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 .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the reference character(s) not mentioned in the description. Specifically, FIG. 2, element 229 is labeled as “optimal state profiles”. Examiner does not find reference to element of “optimal state profiles” in the specification. Examiner notes element 229 is referred to in specification in at least [0022] , [0026], [0029], [0031], [0047], [0061] as “converged state profiles”.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character “229” has been used in FIG. 2 with label “optimal state profiles” (discussed directly above) and also in FIG. 3, where “229” is labeled as “converged state profiles”. Figure labels should be made consistent.
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
FIG. 3 is discussed in [0026], but includes reference to multiple elements
which do not appear in FIG. 3, but do appear in FIG. 2, including at least “weather predictor 200”, “memory 202”, “software 220”. Examiner suggests explicit reference to FIG. 2 be included for clarity, and points to Applicant’s disclosure, paragraph [0050], which recites “[0050]: “Method 400 may be implemented, at least in part, within software 220 of weather predictor 200 of FIG. 2.” as an example which is clear in making such a reference.
FIG 4 is discussed in [0051]-[0063] but narrative includes references to
multiple elements which do not appear in FIG. 4, but appear in FIG. 3 including at least “comparator 330”, “radiance difference 330”, “forecast satellite radiances 324”, “radiative transfer model 220”, “forecasted state profiles 319”, among others. For clarity, reference to FIG. 3 should be made explicit in discussion. Examiner suggests explicit reference to FIG. 2 be included for clarity, and points to Applicant’s disclosure, paragraph [0050], as above.
• Paragraphs [0050] and [0053] recite a list reading “steps 420, 430, 440, 450, 460, and 460.” This is assumed to be a typographical error and should be corrected.
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 1-20 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.
Specifically, independent Claims 1 and 15 recite: “generating radiance differences as a difference between measured radiances from satellites and forecast satellite radiances generated by a radiative transfer model and forecasted state profiles output by a numerical weather prediction (NWP) model”. This language renders Claim 1 to be indefinite. The language indicates “radiance differences” as being generated as a “difference between”, followed by a list of multiple data types. Using broadest reasonable interpretation (BRI) and plain meaning, one of ordinary skill would understand “difference between” to denote introduction or calculation of some distinct contrast, variance, or gap separating two or more subjects, conditions, or data points. More specifically, in the context of computational methods for numerical data analysis, one of ordinary skill would consider “difference between” to imply calculation or quantification of a mathematical disparity (e.g., subtracting value A from value B) or physical means of causing a change to produce a measurable gap or discontinuity. Guidance from specification supports interpretation of meaning in the mathematical / computational in at least FIGURE 3 with [0026], wherein there is an indication that radiance differences (339) are generated by a comparator function 330 (“generating radiance differences”), where the first quantity being compared is “measured satellite radiances 204” with a second quantity “forecast satellite radiances 324” in some combination with “radiative transfer model 320”, where “radiative transfer model 320” is some combination of Jacobian model 322 and forecast state profiles 329 with input from NWP forecast model 310. If the limitation is intended to convey the process depicted in FIG. 3 and described in [0026] language should be revised to convey the concept consistently and improve clarity.
Dependent Claims 2-14, and 16-20 are rejected based on dependence to rejected independent claims 1 and 15, respectively.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are held to be patent ineligible, as explained below.
With attention to independent Claims 1 limitations, and similarly limitations of Claim 15, abstract ideas are recited as follows: (bold emphasis added)
“generating radiance differences as a difference between measured radiances from satellites and forecast satellite radiances generated by a radiative transfer model and forecasted state profiles output by a numerical weather prediction (NWP) model”
when the radiance differences exceed a noise threshold, generating updated state profiles by:
generating radiance-sensitivities using a Jacobian model and the forecasted state profiles;
constructing a Kalman-gain matrix from background error covariance (BEC) matrices and the radiance-sensitivities;
generating filtered state-profile changes from the Kalman-gain matrix and the radiance differences ; and
updating the state profiles by adding the filtered state-profile changes to the forecasted state profiles to yield the updated state profiles.”
Detailed explanation of evaluation steps regarding patent eligibility is presented below:
STEP 1 – Determination of statutory category: Both Claim 1 and Claim 15 fall within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101, namely Claim 1 recites a method (process); Claim 15 recites a machine/manufacture (“weather predictor”).
STEP 2A-PRONG ONE – Determination regarding whether claim recites a judicial exception: Applying broadest reasonable interpretation (BRI), Claim limitations noted above with bold emphasis, recite a judicial exception. These limitations constitute a judicial exception of Abstract Idea because under BRI and using 2024 Revised Patent Subject Matter Eligibility Guidance, the limitations fall into the grouping of subject matter that covers performing mathematics or mental steps. (MPEP 2106.04(a)(2), I.A,C, III.B,C) Examiner notes execution of the claimed limitations involve performing mathematics using at least some generic computer components, but that it may be possible to carry out some components using mental steps depending on the complexity of the mathematical processes. Evidence for use of computation is found in Claim 15 reciting, “a processor; and a memory storing machine-readable instructions that, when executed by the processor, control the processor”, with otherwise parallel limitations with Claim 1. Thus it is interpreted that the limitations common to both Claim 1 and Claim 15 involve using generic computational components and generic artificial intelligence (AI)/machine learning (ML) technology (at least in specification [0033] and [0045]) in development of “model(s)”, and includes training neural network(s) (at least specification [0045]. Further review reveals computational components to perform input and/or output of data and/or results, perform quantitative evaluations, analysis and/or calculations for weather prediction. Claims 1 or 15 limitations do not recite details regarding how the computational algorithm or model functions are trained, such that claims are found to utilize algorithmic tools that provide nothing more than mere instructions to implement the abstract idea on a general purpose computer. (MPEP 2106.05(f)).
Evaluation under STEP 2A-PRONG ONE finds Claim 1, and using similar reasoning and rationale, Claim 15, recites a judicial exception of Abstract Idea.
STEP 2A-PRONG TWO: Evaluation of additional elements to determine whether
the claim integrates the judicial exception into a practical application of that exception:
Claim 1, and similarly Claim 15, does not recite significantly more than the judicial exception to integrate the recited abstract idea into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; or effecting a transformation or reduction of a particular article to a different state or thing.
Claims 1 and 15 limitations do recite additional element of “measured radiances from satellites”. Examiner notes this additional element is interpreted as necessary data gathering required to provide numerical input values for carrying out the judicial exception as defined in analysis above. As recited in MPEP section 2106.05(g), necessary data gathering (i.e. receiving data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). Examiner notes limitations reciting necessary data gathering , even when linked to a particular data source or a type of data, are considered to be insignificant extra solution activity.
As noted above, Claim 15 recites the additional element “a processor; and
a memory storing machine-readable instructions that, when executed by the processor, control the processor”, interpreted as generic computer components recited at a high level of generality. As noted above, generic computer elements, such as those explicitly recited in Claim 15, are not considered significantly more than the abstract idea. Using guidance from MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. (see Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.) And, as for other identified additional elements, this additional element is considered as generally linking the use of a judicial exception to a particular technological environment or field of use, but does not integrate a judicial exception into a practical application. (MPEP § 2106.05(h)).
STEP 2B – Consideration of whether the claim amounts to significantly more than the abstract idea: As discussed above, Claim 1 and similarly Claim 15 does not recite limitations which amount to significantly more than the judicial exception to integrate the recited abstract idea into a practical application. Further there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; or effecting a transformation or reduction of a particular article to a different state or thing.
Additional elements, as discussed above, do not amount significantly more than the judicial exception, reciting insignificant extra solution activity considered as necessary data gathering , even when linked to a particular data source or a type of data, are considered to be insignificant extra solution activity, with limitation elements recited in generality, representing insignificant field of use limitations not meaningful to indicate a practical application, or necessary data gathering required to perform the abstract idea. As above, Examiner points to Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). (MPEP section 2106.05(g))
Thus, Claims 1 and 15 are directed to the judicial exception of Abstract Idea.
Further eligibility consideration includes evaluation of Claims 2-14 with direct or
indirect dependency to Claim 1 and Claims 16-20 with dependency to Claim 15. Evaluation of dependent claims reveal limitations with additional elements which further limit performing the mathematical process and that do not integrate the judicial exception into a practical idea, directed to limiting mathematical processes or calculations to determine quantitative or qualitative results. Further, additional elements recited in dependent claims are directed to necessary data gathering or insignificant field of use limitations. When considered individually or as a whole, the additional elements identified in dependent claims do not amount to more than the judicial exception nor integrate the judicial exception into a practical application.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 10, 13- 16, and 20, are rejected under 35 U.S.C. § 103(a) as being unpatentable over QU ( CN 109782374 B)*, in view of SHAHABADI (Shahabadi et al., “Validation of a weather forecast model at radiance level against satellite observations allowing quantification of temperature, humidity, and cloud-related biases”, J. Adv. Model. Earth Syst., 8, 1453–1467, 2016.), RÜDIGER (Rüdiger, et al., “Evaluation of the observation operator Jacobian for leaf area index data assimilation with an extended Kalman filter”, J. Geophys. Res., 115, 2010), and NOAA (National Oceanic and Atmospheric Administration Physical Sciences Laboratory News Item, “PSL Helps Improve Data Assimilation for Global Weather Prediction”, Contact: Jeff Whitaker, Webpage, 2012)
With regard to Claims 1 and 15, QU teaches:
(Claim 1) A weather prediction method; (Claim 15) A weather predictor comprising: a processor; and a memory storing machine-readable instructions (QU is in same technical field, Abstract: “method and device for optimizing numerical value weather forecasting”; and [0021]: “non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, the computer instructions cause the computer to perform the method”)
generating radiance differences as a difference between measured radiances from satellites and forecast satellite radiances generated by a radiative transfer model and forecasted state profiles output by a numerical weather prediction (NWP) model;
(QU, [0001]: “Based on the radiance generated by ground radiation received by observation satellites (i.e., “measured radiances from satellites”)”; process steps outlined in [0006]-[0011]; [0037]: “initial field of the numerical forecast model (i.e., “numerical prediction (NWP) model”) can be optimized by using the water vapor content observed by satellites to constrain the layered water vapor simulated by the numerical forecast model”; [0038]: “the inversion…each altitude layer of the atmosphere according to the radiance observed by the satellite includes: [0039]: “difference between (i.e., “radiance differences”) the radiance simulated by the radiation transmission model (i.e., “radiative transfer model”*) and the radiance received by the satellite at each observation point of the satellite” (i.e., “forecast satellite radiances generated by a radiative transfer model”); further, see [0040]: “inversion operation includes: [0041]: “Obtain the radiance obtained by the radiant transmission model…as the simulated radiance”)
when the radiance differences exceed a noise threshold, generating updated state profiles (QU, [0042]: “Determine whether the difference between the simulated radiance at each observation point of the satellite and the radiance received by the observation satellite is less than the preset error. If not, adjust the current stratified water vapor content of each atmosphere”; [0043]: “method continuously adjusts the stratified water vapor of each atmosphere in the RTM until the difference between the radiance simulated by the RTM and the radiance received by the observation satellite reaches a preset error”; Examiner interprets “noise threshold” using guidance from specification in at least ([0053]) to mean any predetermined quantity based on noise statistic or uncertainty related measures, which in context of instant application may include radiative transfer model noise or RMS noise from instruments including satellites, or from some combination of the two, analogous to “preset error”.; and [0044]: “process of adjusting the current stratified water vapor content of each atmosphere, it is actually a process of adjusting the state variables mainly based on the current stratified water vapor content (i.e., “updated state profiles”) of each atmosphere”)
QU does not teach:
generating radiance-sensitivities using a Jacobian model and the forecasted
state profiles;
constructing a Kalman-gain matrix from background error covariance (BEC) matrices and the radiance-sensitivities;
generating filtered state-profile changes from the Kalman-gain matrix and the radiance differences ; and
updating the state profiles by adding the filtered state-profile changes to the forecasted state profiles to yield the updated state profiles .
SHAHABADI teaches:
generating radiance-sensitivities using a Jacobian model and the forecasted state profiles; (SHAHABADI is in same technical field, Abstract: “radiative transfer model (RTM) is adapted for simulating all-sky infrared radiance spectra… in order to validate its forecasts (i.e., forecasted state profiles”)”; and, P1453: “Using a forward model to generate synthetic radiances from model-simulated atmospheric profiles and comparing them with satellite-measured radiances”, and P1454, “radiances are assimilated to provide initial analyses leading to GEM forecasts”(i.e., “); and “propose the use of radiative sensitivity kernels, also referred to as Jacobians (i.e., “using a Jacobian model”). These kernels…allow testing the first-order consistency between the radiance bias and meteorological variable biases”; and P1455: “2.3. Kernel Method…spectral kernel method in analyzing the radiance bias (i.e., “generating radiance sensitivities”)… contribution of each meteorological variable to the overall radiance bias is measured”; Examiner applies BRI to interpret “radiance sensitivities” with guidance from specification, in at FIG3, element326, depicting Jacobian model feeds into 326 and then goes into the Kalman filter cycle with [0033], [0034.)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to modify QU to include: generating radiance-sensitivities using a Jacobian model and the forecasted state profiles, as taught by SHAHABADI, as part of a method for iterative updating of weather forecast modeling because this would make effective use of proven assimilation techniques by using acquired data in model development. One of ordinary skill would realize the benefit of combining the more detailed techniques as explicitly taught by SHAHABADI using Jacobian matrix formulations, which are known to accurately represent sensitivity of observable data to changes in prognostic variables, to achieve a more efficient modeling process that would maximize the impact of radiance satellite data, which is part of the process disclosed by QU, and allow for precise atmospheric adjustments, quick data correction, and effectively use satellite measurements for timely updates to a wide variety of prognostic variables, including water vapor and temperature.
RÜDIGER teaches:
constructing a Kalman-gain matrix from background error covariance (BEC) matrices and the sensitivities; (RÜDIGER is in related technical field, using assimilation methods relevant to weather prediction using land-surface modeling, see P2, Col2, “2. Description of Land Surface Model and Assimilation Scheme, 2.1...[5]… Interaction between the Soil, Biosphere and Atmosphere (ISBA) model […] is currently used operationally at Météo‐France as the land surface component of their numerical weather forecasting models […]”; and P2Col2, 2.2 Extended Kalman Filter, [6] “equation for the model state analysis is
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where x is the model state and the superscripts a and b denote the analysis and background states, respectively; t is the time step indicator; B is the background error covariance matrix; R is the observation error covariance matrix; h is the observation operator”; further, observation operator is defined, see Eq.(3) and P3,Col2, “4. Results, 4.1 Jacobian Estimates “ Jacobian of the observation operator h (as defined in equation (3)) required to estimate the Kalman gain of the analysis equation” and P8 Kalman gain k defined by
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where [Symbol font/0x73]b and [Symbol font/0x73]o are the standard deviations of background and observation errors”; Examiner asserts one of ordinary skill in the art would know in the EKF context, the Jacobian of the observation operator describes sensitivity of observable data to changes in various prognostic variables, as discussed above.; Examiner notes references cited by RÜDIGER: Noilhan, et al 1989, Noilhan et al. 1996, and Giard, et al. 2000, and Mahfouf et al. 2009, are provided and listed below as pertinent prior art.)
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generating filtered state-profile changes from the Kalman-gain matrix and the differences; (RÜDIGER, as above, P2, 2.2 [6] “equation for model state analysis Eq. (1)”; Examiner interprets “filtered state-profile changes” as analogous to “model state” based on Extended Kalman Filter, as in reference, defined as above to be a function of Kalman gain matrix, Eq. (9) as above; further, see P3Col1, Eq.(2) with [7]: “Jacobian matrix H of the linearized observation operator is defined as …the Jacobian matrix is estimated using a finite difference approximation by perturbing the initial model state by a small amount dx and estimating the difference dy between a perturbed simulated observation”; Examiner interprets “differences” as analogous to reference, to mean consideration of differences between observable data and model data.)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify QU, as modified by SHAHABADI and taught above, to include: constructing a Kalman-gain matrix from background error covariance (BEC) matrices and the radiance-sensitivities and generating filtered state-profile changes from the Kalman-gain matrix and the radiance differences, as taught by RÜDIGER because use of a Kalman gain matrix is known as a way to optimize statistical weighting and reduce errors. One of ordinary skill would see an obvious benefit of implementing the detailed Kalman method as taught by RÜDIGER with the modeling and data acquisition method of QU, as modified by SHAHABADI of using Jacobian matrices to consider observables sensitivities to measured prognostic variables, even with sparse data acquisition, to enhance balance forecast uncertainty and affect observational impacts smoothly across vertical and horizontal grid points in the resulting model.
NOAA teaches:
updating the state profiles by adding the filtered state-profile changes to the forecasted state profiles to yield the updated state profiles. (NOAA is in same technical field, see NOAA.gov; and P1: “changed their global atmospheric data assimilation system from a purely variational system to a hybrid system, which incorporates an ensemble Kalman filter (EnKF)”; As above, Examiner interprets “state-profile” to mean the resulting model, analogous to reference “data assimilation system” aimed at weather forecast model development using Kalman filtering methods; Examiner interprets limitation “adding the filtered state-profile changes to the forecasted profiles” to mean general the concept of combining model developed using Kalman filtering model with model developed using numerical weather prediction (NWP) method, with guidance from Applicant’s specification in at least [0005], and [0035] with Eq. (2.2), and [0059].)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify QU, as modified by SHAHABADI and RÜDIGER as taught above, to include: updating the state profiles by adding the filtered state-profile changes to the forecasted state profiles to yield the updated state profiles . as taught by and NOAA because using both the Kalman method filtered data and forecast data would allow for improved bias removal and the ability to account for drift that may occur between simulated models and real environment data. One of ordinary skill would see the obvious improvement of implementing the NOAA method of data assimilation, which teaches an overall improvement in forecast models, into the method of data collection and modeling disclosed by QU, and modified using the teaching of SHAHABADI and RÜDIGER to Jacobian matrix methods to consider radiance sensitivities and the well-known Kalman approach to result in a more accurate and robust model that takes advantage of multiple data sources and modeling techniques, with reasonable expectation of success.
With regard to Claims 2 and 16, QU, in view of SHAHABADI and RÜDIGER and further in view of NOAA, teaches the limitations of Claims 1 and 15.
QU further teaches:
(i) repeating the step of generating radiance differences to yield updated radiance differences, where the updated state profiles replace the forecasted state profiles; (QU teaches generation of radiance differences, as above see [0038]-[0039; and updates to state profiles, as above, [0044]: “process of adjusting the current stratified water vapor content of each atmosphere, it is actually a process of adjusting the state variables mainly based on the current stratified water vapor content (i.e., “updated state profiles”) of each atmosphere”; Examiner interprets “repeating” generally to mean iterative methodology, was would be understood in implementation of a forecasting modeling process that uses real-time data, and as taught by reference, see [0045]: “method for optimizing numerical weather prediction by assimilation and inversion…Through continuous iterative inversion of RTM, the radiance of each observation point of the observation satellite simulated by RTM and the actual radiation received by the observation satellite are finally obtained”)
(ii) repeating the step of generating updated state profiles when the updated radiance differences exceed the noise threshold. (QU, as above, [0045] teaching iterative method, and as above, [0042]: “Determine whether the difference between the simulated radiance at each observation point of the satellite and the radiance received by the observation satellite is less than the preset error. If not, adjust the current stratified water vapor content of each atmosphere”; [0043]: “method continuously adjusts the stratified water vapor of each atmosphere in the RTM until the difference between the radiance simulated by the RTM and the radiance received by the observation satellite reaches a preset error”; Examiner notes interpretation of “noise threshold” as discussed above.)
With regard to Claim 10, QU, in view of SHAHABADI and RÜDIGER and further in view of NOAA, teaches the limitations of Claim 1.
QU further teaches:
before generating the radiance differences, generating the forecast satellite radiances with the radiative transfer model. (QU, as above, teaches generating radiance differences using forecast satellite radiance based on radiative transfer model, [0039]: “difference between (i.e., “radiance differences”) the radiance simulated by the radiation transmission model (i.e., “radiative transfer model”*) and the radiance received by the satellite at each observation point of the satellite” (i.e., “forecast satellite radiances generated by a radiative transfer model”); further, see [0012]: “acquisition module is used to acquire the initial field of the numerical weather forecast mode and the radiance observed by the satellite”, Examiner notes this step is performed prior to determination of “differences.)
With regard to Claim 13, QU, in view of SHAHABADI and RÜDIGER and further in view of NOAA, teaches the limitations of Claim 1.
QU further teaches:
updating the state profiles further comprising removing, from the updated state profiles, updated state profiles that are not physically realizable (QU teaches concept of physically unrealistic data removal, [0050]: “method provided in this embodiment only considers the observation points in the clear sky state. Therefore, the non-clear sky points are removed by cloud detection before inversion, and the inversion is performed only based on the observation points in the observation satellite that are in the clear sky state.”)
With regard to Claims 14 and 20, QU, in view of SHAHABADI and RÜDIGER and further in view of NOAA, teaches: the limitations of Claims 1 and 15.
RÜDIGER further teaches:
the filtered state-profile changes being a product of the Kalman-gain matrix and the differences . (RÜDIGER teaches model state analysis matric based on extended Kalman filter, Pg2,2.2 “extended Kalman Filter [6], Eq. (1) (i.e., “filtered state profile changes”) depicting a product of Kalman gain matrix with differences as computed by Jacobian. as would be understood and known by one of ordinary skill in the art in a Kalman filter-based model process.)
It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to further modify QU, as modified by SHAHABADI, RÜDIGER and further modified by NOAA as taught above, to include: the filtered state-profile changes being a product of the Kalman-gain matrix and the differences, as further taught by RÜDIGER, because this would be a way to effectively incorporate differences based on radiance measurements, as taught above by SHAHABADI and used to modify the method disclosed by QU. Further, using this step allows for efficient handling of error corrections and timely and more accurate model results, since it includes physical data.
Allowable Subject Matter
Claims 4-9, 11-12, and 18-19 are 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.
The following is a statement of reasons for the indication of allowable subject matter:
With regard to Claims 3 and 17, the best discovered prior art individually or in an obvious combination fail to teach “method further comprising, when the radiance differences are less than a noise threshold based on noise from each of the satellites, the radiative transfer model and the NWP model”. Specifically, prior art was discovered, as noted in Claim 1, teaching consideration of differences between satellite radiance data and radiative transfer model. However, the concept of consideration of noise from “each” of the satellites, RTM and NWP models was not found. Examiner notes limitations claiming generating subsequent state profiles with NWP model and iterative determination of radiance differences was found, as discussed above in similar language recited in Claim 1.
With regard to Claims 4 and 18, the best discovered prior art individually or in an obvious combination fail to teach explicitly, “forecasted state profiles including, for each of a plurality of horizontal grid points within a regionally defined domain, vertical state profiles of a plurality of state profile variables” was not found. Several references, including RUDIGER teach vertical profiling and horizontal grid points, but the inventive concept of developing forecasted state profile for each point on a horizontal grid point within a regionally defined domain that consists of a vertical profile. Examiner notes the language regarding the variety of state profile prognostic variables is found using an obvious combination (i.e., “air temperature, humidity, altitude, hydrometeor density, hydrometeor size, vapor density, cloud content density, rain content density, ice content density, snow content density, graupel content density, mean rain particle size, and mean ice particle size”), in prior art by SHAHABADI, as cited above in combination with dissertation by GEISS, not cited, but included below as pertinent art of record.
Claims 5-9, with direct or indirect dependence to Claim 4 are likewise objected to. Examiner notes prior art was found that does teach or suggest limitations found in these claims, but not such that the deficiencies identified and discussed above for Claims 4 and 18 can be overcome.
With regard to Claims 11 and 19, the best discovered prior are individually or in an obvious combination fail to teach “generating the background error covariance (BEC) matrices by interpolating the updated state profiles over a library of clustered state profiles obtained by a classification method”. Specifically, while use of generating background error covariance matrices as part of Kalman-based modeling methods and would be known to one of ordinary skill in the art, the specific step or “interpolation” using “library of clustered state profiles”, where interpretation is as discussed above, was not found. Examiner notes review article by LAKSHMIVARAHAN, not cited, but included below as pertinent art of record, teaches implementation of a classification scheme, and does suggest use of interpolation methods to reduce influence of clustered observations but does not disclose use of “library of clustered state profiles”.
With regard to Claim 12, the best discovered prior art individually or in an obvious combination fail to teach: “generating the filtered state-profile changes (i) yielding unscaled filtered state-profile changes and (ii) further comprising scaling the unscaled filtered state-profile changes to yield the filtered state-profile changes.” Specifically, thought the concept of scaling in the genre of Kalman-based modeling would be known to one of ordinary skill in the art, the concept of explicit intentional use of “unscaled filtered state-profile changes”, using BRI and plain meaning in interpretation of state-profile as discussed above, was not found in prior art made available on or before the claimed priority date.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
DENG (US-20210256358-A1) – teaches machine learning-based methods for time series data prediction (generally), with focus on data association for predictive operations using extended KALMAN and JACOBIAN formulation for model development, with real-time, acquired data incorporated into model development; includes detailed mathematical formalism for model development process.
LEBLANC (US-20140303893-A1 ) – teaches localized weather forecasting methods, including multi-variable data assimilation techniques for accurate modeling.
QU (CN 115238514 A) – teaches acquisition of satellite data and simulation comparison to determine numerical forecasting data using multiple prognostic variables with focus on geometry relative to solar influence; with use of radiation transmission model and radiative transfer model formalities for model development; focus on improving efficiency and speed of model development for accurate model forecasting. English translation provided.
SUN (CN 110020462 A) – teaches data assimilation technique (“fusion”) for generation of numerical weather forecasting using multivariable prognostic data values based on 3D and 4D Kalman filtering method; includes use of satellite data in model development with a focus on integration of real time meteorologic data; English translation provided.
ZHU (CN 112418558 B) – teaches total radiation correction method based on multi-source weather forecast; directed to related field (focused on prediction of atmospheric condition/weather in context of photovoltaic power generation); includes correction method to improve multi-source weather forecast modeling technique; fusing the numerical weather forecast data aimed at improved weather forecasting, analogous to data assimilation methods. English translation provided.
BISHOP (Bishop et al., “Gain Form of the Ensemble Transform Kalman Filter and Its Relevance to Satellite Data Assimilation with Model Space Ensemble Covariance Localization”, Mon. Wea. Rev., 145, 4575–4592, 2017) – teaches
DE FEIS (De Feis et al., "Optimal Interpolation for Infrared Products from Hyperspectral Satellite Imagers and Sounders", Sensors 20, no. 8: 2352 2020) – teaches data assimilation based on Kalman filtering method with focus on particular atmospheric data types; includes Bayesian estimation for model improvement and considered radiance measured from satellites in infrared regime.
LAKSHMIVARAHAN (Lakshmivarahan et al., "Ensemble Kalman filter", IEEE Control Systems Magazine, vol. 29, no. 3, pp. 34-46, June 2009) – teaches state of technological development for meteorological data assimilation methods provided broad overview of field and current trends, including details of Kalman filtering methods as applied to weather/environmental forecasting.
SCHRÖTTLE (Schröttle et al., “Assimilating Visible and Infrared Radiances in Idealized Simulations of Deep Convection”, American Meteorological Society-MONTHLY WEATHER REVIEW, Vol 148, pp4357- 4375, November 2020) – teaches data assimilations using radiance measurements to develop improved forecast models; includes investigation for best practice for assimilation of radiance differences.
References cited by RÜDIGER and pertinent to instant application:
NOILHAN (Noilhan et al., 1989) and NOILHAN (Noilhan et al., 1996) – teaches detailed principles of multiple data assimilation methods for weather forecasting, with latter reference containing more current modeling methodologies.
GIARD (Giard et al., 2000) – teaches details of numerical weather forecasting methods.
MAHFOUF (Mahfouf et al. [2009) – teaches detailed of extended Kalman Filtering method.
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/TONI D SAUNCY/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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