ctionDETAILED 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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/02/2026 has been entered.
Response to Arguments
Applicant’s arguments, see pg. 1-10, filed 07/02/2026, with respect to 1-2, 4-9 and 11-16 have been fully considered and are persuasive. The 112 rejection of 04/02/2026 has been withdrawn.
Applicant's arguments filed 07/02/2026 regarding the 101 rejection have been
fully considered but they are not persuasive.
Regarding Claim 1, the claim does not appear to recite a specific improvement to the functioning of a computer, sensor, ASOS device, network, output device, or other technology. The use of physical environmental data gives the claim some practical context. However, a practical field of use alone is not enough under the 2019 PEG. The claim states that the data is:
“observed by an automated synoptic observing system (ASOS)”
But the ASOS appears to be used merely as a source of meteorological data. The claim does not improve the ASOS itself, change how the ASOS measures data, improve sensor calibration, improve sampling accuracy, reduce sensor error, reduce network load, improve computer memory usage, or improve processing architecture.
Similarly, the claim recites:
“outputting, by an output part, information related to issuance of the heatwave warning…”
and:
“transmitting, by a communicator, the information… to another device through a wired or wireless network”
These are generic output/transmission limitations. They do not recite a particular communication protocol, hardware architecture, signal transformation, or technical mechanism that improves warning delivery technology.
The core advance appears to be the mathematical/statistical generation and selection of sensible-temperature calculation formulae, not a technological improvement to a computer or other machine.
The ASOS-observed meteorological data is best characterized as data gathering or input collection. Under the 2019 PEG, mere data gathering is generally insignificant extra-solution activity when it is used only to provide inputs to an abstract calculation.
The output/transmission of the warning is likewise likely post-solution activity, because it merely presents or communicates the result of the mathematical analysis.
Although the claim mentions:
• ASOS;
• An output part; and
• A communicator;
the claimed method does not appear to be tied to a particular machine in a manner that imposes a meaningful limit on the judicial exception.
The ASOS is recited only as the source of weather observations. The output part and communicator are generic result-delivery components. The claim does not require a specifically configured sensor arrangement, specialized circuitry, or non-conventional device architecture.
Accordingly, the machine recitations likely do not integrate the abstract idea into a practical application. It is for these reasons, the examiner maintains the 101 rejection.
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 1-2, 4-9 and 11-16 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 method of issuing a heatwave warning calculating a sensible temperature in consideration of outdoor ground heating, the method comprising: classifying data which includes a globe temperature, an atmospheric temperature, a relative humidity, and a ground surface temperature and is observed by an automated synoptic observing system (ASOS) for a certain period of time, as precipitation data and non-precipitation data according to whether there is precipitation;
clustering the non-precipitation data into K clusters; and
deriving K+1 sensible temperature calculation formulae by performing regression analysis on the K clusters and the precipitation data;
classifying new input data as a non-precipitation data group or a precipitation data group by using a classification model for data classification, the classification model being a model trained in advance using training data, wherein the classifying comprises (i) classifying the new input data as the non-precipitation data group when a precipitation value among meteorological variables related to the new input data is less than a reference value, and (ii) classifying the new input data as the precipitation data group when the precipitation value is the reference value or more; based on the new input data being classified as the non-precipitation data group, (i) calculating a first value which is a sum of squares of errors between the new input data and existing data belonging to a first cluster of the non-precipitation data, (ii) calculating a second value which is a sum of squares of errors between the new input data and existing data belonging to a second cluster of the non-precipitation data, and (iii) classifying the new input data into the first cluster when the first value is smaller than the second value and classifying the new input data into the second cluster when the second value is smaller than the first value; selecting one of the K+1 sensible temperature calculation formulae based on the classification result, wherein the selecting comprises (i) selecting a first sensible temperature calculation formula among the K+1 sensible temperature calculation formulae when the new input data is classified into the first cluster, (ii) selecting a second sensible temperature calculation formula among the K+1 sensible temperature calculation formulae when the new input data is classified into the second cluster, and (iii) selecting a third sensible temperature calculation formula among the K+1 sensible temperature calculation formulae when the new input data is classified as the precipitation data group; predicting a sensible temperature for the new input data based on the selected sensible temperature calculation formula;
determining whether to issue the heatwave warning based on comparison of the predicted sensible temperature, and criteria for heatwave warnings; and; in response to determining to issue the heatwave warning, outputting, by an output part, information related to issuance of the heatwave warning in at least one of visual, auditory and tactile forms, or transmitting, by a communicator, the information related to issuance of the heatwave warning to another device through a wired or wireless network, wherein the clustering of the non-precipitation data into the K clusters comprises clustering the non-precipitation data into the K clusters by using a K-means clustering algorithm which is an unsupervised machine learning algorithm for clustering data having similar features into the K clusters.
Similar limitations comprise the abstract ideas of Claims 8. Further, Claim 15 recites:
A heatwave warning method based on a sensible temperature in consideration of outdoor ground heating, the heatwave warning method comprising:
classifying new input data into a group and cluster as anon-precipitation data group or a precipitation data group by using a classification model for data classification, the classification model being a model trained in advance using training data, wherein the classifying comprises (i) classifying the new input data as the non-precipitation data group when a precipitation value among meteorological variables related to the new input data is less than a reference value, and (ii) classifying the new input data as the precipitation data group when the precipitation value is the reference value or more; based on the new input data being classified as the non-precipitation data group, (i) calculating a first value which is a sum of squares of errors between the new input data and existing data belonging to a first cluster of the non-precipitation data, (ii) calculating a second value which is a sum of squares of errors between the new input data and existing data belonging to a second cluster of the non-precipitation data, and (iii) classifying the new input data into the first cluster when the first value is smaller than the second value and classifying the new input data into the second cluster when the second value is smaller than the first value;
selecting one of a plurality of prestored sensible temperature calculation formulae based on the basis of the classified group and cluster classification result, wherein the selecting comprises (i) selecting a first sensible temperature calculation formula among the sensible temperature calculation formulae when the new input data is classified into the first cluster, (ii) selecting a second sensible temperature calculation formula among the sensible temperature calculation formulae when the new input data is classified into the second cluster, and (iii) selecting a third sensible temperature calculation formula among the sensible temperature calculation formulae when the new input data is classified as the precipitation data group; predicting a sensible temperature for the new input data using the selected sensible temperature calculation formula; determining whether to issue a heatwave warning based on comparison of the basis of the predicted sensible temperature, and criteria for heatwave warnings; and in response to determining to issue the heatwave warning, outputting, by an output part, information related to issuance of the heatwave warning in at least one of visual, auditory and tactile forms, or transmitting, by a communicator, the information related to issuance of the heatwave warning to another device through a wired or wireless network, wherein the plurality of sensible temperature calculation formulae are derived by classifying data which includes a globe temperature, an atmospheric temperature, a relative humidity, and a ground surface temperature and is observed by an automated synoptic observing system (ASOS) for a certain period of time, as precipitation data and non-precipitation data according to whether there is precipitation, clustering the non-precipitation data into K clusters, and then performing regression analysis on the K clusters and the precipitation data, and wherein the clustering of the non-precipitation data into the K clusters comprises clustering the non-precipitation data into the K clusters by using a K-means clustering algorithm which is an unsupervised machine learning algorithm for clustering data having similar features into the K clusters.
Similar limitations comprise the abstract ideas of Claims 16.
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 (process).
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 “clustering the non-precipitation data into K clusters; and deriving K+1 sensible temperature calculation formulae by performing regression analysis on the K clusters and the precipitation data; based on the new input data being classified as the non-precipitation data group, (i) calculating a first value which is a sum of squares of errors between the new input data and existing data belonging to a first cluster of the non-precipitation data, (ii) calculating a second value which is a sum of squares of errors between the new input data and existing data belonging to a second cluster of the non-precipitation data” are treated by the Examiner as belonging to mathematical concept grouping, while the steps of “classifying data which includes a globe temperature, an atmospheric temperature, a relative humidity, and a ground surface temperature and is observed by an automated synoptic observing system (ASOS) for a certain period of time, as precipitation data and non-precipitation data according to whether there is precipitation; classifying new input data as a non-precipitation data group or a precipitation data group by using a classification model for data classification, the classification model being a model trained in advance using training data, wherein the classifying comprises (i) classifying the new input data as the non-precipitation data group when a precipitation value among meteorological variables related to the new input data is less than a reference value, and (ii) classifying the new input data as the precipitation data group when the precipitation value is the reference value or more; selecting one of the K+1 sensible temperature calculation formulae based on the classification result, wherein the selecting comprises (i) selecting a first sensible temperature calculation formula among the K+1 sensible temperature calculation formulae when the new input data is classified into the first cluster, (ii) selecting a second sensible temperature calculation formula among the K+1 sensible temperature calculation formulae when the new input data is classified into the second cluster, and (iii) selecting a third sensible temperature calculation formula among the K+1 sensible temperature calculation formulae when the new input data is classified as the precipitation data group and predicting a sensible temperature for the new input data based on the selected sensible temperature calculation formula and determining whether to issue the heatwave warning based on comparison of the predicted sensible temperature and criteria for heatwave warnings, wherein the clustering of the non-precipitation data into the K clusters comprises clustering the non-precipitation data into the K clusters by using a K-means clustering algorithm which is an unsupervised machine learning algorithm for clustering data having similar features into the K clusters” are treated as belonging to mental process grouping and/or mathematical concept grouping.
An additional example regarding Claim 15, steps of “classifying new input data into a group and cluster as a non-precipitation data group or a precipitation data group by using a classification model for data classification, the classification model being a model trained in advance using training data, wherein the classifying comprises (i) classifying the new input data as the non-precipitation data group when a precipitation value among meteorological variables related to the new input data is less than a reference value, and (ii) classifying the new input data as the precipitation data group when the precipitation value is the reference value or more; based on the new input data being classified as the non-precipitation data group, (i) calculating a first value which is a sum of squares of errors between the new input data and existing data belonging to a first cluster of the non-precipitation data, (ii) calculating a second value which is a sum of squares of errors between the new input data and existing data belonging to a second cluster of the non-precipitation data, and (iii) classifying the new input data into the first cluster when the first value is smaller than the second value and classifying the new input data into the second cluster when the second value is smaller than the first value;;
selecting one of a plurality of prestored sensible temperature calculation formulae based on the basis of the classified group and cluster classification result, wherein the selecting comprises (i) selecting a first sensible temperature calculation formula among the sensible temperature calculation formulae when the new input data is classified into the first cluster, (ii) selecting a second sensible temperature calculation formula among the sensible temperature calculation formulae when the new input data is classified into the second cluster, and (iii) selecting a third sensible temperature calculation formula among the sensible temperature calculation formulae when the new input data is classified as the precipitation data group; predicting a sensible temperature for the new input data using the selected sensible temperature calculation formula; and
determining whether to issue a heatwave warning based on comparison of the basis of the predicted sensible temperature and criteria for heatwave warnings; andin response to determining to issue the heatwave warning, outputting, by an output part, information related to issuance of the heatwave warning in at least one of visual, auditory and tactile forms, or transmitting, by a communicator, the information related to issuance of the heatwave warning to another device through a wired or wireless network, wherein the plurality of sensible temperature calculation formulae are derived by classifying data which includes a globe temperature, an atmospheric temperature, a relative humidity, and a ground surface temperature and is observed by an automated synoptic observing system (ASOS) for a certain period of time, as the precipitation data and the non-precipitation data according to whether there is precipitation, clustering the non-precipitation data into K clusters, and then performing regression analysis on the K clusters and the precipitation data, and wherein the clustering of the non-precipitation data into the K clusters comprises clustering the non-precipitation data into the K clusters by using a K-means clustering algorithm which is an unsupervised machine learning algorithm for clustering data having similar features into the K clusters” are treated as belonging to mental process grouping.
Similar limitations comprise the abstract ideas of Claims 8 and16, respectively.
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:
In Claim 16: predictor, determiner
The additional element a predictor and determiner are generally recited and are not qualified as particular machines. Further additional elements include: “in response to determining to issue the heatwave warning, outputting, by an output part, information related to issuance of the heatwave warning in at least one of visual, auditory and tactile forms, or transmitting, by a communicator, the information related to issuance of the heatwave warning to another device through a wired or wireless network.” This is considered by MPEP 2106.05(g) as insignificant extra solution activity, mere data outputting.
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, 4-7 and 9, 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.
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter:
Claims 1-2, 4-9 and 11-16 would be allowable if written overcome the 101 rejection set forth in this office action.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding Claim 1, Kim et al. (KR20210036085A, 2021-04-02) and Yang et al. (CN102706455B, 2014-03-12) both teach a method of measuring/estimating a sensible temperature, but both references along with all other prior art fail to teach classifying data which includes a globe temperature, an atmospheric temperature, a relative humidity, and a ground surface temperature and is observed by an automated synoptic observing system (ASOS) for a certain period of time, as precipitation data and non- precipitation data according to whether there is precipitation; clustering the non-precipitation data into K clusters; deriving K+1 sensible temperature calculation formulae by performing regression analysis on the K clusters and the precipitation data; classifying new input data as a non-precipitation data group or a precipitation data group by using a classification model for data classification, the classification model being a model trained in advance using training data, wherein the classifying comprises (i) classifying the new input data as the non-precipitation data group when a precipitation value among meteorological variables related to the new input data is less than a reference value, and (ii) classifying the new input data as the precipitation data group when the precipitation value is the reference value or more; based on the new input data being classified as the non-precipitation data group, (i) calculating a first value which is a sum of squares of errors between the new input data and existing data belonging to a first cluster of the non-precipitation data, (ii) calculating a second value which is a sum of squares of errors between the new input data and existing data belonging to a second cluster of the non-precipitation data, and (iii) classifying the new input data into the first cluster when the first value is smaller than the second value and classifying the new input data into the second cluster when the second value is smaller than the first value; selecting one of the K+1 sensible temperature calculation formulae based on the classification result, wherein the selecting comprises (i) selecting a first sensible temperature calculation formula among the K+1 sensible temperature calculation formulae when the new input data is classified into the first cluster, (ii) selecting a second sensible temperature calculation formula among the K+1 sensible temperature calculation formulae when the new input data is classified into the second cluster, and (iii) selecting a third sensible temperature calculation formula among the K+1 sensible temperature calculation formulae when the new input data is classified as the precipitation data group; and predicting a sensible temperature for the new input data based on the selected sensible temperature calculation formula; and determining whether to issue the heatwave warning based on comparison of the predicted sensible temperature and criteria for heatwave warnings; and; in response to determining to issue the heatwave warning, outputting, by an output part, information related to issuance of the heatwave warning in at least one of visual, auditory and tactile forms, or transmitting, by a communicator, the information related to issuance of the heatwave warning to another device through a wired or wireless network, wherein the clustering of the non-precipitation data into the K clusters comprises clustering the non-precipitation data into the K clusters by using a K-means clustering algorithm which is an unsupervised machine learning algorithm for clustering data having similar features into the K clusters. It is for this reason, Claim 1 and all of its dependencies are allowed.
Claim 8, 15 and 16 includes analogous, though not necessarily coextensive, features in conjunction with Claim 1, an is, therefore, along with its dependencies, for similar rationale as disclosed above, allowed.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J SINGLETARY whose telephone number is (571)272-4593. The examiner can normally be reached Monday-Friday 8:00am-5:00pm.
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/MICHAEL J SINGLETARY/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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