UA) algorithm as a global optimization algorithm.
However, Ajami discloses the random number following the normal distribution is quantitatively expressed as a random number for the surface runoff, the random number following the normal distribution is quantitatively expressed as a random number of normal distribution with a mean of mIRUN and a variance of
δ
2
I
R
U
N
(“the implemented changes into the hydrologic input-output system included introduction of a random multiplier to each time step, drawn from the same normal distribution with unknown mean and variance (m and σm2).” Ajami [P.28]); the random number following the normal distribution is quantitatively expressed as a random number of normal distribution with a mean of mSRUN and a variance of
δ
2
S
R
U
N
(“the implemented changes into the hydrologic input-output system included introduction of a random multiplier to each time step, drawn from the same normal distribution with unknown mean and variance (m and σm2).” Ajami [P.28]); a random number following the normal distribution is quantitatively expressed as a random number of normal distribution with a mean of mBAS and a variance of
δ
2
B
A
S
(“the implemented changes into the hydrologic input-output system included introduction of a random multiplier to each time step, drawn from the same normal distribution with unknown mean and variance (m and σm2).” Ajami [P.28]); variances
δ
2
I
R
U
N
,
δ
2
S
R
U
N
, and
δ
2
B
A
S
respectively represent uncertainties of runoff production structures including a surface runoff structure, an interflow structure and a base flow structure (Ajami discloses variances representing uncertainties in at least [P.15, 24, 26, 28, etc.]); and using a Shuffled Complex Evolution (SCE-UA) algorithm as a global optimization algorithm (“The Shuffled Complex Evolution Metropolis (SCEM) was built upon the principles of the effective and efficient global optimization technique, the Shuffled Complex Evolution (SCE-UA)” Ajami [P.18])
Guan and Ajami are analogous art as they both relate to hydrological modeling. Guan discloses a study which “aims to assess the capacity and performance of the hydrological models in simulating the discharge under a changing environment. Four well-documented and applied hydrological models, i.e., the Xin’anjiang (XAJ) model, GR4J model, SIMHYD model, and RCCC-WBM (Water Balance Model developed by Research Center for Climate Change) model, were selected for this assessment.” Guan [Abstract]. And Ajami discloses “a new framework, the Integrated Bayesian Uncertainty Estimator (IBUNE), to account for the major uncertainties of hydrologic rainfall-runoff predictions explicitly. IBUNE distinguishes between the various sources of uncertainty including parameter, input, and model structural uncertainty. An input error model in the form of a Gaussian multiplier has been introduced within IBUNE. These multipliers are assumed to be drawn from an identical distribution with an unknown mean and variance which were estimated along with other hydrological model parameters by a Monte Carlo Markov Chain (MCMC) scheme” Ajami [Abstract]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Guan’s disclosed hydrological models to include random multipliers drawn from a normal distribution with mean/variance for surface runoff, interflow, and base flow, including utilizing a SCE-UA algorithm as a global optimization algorithm as Ajami discloses, in order to achieve realistic model simulations and correct uncertainty bounds Ajami [Abstract].
Regarding claim 2, Guan in view of Ajami disclose the hydrological model of claim 1, Guan further discloses wherein in the step S(1), the precipitation and evaporation from water surface observed by the hydrometeorological stations in the watershed are further converted to the average areal precipitation in the watershed and the average areal evaporation from water surface (“Daily areal average precipitation (sum of rainfall and snowfall) and potential evapotranspiration data series are required to drive the model and calculate the discharge.” Guan [Pg.6 P.1]), and a multi-site arithmetic averaging method is adopted as a conversion method:
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39
117
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where PEi,t represents the precipitation or evaporation from water surface observed by a station i at time t, n represents a total number of stations in the watershed, and
P
E
-
t
represents the average areal precipitation in the watershed or the evaporation from water surface at time t. (“Long-term variations of annual areal average precipitation (calculated by arithmetic averaging method) and temperature are shown in Figure 4. Trends test of precipitation and temperature of the six catchments are summarized in Table 2, in which the slope coefficient (S) of the regression line illustrates the magnitude of the upward or downward trend of a time series.” Guan [Pg.8 3.1])
Regarding claim 3, Guan in view of Ajami disclose the hydrological model of claim 1, Guan further discloses wherein a formula for the step S(4) calculating the confluence is as follows:
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42
325
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where Qt is a flow rate at the outlet of the watershed at time t, m³/s; CR is a coefficient of extinction for channel storage,
I
R
U
N
~
t represents an actual surface runoff at time t,
S
R
U
N
~
t represents an actual interflow at time t, and
B
A
S
~
t represents an actual base flow at time t (The examiner interprets the formula
Q
t as a standard linear reservoir routing method. The sum inside the parentheses represents the total unrouted water generated within the catchment at time t, the model uses
C
R
to distribute the flow over time, and the outflow (i.e. streamflow/runoff) is a weighted combination of the historical discharge (
Q
t-1) and the newly generated runoff. Guan discloses this calculation including Figure 7 (see below) [Pg.11])
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534
900
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Regarding claim 4, Guan in view of Ajami disclose the hydrological model of claim 1, Guan further discloses wherein in the step S(5), the parameters of the hydrological model comprise intercepted precipitation storage capacity (Guan [Fig.3]), maximum infiltration loss (Guan [Fig.3]), soil moisture storage capacity (Guan [Fig.3]), interflow outflow coefficient (Guan [Fig.3]. Examiner interprets “SUB” as interflow outflow coefficient due to Applicant’s disclosure Spec. [P.0023, 0025]), infiltration loss index (Guan [Fig.3]), subsurface water replenishment coefficient (Guan [Fig.3]. See “CRAK”), subsurface runoff coefficient (Guan [Fig.3]. See “Kg”).
Guan fails to specifically disclose mean random multiplier of the surface runoff, mean random multiplier of the interflow, mean random multiplier of the base flow, variance of a random multiplier of the surface runoff, variance of a random multiplier of the interflow, variance of a random multiplier of the base flow, and coefficient of extinction for channel storage.
However, Ajami discloses mean random multiplier of the surface runoff, mean random multiplier of the interflow, mean random multiplier of the base flow, variance of a random multiplier of the surface runoff, variance of a random multiplier of the interflow, variance of a random multiplier of the base flow (“the implemented changes into the hydrologic input-output system included introduction of a random multiplier to each time step, drawn from the same normal distribution with unknown mean and variance (m and σm2).” Ajami [P.28]), and coefficient of extinction for channel storage (Ajami [Pg.3 Table 1] discloses an upper zone recession coefficient and a lower zone supplementary recession coefficient.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Guan’s disclosed hydrological models (including surface runoff, interflow, and base flow) to include mean random multipliers and variance of random multipliers and coefficient of extinction for channel storage as Ajami discloses, in order to achieve realistic model simulations and correct uncertainty bounds - Ajami [Abstract].
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Guan, Xiaoxiang, et al. "The capacity of the hydrological modeling for water resource assessment under the changing environment in semi-arid river basins in China." Water 11.7 (2019): 1328 (hereinafter referred to as “Guan”) in view of Ajami, Newsha K., Qingyun Duan, and Soroosh Sorooshian. "An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction." Water resources research 43.1 (2007). (hereinafter referred to as “Ajami”), in further view of Zhou, Shuai, et al. "Quantifying the uncertainty interaction between the model input and structure on hydrological processes." Water Resources Management 35.12 (2021): 3915-3935 (hereinafter referred to as “Zhou”).
Regarding claim 5, Guan in view of Ajami disclose the hydrological model of claim 1 but fail to specifically disclose A method for quantifying an impact of an uncertainty of a runoff production structure on a surface-subsurface hydrological process
I
R
U
N
~
t of the surface runoff is randomly generated from the normal distribution
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30
239
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with a mean of IRUNt and a variance of
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27
108
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Greyscale
, and cyclic sampling is performed N times to obtain a quantitative estimate of the impact of the surface runoff production structure uncertainty on the surface runoff; for an interflow simulation that considers the uncertainty of an interflow runoff production structure, after parameter optimization, an estimated value
S
R
U
N
~
t of the interflow is randomly generated from the normal distribution
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28
247
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with a mean of SRUNt and a variance of
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25
117
media_image4.png
Greyscale
, and cyclic sampling is performed N times to obtain a quantitative estimate of the impact of the uncertainty of the interflow runoff production structure on the interflow; and for a base flow simulation that considers a uncertainty of the base flow production structure, after parameter optimization, an estimated value
B
A
S
~
t of the base flow is randomly generated from the normal distribution
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33
194
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with a mean of BASt and a variance of
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23
98
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Greyscale
, and cyclic sampling is performed N times to obtain a quantitative estimate of the impact of the uncertainty of the base flow production structure on the base flow.
Zhou discloses A method for quantifying an impact of an uncertainty of a runoff production structure on a surface-subsurface hydrological process (“the objective of this study is to investigate the impacts of the uncertainties of rain gauge station input levels and hydrological models on flows with different magnitudes” Zhou [Abstract] (see Guan/Ajami combination), wherein random Monte Carlo sampling is used to simulate an impact of runoff production structure uncertainty on the surface-subsurface hydrological process (“subsampling was used to dynamically quantify the contribution rates of rain gauge station input levels, hydrological models, and their interaction to the runoff simulation uncertainty.” Zhou [Abstract], “using the Monte Carlo method to simulate 200 random samples and then driving them with three lumped hydrological models (HyMod, XAJ and HBV) before quantitatively assessing the impacts of rain gauge station input levels and hydrological models on the magnitudes of flows and monthly flows.” Zhou [Pg.3933 P.1]); for a surface runoff simulation that considers the uncertainty of a surface runoff production structure, after parameter optimization, an estimated value
I
R
U
N
~
t of the surface runoff is randomly generated from the normal distribution
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30
239
media_image1.png
Greyscale
with a mean of IRUNt and a variance of
PNG
media_image2.png
27
108
media_image2.png
Greyscale
, and cyclic sampling is performed N times to obtain a quantitative estimate of the impact of the surface runoff production structure uncertainty on the surface runoff; for an interflow simulation that considers the uncertainty of an interflow runoff production structure, after parameter optimization, an estimated value
S
R
U
N
~
t of the interflow is randomly generated from the normal distribution
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28
247
media_image3.png
Greyscale
with a mean of SRUNt and a variance of
PNG
media_image4.png
25
117
media_image4.png
Greyscale
, and cyclic sampling is performed N times to obtain a quantitative estimate of the impact of the uncertainty of the interflow runoff production structure on the interflow; and for a base flow simulation that considers a uncertainty of the base flow production structure, after parameter optimization, an estimated value
B
A
S
~
t of the base flow is randomly generated from the normal distribution
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33
194
media_image5.png
Greyscale
with a mean of BASt and a variance of
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media_image6.png
23
98
media_image6.png
Greyscale
, and cyclic sampling is performed N times to obtain a quantitative estimate of the impact of the uncertainty of the base flow production structure on the base flow. (Zhou discloses flow simulation that considers model structure uncertainty and estimates flow values (see Figs. 7 and 9, Pg.3927 and 3929) randomly generated from normal distribution (see Pg.3922), after parameter optimization (see Pg.3920 3.3 Parameter Calibration), with mean and variance (see Pg.3923 P.1), and cyclic sampling to obtain a quantitative impact estimate of structure uncertainty on flows (see Pg.3933 P.1), to include surface runoff, interflow, and underground runoff (i.e. base flow) (see Pg.3919 3.2 P.2)).
Zhou is analogous art as it relates to hydrological model uncertainty. Zhou investigates “the impacts of the uncertainties of rain gauge station input levels and hydrological models on flows with different magnitudes” [Abstract]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Guan/Ajami to include quantifying an impact of uncertainties on a hydrological process, including random Monte Carlo sampling, as Zhou discloses, in order to “provide a decision-making basis and scientific guidance for the management and planning of water resources within basins under the influence of a changing environment” Zhou [Abstract].
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Guan, Xiaoxiang, et al. "The capacity of the hydrological modeling for water resource assessment under the changing environment in semi-arid river basins in China." Water 11.7 (2019): 1328 (hereinafter referred to as “Guan”) in view of Ajami, Newsha K., Qingyun Duan, and Soroosh Sorooshian. "An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction." Water resources research 43.1 (2007). (hereinafter referred to as “Ajami”), in further view of Zhou, Shuai, et al. "Quantifying the uncertainty interaction between the model input and structure on hydrological processes." Water Resources Management 35.12 (2021): 3915-3935 (hereinafter referred to as “Zhou”), and in further view of Li, You, et al. "Improving runoff simulation and forecasting with segmenting delay of baseflow from fast surface flow in montane high-vegetation-covered catchments." Water 13.2 (2021): 196 (hereinafter referred to as “Li”).
Regarding claim 6, Guan in view of Ajami, in further view of Zhou disclose the method of claim 5, Guan further discloses wherein the three runoff components, in a same sampling scenario, are superimposed to obtain a runoff yield of the whole watershed (Guan discloses Figure 7 [Pg.11 see SIMHYD] which captures total stream flow (i.e. whole watershed yield including the three runoff components) of two separate river catchments. See Fig.7 below.),
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538
916
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Guan fails to specifically disclose then according to a watershed confluence formula and an optimized confluence parameters CR, the flow rates at the outlet of the watershed under different random sampling scenarios are calculated to obtain N kinds of processes of flow at the outlet of the watershed, which represents the impact of the uncertainty of the runoff production structure on a simulation of the flow rate at the outlet of the watershed.
Li discloses then according to a watershed confluence formula and an optimized confluence parameters CR, the flow rates at the outlet of the watershed under different random sampling scenarios are calculated to obtain N kinds of processes of flow at the outlet of the watershed, which represents the impact of the uncertainty of the runoff production structure on a simulation of the flow rate at the outlet of the watershed (“results are summarized in Figure 6 and Table 8 (see below)” Li [Pg.16-17]).
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469
974
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310
765
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Li is analogous art as it relates to hydrological modeling and improvement thereof. Li discloses “four typical montane catchments with different climatic conditions and high vegetation coverage, located in the topographically varying mountains of the
eastern Tibetan Plateau, were selected for runoff and flood process simulations using the proposed SVSMRG-SBS model” [Abstract]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Guan/Ajami/Zhou to include watershed outlet flow rates, as calculated by Li, in order “to improve runoff and flood simulation accuracy in montane
high-vegetation-covered catchments” Li [Abstract].
Regarding claim 7, Guan in view of Ajami, in further view of Zhou, and in further view of Li disclose the method of claim 6, Guan fails to specifically disclose wherein N is set to 1000, that is, cyclic sampling is performed for 1000 times.
Ajami further discloses wherein N is set to 1000, that is, cyclic sampling is performed for 1000 times (“the HYMOD model parameters, after 20,000 samples, are given in Figure 6.” Ajami [P.22]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Guan to include cyclic sampling performed 1000 times, as Ajami discloses, in order to obtain “the most probable parameter set” Ajami [P.18].
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon is
considered pertinent to applicant's disclosure:
Tian, Ye, Yue-Ping Xu, Martijn J. Booij, and Guoqing Wang. "Uncertainty in future high flows in Qiantang River Basin, China." Journal of Hydrometeorology 16, no. 1 (2015): 363-380. “the impacts of uncertainties from future emissions scenarios, hydrological model structures, and parameters on high flows were investigated using the regional climate model PRECIS” [Pg.377 5.Conclusions].
Wang, G. Q., J. Y. Zhang, J. L. Jin, Y. L. Liu, R. M. He, Z. X. Bao, C. S. Liu, and Y. Li. "Regional calibration of a water balance model for estimating stream flow in ungauged areas of the Yellow River Basin." Quaternary International 336 (2014): 65-72. “a regional calibration approach to constructing regional relationships between the parameters of the Snowmelt-based Water Balance Model (SWBM) and catchment characteristics.” [Abstract]
Kuczera, George, and Eric Parent. "Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm." Journal of hydrology 211, no. 1-4 (1998): 69-85.
Liu, Yanli, Jianyun Zhang, Guoqing Wang, Jiufu Liu, Ruimin He, Hongjie Wang, Cuishan Liu, and Junliang Jin. "Quantifying uncertainty in catchment-scale runoff modeling under climate change (case of the Huaihe River, China)." Quaternary International 282 (2012): 130-136. “This study proposed a framework to evaluate the whole uncertainty system and illustrate how to quantify the different uncertainties. Uncertainties were presented in terms of differences between the simulated and observed discharges” [Pg.135 5.Conclusions]
Duan, Qingyun, Soroosh Sorooshian, and Vijai K. Gupta. "Optimal use of the SCE-UA global optimization method for calibrating watershed models." Journal of hydrology 158, no. 3-4 (1994): 265-284. “a global optimization method known as the SCE-UA (shuffled complex evolution method developed at The University of Arizona) has shown promise as an effective and efficient optimization technique for calibrating watershed models.” [Abstract]
Vrugt, Jasper A., Hoshin V. Gupta, Willem Bouten, and Soroosh Sorooshian. "A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters." Water resources research 39, no. 8 (2003). “This paper has presented a Markov Chain Monte Carlo sampler, which is well suited for the practical assessment of parameter uncertainty in hydrological models.” [Pg.15 4.Summary]
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Anthony Chavez whose telephone number is (571) 272-1036. The examiner can normally be reached Monday - Thursday, 8 a.m. - 5 p.m. ET.
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/ANTHONY CHAVEZ/ Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186
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