DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 6 and 8 are objected to because of the following informalities:
Claim 6 recites “most closest” which should instead just recite “closest”.
Claim 8 recites “most closest” which should instead just recite “closest”.
Appropriate correction is required.
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.
Claims 1-8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “WRF parameterization scheme” in which the acronym is not made clear by the claims. When read in light of the specification, WRF is intended to mean “weather research and forecasting model” and such definition should be claimed accompanying the first recitation of the acronym to ensure clarity on the scope of what is being claimed.
Claims 2-8 incorporate the deficiency of claim 1 and are rejected under the same rationale
Claim 6 recites “the step of (S3)” which lacks antecedent basis.
Claims 7-8 incorporate the deficiency of claim 6 and are rejected under the same rationale.
Claim 7 recites “WFP mode” which lacks antecedent basis.
Claim 8 incorporates the deficiency for claim 7 and is rejected under the same rationale.
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The following section follows the 2019 Patent Eligibility Guidance (PEG) for analyzing subject matter eligibility:
Step 1 - Statutory Category:
Step 1 of the PEG analysis 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).
Step 2A Prong One - Judicial exception:
In Step 2A Prong 1, examiners evaluate whether the claim recites a judicial exception (an abstract idea, law of nature, or a natural phenomenon).
Step 2A Prong Two - Integration into a practical application:
If claims recite a judicial exception, the claim requires further analysis in Step 2A Prong 2. In Step 2A Prong 2, examiners evaluate whether the claim as a whole integrates the exception into a practical application. This evaluation considers any additional elements in the claim beyond any recited judicial exceptions.
Step 2B - Significantly More:
If the additional elements identified in Step 2A Prong 2 do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception and requires further analysis under Step 2B- Significantly More. This evaluation is to evaluate if the additional elements of the claim provide an inventive concept.
As noted in the MPEP 2106.05(II): The identification of the additional element(s) in the claim from Step 2A Prong 2, as well as the conclusions from Step 2A Prong 2 on the considerations discussed in MPEP 2106.05(a) -(c), (e), (f), and (h) are to be carried over. Claim limitations identified as Insignificant Extra-Solution Activities are re-evaluated to determine if the elements are beyond what is well -understood, routine, and conventional (WURC) activity, as dictated by MPEP 2106.05(II).
The additional elements are evaluated to determine if any additional element or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP § 2106.05(d).
Independent Claims:
Claim 1:
Step 1: Claim 1 and its dependent claims 2-8 are directed to a method which falls within one of the four statutory categories of a process.
Step 2A Prong 1: Claim 1 recites a judicial exception, noted in bold:
(S1) constructing a database having a corresponding relation between a historical surface pressure distribution situation and an optimal parameterization scheme combination; and The claim limitation can be reasonably read to entail evaluating historical surface pressure data and optimal parameterization schemes so as to make a correlation between the data in the form of a database. This task can be performed within the human mind or using a pen and paper as an assistive physical aid, for example by writing down a collection of the data with a corresponding relation as described. Other examples of physical databases on paper include items such as a phone book, dictionary, filing system, etc. There are no requirements as to how the database is constructed such that it would not be achievable in the human mind and using assistive aids such as pen and paper. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
obtaining an optimal parameterization scheme combination corresponding to the historical surface pressure distribution situation by querying a historical surface pressure distribution situation closest to an actual precipitation forecast surface pressure distribution situation in the database, The claim limitation can be reasonably read to entail evaluating the historical surface pressure distribution situation with regard to the information in the database with regard to an actual precipitation forecast. This task can be performed within the human mind or using a pen and paper as an assistive physical aid, for example by making observations and judgements so as to determine the optimal scheme. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
before the step of (S1), the method further comprises a step of setting a forecast period of numerical precipitation forecast according to forecast demands;. The claim limitation can be reasonably read to entail making a judgment as to a forecast period according to a demand. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
the step of (S1) specifically comprises: (S11) determining a start time and an end time of the forecast period; The claim limitation can be reasonably read to entail making a judgement as to a start and end time, in which a judgment and determination of this kind can be made in the human mind. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
(S12) determining a parameterization scheme combination sample set; The claim limitation can be reasonably read to entail making a judgement as to a parameterization scheme combination sample set, in which a judgment and determination of this kind can be made in the human mind. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
(S13) determining a WRF operation scheme; The claim limitation can be reasonably read to entail making a judgement as to a WRF operation scheme, in which a judgment and determination of this kind can be made in the human mind. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Therefore, the claim recites a judicial exception.
Step 2A Prong 2: Additional elements were identified and are noted in italics.
and running WRF by the optimal parameterization scheme combination corresponding to the historical surface pressure distribution situation, so as to carry out an actual precipitation forecast, wherein:- This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for merely reciting an equivalent of the words “apply it” with regard to the judicial exception
(S14) carrying out the WRF by each parameterization scheme combination;- This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for merely reciting an equivalent of the words “apply it” with regard to the judicial exception
(S15) obtaining surface pressure distribution data at a beginning of each WRF operation and a parameterization scheme combination with a minimum forecast error of the each WRF operation; and- This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering
(S16) obtaining surface pressure distribution data at the beginning of all WRF operations and all parameterization scheme combinations with the minimum forecast error of the all WRF operations by repeating the steps of (S14) and (S15) and storing, so as to obtain the database having the corresponding relation between the historical surface pressure distribution situation and the optimal parameterization scheme combination.- This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering (obtaining), insignificant computer implementation (repeating steps, storing information). The limitation is further identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of generic computers to implement the functionality of the judicial exception. Further, the limitation is identified as Field of Use and Technological Environment (MPEP 2106.05(h)) for generally linking the use of the judicial exception to a particular field of use and technological environment.
The courts have found that merely including instructions to implement an abstract idea on a computer, merely using a computer as a tool to perform an abstract idea, and reciting generically the words “apply it’ with regard for the judicial exception (Mere Instructions to Apply an Exception (MPEP 2106.05(f))); adding insignificant extra- solution activity to the judicial exception (Insignificant Extra Solution Activity (MPEP 2106.05(g))); and generally linking the use of the judicial exception to a particular technological environment and field of use (Field of Use and Technological Environment (MPEP 2106.05(h))) does not integrate the judicial exception into a practical application.
When viewed independently and within the claim as a whole, the additional elements no not appear to integrate the judicial exception into a practical application because the additional elements do not impose meaningful limits on the claim nor do they interact with the judicial exception in such a way to effectively integrate the exception.
Step 2B: As discussed in Step 2A Prong 2, additional elements were identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) which must be further evaluated to determine if they are beyond WURC activities. Additional elements identified otherwise and conclusions from Step 2A Prong 2 are carried over for evaluating if the claim, as a whole, amounts to an inventive concept that is significantly more than the judicial exception:
(S15) obtaining surface pressure distribution data at a beginning of each WRF operation and a parameterization scheme combination with a minimum forecast error of the each WRF operation; and-
(S16) obtaining surface pressure distribution data at the beginning of all WRF operations and all parameterization scheme combinations with the minimum forecast error of the all WRF operations by repeating the steps of (S14) and (S15) and storing, so as to obtain the database having the corresponding relation between the historical surface pressure distribution situation and the optimal parameterization scheme combination.
Under broadest reasonable interpretation, obtaining data encompasses receiving data over a network which has been found by the courts to be a well understood, routine, and conventional activity when claimed in a merely generic manner such as in the claims. Likewise, storing information and repetitively performing tasks by way of a computer have been found by the courts as well understood, routine, and conventional activities when claimed generically. The courts have found that simply appending insignificant extra solution activities that are well-understood, routine, and conventional activities to the judicial exception does not qualify the limitations as “significantly more” than the recited judicial exception. The remaining additional elements were identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) and Field of Use and Technological Environment (MPEP 2106.05(h)), as stated previously. The courts have found that merely using a computer as a tool to perform a mental process or reciting the words “apply it” to the judicial exception and generally linking the use of a judicial exception to a particular technological environment does not qualify the limitations as “significantly more” than the recited judicial exception.
With the additional elements viewed independently and as part of the ordered combination, the claim as a whole does not appear to amount to significantly more than the recited judicial exception because the claim is using generic computing components recited at a high level of generality and functioning in their normal capacity in conjunction with well-understood, routine, and conventional activity to enable the performance of a task that can practically be performed within the human mind or using pen and paper as an assistive physical aid. Therefore, the claim does not include additional elements, alone or in combination that are sufficient to amount to significantly more than the recited judicial exception.
Conclusion: Based on this rationale, the claim has been deemed to be ineligible subject matter under 35 U.S.C. 101.
Dependent Claims:
Examiner notes limitations identified as judicial exceptions are indicated in italicized bold and limitations identified as additional elements are indicated using italics.
Claim 2
Step 1: Regarding dependent claim 2, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 2 does not recite any additional judicial exceptions.
Step 2A Prong 2: Claim 2 additionally recites the limitation wherein the parameterization scheme combination sample set comprises microphysical parameterization schemes and cumulus convection parameterization schemes.. This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)). The courts have ruled generally linking the use of the judicial exception to a particular technological environment or field of use does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to generally linking the exception to a field of use and technological environment are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 3
Step 1: Regarding dependent claim 3, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 3 additionally recites the wherein the step of (S13) specifically comprises on a basis of determining the start time, the end time and the parameterization scheme combination sample set, determining the WRF parameterization scheme in combination with the forecast period., which can reasonably be read to entail evaluating the start time, end time, and sample set so as to make a judgement of the WRF parameterization scheme in combination with the forecast period. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Step 2A Prong 2 & Step 2B: Claim 3 does not recite any additional elements that would integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 4
Step 1: Regarding dependent claim 4, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 4 additionally recites the limitations:
calculating an precipitation forecast error of the each parameterization scheme combination of the each WRF operation, which can reasonably be read to entail performing a calculation of an error. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas as a mathematical concept. Additionally, this task can be performed in the human mind or using assistive physical aids and therefore is further a recitation of the abstract idea of mental process.
selecting the parameterization scheme combination with the minimum forecast error of the each WRF operation as an optimal parameterization scheme combination of the each WRF operation, which can reasonably be read to entail making a judgement according to an error so as to choose an appropriate parameterization scheme combination as being optimal. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Further, because the evaluation for a minimum forecast error is the mathematical concept of a mathematical relationship, this claim limitation includes the recitation of the judicial exception of abstract ideas as a mathematical concept.
and building a corresponding relation between the optimal parameterization scheme combination and the surface pressure distribution data at the beginning of the each WRF operation. which can reasonably be read to entail evaluating the optimal parameterization scheme combination and the surface pressure distribution data so as to inform the judgement of a relation between them. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Step 2A Prong 2: Claim 4 additionally recites the limitation wherein the step of (S 15) specifically comprises obtaining the surface pressure distribution data at the beginning of the each WRF operation,. This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)). The courts have ruled appending insignificant extra solution activity to the judicial exception does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: Under broadest reasonable interpretation, obtaining the distribution data encompasses receiving data over a network which has been found by the courts to be a well understood, routine, and conventional computer function when claimed generically. The courts have found that limitations that amount to appending insignificant extra solution activity which has been identified as well understood, routine, and conventional activities are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 5
Step 1: Regarding dependent claim 5, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 5 additionally recites the limitation wherein the precipitation forecast error of the each parameterization scheme combination is calculated by a formula of
Δ
P
=
∑
d
=
1
λ
P
r
e
d
-
∑
d
=
1
λ
O
b
s
d
, which can reasonably be read to entail a mathematical formula/calculation and is therefore the recitation of a mathematical concept. Further, since this calculation can be performed by the human mind, the limitation further recites a mental process as an abstract idea.
Step 2A Prong 2: Claim 5 additionally recites the limitation wherein
Δ
P
is the precipitation forecast error of the each parameterization scheme combination,
λ
is the forecast period,
P
r
e
d
is a precipitation forecast of the
d
t
h
day,
O
b
s
d
is an observed precipitation value of the
d
t
h
day. This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)).The courts have ruled generally linking the use of the judicial exception to a particular technological environment or field of use does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to generally linking the judicial exception to a particular technological environment and field of use are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 6
Step 1: Regarding dependent claim 6, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 6 additionally recites the limitation (S22) querying a historical surface pressure distribution situation which is most closest to the surface pressure distribution situation at the beginning of the actual precipitation forecast in the database, which can reasonably be read to entail making a judgement and observation so as to determine a closest situation. This task can be performed within the human mind or using a pen and paper as an assistive physical aid, for example by looking at entries on paper and making such an analysis. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Step 2A Prong 2: Claim 6 additionally recites the limitations:
(S21) obtaining a surface pressure distribution situation at a beginning of the
actual precipitation forecast; -This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering
wherein an optimal parameterization scheme combination corresponding to the historical surface pressure distribution situation is an optimal parameterization scheme combination of the actual precipitation forecast; and-This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h))
(S23) carrying out the actual precipitation forecast by carrying out the WRF operation with the optimal parameterization scheme combination of the actual precipitation forecast. -This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f))
The courts have ruled appending insignificant extra solution activity to the judicial exception (Insignificant Extra Solution Activity (MPEP 2106.05(g))), reciting the words “apply it “ or equivalent to the judicial exception or invoking the use of generic computing components as a tool (Mere Instructions to Apply an Exception (MPEP 2106.05(f))), or generally linking the use of the judicial exception to a particular technological environment or field of use (Field of Use and Technological Environment (MPEP 2106.05(h))) does not integrate the judicial exception into a practical application. With the additional elements viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: Under broadest reasonable interpretation, obtaining information such as a surface pressure distribution situation entails receiving data over a network which has been found by the courts to be well understood, routine, and conventional when claimed in a merely generic manner. The courts have found that limitations that amount to appending insignificant extra solution activities to the judicial exception that are well understood, routine, and conventional, merely reciting the words “apply it” with regard to the judicial exception, and generally linking the judicial exception to a particular technological environment and field of use are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 7
Step 1: Regarding dependent claim 7, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 7 additionally recites the limitation:
each of the multiple historical surface pressure distribution situations is obtained by analyzing FNL (final operational global analysis) data in the WFP mode, which can reasonably be read to entail performing an evaluation (analysis) and making a judgment so as to derive the historical surface pressure distribution situations. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
the surface pressure distribution situation at the beginning of the actual precipitation forecast is obtained by analyzing the FNL data,, which can reasonably be read to entail performing an evaluation (analyzing) and making a judgment so as to derive the l surface pressure distribution situation. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Step 2A Prong 2: Claim 7 additionally recites the limitations
wherein the database comprises multiple historical surface pressure distribution situations; This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h))
and storing surface pressure distribution data of a research area in a corresponding historical surface pressure distribution matrix file in a form of rows and columns, so as to form the historical surface pressure distribution situation; This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g))
and storing the surface pressure distribution data of the research area in an actual precipitation forecast surface pressure distribution matrix file in the form of rows and columns, so as to form the surface pressure distribution situation at the beginning of the actual precipitation forecast. This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g))
The courts have ruled generally linking the use of the judicial exception to a particular technological environment or field of use and appending insignificant extra solution activity to the judicial exception does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: Under broadest reasonable interpretation, storing data such as in the claims encompasses storing information in memory which has been found by the courts to be well understood, routine, and conventional activity. The courts have found that limitations that amount to appending insignificant extra solution activity that has been found to be well understood routine and conventional to the judicial exception, as well as generally linking the use of the judicial exception to a particular technological environment and field of use are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 8
Step 1: Regarding dependent claim 8, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 8 additionally recites the limitation wherein the step of (S22) specifically comprises calculating a degree of deviation between the surface pressure distribution situation at the beginning of the actual precipitation forecast and each historical surface pressure distribution situation in the database, which can be reasonably read to entail performing a mathematical calculation for a degree of deviation. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas as a mathematical concept. Additionally, such a calculation can be performed practically in the human mind or using assistive aids. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
finding a historical surface pressure distribution situation with a smallest degree of deviation by traversing all historical surface pressure distribution situations in the database, which can be reasonably read to entail evaluating database entries to make a judgment of the smallest degree of deviation. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Additionally, the recitation of a smallest degree of deviation is the recitation of mathematical relationships between numbers to identify the smallest. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas as a mathematical concept.
the degree of deviation is calculated by a formula of
ε
h
=
∑
i
=
1
m
∑
j
=
1
n
P
i
,
j
-
P
i
,
j
h
, here
ε
h
is a degree of deviation between the surface pressure distribution situation at the beginning of the actual precipitation forecast and a
h
t
h
historical surface pressure distribution situation,
i
is row number,
j
is column number,
m
is a largest row number,
n
is a largest column number,
P
i
,
j
is a pressure value of
i
t
h
row,
j
t
h
column in the actual precipitation forecast surface pressure distribution matrix file,
P
i
,
j
h
is a pressure value of the
i
t
h
row,
j
t
h
column in the historical surface pressure distribution matrix file., which can reasonably be read to entail performing a calculation according to the given formula with is the recitation of mathematical calculations and formulas. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas as a mathematical concept. A human being is further capable of performing this calculation and accordingly the limitation is further identified as an abstract idea of mental process.
Step 2A Prong 2: Claim 8 additionally recites the limitations:
wherein the historical surface pressure distribution situation with the smallest degree of deviation is the historical surface pressure distribution situation which is most closest to the surface pressure distribution situation at the beginning of the actual precipitation forecast, which has been identified as Field of Use and Technological Environment (MPEP 2106.05(h))
the optimal parameterization scheme combination corresponding to the historical surface pressure distribution situation with the smallest degree of deviation is the optimal parameterization scheme combination of the actual precipitation forecast, which has been identified as Field of Use and Technological Environment (MPEP 2106.05(h))
The courts have ruled linking the judicial exception to a particular technological environment and field of use does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to linking the judicial exception to a particular technological environment and field of use are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-4 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Moosavi et al (Moosavi, A. Rao, V., and Sandu, A., “A Learning Based Approach for Uncertainty Analysis in Numerical Weather Prediction Models”, February 2018, arXiv.com), hereinafter referred to as Moosavi.
Regarding claim 1, Moosavi discloses A method for dynamically changing a WRF parameterization scheme combination based on a surface pressure distribution situation, the method comprising steps of: A methodology is disclosed for indicating the best model configurations for WRF prediction, wherein the model configurations include a parameter scheme ((Moosavi, Page 19, ¶6) " The experiments validate the new approach, and illustrates how it is able to estimate model errors, indicate best model configurations, and pinpoint to those physical packages that influence most the WRF prediction accuracy"); ((Moosavi, Page 10, ¶4) "The model configuration parameters represent various combinations of micro-physics schemes, cumulus parameterizations, short wave, and long wave radiation schemes."). The schemes are determined according to a state vector of the atmosphere at different spatial locations ((Moosavi, Page 4, ¶3) " The state vector
x
t
∈
R
n
contains the dynamic variables of the atmosphere such as temperature, pressure, precipitation, tracer concentrations, at all spatial locations covered by the model, and at t. All the physical parameters of the model are lumped into
Θ
∈
R
l
."). The domain includes the troposphere portion of the atmosphere which is understood to extend from the surface upwards ((Moosavi, Page 10, ¶3) "In this study we use the non-hydrostatic WRF model version 3.3. The simulation domain, shown in Fig. 1, covers the continental United States and has dimensions of 60_73 horizontal grid points in the west-east and south-north directions respectively, with a horizontal grid spacing of 60km [54]. The grid has 60 vertical levels to cover the troposphere and lower part of the stratosphere between the surface to approximately 20km.")
(S1) constructing a database having a corresponding relation between a historical surface pressure distribution situation and an optimal parameterization scheme combination; and A dataset is built to include features comprising different physical configurations including physics schemes corresponding to time-sensitive modeled WRF forecast data ((Moosavi, Page 17, ¶1) "To build the training dataset, we run WRF with each of the 252 different physical configurations. The forecast window is 6 hours and the WRF model forecast final simulation time is at 12pm. The hourly WRF forecast and discrepancy between the analysis and WRF forecast is used as training features. The input features are: - different physics schemes (
Θ
), - the norms of the WRF model predictions at previous time windows, as well as at the current time (
o
t
=
12
p
m
2
,
o
T
2
,
7
a
m
≤
T
<
12
p
m
), and -the norms of past observed discrepancies (
Δ
T
2
,
7
a
m
≤
12
p
m
)."). The forecast data includes model variables include pressure projected onto the observation space, as a distributed pressure ((Moosavi, Page 13, ¶2) "The hourly WRF forecasts projected onto observation space o_ , am _ _ _ 12pm. The WRF state (xt) includes all model variables such as temperature, pressure, precipitation, etc."). Surface physics is suggested as being a key consideration in evaluated model accuracy ((Moosavi, Page 2, ¶4) "Among different atmospheric phenomena, the prediction of precipitation is extremely challenging and is obtained by solving the atmospheric dynamic and thermodynamic equations [38]. Model forecasts of precipitation are very sensitive to physics options such as the micro-physics, cumulus, long wave, and short wave radiation [13, 31, 38]. Other physics settings that can affect the WRF precipitation predictions include surface physics, planetary boundary layer (PBL), land-surface (LS) parameterizations, and lateral boundary condition. Selecting the right physical process representations and parameterizations is a challenge."). The domain includes the troposphere portion of the atmosphere which is understood to extend from the surface upwards ((Moosavi, Page 10, ¶3) "In this study we use the non-hydrostatic WRF model version 3.3. The simulation domain, shown in Fig. 1, covers the continental United States and has dimensions of 60_73 horizontal grid points in the west-east and south-north directions respectively, with a horizontal grid spacing of 60km [54]. The grid has 60 vertical levels to cover the troposphere and lower part of the stratosphere between the surface to approximately 20km."). The schemes are evaluated in terms of how they best work, thereby indicating that a scheme of the database may be identified as optimal ((Moosavi, Page, 8, ¶1) "Each physical package contains several alternative configurations (e.g., parameterizations or numerical solvers) that affect the accuracy of the forecasts produced by the NWP model. A particular scheme in a certain physical package best captures the reality under some specific conditions (e.g., time of the year, representation of sea-ice, etc.).")
(S2) obtaining an optimal parameterization scheme combination corresponding to the historical surface pressure distribution situation by querying a historical surface pressure distribution situation closest to an actual precipitation forecast surface pressure distribution situation in the database,
before the step of (S1), the method further comprises a step of setting a forecast period of numerical precipitation forecast according to forecast demands; A time window is prescribed for precipitation forecasting in an application-specific way ((Moosavi, Page 8, ¶1) " The primary focus of this study is the accuracy of precipitation forecasts, therefore we seek to learn the impacts of all the physical packages that affect precipitation. To this end, we define the following mapping:
Φ
p
h
y
s
i
c
s
Δ
t
≈
θ
;
(11) that estimates the configuration of the physical packages such that the WRF run generates a forecast with an error consistent with the prescribed level Δt (where Δt defined in equation (5) is the forecast error in observation space at time t.)"); ((Moosavi Page 8, ¶3) " In order to estimate the combinations of physical process configuration that contribute most to the uncertainty in predicting precipitation we take the following approach. The dataset consisting of the observable discrepancies during the current time window Δt is split into a training part and a testing part. In the test phase we use the approximated function
ϕ
^
p
h
y
s
i
c
s
to estimate the physical process settings
θ
^
j
1
that are consistent with the observable errors
Δ
t
,
j
{
1
}
. Here we select
Δ
t
,
j
{
1
}
=
Δ
t
,
j
t
e
s
t
for each j
ϵ
{
t
e
s
t
d
a
t
a
s
e
t
}
. Note that in this case, since we know what physics has been used for the current results, one can take b
θ
^
j
{
1
}
j to be the real parameter values
θ
j
{
1
}
used to generate the test data. However, in general, one selects
Δ
t
,
j
{
1
}
in an application-specific way and the corresponding parameters need to be estimated.")
the step of (S1) specifically comprises: (S11) determining a start time and an end time of the forecast period; A start time of 6am and end time of 12pm is chosen for the forecast window of the model ((Moosavi, Page 13, ¶1) " For training purposes, we use the NCEP analysis of the May 1st 2017 at 6am as initial conditions for the WRF model. The forecast window is 6 hours and the WRF model forecast final simulation time is 12pm. ")
(S12) determining a parameterization scheme combination sample set; The exemplary parameterization scheme set includes microphysics, cumulus physics, short wave radiation physics, and long wave radiation physics. ((Moosavi, Page 10, ¶4) " The model configuration parameters represent various combinations of micro-physics schemes, cumulus parameterizations, short wave, and long wave radiation schemes");((Moosavi, Page 15, ¶1) " Figure 3(a) shows the WRF forecast for 6pm for the state of Virginia using the following physics packages (the physics options are given in parentheses):- Micro-physics (Kessler),- Cumulus-physics (Kain), -Short-wave radiation physics (Dudhia), - Long-wave radiation physics (Janjic).")
(S13) determining a WRF operation scheme; Multiple combinations of the set are established for operation as part of the simulation process ((Moosavi, Page 11, ¶2-3) " A total number of 252 combinations of the four physical modules are used in the simulations. The micro-physics schemes include: Kessler [26], Lin [30], WSM3 Hong [21], WSM5 Hong [21], Eta (Ferrier), WSM6 [22], Goddard [50], Thompson [51], Morrison [36]. The cumulus physics schemes applied are: Kain-Fritsch [25], Betts-Miller-Janjic [23], Grell Freitas[18]. The long wave radiation physics include: RRTM [32], Cam [9]. Short wave radiation physics include: Dudhia [11], Goddard [8], Cam [9]. For each of the 252 different physics combinations, the effect of each physics combination on precipitation is investigated. The NCEP analysis grid points are 428 X 614, while the WRF computational model have 6073 grid points. For obtaining the discrepancy between the WRF forecast and NCEP analysis we linearly interpolate the analysis to transfer the physical variables onto the model grid. Figure 1(a) and 1(b) shows the NCEP analysis at 6am and 12pm on 5/1/2017 which are used as initial condition and \true" (verification) state, respectively. The WRF forecast corresponding to the physics micro-physics: Kessler, cu-physics: Kain-Fritsch, ra-lw-physics: Cam , ra-sw-physics: Dudhia is illustrated in Figure 1(c). Figure 2 shows contours of discrepancies at 12pm (t=12pm) discussed in equation (5) for two different physical combinations, which illustrates the effect that changing the physical schemes has on the forecast."); ((Moosavi, Page 8, ¶1) " Typical NWP models incorporate an array of different physical packages to represent multiple physical phenomena that act simultaneously. Each physical package contains several alternative configurations (e.g., parameterizations or numerical solvers) that affect the accuracy of the forecasts produced by the NWP model. A particular scheme in a certain physical package best captures the reality under some specific conditions (e.g., time of the year, representation of sea-ice, etc.). The primary focus of this study is the accuracy of precipitation forecasts, therefore we seek to learn the impacts of all the physical packages that affect precipitation.").
(S14) carrying out the WRF by each parameterization scheme combination; The different combinations are used as the basis for simulation for the WRF model, wherein WRF forecast results are depicted, thereby indicating that the WRF has been carried out ((Moosavi, Page 11, ¶2-3) " A total number of 252 combinations of the four physical modules are used in the simulations. The micro-physics schemes include: Kessler [26], Lin [30], WSM3 Hong [21], WSM5 Hong [21], Eta (Ferrier), WSM6 [22], Goddard [50], Thompson [51], Morrison [36]. The cumulus physics schemes applied are: Kain-Fritsch [25], Betts-Miller-Janjic [23], Grell Freitas[18]. The long wave radiation physics include: RRTM [32], Cam [9]. Short wave radiation physics include: Dudhia [11], Goddard [8], Cam [9]. For each of the 252 different physics combinations, the effect of each physics combination on precipitation is investigated. The NCEP analysis grid points are 428 X 614, while the WRF computational model have 6073 grid points. For obtaining the discrepancy between the WRF forecast and NCEP analysis we linearly interpolate the analysis to transfer the physical variables onto the model grid. Figure 1(a) and 1(b) shows the NCEP analysis at 6am and 12pm on 5/1/2017 which are used as initial condition and \true" (verification) state, respectively. The WRF forecast corresponding to the physics micro-physics: Kessler, cu-physics: Kain-Fritsch, ra-lw-physics: Cam , ra-sw-physics: Dudhia is illustrated in Figure 1(c). Figure 2 shows contours of discrepancies at 12pm (t=12pm) discussed in equation (5) for two different physical combinations, which illustrates the effect that changing the physical schemes has on the forecast.").
(S15) obtaining surface pressure distribution data at a beginning of each WRF operation and a parameterization scheme combination with a minimum forecast error of the each WRF operation; and The model describes the dynamic variables of the atmosphere, including pressure at time intervals including t=1 as an initial time for a time window ((Moosavi, Page 4, ¶3) " Consider the following NWP computer model M, that describes the time-evolution of the state of the atmosphere: [[equation omitted]] (1a) The state vector
x
t
ϵ
R
n
contains the dynamic variables of the atmosphere such as temperature, pressure, precipitation, tracer concentrations, at all spatial locations covered by the model, and at t. All the physical parameters of the model are lumped into
θ
ϵ
R
l
. "). The model is configured with the parameterization schemes that characterize the operation of the forecasting model ((Moosavi, Page 10, ¶4) "The model configuration parameters represent various combinations of micro-physics schemes, cumulus parameterizations, short wave, and long wave radiation schemes. Specifically, each process is represented by the schema values of each physical parameter it uses, as detailed in WRF model physics options and references [57]."). Multiple combinations of configurations are established and the model is run with each corresponding configuration to determine a forecast and corresponding error for a given forecast time window ((Moosavi, Page 14, ¶1) " The output variable is the discrepancy between the NCEP analysis and the WRF forecast at 12pm, i.e., the observable discrepancies for the current forecast window (t=12pm). In fact, for each of the 252 different physical configurations, the WRF model forecast as well as the difference between the WRF forecast and the analysis are provided as input-output-error. combinations for learning the function "); ((Moosavi, Page 17, ¶1) "To build the training dataset, we run WRF with each of the 252 different physical configurations. The forecast window is 6 hours and the WRF model forecast final simulation time is at 12pm. The hourly WRF forecast and discrepancy between the analysis and WRF forecast is used as training features."). The lowest model forecasting error is determined ((Moosavi, Page 17, ¶5) " Based on our prediction of the norm of model error, the best physics combination that leads to lowest norm of precipitation error over the entire continental U.S. for the given meteorological conditions is:")
(S16) obtaining surface pressure distribution data at the beginning of all WRF operations and all parameterization scheme combinations with the minimum forecast error of the all WRF operations by repeating the steps of (S14) and (S15) and The model describes the dynamic variables of the atmosphere, including pressure at time intervals ((Moosavi, Page 4, ¶3) " Consider the following NWP computer model M, that describes the time-evolution of the state of the atmosphere: [[equation omitted]] (1a) The state vector
x
t
ϵ
R
n
contains the dynamic variables of the atmosphere such as temperature, pressure, precipitation, tracer concentrations, at all spatial locations covered by the model, and at t. All the physical parameters of the model are lumped into
θ
ϵ
R
l
. "). The model is configured with the parameterization schemes that characterize the operation of the forecasting model ((Moosavi, Page 10, ¶4) "The model configuration parameters represent various combinations of micro-physics schemes, cumulus parameterizations, short wave, and long wave radiation schemes. Specifically, each process is represented by the schema values of each physical parameter it uses, as detailed in WRF model physics options and references [57]."). Multiple combinations of configurations are established and the model is run with each (as in repeating for all) corresponding configuration to determine a forecast and corresponding error for a given forecast time window ((Moosavi, Page 14, ¶1) " The output variable is the discrepancy between the NCEP analysis and the WRF forecast at 12pm, i.e., the observable discrepancies for the current forecast window (t=12pm). In fact, for each of the 252 different physical configurations, the WRF model forecast as well as the difference between the WRF forecast and the analysis are provided as input-output-error. combinations for learning the function "); ((Moosavi, Page 17, ¶1) "To build the training dataset, we run WRF with each of the 252 different physical configurations. The forecast window is 6 hours and the WRF model forecast final simulation time is at 12pm. The hourly WRF forecast and discrepancy between the analysis and WRF forecast is used as training features."). The lowest model forecasting error is determined ((Moosavi, Page 17, ¶5) " Based on our prediction of the norm of model error, the best physics combination that leads to lowest norm of precipitation error over the entire continental U.S. for the given meteorological conditions is:")
storing, so as to obtain the database having the corresponding relation between the historical surface pressure distribution situation and the optimal parameterization scheme combination. A dataset is built for training the model, wherein the model learns the relationship between error of the model’s forecasting and the physical packages that characterize the model ((Moosavi, Page 17, ¶1) " To build the training dataset, we run WRF with each of the 252 different physical configurations. "); ((Moosavi, Page 18, ¶4) " From all the collected data points, 80% (202 samples) are used for training the learning model, and the remaining 20% (50 samples) are used for testing purposes. "). The dataset is used to train the model that quantifies the relationship between model error derived from results compared to past observations and the parameterization scheme, thereby indicating that the dataset has such a corresponding relation ((Moosavi, Page 1, ¶Abstract) "The discrepancies between model results and observations at past times are used to learn the relationships between the choice of physical processes and the resulting forecast errors.");((Moosavi, Page 19, ¶4) "We construct probabilistic approaches to learn the relationships between the configuration of the physical processes used in the simulation and the observed model forecast errors. These relationships are then used to solve two important problems related to model errors, as follows: estimating the systematic model error in a quantity of interest at future times, and identifying the physical processes that contribute most to the forecast uncertainty in a given quantity of interest under specified conditions."). The best physics combination can be determined, as an optimal scheme ((Moosavi, Page 18, ¶1) " According to the true model errors, the best physics combination leading to the lowest norm of model error is achieved using the BMJ cumulus parameterization, combined with the WSM5 micro-physics, Cam long wave, and Cam short wave radiation physics.").
Regarding claim 2, Moosavi discloses The method for dynamically changing the WRF parameterization scheme combination based on the surface pressure distribution situation according to claim 1, as given above and further discloses:
wherein the parameterization scheme combination sample set comprises microphysical parameterization schemes and cumulus convection parameterization schemes. A combination of four physical parameter schemes used in simulation includes microphysics and cumulus physics ((Moosavi, Page 15, ¶1) " Figure 3(a) shows the WRF forecast for 6pm for the state of Virginia using the following physics packages (the physics options are given in parentheses):- Micro-physics (Kessler), - Cumulus-physics (Kain),- Short-wave radiation physics (Dudhia),- Long-wave radiation physics (Janjic)."); ((Moosavi, Page 11, ¶2) " A total number of 252 combinations of the four physical modules are used in the simulations. ").
Regarding claim 3, Moosavi discloses The method for dynamically changing the WRF parameterization scheme combination based on the surface pressure distribution situation according to claim 2, as given above and further discloses:
wherein the step of (S 13) specifically comprises on a basis of determining the start time, the end time and the parameterization scheme combination sample set, determining the WRF parameterization scheme in combination with the forecast period. A forecasting window is established from start/end times in conjunction with a parameterization scheme for carrying out WRF forecasting ((Moosavi, Page 14, ¶1) "The output variable is the discrepancy between the NCEP analysis and the WRF forecast at 12pm, i.e., the observable discrepancies for the current forecast window (t=12pm). In fact, for each of the 252 different physical configurations, the WRF model forecast as well as the difference between the WRF forecast and the analysis are provided as input-output -error. combinations for learning the function."); ((Moosavi, Page 17, ¶1) "To build the training dataset, we run WRF with each of the 252 different physical configurations. The forecast window is 6 hours and the WRF model forecast final simulation time is at 12pm. The hourly WRF forecast and discrepancy between the analysis and WRF forecast is used as training features. The input features are:- different physics schemes (_),- the norms of the WRF model predictions at previous time windows, as well as at the current time (kot=12pmk2; ko_k2, 7am _ _ < 12pm), and - the norms of past observed discrepancies (k__k2, 7am _ _ < 12pm).")
Regarding claim 4, Moosavi discloses The method for dynamically changing the WRF parameterization scheme combination based on the surface pressure distribution situation according to claim 3 as given above and further discloses:
wherein the step of (S15) specifically comprises obtaining the surface pressure distribution data at the beginning of the each WRF operation, The WRF model forecasting is performed for each of the different parameter configurations ((Moosavi, Page 14, ¶1) " In fact, for each of the 252 different physical configurations, the WRF model forecast as well as the difference between the WRF forecast and the analysis are provided as input-output combinations for learning the function _error."). The forecasting is performed for initial conditions ((Moosavi, Page 18, ¶3) "To build the dataset, the WRF model is simulated for each of the 252 different physical configurations, and the mismatches between the WRF forecasts and the NCEP analysis at the end of the current forecast window are obtained. Similar to the previous experiment, the initial conditions used in the WRF model is the NCEP analysis for the May 1st 2017 at 6am. The forecast window is 6 hours and the WRF model forecast is obtained for time 12pm."). The WRF forecasting model comprises pressure values over a given area ((Moosavi, Page 4, ¶3) "The state vector contains the dynamic variables of the atmosphere such as temperature, pressure, precipitation, tracer concentrations, at all spatial locations covered by the model, and at t. All the physical parameters of the model are lumped into "). The modeled values may be obtained in the tropospheric level of the atmosphere ((Moosavi, Page 10, ¶3) " In this study we use the non-hydrostatic WRF model version 3.3. The simulation domain, shown in Fig. 1, covers the continental United States and has dimensions of 60_73 horizontal grid points in the west-east and south-north directions respectively, with a horizontal grid spacing of 60km [54]. The grid has 60 vertical levels to cover the troposphere and lower part of the stratosphere between the surface to approximately 20km.")
calculating an precipitation forecast error of the each parameterization scheme combination of the each WRF operation, The difference between the WRF model forecast and the NCEP analysis are determined for each different configuration set ((Moosavi, Page 14, ¶1) " The output variable is the discrepancy between the NCEP analysis and the WRF forecast at 12pm, i.e., the observable discrepancies for the current forecast window (_t=12pm). In fact, for each of the 252 different physical configurations, the WRF model forecast as well as the difference between the WRF forecast and the analysis are provided as input-output combinations for learning the function error."). The model error evaluated is for precipitation forecasting ((Moosavi, Page 13, ¶1) " Our goal is to use the learning algorithms to correct the bias created due to model errors and hence improve the forecast for precipitation.")
selecting the parameterization scheme combination with the minimum forecast error of the each WRF operation as an optimal parameterization scheme combination of the each WRF operation, and The lowest model error is correlated to a specific parameterization scheme and chosen as the best ((Moosavi, Page 18, ¶1) " According to the true model errors, the best physics combination leading to the lowest norm of model error is achieved using the BMJ cumulus parameterization, combined with the WSM5 micro-physics, Cam long wave, and Cam short wave radiation physics."). The error value is given for each of 252 runs with different physics combinations ((Moosavi, Page 17, ¶5) " The RMSE is taken over the 252 runs with different physics combinations.")
building a corresponding relation between the optimal parameterization scheme combination and the surface pressure distribution data at the beginning of the each WRF operation. The relationship between the physical configurations and model error is determined by training learning models to quantify an approximated function representative of the relationship ((Moosavi, Page 17, ¶3) " We use two different learning algorithms, namely, RF with ten trees in the forest and ANN with four hidden layers, the hyperbolic tangent sigmoid activation function in each layer and linear activation function at last layer. The number of layers and neurons at each layer is tuned empirically. The total number of samples in the training set is 252 with 15 of features. During the training phase the model learns the effect of interaction of different physical configurations on model error and obtains the approximated function error.")
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Moosavi as applied to claims 1-4 above, and further in view of Chen et al (CN103793511A), hereinafter referred to as Chen.
Regarding claim 5, Moosavi discloses (except the limitations surrounded by brackets ([[..]])) The method for dynamically changing the WRF parameterization scheme combination based on the surface pressure distribution situation according to claim 4, as stated previously and further discloses:
wherein the precipitation forecast error of the each parameterization scheme combination is calculated by a formula of
Δ
P
=
∑
d
=
1
λ
P
r
e
d
-
∑
d
=
1
λ
O
b
s
d
, wherein
Δ
P
is the precipitation forecast error of the each parameterization scheme combination,
λ
is the forecast period,
P
r
e
d
is a precipitation forecast of the dth [[day]],
O
b
s
d
is an observed precipitation value of the dth [[day]]. The difference between observations and predicted values indicate the model error for a given time period T. ((Moosavi, Page 5, ¶1) " The difference between the observations (6b) of the real system and the model predicted values of these observables (4) represent the model error in observation space: _t = ot yt 2 Rm; t = 1; _ _ _ ; T: "). The error value is calculated for all parameterization scheme combinations ((Moosavi, Page 17, ¶3) " The total number of samples in the training set is 252 with 15 of features. During the training phase the model learns the effect of interaction of different physical configurations on model error and obtains the approximated function b_error.").
Moosavi fails to disclose daily time consideration as the time window for evaluation; however Chen discloses performing weather forecasting for a day interval ((Chen, ¶17) " B2: Daily simulation calculations are performed on historical meteorological data for each characteristic month using the parameter variables included in the main program parameter scheme to obtain the wind speed values predicted by the WRF model; [0018]")((Chen, ¶98) " Finally, in the actual forecasting process, weather forecast data for the next 24 hours (i.e. the next day) is obtained.")
It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have modified the teachings of Moosavi to particularly consider daily forecasting as the particular time interval for performing weather forecasting evaluations because simple substitution of one known element for another would yield predictable results. By instead using the daily interval disclosed by Chen into the forecasting of Moosavi, one having skill would reasonably be able to analyze weather forecasting on a regularly-occurring and predictable basis by which weather forecasting is often performed..
Regarding claim 6, the proposed combination discloses The method for dynamically changing the WRF parameterization scheme combination based on the surface pressure distribution situation according to claim 5, as above and further in view of Moosavi discloses:
wherein the step of (S3) specifically comprises: (S2 l) obtaining a surface pressure distribution situation at a beginning of the actual precipitation forecast; An NCEP analysis is used as a true state value of the atmosphere as the initial condition for forecasting with the WRF model ((Moosavi, Page 18, ¶3) " Similar to the previous experiment, the initial conditions used in the WRF model is the NCEP analysis for the May 1st 2017 at 6am. The forecast window is 6 hours and the WRF model forecast is obtained for time 12pm.")
(S22) querying a historical surface pressure distribution situation which is most closest to the surface pressure distribution situation at the beginning of the actual precipitation forecast in the database, wherein an optimal parameterization scheme combination corresponding to the historical surface pressure distribution situation is an optimal parameterization scheme combination of the actual precipitation forecast; and A learning model is trained on historical WRF forecasts and the current forecast ((Moosavi, Page 7, ¶1) " We use a machine learning algorithm to approximate the function _error. The learning model is trained using a dataset that consists of the following inputs: _ WRF physical packages that affect the physical quantity of interest (_), - historical WRF forecasts ([[…]]), - historical model discrepancies ([[..]]), _-WRF forecast at the current time (ot), - the available model discrepancy at the current time (_t) since we have access to the observations from reality yt at the current time step."). The model is then used (as a query) to identify the physical packages comprising parameterizations that best captures the reality under current specific conditions for precipitation forecasting ((Moosavi, Page 6, ¶1) " Typical NWP models incorporate an array of different physical packages to represent multiple physical phenomena that act simultaneously. Each physical package contains several alternative configurations (e.g., parameterizations or numerical solvers) that affect the accuracy of the forecasts produced by the NWP model. A particular scheme in a certain physical package best captures the reality under some specific conditions (e.g., time of the year, representation of sea-ice, etc.). The primary focus of this study is the accuracy of precipitation forecasts, therefore we seek to learn the impacts of all the physical packages that affect precipitation.")
(S23) carrying out the actual precipitation forecast by carrying out the WRF operation with the optimal parameterization scheme combination of the actual precipitation forecast. The norm model error indicates the best combination of parameterizations, wherein the norm model error is derived from performing WRF forecasting, thereby indicating that the forecasting has been performed for all parameter combinations including that identified as the best ((Moosavi, Page 17, ¶7) " Based on our prediction of the norm of model error, the best physics combination that leads to lowest norm of precipitation error over the entire continental U.S. for the given meteorological conditions is: - the BMJ cumulus parameterization, combined with- the WSM5 micro-physics, - Cam long wave, and - Dudhia short wave radiation physics. According to the true model errors, the best physics combination leading to the lowest norm of model error is achieved using the BMJ cumulus parameterization, combined with the WSM5 micro-physics, Cam long wave, and Cam short wave radiation physics."); ((Moosavi, Page 16, ¶1) " We now seek to estimate the two-norm of precipitation model error over the entire continental U.S., which gives a global metric for the accuracy of the WRF forecast, and helps provide insight about the physics configurations that result in more accurate forecasts."); ((Moosavi, Page 18, ¶3) " For each of the 252 different physical configurations, this process is repeated and statistical characteristics of the WRF forecast model error __ t=12pm, and the norm of model error k_t=12pmk2 are used as feature values of the function _physics.")
Regarding claim 7, The proposed combination discloses The method for dynamically changing the WRF parameterization scheme combination based on the surface pressure distribution situation according to claim 6, as stated previously and further in view of Moosavi discloses:
wherein the database comprises multiple historical surface pressure distribution situations; The dataset consists of historical WRF forecasts ((Moosavi, Page 7, ¶1) " We use a machine learning algorithm to approximate the function _error. The learning model is trained using a dataset that consists of the following inputs: - WRF physical packages that affect the physical quantity of interest (_),- historical WRF forecasts (o_ for _ _ t 1),")
each of the multiple historical surface pressure distribution situations is obtained by analyzing FNL (final operational global analysis) data in the WFP mode, and storing surface pressure distribution data of a research area in a corresponding historical surface pressure distribution matrix file in a form of rows and columns, so as to form the historical surface pressure distribution situation; The computer model is characterized by information about a global physical state wherein the model state is related to observations through an operator that maps the state onto the observation space ((Moosavi, Page 4, ¶6-7- Page 5, ¶1) " Although the global physical state _t is unknown, we obtain information about it by measuring of a finite number of observables yt 2 Rm, as follows:[[..]; (3) Here h is the observation operator that maps the true state of atmosphere to the observation space, and the observation error _t is assumed to be normally distributed. In order to relate the model state to observations we also consider the observation operator H that maps the model state onto the observation space; the model-predicted values ot 2 Rm of the observations (3) are: [[...]] (4)"). The observation domain is described as a grid ((Moosavi, Page 10, ¶3) " In this study we use the non-hydrostatic WRF model version 3.3. The simulation domain, shown in Fig. 1, covers the continental United States and has dimensions of 60_73 horizontal grid points in the west-east and south-north directions respectively, with a horizontal grid spacing of 60km [54]. The grid has 60 vertical levels to cover the troposphere and lower part of the stratosphere between the surface to approximately 20km."). Such modeling is performed for all training data, as the historical data ((Moosavi, Page 13, ¶1) " The number of grid points over the state of Virginia is 14 _ 12. Therefore for each physical combination we have 168 grid points, and the total number of samples in the training data set is 252_168 = 42; 336 with 15 features.")
the surface pressure distribution situation at the beginning of the actual precipitation forecast is obtained by analyzing the FNL data, and storing the surface pressure distribution data of the research area in an actual precipitation forecast surface pressure distribution matrix file in the form of rows and columns, so as to form the surface pressure distribution situation at the beginning of the actual precipitation forecast. The NCEP analysis provides an initial condition that is applied to the WRF computational model with grid points through interpolation ((Moosavi Page 11, ¶2) " For each of the 252 different physics combinations, the effect of each physics combination on precipitation is investigated. The NCEP analysis grid points are 428 X 614, while the WRF computational model have 60X73 grid points. For obtaining the discrepancy between the WRF forecast and NCEP analysis we linearly interpolate the analysis to transfer the physical variables onto the model grid. Figure 1(a) and 1(b) shows the NCEP analysis at 6am and 12pm on 5/1/2017 which are used as initial condition and \true" (verification) state, respectively. The WRF forecast corresponding to the physics micro-physics: Kessler, cu-physics: Kain-Fritsch, ra-lw-physics: Cam , ra-sw-physics: Dudhia is illustrated in Figure 1(c). Figure 2 shows contours of discrepancies at 12pm (_t=12pm) discussed in equation (5) for two different physical combinations, which illustrates the effect that changing the physical schemes has on the forecast."). The computer model is characterized by information about a global physical state wherein the model state is related to observations through an operator that maps the state onto the observation space ((Moosavi, Page 4, ¶6-7- Page 5, ¶1) " Although the global physical state _t is unknown, we obtain information about it by measuring of a finite number of observables yt 2 Rm, as follows:[[..]; (3) Here h is the observation operator that maps the true state of atmosphere to the observation space, and the observation error _t is assumed to be normally distributed. In order to relate the model state to observations we also consider the observation operator H that maps the model state onto the observation space; the model-predicted values ot 2 Rm of the observations (3) are: [[...]] (4)"). The observation domain is described as a grid ((Moosavi, Page 10, ¶3) " In this study we use the non-hydrostatic WRF model version 3.3. The simulation domain, shown in Fig. 1, covers the continental United States and has dimensions of 60_73 horizontal grid points in the west-east and south-north directions respectively, with a horizontal grid spacing of 60km [54]. The grid has 60 vertical levels to cover the troposphere and lower part of the stratosphere between the surface to approximately 20km.").
Allowable Subject Matter
Claim 8 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims as well as rewritten to overcome the rejection set forth under 35 U.S.C. § 101.
The following is a statement of reasons for the indication of allowable subject matter: The prior art of record, both alone and in combination, fails to disclose or fairly suggest at least the following limitations of claim 8 when considered with the cumulative preceding claims by which claim 8 depends:
finding a historical surface pressure distribution situation with a smallest degree of deviation by traversing all historical surface pressure distribution situations in the database,
…
the degree of deviation is calculated by a formula of
ε
h
=
∑
i
=
1
m
∑
j
=
1
n
P
i
,
j
-
P
i
,
j
h
,
here,
ε
h
is a degree of deviation between the surface pressure distribution situation at the beginning of the actual precipitation forecast and a
h
t
h
historical surface pressure distribution situation,
i
is row number,
j
is column number, m is a largest row number, n is a larges column number,
P
i
,
j
is a pressure value of
i
t
h
row,
j
t
h
column in the actual precipitation forecast surface pressure distribution matrix file,
P
i
,
j
h
is a pressure value of the
i
t
h
row,
j
t
h
column in the historical surface pressure distribution matrix file.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
CN110619433 discloses (description, paragraphs [0037]-[0074]): determining a simulation region; determining an alternative parameterization solution according to longitude and latitude positions and a typical climate type of the simulation region; collecting daily precipitation data of a forecast region in the past 10 years; collecting forecast initial field and boundary field data of a numerical value forecast model three days before the occurrence of each precipitation event (equivalent to setting a prediction period of a numerical value precipitation forecast according to a forecast demand, and determining a start time and an end time of the prediction period; determining a sample set of a parameterization solution combination); according to the forecast initial field and boundary field data, using a single variable method to only change parameters of one parameterization solution at a time, and then using the precipitation set to simulate precipitation events of different typical precipitation types (equivalent to determining a WRF mode operation solution; operating the WRF mode by using each parameterization solution combination); comparing a simulation result with precipitation observation data of a corresponding ground meteorological station, to obtain an accuracy score of the simulation result; according to the accuracy score of the simulation result, determining an optimal parameterization solution combination of a grid rainstorm numerical value model to be rapidly selected during power supply grid rainstorm service forecast (equivalent to cyclic execution, to obtain the parameterization solution combination with the minimum prediction error when operating the WRF mode each time; using the optimal parameterization solution combination to operate the WRF mode to carry out the actual precipitation forecast)- Note this description is as-given in written opinion document of priority document WO 2021248987 A1.
US 9274251 B2 discloses the tuning of an WRF model to fit local conditions by choosing parameterization schemes provide the best possible forecast which have the highest spatial and temporal correlation factors, the modification of model forecasting according to spatial and temporal needs, using real-time conditions to select proper parameterization schemes for weather forecasting, assembly and utilization of a database for the most effective parameterization schemes for a given need whereby skill scores are generated for the effectiveness of a parameterization scheme in the database, the consideration of surface pressure with respect to eta levels in WRF forecasting.
CN110705796 A discloses an ensemble forecasting method for heavy rainfall that includes selecting initial field data for different forecasting times from historical data, selecting a numerical forecasting model, selecting different parameterization schemes and performing the forecasting given the selected components. Correction coefficients are calculated for different parameterization schemes and used to tune the forecasting model.
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/E.G.L./Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
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