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 filed on 06/25/24 are accepted.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
With respect to step 1 of the patent subject matter eligibility analysis, the claims are directed to a process, machine, manufacture, or composition of matter. Independent claim 1 is directed to a method, which is a process. Independent claim 11 is directed to a non-transitory computer-readable media, which is a manufacture. All other claims depend on independent claims 1 and 11. As such, claims 1-20 are directed to a statutory category.
With respect to step 2A, prong one, the claims recite an abstract idea, law of nature, or natural phenomenon. Specifically, the following limitations recite mathematical concepts and/or mental processes.
Claim 1
filtering the plurality of weather models according to the plurality of parameters to generate a filtered set of weather models (Paragraph 0029 of the applicant’s original specification states, “Model filtering module 206 may filter the plurality of weather models according to the plurality of parameters to generate a filtered set of weather models. The filtering may be performed by cropping each of the plurality of weather models to portions of those weather models corresponding to the plurality of parameters. For example, a GEFS model may include data for many pressure levels, but a parameter variable may indicate that a user is interested in Z500mb. In such a case, model filtering module 206 may filter to only show data corresponding to that pressure level, obscuring out data for other pressure levels.” Such filtering represents the formation of mathematical relationships between a full set of data and a subset of that data. Also, at a general level, such filtering is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind. The human mind is able to look at a limited set of data and choose a subset of that data, based on some sort of user goal. The limitation therefore recites abstract mathematical concepts and/or mental processes.)
for each weather model of the filtered set of weather models, determining an information gain metric (Paragraph 0031 of the applicant’s original specification states, “information gain module 208 may input the filtered set of weather models into an empirical orthogonal function (EOF) …” EOF is a specific mathematical formula/equation/calculation. Also, without more details claimed, a general “determining an information gain metric …” is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind. The limitation therefore recites abstract mathematical concepts and/or mental processes.)
sampling a subset of the weather models based on the information gain metric (Paragraph 0035 of the applicant’s original specification states, “Sampling module 210 may determine at least some of the subset of the weather models by selecting outliers in graph 530. That is, for each quadrant of graph 530, the ensemble members having a largest distance from the origin point may be defined as the outliers.” This criteria for sampling is defined by specific mathematical relationships (i.e. distance from origin point). Also, without more details claimed, a general sampling of a subset of data, based on some sort of metric, is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind. The limitation therefore recites abstract mathematical concepts and/or mental processes.)
aggregating the sampled subset of the weather models into an ensemble filter that is downscaled (The claim does not positively recite any details, as to what “aggregating the sampled subset … into an ensemble filter that is downscaled …” entails. Broadly speaking, the examiner interprets “aggregating” as merely grouping data together. A broad and general interpretation of such a data grouping action is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind. Also, ensemble filters are defined by mathematics. The limitation therefore recites abstract mathematical concepts and/or mental processes.)
generating a forecast for the weather event using the ensemble filter (Paragraph 0039 of the applicant’s original specification states, “Weather forecast tool 130 may aggregate 760 the sampled subset of the weather models into an ensemble filter, and may generate 770 a forecast for the weather event using the ensemble filter (e.g., using fine forecast module 212).” As discussed above, an ensemble filter is defined by mathematics, and using the ensemble filter is therefore an application of mathematical calculations/relationships. Furthermore, a general generation of a forecast is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind. For example, the human mind can think, “if the relative humidity is greater than X, the dew point depression is less than Y, and the temperature is Z, then rain may be coming.” The limitation therefore recites abstract mathematical concepts and/or mental processes.)
Independent claim 11 represents a variation of claim 1 and recites similar abstract limitations.
Dependent claims 2-10 and 12-20 depend on independent claims 1 and 11. They also recite the independent claims’ abstract limitations, by virtue of their dependence. In addition, some of the claims also recite their own abstract mathematical concepts and/or mental processes.
Claims 2 and 12
wherein the plurality of parameters comprises a variable, a region, and a time range (General parameters, such as variables, regions, and time ranges can be processed by the human mind. Also, these parameters appear to be inputs into a mathematical relationship/calculation.)
Claims 3 and 13
wherein the variable comprises a pressure coordinate (This represents a mathematical variable. Also, a single pressure coordinate can be processed by the human mind.)
Claims 4 and 14
wherein filtering the plurality of weather models according to the plurality of parameters to generate the filtered set of weather models comprises cropping each of the plurality of weather models to portions of those weather models corresponding to the plurality of parameters (As discussed above, the act of cropping represents a mathematical relationship, in the form of choosing a subset of data from a set of data. Also, such a filtering action, at a broad and general level, is an observation, evaluation, judgment, and/or opinion that can be performed in the human mind.)
Claims 5 and 15
wherein for each weather model of the filtered set of weather models, determining the information gain metric comprises: inputting the filtered set of weather models into a function configured to output a plurality of maps indicating where variability is concentrated; selecting a subset of the plurality of maps having a variability quality; and inputting the subset of the plurality of maps into a model, the model configured to output an amplitude of contribution to the variability for each of the plurality of ensemble filters (This claim discloses specific mathematical relationships and/or calculations.)
Claims 6 and 16
wherein sampling the subset of the weather models based on the information gain metric comprises weighting the sampling based on the amplitude of contribution to the variability (This claim discloses specific mathematical relationships and/or calculations.)
Claims 7 and 17
wherein the sampled subset of the weather models comprises one or more of the subset of weather models that are outliers in their amplitude of contribution (This claim discloses specific mathematical relationships and/or calculations. The outliers represent a specific mathematical relationship to normal data points.)
Claims 8 and 18
wherein the function is an empirical orthogonal function configured to output a measure of variability corresponding to each map of the plurality of maps (This limitation describes a specific mathematical function.)
Claims 9 and 19
wherein the plurality of maps are ranked into a ranked order based on their corresponding measure of variability, and wherein the variability quality is an amount of maps to be selected from a top of the ranked order (This limitation recites specific mathematical relationships, in the form of a ranked order.)
Claims 10 and 20
wherein the model is a principal component analysis model (Principal component analysis is a mathematical technique that is defined by specific mathematical relationships, formulas/equations, and/or calculations.)
With respect to step 2A, prong two, the claims do not recite additional elements that integrate the judicial exception into a practical application. The following limitations are considered “additional elements” and explanation will be given as to why these “additional elements” do not integrate the judicial exception into a practical application.
Claim 1
A method (The general disclosure of a method is not indicative of integration into a practical application because it only serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).)
receiving a request to output a tuned weather forecast for a weather event, the request comprising a selection of a plurality of parameters corresponding to the weather event (Receiving a request comprising a selection of data parameters merely serves to add insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). The collection of the data merely provides the necessary data (i.e. routine data gathering) for the data processing limitations, which represent the “solution”. Also, the general contextual reference to “weather” merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.95(h)). The limitation is therefore not indicative of integration into a practical application.)
accessing a plurality of weather models configured to predict a coarse weather forecast (General access of models to process data merely serve to add insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). The accessing of the data models merely allows the data to be processed. Also, the general contextual reference to “weather” merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.95(h)). The limitation is therefore not indicative of integration into a practical application.)
Independent claims 11 represents a variation of claim 1 and recites similar limitations that are not indicative of integration into a practical application. The unique limitations belonging to claim 11 are generic recitations of different computer elements, such as a memory, processors, non-transitory computer-readable medium, etc … Merely using a computer as a tool to perform an abstract idea is not indicative of integration into a practical application (see MPEP 2106.05(f)).
Dependent claims 2-10 and 12-20 depend on independent claims 1 and 11. They also recite the independent claims’ limitations that are not indicative of integration into a practical application, by virtue of their dependence.
With respect to step 2B, the claims do not recite additional elements that amount to significantly more than the judicial exception. The claimed invention does not add significantly more because, as discussed above in step 2A, prong two, the claims do nothing more than merely use a computer as a tool to perform an abstract idea; add insignificant extra-solution activity to the judicial exception; and/or generally link the use of the judicial exception to a particular technological environment or field of use. The claims are directed to receiving and processing data. This is well-understood, routine, and conventional. Simply appending well-understood, routine, and conventional activities previously known to the industry, and specified at a high level of generality, to the judicial exception is not indicative of an inventive concept (aka “significantly more”) (see MPEP 2106.05(d) and Berkheimer Memo).
Examiner’s Note - Allowable Subject Matter
Independent claim 1 discloses the below limitations, which when considered in the context of the claims as a whole, were not found, taught, suggested, or disclosed in the prior art. Please note that the claims cannot be allowed until the above 35 U.S.C. 101 rejection is overcome.
sampling a subset of the weather models based on the information gain metric
aggregating the sampled subset of the weather models into an ensemble filter that is downscaled
generating a forecast for the weather event using the ensemble filter
The closest art that was found was Gronquist et al NPL (Gronquist, P.; Yao, C.; Ben-Nun, T.; Dryden, N.; Dueben, P.; Li, S.; and Hoefler, T. – “Deep Learning for Post-Processing Ensemble Weather Forecasts; arXiv:2005.08748v2 [cs.LG] 21 Sep 2020).
With respect to claim 1, Gronquist et al NPL discloses:
A method (abstract states, “We propose a mixed model that uses only a subset of the original weather trajectories combined with a post-processing step using deep neural networks. These enable the model to account for non-linear relationships that are not captured by current numerical models or post-processing methods.”)
receiving a request to output a tuned weather forecast for a weather event, the request comprising a selection of a plurality of parameters corresponding to the weather event (figure 1; page 3, first sentence after section “2.2 Data Preprocessing” states, “We first select the relevant inputs and labels to each of our respective models … This simplifies and speeds up training, while not impacting the results.”)
accessing a plurality of weather models configured to predict a coarse weather forecast (abstract states, “We propose a mixed model that uses only a subset of the original weather trajectories …” The examiner broadly construes the “original weather trajectories” to represent the “coarse weather forecast.”)
filtering the plurality of weather models according to the plurality of parameters to generate a filtered set of weather models (figure 2; page 4, paragraph 1 after figure 1 discloses filters)
for each weather model of the filtered set of weather models, determining an information gain metric (page 5, paragraph 3 after section 3.1 states, “The core of the DNN is based on the ResNet architecture … allowing the network to learn differently-sized receptive fields (local regions).” Paragraph 00030 of the applicant’s original specification states, “The information gain metric may yield further information for the filtered models (e.g., for the region, weather event, variable, and timeline of interest) …” Here, the differently-sized receptive fields (local regions) will be broadly construed to serve as the information gain metric.)
With respect to claim 1, Gronquist et al NPL differs from the claimed invention in that is does not explicitly disclose:
sampling a subset of the weather models based on the information gain metric
aggregating the sampled subset of the weather models into an ensemble filter that is downscaled
generating a forecast for the weather event using the ensemble filter
Although Groquist mentions sampling (page 2, paragraph 1 states, “Larger ensembles allow for a better sampling of the probability density function (PDF) of predictions.”), it does not teach sampling a subset of the weather models based on the information gain metric; aggregating the sampled subset of the weather models into an ensemble filter that is downscaled; and generating a forecast of the weather event using the ensemble filter.
The examiner recognized that downscaling is a well-known and well-established characteristic of ensemble prediction systems and considered making an obviousness argument. However, the examiner gave weight to the claimed “sampling a subset of the weather models based on the information gain metric” and then aggregating only that sampled subset into an ensemble filter. (emphasis mine).
The examiner considered that general references of ensemble prediction and filtering were not sufficient to anticipate or render obvious the claimed limitations, as a whole.
Independent claim 11 represents a variation of independent claim 1. It recites similar limitations that were not found, taught, disclosed, or suggested in the prior art.
Claims 2-10 and 12-20 depend on independent claims 1 and 11. They also disclose limitations that were not found, taught, disclosed, or suggested in the prior art, as a result of their dependency.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Price (US PgPub 20230143145) discloses increasing accuracy and resolution of weather forecasts using deep generative models.
Albert et al (US PgPub 20210064802) discloses method and system for increasing the resolution of physical gridded data.
Eckel et al (US PgPub 20200200897) discloses quantitative precipitation estimate quality control.
Giorgetti (WO2012080944A1) discloses medium-long term meteorological forecasting method and system.
Verma, S.; Srivastava, K.; Tiwari, A.; and Verma, S. – “Deep Learning Techniques in Extreme Weather Events: A Review”; arXiv:2308.10995v1 [physics.ao-ph] 18 Aug 2023.
Rusu, A.; Rusu, A.; and Williams, J. – “Visualization System Suite for Probabilistic Weather Forecasting”; 2023 IEEE International Systems Conference (SysCon) (2023).
Bouallegue, Z.B.; Weyn, J.A.; Clare, M.; Dramsch, J.; Dueben, P.; and Chantry, M. – “Improving Medium-Range Ensemble Weather Forecasts with Hierarchical Ensemble Transformers”; arXiv:2303.17195v3 [physics.ao-ph] 20 Oct 2023.
Moosavi, A.; Rao, V.; and Sandu, A. – “A Learning Based Approach for Uncertainty Analysis in Numerical Weather Prediction Models”; Computer Science Technical Report CSTR-2018-2; arXiv:1082.08055v1 [cs.NA] 20 Feb 2018.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEONARD S LIANG whose telephone number is (571)272-2148. The examiner can normally be reached M-F 10:00 AM - 7 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ARLEEN M VAZQUEZ can be reached at (571)272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LEONARD S LIANG/ Examiner, Art Unit 2857 09/29/26
