DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-14 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1 and 8 recite the limitation determine an exceedance rate threshold associated with a predetermined probability. Although the specification states that an exceedance rate threshold can be determined from a predetermined probability, it does not properly describe how that predetermined probability is determined or used to further determine an exceedance rate threshold. The specification as best understood by the Examiner, only states that a “vehicle may provide the probability due to an algorithm that adjusts based on a probability that the precipitation rate will exceed a sensor's maximum precipitation rate by a certain amount (Specification p. 28, line 9)”, which seems to describe an algorithm that provides a probability based on another probability, but still does not indicate how the predetermined probability is obtained. Additionally, figure 8 describes the current limitation of interest and its preceding limitation of determining a probability associated with a predetermined exceedance rate threshold in the alternative., but figure 4 470 and 480 only describes the preceding limitation, which is also properly described within the specification beginning on p. 26 line 30, without properly describing the current limitation of interest.
Claims 2-7 and 9-14 are rejected for their dependences on claims 1 and 8, respectively.
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 1-14 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.
Claims 1 and 8 recite:
climatological metrics relationship;
climatological metrics of the precipitation rates;
an operational metrics relationship;
operational metrics of the precipitation rate;
a probability associated with a predetermined exceedance rate threshold; and
an exceedance rate threshold associated with a predetermined probability.
These terms and limitations are vague as to what is being claimed. For example a metrics relationship can be both a relationship between two separate metrics, or within multiple readings of a single metric/parameter. Additionally, “operational metrics”, as written can be interpreted as metrics related to operation of something. It is not clear, what the metrics and their relationships are, or how any of the metrics or their relationships are derived/determined from the obtained precipitation rate. It is also not clear what the exceedance rate threshold or its probability is referring to.
Additionally in claim 1, in the limitation determine an operational metrics relationship between at least two operational metrics of the precipitation rate it is not clear which “precipitation rate” of the multiple precipitation rates is being referred to.
Claims 2-7 and 9-14 are rejected for their dependences on claims 1 and 8, 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 1-14 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.
Specifically, representative Claim 1 recites:
A system for scaling and statistical adjustments of precipitation rates for apparatuses having precipitation sensitive sensors, the system comprising:
a weather information station configured to provide precipitation rates for areas of a region; and
a processor in communication with the weather information station, the processor being configured to:
obtain the precipitation rates of the areas of the region;
determine a climatological metrics relationship between at least two climatological metrics of the precipitation rates;
determine an operational metrics relationship between at least two operational metrics of the precipitation rate;
compare the climatological metrics relationship with the operational metrics relationship; and
determine at least one of:
a probability associated with a predetermined exceedance rate threshold; and
an exceedance rate threshold associated with a predetermined probability.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (machine).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, steps of “determine a climatological metrics relationship between at least two climatological metrics of the precipitation rates (determination of relation between values);
determine an operational metrics relationship between at least two operational metrics of the precipitation rate (determination of relation between values);
compare the climatological metrics relationship with the operational metrics relationship (comparison of values); and
determine at least one of:
a probability associated with a predetermined exceedance rate threshold (statistical analysis determination); and
an exceedance rate threshold associated with a predetermined probability (statistical analysis determination)” are treated by the Examiner as belonging to mathematical concept grouping, and mental process grouping.
Similar limitations comprise the abstract ideas of Claim 8.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The above claims comprise the following additional elements:
Claim 1: A system for scaling and statistical adjustments of precipitation rates for apparatuses having precipitation sensitive sensors, the system comprising:
a weather information station configured to provide precipitation rates for areas of a region; and
a processor in communication with the weather information station, the processor being configured to:
obtain the precipitation rates of the areas of the region;;
Claim 8: A method for scaling and statistical adjustments of precipitation rates for apparatuses having precipitation sensitive sensors, the method comprising: obtaining precipitation rates for areas of a region;
The additional element in the preamble of “A system/method for scaling and statistical adjustments of precipitation rates for apparatuses having precipitation sensitive sensors” is not qualified for a meaningful limitation because it only generally links the use of the judicial exception to a particular technological environment or field of use. A weather information station configured to provide precipitation rates for areas of a region and obtaining precipitation rates for areas of a region represent mere data gathering steps and only adds an insignificant extra-solution activity to the judicial exception. A processor (generic processor) is generally recited and are not qualified as a particular machine.
In conclusion, the above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis).
The claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-7 and 11-14 provide additional features/steps which are part of an expanded algorithm, so these limitations should be considered part of an expanded abstract idea of the independent claims.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 4-6, 8, and 11-13 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kodra et al. (US 20170176640 A1), hereinafter “Kodra”.
Regarding Claim 1, Kodra teaches a system for scaling and statistical adjustments of precipitation rates for apparatuses having precipitation sensitive sensors, the system comprising:
a weather information station configured to provide precipitation rates for areas of a region (Kodra [0062] The system and method can take as input climate data from multiple sources. These inputs can include publicly available data sets outputted and archived from, for example and without limitation, […] satellite and radar observations, and direct station observations. Also see [0063] The system and method provide an “asynchronous” mapping in which, for observations, extremes of climate variables and their covariates (for example, rainfall extremes and temperature) are sorted by value from lowest to highest.); and
a processor in communication with the weather information station, the processor being configured to:
obtain the precipitation rates of the areas of the region (Kodra [0010] obtaining, from one or more climate observational datasets, observational data comprising historical observed climate data;);
determine a climatological metrics relationship between at least two climatological metrics of the precipitation rates (Kodra [0011] providing a statistical distribution of extremes or climate indices for one or more variable climate features using the climate model data and the observational model data. Specifically the climate model data);
determine an operational metrics relationship between at least two operational metrics of the precipitation rate (Kodra [0011] providing a statistical distribution of extremes or climate indices for one or more variable climate features using the climate model data and the observational model data. Specifically the observational model data);
compare the climatological metrics relationship with the operational metrics relationship (Kodra [0081] The present framework can harness data from years in these historical simulations and compares them to real observed data from the same period.); and
determine at least one of (Note that this limitation has no relation to the other limitations of the processor):
a probability associated with a predetermined exceedance rate threshold (Kodra [0014] a confidence bound of the prediction of the future climate variable for the determined future time period. Also see [0088] As an example, the system can be used to provide a probability of rainfall level exceedance, such as the probability of rainfall greater than, e.g., 3 inches in 24 hours in a particular geographic region. […] The system can be used for weather, environmental, and weather extremes predictions); and
an exceedance rate threshold associated with a predetermined probability (Kodra [0015] a prediction bound for the future climate variable for the determined future time period Also see [0088] As an example, the system can be used to provide a probability of rainfall level exceedance, such as the probability of rainfall greater than, e.g., 3 inches in 24 hours in a particular geographic region. […] The system can be used for weather, environmental, and weather extremes predictions).
Regarding Claim 4, Kodra further teaches wherein the probability associated with the predetermined exceedance rate threshold and the predetermined probability are a probability that a maximum precipitation rate of one or more areas of the region is greater than the predetermined exceedance rate threshold (Kodra [0088] As an example, the system can be used to provide a probability of rainfall level exceedance, such as the probability of rainfall greater than, e.g., 3 inches in 24 hours in a particular geographic region. […] The system can be used for weather, environmental, and weather extremes predictions).
Regarding Claim 5, Kodra further teaches to perform a spatial-scale adjustment of the precipitation rates of the areas of the region to a spatial-scale of an operational region (Kodra [0116] The system and method can be used at various geospatial and temporal scales to meet stakeholder-specific location and time of event needs. The system and method can provide a commercial framework that provides for an improvement in regional and local spatial scale prediction of an uncertainty in extreme weather events).
Regarding Claim 6, Kodra further teaches to perform a temporal-scale adjustment of the precipitation rates of the areas of the region to a temporal-scale of an operational period(Kodra [0116] The system and method can be used at various geospatial and temporal scales to meet stakeholder-specific location and time of event needs. The system and method can provide a commercial framework that provides for an improvement in regional and local spatial scale prediction of an uncertainty in extreme weather events).
Regarding Claim 8, Kodra teaches a method for scaling and statistical adjustments of precipitation rates for apparatuses having precipitation sensitive sensors, the method comprising:
obtaining precipitation rates for areas of a region (Kodra [0010] obtaining, from one or more climate observational datasets, observational data comprising historical observed climate data;);
determining a climatological metrics relationship between at least two climatological metrics of the precipitation rates (Kodra [0011] providing a statistical distribution of extremes or climate indices for one or more variable climate features using the climate model data and the observational model data. Specifically the climate model data);
determining an operational metrics relationship between at least two operational metrics of the precipitation rates (Kodra [0011] providing a statistical distribution of extremes or climate indices for one or more variable climate features using the climate model data and the observational model data. Specifically the observational model data);
comparing the climatological metrics relationship with the operational metrics relationship (Kodra [0081] The present framework can harness data from years in these historical simulations and compares them to real observed data from the same period.); and
determining at least one of (Note that this limitation has no relation to the other limitations of the processor):
a probability associated with a predetermined exceedance rate threshold (Kodra [0014] a confidence bound of the prediction of the future climate variable for the determined future time period. Also see [0088] As an example, the system can be used to provide a probability of rainfall level exceedance, such as the probability of rainfall greater than, e.g., 3 inches in 24 hours in a particular geographic region. […] The system can be used for weather, environmental, and weather extremes predictions)); and
an exceedance rate threshold associated with a predetermined probability (Kodra [0015] a prediction bound for the future climate variable for the determined future time period. Also see [0088] As an example, the system can be used to provide a probability of rainfall level exceedance, such as the probability of rainfall greater than, e.g., 3 inches in 24 hours in a particular geographic region. […] The system can be used for weather, environmental, and weather extremes predictions)).
Regarding Claim 11, Kodra further teaches wherein the probability associated with the predetermined exceedance rate threshold and the predetermined probability are a probability that a maximum precipitation rate of one or more areas of the region is greater than the predetermined exceedance rate threshold (Kodra [0088] As an example, the system can be used to provide a probability of rainfall level exceedance, such as the probability of rainfall greater than, e.g., 3 inches in 24 hours in a particular geographic region. […] The system can be used for weather, environmental, and weather extremes predictions).
Regarding Claim 12, Kodra further teaches to perform a spatial-scale adjustment of the precipitation rates of the areas of the region to a spatial-scale of an operational region (Kodra [0116] The system and method can be used at various geospatial and temporal scales to meet stakeholder-specific location and time of event needs. The system and method can provide a commercial framework that provides for an improvement in regional and local spatial scale prediction of an uncertainty in extreme weather events).
Regarding Claim 13, Kodra further teaches to perform a temporal-scale adjustment of the precipitation rates of the areas of the region to a temporal-scale of an operational period(Kodra [0116] The system and method can be used at various geospatial and temporal scales to meet stakeholder-specific location and time of event needs. The system and method can provide a commercial framework that provides for an improvement in regional and local spatial scale prediction of an uncertainty in extreme weather events).
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.
Claim(s) 2 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kodra (as stated above).
Regarding Claim 2, Kodra (as stated above) further teaches the at least two climatological metrics comprises a maximum of a climatological sample of the precipitation rates of the areas of the region (Kodra [0067] In one embodiment, using temperature and rainfall extremes as an example, each model contains […] precipitation extremes data (d)); and
the at least two operational metrics comprises a maximum of an operational sample of the precipitation rates of the areas of the region (Kodra [0069] A number K of datasets 20 of observations (g), each indexed by subscript k, contain spatio-temporal observational data 22, such as […] precipitation extremes data (i)).
Kodra (as stated above does not explicitly teach a mean of a climatological sample of the precipitation rates; and
a mean of an operational sample of the precipitation rates.
However, Kodra teaches a historical mean temperature and observational mean temperature (Kodra [0068] and [0069]).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Kodra (as stated above) to explicitly teach a mean of a climatological sample of the precipitation rates; and
a mean of an operational sample of the precipitation rates, by using mean precipitation data in instance where only precipitation data is of interest.
Regarding Claim 9, Kodra (as stated above) further teaches the at least two climatological metrics comprises a maximum of a climatological sample of the precipitation rates of the areas of the region (Kodra [0067] In one embodiment, using temperature and rainfall extremes as an example, each model contains […] precipitation extremes data (d)); and
the at least two operational metrics comprises a maximum of an operational sample of the precipitation rates of the areas of the region (Kodra [0069] A number K of datasets 20 of observations (g), each indexed by subscript k, contain spatio-temporal observational data 22, such as […] precipitation extremes data (i)).
Kodra (as stated above does not explicitly teach a mean of a climatological sample of the precipitation rates; and
a mean of an operational sample of the precipitation rates.
However, Kodra teaches a historical mean temperature and observational mean temperature (Kodra [0068] and [0069]).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Kodra (as stated above) to explicitly teach a mean of a climatological sample of the precipitation rates; and
a mean of an operational sample of the precipitation rates, by using mean precipitation data in instance where only precipitation data is of interest.
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kodra (as stated above) in view of Osumi et al. (US 20200103555 A1), hereinafter “Osumi”.
Regarding Claim 7, Kodra (as stated above) further teaches the exceedance rate threshold is based on a maximum precipitation rate (Kodra [0088] As an example, the system can be used to provide a probability of rainfall level exceedance, such as the probability of rainfall greater than, e.g., 3 inches in 24 hours in a particular geographic region. […] The system can be used for weather, environmental, and weather extremes predictions).
Kodra (as stated above) is not relied upon to teach a sensor in a vehicle.
Osumi teaches a sensor in a vehicle (Osumi [0126] the rainfall amount data collection unit 34 collects rainfall amount data that is detected by the rainfall amount sensor, in one or more vehicles positioned in a predetermined area within a predetermined period. Based on the collected rainfall amount data, the precipitation index estimation unit 48 estimates a precipitation index indicating an intensity of precipitation in the predetermined area within the predetermined period.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Kodra (as stated above) in view of Osumi to explicitly teach a sensor in a vehicle, to measure vary localized precipitation rates with high accuracy (Osumi [0004]).
Regarding Claim 14, Kodra (as stated above) further teaches the exceedance rate threshold is based on a maximum precipitation rate (Kodra [0088] As an example, the system can be used to provide a probability of rainfall level exceedance, such as the probability of rainfall greater than, e.g., 3 inches in 24 hours in a particular geographic region. […] The system can be used for weather, environmental, and weather extremes predictions).
Kodra (as stated above) is not relied upon to teach a sensor in a vehicle.
Osumi teaches a sensor in a vehicle (Osumi [0126] the rainfall amount data collection unit 34 collects rainfall amount data that is detected by the rainfall amount sensor, in one or more vehicles positioned in a predetermined area within a predetermined period. Based on the collected rainfall amount data, the precipitation index estimation unit 48 estimates a precipitation index indicating an intensity of precipitation in the predetermined area within the predetermined period.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Kodra (as stated above) in view of Osumi to explicitly teach a sensor in a vehicle, to measure vary localized precipitation rates with high accuracy (Osumi [0004]).
Examiner notes that there are currently no prior art rejection for Claims 3 and 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
LeBlanc et a. (WO 2014161076 A1) discloses a Method And System For Displaying Nowcasts Along A Route On A Map.
Pohl et al. (US 20200142419 A1) discloses Inclement Weather Condition Avoidance.
Saleh et al. (US 20200276977 A1) discloses Micro-Weather Reporting.
Peacock et al. (US 20170131435 A1) discloses Localized Weather Prediction.
Gail et al. (US 20140067270 A1) discloses a Weather Information System.
Elkabetz et al. (US 20170371074 A1) discloses a Real-Time Precipitation Forecasting System.
Wang et al. (Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLOS ONE 11(6): e0156720. June 8 2016. https://doi.org/10.1371/journal.pone.0156720) discloses algorithms to improve accuracy and to extend the functionality of the new ClimateNA software package and validating the effectiveness of the improved downscaling approaches and the accuracy of new climate variables against observations from weather stations.
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/CHRISTIAN T BRYANT/Examiner, Art Unit 2863 04/01/2025