DETAILED ACTION
Claims 1-20 are presented for examination.
This office action is in response to submission of application on 08-AUG-2022.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/08/2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 (Statutory Category – Process)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claim recites a mental process, specifically:
MPEP 2106.04(a)(2)(Ill) “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.”
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
2106.04(a)(2)(I)(A) “Mathematical Relationships A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols. For example, pressure (p) can be described as the ratio between the magnitude of the normal force (F) and area of the surface on contact (A), or it can be set forth in the form of an equation such as p = F/A.”
2106.04(a)(2)(I)(B) “Mathematical Formulas or Equations A claim that recites a numerical formula or equation will be considered as falling within the "mathematical concepts" grouping. In addition, there are instances where a formula or equation is written in text format that should also be considered as falling within this grouping. For example, the phrase "determining a ratio of A to B" is merely using a textual replacement for the particular equation (ratio = A/B). Additionally, the phrase "calculating the force of the object by multiplying its mass by its acceleration" is using a textual replacement for the particular equation (F= ma).”
2106.04(a)(2)(I)(C) “Mathematical Calculations A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.”
identifying … for each of the modified plurality of climate impact and hazard models, at least one model parameter associated with a respective modified climate impact and hazard model, by processing the respective modified climate impact and hazard model with a model enhancing machine learning model;
The claim recites identifying the parameter associated with the plurality of climate impact and hazard model. The parameter is identified based on an evaluation of how the climate impact and hazard model is affect, which amounts to an opinion or judgement. The machine learning model is recited at a high level of generality.
determining … for each of the plurality of modified climate impact and hazard models, at least one operation based on the user-specified requirement, the at least one intelligent workflow, and the at least one model parameter corresponding to the respective modified climate impact and hazard model; and
The determination an operation based on the requirement is further evaluation of the climate impact and hazard models. The operation associated with these models is based on an opinion or judgement.
Therefore, the claim recites a mental process.
Step 2A – Prong 2: Integrated into a Practical Solution?
MPEP 2106.05(g) Insignificant Extra-Solution Activity has found mere data gathering and post solution activity to be insignificant extra-solution activity.
The following step is merely gathering the data on elements to be used in the calculation:
modifying … the plurality of climate impact and hazard models based on a user-specified requirement and at least one intelligent workflow;
Post solution activity:
executing … for each of the plurality of modified climate impact and hazard models, the at least one operation corresponding to the respective modified climate impact and hazard model, based on the at least one intelligent workflow.
MPEP 2106.05(f) Mere Instructions To Apply An Exception has found simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
, by one or more processors,
, by the one or more processors,
, by the one or more processors,
, by the one or more processors,
The additional elements have been considered both individually and as an ordered combination in to determine whether they integrate the exception into a practical application.
Therefore, no meaningful limits are imposed on practicing the abstract idea.
The claim is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No, as discussed with respect to Step 2A, the additional limitation is mere data gathering/post solution activity (Insignificant Extra-Solution Activity) and a general purpose computer do not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B.
Further, in regards to step 2B and as cited above in step 2A, MPEP 2106.05(g) “Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir.2011)” is merely data gathering.
The additional elements have been considered both individually and as an ordered combination in the significantly more consideration.
The claim is ineligible.
2. The computer-implemented method of claim 1, further comprising unifying the plurality of modified climate impact and hazard models.
Unifying the models is aggregating the models and can be done by an evaluation. Step 2A Prong 1.
3. The computer-implemented method of claim 1, wherein modifying the plurality of climate impact and hazard models comprises at least one selected from a group consisting of: onboarding, managing, and scaling each of the plurality of climate impact and hazard models based on the user-specified requirement and the at least one intelligent workflow.
The modification based on a user-specified requirement is mere data gathering. MPEP 2106.05(g).
4. The method of claim 1, wherein modifying the plurality of climate impact and hazard models includes onboarding each of the plurality of climate impact and hazard models by generating and providing geospatial data interfaces, pre-processing, post-processing, and validation tools.
The tools are recited at a high level of generality. These tools are used as pre-solution and post-solution activity. MPEP 2106.05(g).
5. The computer-implemented method of claim 1, further comprising storing the plurality of modified climate impact and hazard models on a hybrid cloud environment.
Storing the models is post solution activity. MPEP 2106.05(g). The hybrid cloud environment is “apply it” on a general purpose computer. MPEP 2106.05(f).
6. The computer-implemented method of claim 5, further comprising: storing, by the one or more processors, the plurality of modified climate impact and hazard models, wherein storing the modified climate impact and hazard models includes managing, consolidating, versioning, and benchmarking each of the plurality of modified climate impact and hazard models.
The processors are general purpose computers. MPEP 2106.05(f). Managing, consolidating, versioning, and benchmarking are post solution activity. MPEP 2106.05(g).
7. The computer-implemented method of claim 1, wherein identifying the at least one model parameter associated with the respective modified climate impact and hazard model is further based on ontologies and knowledge graphs corresponding to the respective modified climate impact and hazard model.
Identifying a parameters based on ontologies and knowledge graphs is an evaluation of the ontologies and knowledge graphs to make a judgement or opinion. Step 2A Prong 1.
8. The computer-implemented method of claim 1, wherein the at least one operation includes at least one selected from the group consisting of: model calibration, uncertainty quantification, model validation, localization and scaling of the modified climate impact and hazard model.
Model calibration, uncertainty quantification, model validation, localization and scaling is an evaluation of climate impact and hazard models. Step 2A Prong 1.
9. The computer-implemented method of claim 1, wherein the at least one operation includes model calibration, and executing the operation comprises:
generating, by the one or more processors, a ground truth test data; inputting, by the one or more processors, the ground truth test data to a ground truth machine learning model;
The processors are general purpose computers. MPEP 2106.05(f). The ground truth test data is mere data gathering. MPEP 2106.05(g).
generating, by the one or more processors, a predicted output, based on inputting the test data to the modified climate impact and hazard model; and
adapting, by the one or more processors, parameters of the modified climate impact and hazard model based on a comparison between the ground truth and predicted output.
The processors are general purpose computers. MPEP 2106.05(f). Generating a predicted output and adapting the parameters is an evaluation of the ground truth and the predicted output. Step 2A Prong 1.
10. The computer-implemented method of claim 1, wherein the at least one operation includes uncertainty quantification, and executing the at least one operation comprises learning a joint distribution of data input to the modified climate impact and hazard model, and uncertainty of the modified climate impact and hazard model.
A joint distribution of the data input for the climate impact and hazard model is an evaluation when determining the uncertainty quantification. Step 2A Prong 1.
11. The computer-implemented method of claim 1, wherein the at least one operation includes model validation, and executing the at least one operation comprises: validating the modified climate impact and hazard model based on the user-specified requirement.
Validation of the models is an evaluation. Step 2A Prong 1.
12. The computer-implemented method of claim 1, wherein the at least one operation includes scaling, and executing the at least one operation includes adapting the at least one model parameter of the modified climate impact and hazard model based on the user-specified requirement.
Adapting the model parameters is post solution activity based on the evaluation. MPEP 2106.05(g).
13. The computer-implemented method of claim 1, wherein the plurality of climate impact and hazard models includes at least one selected from a group consisting of: flooding models, wildfire models, drought models, rainfall models, heat wave models, and cold wave models.
Further narrowing the group of models is mere data gathering. MPEP 2106.05(g).
14. The computer-implemented method of claim 1, wherein the user-specified requirement includes at least one selected from a group consisting of: a geographical location, a time period, and a modelled hazard.
Further user-specified requirements is mere data gathering. MPEP 2106.05(g).
Claims 15-20 are system claims, containing substantially the same elements as method Claims 1, 4, and 6-9, respectively, and are rejected on the same grounds under 35 U.S.C. 101 as Claims 1, 4, and 6-9 respectively, Mutatis mutandis. The additional components of “one or more computer processors; at least one computer-readable storage medium, and program instructions stored on the at least on computer readable storage medium, the computer system further comprising:” are interpreted as a general purpose computer and mere instructions to apply. MPEP 2106.05(f).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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.
Claims 1-3, 5, 8-10, 12-15, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over
Cloke et al., “Modelling climate impact on floods with ensemble climate projections” [2012] (hereinafter ‘Cloke’) in view of
Mattmann et al., “Cloud computing and virtualization within the regional climate model and evaluation system” [2013] (hereinafter ‘Mattmann’).
Regarding Claim 1: A computer-implemented method for augmenting a plurality of climate impact and hazard models, the method comprising:
Cloke teaches identifying, by the one or more processors, for each of the modified plurality of climate impact and hazard models, (Pg. 286 Table 3 Cloke “Table 3. RCM climate projections used in this study”)
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Cloke teaches at least one model parameter associated with a respective modified climate impact and hazard model, by processing the respective modified climate impact and hazard model with a model enhancing machine learning model; (Pg. 283 right col 1st paragraph Cloke “…Both uncorrected and MOS-corrected precipitation ensembles are generated together with consideration of hydrological model parameter uncertainty…”)
Cloke teaches determining, by the one or more processors, for each of the plurality of modified climate impact and hazard models, at least one operation based on the user-specified requirement, the at least one intelligent workflow, and the at least one model parameter corresponding to the respective modified climate impact and hazard model; and (Pg. 289 left col last paragraph – right col 1st paragraph Cloke “…The HBV model was then forced by the future RCM projections (again including parameter uncertainty). The precipitation inputs were included as both uncorrected and as MOS corrected. The MOS correction was undertaken for each individual GCM/RCM combination. Figure 8 shows the ensemble mean of the annual maximum discharge from HBV simulations averaged across time slices for the UKCP09 and ENSEMBLES. The results are separated into those simulations forced by uncorrected and those forced by MOS-corrected projections…”)
Cloke teaches
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executing, by the one or more processors, for each of the plurality of modified climate impact and hazard models, the at least one operation corresponding to the respective modified climate impact and hazard model, based on the at least one intelligent workflow. (Pg. 293 Fig. 9 Cloke “…Perturbed physics experiment for the probability that the flood warning level is exceeded for the Montford catchment. The contours show the probability of exceeding the threshold and the shaded plot the density of runs from the perturbed physics experiment. The thicker dots denote the mean of the groups of RCMs. The squares indicate the RCMs after MOS…”)
Cloke does not appear to explicitly disclose
modifying, by one or more processors, the plurality of climate impact and hazard models based on a user-specified requirement and at least one intelligent workflow;
However, Mattmann teaches modifying, by one or more processors, the plurality of climate impact and hazard models based on a user-specified requirement and at least one intelligent workflow; (Pg. 3 right col 2nd paragraph Mattmann “…The right side of the diagram shows the RCMET Regional Climate Model Evaluation Toolkit (RCMET). It provides users with the ability to take in the reference data from RCMED and climate model output data produced elsewhere and to regrid these datasets in order to match them spatially and temporally in preparation for the comparison of the reference- and model data for the evaluation of model output against the user-selected reference data…”)
Cloke and Mattmann are analogous art because they are from the same field of endeavor, climate modeling.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the determining, by the one or more processors, for each of the plurality of modified climate impact and hazard models, at least one operation based on the user-specified requirement, the at least one intelligent workflow, and the at least one model parameter corresponding to the respective modified climate impact and hazard model as disclosed by Cloke by modifying, by one or more processors, the plurality of climate impact and hazard models based on a user-specified requirement and at least one intelligent workflow as disclosed by Mattmann.
One of ordinary skill in the art would have been motivated to make this modification in order to allow a user to create user create dynamics of regional climate models as discussed on pg. 1 right col 1st paragraph by Mattmann “…In this paper we explain the contribution of cloud computing to RCMES’s specifically describing studies of various cloud databases we evaluated for RCMED, and virtualization toolkits for RCMET, and their potential strengths in delivering user-created dynamic regional climate model evaluation virtual machines for our users…”
Regarding Claim 2: Cloke and Mattmann teaches The computer-implemented method of claim 1, further comprising
Mattmann teaches unifying the plurality of modified climate impact and hazard models. (Pg. 290 right col last paragraph Mattmann “…The left-hand panel shows the ENSEMBLES, the middle panel the UKCP09 and the right-hand panel the combined results…”)
Regarding Claim 3: Cloke and Mattmann teaches The computer-implemented method of claim 1, wherein modifying the plurality of climate impact and hazard models comprises
Cloke teaches at least one selected from a group consisting of: onboarding, managing, and scaling each of the plurality of climate impact and hazard models based on the user-specified requirement and the at least one intelligent workflow. (Pg. 293 left col 1st paragraph Cloke “…We highlight again that in the response surface method there is a crucial assumption that climate change only produces an overall scaling of the precipitation, whereas a hydrological model forced by GCM/RCM predictions can, at least in principle, respond to more detailed changes in climatology…”)
Regarding Claim 5: Cloke and Mattmann teaches The computer-implemented method of claim 1, further comprising
Mattmann teaches storing the plurality of modified climate impact and hazard models on a hybrid cloud environment. (Pg. 8 right col 4th paragraph Mattmann “…In option (A), we deployed RCMET in a cloud-RCMED environment, and VM-based client-side RCMET environment. In this scenario, the RCMED is running on a cloudbased, expandable storage network, allowing for ad-hoc studies, and transient ingestion and presence of remote sensing data…”)
Regarding Claim 8: Cloke and Mattmann teaches The computer-implemented method of claim 1,
Cloke teaches wherein the at least one operation includes at least one selected from the group consisting of: model calibration, uncertainty quantification, model validation, localization and scaling of the modified climate impact and hazard model. (Pg. 283 left col 2nd paragraph Cloke “…Regional climate models (RCMs) are used to dynamically downscale GCM projections to make them more useful in climate impact studies…”)
Regarding Claim 9: Cloke and Mattmann teaches The computer-implemented method of claim 1, wherein the at least one operation includes model calibration, and executing the operation comprises:
Cloke teaches generating, by the one or more processors, a ground truth test data; (Pg. 283 right col last paragraph Cloke “…The observed discharge data were provided by the UK Environment Agency (EA) Midlands region and cover 1950–2007. In particular, the gauging station at Montford is important for predicting flooding in the downstream town of Shrewsbury. The digital elevation model of the Upper Severn catchment was obtained from the NEXTMap Britain dataset through the UK NERC Earth Observation Data Centre. The observed precipitation and temperature data used in this study were from the gridded data on a 5× 5 km grid provided by the UK Met Office. These were interpolated using daily observations as main input, incorporating geographical effects, latitude and longitude, altitude, coastal influence and urban land use through normalization with respect to the monthly 1961–1990 climate (Perry et al., 2008). The spatial distribution of the observations is shown in Figure 1. The accumulated 5-day maximum precipitation (5dmax), expressed as mm day−1, is a useful measure of flood-inducing precipitation in semihumid meso-scale catchments such as the Severn and was used in this study. It is calculated as the annual maximum precipitation, as a mean over the entire catchment after the data have been filtered with a 5-day running mean filter. The rainfall statistics for the Upper Severn catchment upstream of both Montford and Buildwas are summarized in Table 1, as well as the statistics for the used RCMs…”)
Cloke teaches inputting, by the one or more processors, the ground truth test data to a ground truth machine learning model; (Pg. 286 left col 3rd paragraph Cloke “…Response surfaces were created by perturbing the temperature and precipitation observations that were used as input to the HBV hydrologicalmodel over the calibration period 1986–2006…”)
Cloke teaches generating, by the one or more processors, a predicted output, based on inputting the test data to the modified climate impact and hazard model; and (Pg. 295 right col last paragraph Cloke “…A grand ensemble of projections from a number of GCM/RCMs was used to force the HBV hydrological model and analyse the resulting future flood projections for the Upper Severn, UK, and the impact and implications of applying MOS techniques to precipitation fields was examined…”)
Cloke teaches adapting, by the one or more processors, parameters of the modified climate impact and hazard model based on a comparison between the ground truth and predicted output. (Pg. 295 right col last paragraph - pg. 296 left col 1st paragraph Cloke “…The impact of hydrological model parameter uncertainty was taken into account. The resultant grand ensemble of future river discharge projections was compared with a response surface technique combined with perturbed physics ensemble climate model outputs. The ensemble distribution of results shows that future risk of flooding in the Upper Severn increases compared to present conditions, particularly with regard to the probability of exceeding the flood warning threshold at Montford, but the study also highlights the large uncertainties in results and the strong assumptions made in using MOS to produce the estimates of future discharge. MOS has a clear effect on the results when the RCM output was used directly in combination with the hydrological model…”)
Regarding Claim 10: Cloke and Mattmann teaches The computer-implemented method of claim 1,
Cloke teaches wherein the at least one operation includes uncertainty quantification, and executing the at least one operation comprises learning a joint distribution of data input to the modified climate impact and hazard model, and (Pg. 283 right col 1st paragraph Cloke “…(i) an ensemble of singlemodel RCM projections from the current UK Climate Projections (UKCP09); (ii) multi-model ensemble RCM projections from the ENSEMBLES project; and (iii) a joint probability distribution of precipitation and temperature from a GCM-based perturbed physics ensemble…”)
Cloke teaches uncertainty of the modified climate impact and hazard model. (Pg. 283 left col 2nd paragraph Cloke “…This correction procedure is also known as calibration or bias correction and focuses on corrections to moments of the climatology (rather than representation of forecast uncertainty, such as the calibration of ensemble spread and root mean square (RMS) error of the ensemble mean forecast, as is common in weather forecasting)…”)
Regarding Claim 12: Cloke and Mattmann teaches The computer-implemented method of claim 1,
Cloke teaches wherein the at least one operation includes scaling, and executing the at least one operation includes adapting the at least one model parameter of the modified climate impact and hazard model based on the user-specified requirement. (Pg. 283 left col 2nd paragraph Cloke “…Regional climatemodels (RCMs) are used to dynamically downscale GCM projections to make them more useful in climate impact studies…”)
Regarding Claim 13: Cloke and Mattmann teaches The computer-implemented method of claim 1,
Cloke teaches wherein the plurality of climate impact and hazard models includes at least one selected from a group consisting of: flooding models, wildfire models, drought models, rainfall models, heat wave models, and cold wave models. (Pg. 288 left col 3rd paragraph Cloke “…Here the DBS MOS technique is applied to the grand ensemble of RCM projections as described in section 2.3.2. Each individual GCM/RCM pair in the grand ensemble was corrected for the bias, using the period 1960–2000 as baseline for the observed climate. MOS was only applied to the precipitation output as floods are mainly driven by precipitation events…”)
Regarding Claim 14: Cloke and Mattmann teaches The computer-implemented method of claim 1,
Cloke teaches wherein the user-specified requirement includes at least one selected from a group consisting of: a geographical location, a time period, and a modelled hazard. (Pg. 287 right col last paragraph Cloke “…Table 1 shows statistics comparing control period RCM precipitation and observed. Notably, the performance of the RCMprecipitation is dependent on the size and geographical characteristics of the catchment…”)
Claims 15 and 19-20 are system claims, containing substantially the same elements as method Claims 1 and 8-9, respectively, and are rejected on the same grounds under 35 U.S.C. 103 as Claims 1 and 8-9 respectively, Mutatis mutandis.
Claims 4 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over
Cloke et al., “Modelling climate impact on floods with ensemble climate projections” [2012] (hereinafter ‘Cloke’) in view of
Mattmann et al., “Cloud computing and virtualization within the regional climate model and evaluation system” [2013] (hereinafter ‘Mattmann’). Further view of
WOOD et al., U.S. Patent Application Publication 2019/0018918 A1 (hereinafter ‘WOOD’).
Regarding Claim 4: Cloke and Mattmann teaches The method of claim 1,
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Cloke teaches wherein modifying the plurality of climate impact and hazard models includes onboarding each of the plurality of climate impact and hazard models by generating and providing geospatial data interfaces, (Figure 1 Cloke “Upper Severn catchment located in the Midlands Region of England. Observed precipitation grid from UK Met Office at 5 × 5 km, RCM projection grid and Environment Agency river flow gauges are shown…”)
Cloke and Mattmann does not appear to explicitly disclose
pre-processing, post-processing, and validation tools.
However, WOOD teaches pre-processing, post-processing, and validation tools. ([0039] WOOD “…In the context of short term determination, the HMS 100 may receive a high temporal, low spatial resolution data set for global forecasts of precipitation, temperature and surface wind speed derived from a global weather model (e.g., NCEP GFS), for example, where each individual observation represents a 1.0 degree region over a 3-hour time period for a determination window of 7 days from the present. In such context, the HMS 100 may pre-process the received data to a binary format and extract data for a region of interest to obtain forecast meteorological forcing data. Then, the forecast meteorological forcing data may be processed to be statistically consistent with a long term global historical data set, as described above, by correcting for bias using the statistical method of Cumulative Distribution Function (CDF) matching to obtain a refined dataset of forecast meteorological forcing data useful for hydrologic determination. Then, hydrologic conditions may be predicted, and indices may be derived, as a function of the bias-corrected forecast meteorological forcing data. The system may then post-process all forecasts generated to a common format (e.g., NetCDF) files for easier access and rapid distribution…”
[0055] WOOD “…Next, the predicted inundation extend may be repeatedly validated against a satellite-derived inundation extent product to confirm that the dynamic inundation model is capturing flood extents correctly. The remote sensing inundation model referenced above is another application of a random forest classifier model. In this case, satellite passive microwave observations (from a NASA mission such as SMAP) may be used to predict whether or not there is flooding (simply water above the ground surface) in a given grid cell or location. Final conclusions may be determined in automated fashion using predetermined and prestored logic, e.g., of the Determination System 400…”)
Cloke, Mattmann, and WOOD are analogous art because they are from the same field of endeavor, climate modeling.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the wherein modifying the plurality of climate impact and hazard models includes onboarding each of the plurality of climate impact and hazard models by generating and providing geospatial data interfaces as disclosed by Cloke and Mattmann by pre-processing, post-processing, and validation tools as disclosed by WOOD.
One of ordinary skill in the art would have been motivated to make this modification in order to improve forecast accuracy as discussed in [0006] by WOOD “…The present invention provides a system and method for performing hydrologic determination using disparate weather data sources (e.g., in-situ observations, remotely-sensed ( e.g., satellite) observations, and model data resulting from mathematical weather and climate models) in a manner that increases overall forecast accuracy by effectively combining the datasets to eliminate or mitigate inherent limitations or inaccuracies existing in each individual dataset. More particularly, the present invention provides a system and method for modeling hydrologic processes for determination purposes that involves retrieval of remote sensing weather observations, selectively downscaling data from the datasets to harmonize them to common (finer) temporal and spatial scales, bias-correcting the common-scale data to make the common-scale data statistically consistent with a long-term historical dataset, and performing global hydrologic modeling and determination with increased accuracy as a function of the bias-corrected common-scale dataset…”
Claims 16 is a system claim, containing substantially the same elements as method Claim 4 and are rejected on the same grounds under 35 U.S.C. 103 as Claim 4, Mutatis mutandis.
Claims 6, 11, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over
Cloke et al., “Modelling climate impact on floods with ensemble climate projections” [2012] (hereinafter ‘Cloke’) in view of
Mattmann et al., “Cloud computing and virtualization within the regional climate model and evaluation system” [2013] (hereinafter ‘Mattmann’). Further view of
Yeager et al., U.S. Patent Application Publication 2014/0278294 A1 (hereinafter ‘Yeager’).
Regarding Claim 6: Cloke and Mattmann teaches The computer-implemented method of claim 5, further comprising:
Mattmann teaches storing, by the one or more processors, the plurality of modified climate impact and hazard models, (Pg. 8 right col 4th paragraph Mattmann “…In option (A), we deployed RCMET in a cloud-RCMED environment, and VM-based client-side RCMET environment. In this scenario, the RCMED is running on a cloudbased, expandable storage network, allowing for ad-hoc studies, and transient ingestion and presence of remote sensing data…”)
Cloke and Mattmann does not appear to explicitly disclose
wherein storing the modified climate impact and hazard models includes managing, consolidating, versioning, and benchmarking each of the plurality of modified climate impact and hazard models.
However, Yeager teaches wherein storing the modified climate impact and hazard models includes managing, consolidating, versioning, and benchmarking each of the plurality of modified climate impact and hazard models. ([0090] Yeager “…Discrete event simulation can provide time sensitive queue management and other process oriented capabilities but is generally not used for strategic planning or for studying systems that have widespread feedback relationships…” [0177] Yeager “…In many cases, a collection of entities representing individuals or cohorts of the key quantity will be a more natural representation. But at other times, it may be desirable to preserve the well-mixed stock assumption. For such cases, the platform can provide a consolidated dialog which contains the key quantity and its qualities, with distinct units of measure…” [0245] “…In some implementations, the platform, or an external source control system supporting the platform, can keep version history of the model used to generate specific scenarios, so that backing up and analysis are possible…” [0163] “…A data node 608 enables data to be added to an entity type definition in the diagram workspace. Models frequently use data from external sources. Data variables typically contain time series data that may or may not align with the model time frame. Data variables may be used to drive, calibrate, or provide a benchmark for reporting. Data variables may also represent budgets, forecasts, or projections from outside sources or other models. Outside sources may include files, databases, sensors, and other automated systems…”
Cloke, Mattmann, and Yeager are analogous art because they are from the same field of endeavor, climate modeling.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the storing, by the one or more processors, the plurality of modified climate impact and hazard models as disclosed by Cloke and Mattmann by wherein storing the modified climate impact and hazard models includes managing, consolidating, versioning, and benchmarking each of the plurality of modified climate impact and hazard models as disclosed by Yeager.
One of ordinary skill in the art would have been motivated to make this modification in order to improve the difficulties of running realistic models and dynamic systems as discussed in [0074] by Yeager “…We describe here a modeling and simulation approach (and a corresponding platform) that is specifically designed to enable building, debugging, running, and using realistic models of large and complex dynamic systems. In many cases realistic models of these types of dynamic systems are currently impractical due to the difficulty, time, or expense required for a modeling team to build the model, manage data, calibrate and check the model, evaluate the results, and collaborate and communicate policy recommendations…”
Regarding Claim 11: Cloke and Mattmann teaches The computer-implemented method of claim 1, wherein the at least one operation includes model validation, and executing the at least one operation comprises:
Cloke and Mattmann do not appear to explicitly disclose
validating the modified climate impact and hazard model based on the user-specified requirement.
However, Yeager teaches validating the modified climate impact and hazard model based on the user-specified requirement. ([0075] Yeager “…In some implementations, the modeling and simulation approach and platform described here (which we sometimes also call the "platform") extends the capabilities of system dynamics modeling to provide new capabilities that enable effective modeling of large and complex systems that evolve over time. These new capabilities yield the following advantages, among others, 1) making it much easier to represent certain real-world systems, 2) enabling integration of empirical data for distributions as well as time series for realistic calibration and validation of models, 3) providing an interface and methodology that increases the productivity of model builders, or 4) reducing the difficulty of direct use by policy and business people as well as engineers and scientists, or a combination of any two or more of these capabilities…”)
Cloke, Mattmann, and Yeager are analogous art because they are from the same field of endeavor, climate modeling.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the executing, by the one or more processors, for each of the plurality of modified climate impact and hazard models, the at least one operation corresponding to the respective modified climate impact and hazard model, based on the at least one intelligent workflow as disclosed by Cloke and Mattmann by validating the modified climate impact and hazard model based on the user-specified requirement.as disclosed by Yeager.
One of ordinary skill in the art would have been motivated to make this modification in order to improve the difficulties of running realistic models and dynamic systems as discussed in [0074] by Yeager “…We describe here a modeling and simulation approach (and a corresponding platform) that is specifically designed to enable building, debugging, running, and using realistic models of large and complex dynamic systems. In many cases realistic models of these types of dynamic systems are currently impractical due to the difficulty, time, or expense required for a modeling team to build the model, manage data, calibrate and check the model, evaluate the results, and collaborate and communicate policy recommendations…”
Claims 17 is a system claim, containing substantially the same elements as method Claim 6 and are rejected on the same grounds under 35 U.S.C. 103 as Claim 6, Mutatis mutandis.
Claims 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over
Cloke et al., “Modelling climate impact on floods with ensemble climate projections” [2012] (hereinafter ‘Cloke’) in view of
Mattmann et al., “Cloud computing and virtualization within the regional climate model and evaluation system” [2013] (hereinafter ‘Mattmann’). Further view of
DEMBO et al., U.S. Patent Application Publication 2024/0104401 A1 (hereinafter ‘DEMBO’).
Regarding Claim 7: Cloke and Mattmann teaches The computer-implemented method of claim 1,
Cloke and Mattmann do not appear to explicitly disclose
wherein identifying the at least one model parameter associated with the respective modified climate impact and hazard model is further based on ontologies and knowledge graphs corresponding to the respective modified climate impact and hazard model.
However, DEMBO teaches wherein identifying the at least one model parameter associated with the respective modified climate impact and hazard model is further based on ontologies and knowledge graphs corresponding to the respective modified climate impact and hazard model. ([0095] DEMBO “…The server 100 has a hardware processor 129 with a communication path to the non-transitory memory 110 to generate integrated risk data structures using a natural language processing pipeline 165 to extract information from unstructured text, classify risk and a plurality of risk dimensions to define the risk, quantify interconnectedness of risk factors for the associated link values. The server 100 returns structured, codified and accessible data structures to update the knowledge graphs in memory 110. The integrated risk data structures map multiple risk factors to geographic space and time. The server 100 populates the knowledge graph and the causal graph of nodes in the memory 110 by computing values for the risk factor for the time horizon using the integrated climate risk data structures. The server 100 generates multifactor scenario sets using the scenario paths for the climate model to compute the likelihood of different scenario paths for the climate model. The server 100 generates risk metrics for stress tests using the multifactor scenario sets and the knowledge graph. The server 100 transmits at least a portion of the risk metrics and the multifactor scenario sets in response to queries. The server 100 stores the integrated risk data structures and the multifactor scenario sets in the non-transitory memory 100…”
[0121] DEMBO “…The processor 120 has a machine learning pipeline 160 with a natural language processing (NLP) pipeline 165, structured expert pipeline 170, indices 175 (e.g., climate indices), and an integrated model pipeline 185 to generate an ontology of risk (knowledge graph) from unstructured data. The processor 120 uses the machine learning pipeline 160 and expert pipeline 170 to link the computer model to macro financial variables to encode a relationship between risk shocks and financial impact…”)
Cloke, Mattmann, and DEMBO are analogous art because they are from the same field of endeavor, climate modeling.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the executing, by the one or more processors, for each of the plurality of modified climate impact and hazard models, the at least one operation corresponding to the respective modified climate impact and hazard model, based on the at least one intelligent workflow as disclosed by Cloke and Mattmann by wherein identifying the at least one model parameter associated with the respective modified climate impact and hazard model is further based on ontologies and knowledge graphs corresponding to the respective modified climate impact and hazard model as disclosed by DEMBO.
One of ordinary skill in the art would have been motivated to make this modification in order to assess risk factors in the model as discussed in [0078] by DEMBO “…Embodiments described herein relate to computer systems and methods for generating an ontology of climate related risk as knowledge graphs or data structures. The systems and methods process unstructured text using a natural language processing pipeline to extract information from unstructured text, classify the risk, the multitude of dimensions that define a risk, quantify the interconnectedness of risk factors, and return a structured, codified and accessible data structure that can be queried by a client application…”
Claims 18 is a system claim, containing substantially the same elements as method Claim 7 and are rejected on the same grounds under 35 U.S.C. 103 as Claim 7, Mutatis mutandis.
Conclusion
Claims 1-20 are rejected.
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/JOHN E JOHANSEN/Examiner, Art Unit 2187