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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Specification
The disclosure is objected to because of the following informalities:
[0044] recites 3) Setting the of number of decision trees as 100…
It should be corrected as – Setting the
Appropriate correction is required.
Claim Objections
Claim 4, 5 objected to because of the following informalities:
Claim 4 and 5 recite OMUVBd and ERA5. Please provide appropriate full form of these terms. Appropriate correction is required.
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 1-6 are rejected under 35 U.S.C 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) without significantly more.
Specifically, claim 1 recites:
An imputation method for surface ultraviolet irradiance based on feasible cloud information and machine learning, the method comprising:
A) establishing a deep learning model, wherein the deep learning model is designed to be a two-layered stacking ensemble learning model; constructing a first layer of the deep learning model as combination of multiple fundamental machine learning models; constructing a second layer of the deep learning model as Lasso model, which integrates an output from the first layer to obtain a final retrieval result;
B) matching the surface ultraviolet irradiance with input features comprising cloud and meteorological information according to date, latitude and longitude; establishing a statistical relationship between the surface ultraviolet irradiance and the input features by training the deep learning model; and
C) estimating the surface ultraviolet irradiance based on the trained deep learning model in regions with missing satellite observations of the surface ultraviolet irradiance; and
D) inputting the cloud and meteorological information to produce an UV index.
The claim limitations in the abstract idea have been highlighted in bold above.
Under the step 1 of the eligibility analysis, it is determined whether the claims are drawn to a statutory category by considering whether the claimed subject matter fall 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 the statutory category of (process).
Under the step 2A, prong one, it is considered 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 groupings of subject matter when recited as such in a claim limitation, that cover mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental process – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, a step of A) establishing a deep learning model, wherein the deep learning model is designed to be a two-layered stacking ensemble learning model (is considered as mathematical relationship); constructing a first layer of the deep learning model as combination of multiple fundamental machine learning model (is considered as mathematical relationship); constructing a second layer of the deep learning model as Lasso model, which integrates an output from the first layer to obtain a final retrieval result (is considered as mathematical relationship);
B) matching the surface ultraviolet irradiance with input features comprising cloud and meteorological information according to date, latitude and longitude (is considered as a mental process); establishing a statistical relationship between the surface ultraviolet irradiance and the input features by training the deep learning model (is considered as mathematical relationship); and
C) estimating the surface ultraviolet irradiance based on the trained deep learning model in regions with missing satellite observations of the surface ultraviolet irradiance (is considered as mathematical relationship); and
D) inputting the cloud and meteorological information to produce an UV index (is considered as mathematical relationship).
These mathematical and mental steps represent that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind.
Next, under the step 2A, prong two, it is considered whether the claim that recites a judicial exception is integrated into a practical application.
In this step, it is evaluated whether the claim recites meaningful additional elements that integrate the exception into a practical application of that exception.
In claim 1, there are not any additional elements/steps. The additional element in the preamble of “An imputation method for surface…” 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.
In conclusion, the above additional element, 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.
Considering the claim as a whole, one of ordinary skill in the art would not know the practical application of the present invention since the claims do not apply or use the judicial exception in some meaningful way.
The independent claim, therefore, is not patent eligible.
With regards to the dependent claims, the claims 2-6 comprise the analogous subject matter and also comprise additional features/steps which are the part of an expanded abstract idea of the independent claim 1 (additionally comprising mathematical relationship/mental process steps) and, therefore, the dependent claims are not eligible without additional elements that reflect a practical application and qualified for significantly more for substantially similar reason as discussed with regards to claim 1.
Prior arts:
Regarding claim 1, Poutiatine teaches An imputation method for surface ultraviolet irradiance based on feasible cloud information and machine learning, the method comprising:
B) matching the surface ultraviolet irradiance with input features comprising cloud and meteorological information according to date, latitude and longitude (para
[0020] Therefore, the light exposure device can implement Blocks of method S100 to account for local variations in ultraviolet radiation indices—such as when the light exposure device is located in a shaded area, indoors, or under local cloud cover—in calculating cumulative ultraviolet exposure over a sampling interval. For example, outdoors (e.g., on a roof of a building) at a particular geolocation, the light exposure device can record ultraviolet values and, from the ultraviolet values, calculate an ultraviolet index of 6.0 for the particular geolocation. However, indoors (e.g., inside the building) at the particular geolocation, the light exposure device can record lower ultraviolet values and, from the ultraviolet values (e.g., the global ultraviolet value), calculate an ultraviolet index of 0.1 for the particular geolocation.
[0052] Alternatively, the light exposure device can access a current location of the light exposure device entered (e.g., manually) by a user into the mobile computing device and/or the light exposure device. For example, the user may input an instant zip code, city and state, longitude and latitude, etc. into the mobile computing device and/or the light exposure device);
Herein examiner views the light exposure device account (i.e., match) the surface ultraviolet irradiance with input features such as cloud, shade, indoor and meteorological (such as heat, cool) information according to date, latitude and longitude.
D) inputting the cloud and meteorological information to produce an UV index (para [0018] A global ultraviolet value can be recorded by a single static weather station in the North Beach neighborhood, and an approximate ultraviolet index for San Francisco at this time can be calculated based on this single ultraviolet value. However, on this day, weather in the North Beach neighborhood may be cool and foggy while weather in the Marina neighborhood may be warmer and slightly overcast, weather in the “SoMa” neighborhood may be windy and clear, and weather in the Potrero Hill area may be warm and sunny. While the ultraviolet index calculated based on the single ultraviolet value recorded by the weather station may be approximately correct for the North Beach neighborhood, this ultraviolet index may be highly inaccurate (e.g., under-representative) for these other neighborhoods of San Francisco.).
Herein examiner views the foggy, clear (as cloud information) and cool, heat (as meteorological information) information are used to determine (or produce) an UV index.
However, Poutiatine does not teach
establishing a deep learning model, wherein the deep learning model is designed to be a two-layered stacking ensemble learning model; constructing a first layer of the deep learning model as combination of multiple fundamental machine learning models; constructing a second layer of the deep learning model as Lasso model, which integrates an output from the first layer to obtain a final retrieval result;
establishing a statistical relationship between the surface ultraviolet irradiance and the input features by training the deep learning model;
C) estimating the surface ultraviolet irradiance based on the trained deep learning model in regions with missing satellite observations of the surface ultraviolet irradiance.
Kim teaches A) establishing a deep learning model, wherein the deep learning model is designed to be a two-layered stacking ensemble learning model (page 4, line 14. it is possible to increase the existing stacking ensemble technique having two layers to N layers and build each layer with ensemble networks learned by the stacking ensemble technique.); constructing a first layer of the deep learning model as combination of multiple fundamental machine learning models (page 6, line 4: Referring to FIG. 3A, the generator 210 may build a first ensemble network 30-1 including a plurality of classification algorithms (eg, six different classification algorithms) in the first layer.);
constructing a second layer of the deep learning model, which integrates an output from the first layer to obtain a final retrieval result (For example, the learning unit 220 may learn a first sub-classification model including only the first layer. Subsequently, when the generator 210 generates the second layer, the learning unit 220 may learn a second sub-classification model including the first layer and the second layer.);
Ryu teaches a second layer of the deep learning model as Lasso model (para [0016] various machine learning models/algorithms and deep learning models/algorithms, such as a classification algorithm, a regression algorithm, supervised learning, unsupervised learning, reinforcement learning, support vector machine, a decision tree, random forests, least absolute shrinkage and selection operator (LASSO), AdaBoost, XGBoost, an artificial neural network, and the like.,)
Abedini teaches establishing a statistical relationship between the surface ultraviolet irradiance and the input features by training the deep learning model (para [0027] Conditional Random Field (CRF) may smooth the output of the classifier and remove noise predictions. Deep learning segmentation methods (such as Fully Convoloution Network, CRFasRNN) may be also used to identify cloud objects and draw borders.
para [0030] For example, the sky images obtained and processed at 102 and 104 may be used for estimating solar irradiation, for example, through: cloud analysis to obtain sky clearness, or cloudiness indices at 110; and cloud movement prediction to obtain future cloud coverage forecasts at 108. The method may provide a short-term prediction of the cloud layout. For example, at 108, a trained machine learning model predicts future cloud positions or layout
[0032] The output of the cloud coverage prediction at 108, in addition to other meteorological data, may be input for estimating the sun exposure and/or solar irradiation values at 112.);
Herein examiner views estimating sun exposure (i.e., surface ultraviolet irradiance) from cloud coverage (input features) as establishing statistical relationship between them, which is performed using a deep learning method.
Allowable Subject Matter
Regarding claim 1, none of the prior arts alone or in combination neither disclose or render obvious method to teach the limitation: estimating the surface ultraviolet irradiance based on the trained deep learning model in regions with missing satellite observations of the surface ultraviolet irradiance”
However, Examiner cannot comment on the allowability of claims 1 and dependent claims 2-6 until the rejection under 35 U.S.C 101 is adequately addressed.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Albright et al US20070073487A1 discuss predicting solar UV hazard.
Vega-Avila et al US 20170031056 A1 discuss solar energy forecasting.
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/SHARAD TIMILSINA/Examiner, Art Unit 2863
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2863