DETAILED ACTION
This action is in respond to the amendment filed 02/02/2026. Claims 1-20 are pending and have been examined.
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 § 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.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 1:
Claim 1 recites a computer program product comprising a non-transitory computer readable storage medium and is thus a product, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 1 recites
generating a plurality of pairs of climate data points from the climate data, wherein in a pair of climate data points of the plurality of pairs of climate data points, a first climate data point comprises climate attributes of a first half of a given time period and a second climate data point comprises climate attributes of a second half of the given time period (This limitation is a mental process as it encompasses a human mentally generating pairs of climate data points.)
embedding the plurality of pairs of climate data points with a positional embedding, a seasonality embedding and a climate attribute embedding, the positional embedding further comprising a location specific embedding and a data specific embedding (This limitation is a mental process as it encompasses a human mentally embedding the pairs of data.)
randomly masking a portion of the plurality of pairs of climate data points (This limitation is a mental process as it encompasses a human mentally masking some of the pairs of data.)
predicting climate attributes of the masked portion of the plurality of pairs of climate data points to generate a first prediction output; (This limitation is a mental process as it encompasses a human mentally predicting attributes of the data and generating a prediction.)
randomly replacing a portion of the plurality of pairs of climate data points with random climate data points and, (This limitation is a mental process as it encompasses a human mentally replacing some pairs of data.)
and predicting whether a given climate data point of a given pair of climate data points of the plurality of pairs of climate data points is a randomly replaced climate data point of the random climate data points to generate a second prediction output; (This limitation is a mental process as it encompasses a human mentally predicting whether a data point is a random data point and generating a prediction.)
combining the first prediction output and the second prediction output to forecast climate data for a future time period (This limitation is a mental process as it encompasses a human mentally combining the first output and the second output)
modify one or more parameters of the trained machine learning model based on a desired dependent task of the machine learning model (This limitation is a mental process as it encompasses a human mentally modifying parameters.)
generate… a vector representation of the forecast climate data for the future time period. (This limitation is a mental process as it encompasses a human mentally creating a vector representation of the forecast data.)
Therefore, claim 1 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 1 further recites additional elements of
A computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by one or more processors (This element does not integrate the abstract idea into a practical application because it recites generic computing components on which to perform the abstract idea (see MPEP 2106.05(f)).)
train a machine learning model (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of training a model (see MPEP 2106.05(g)).)
receiving climate data comprising a plurality of spatial components and a plurality of temporal components (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
By executing a first machine learning layer of the machine learning model(This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
By executing a second machine learning layer of the machine learning model (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
By executing a third machine learning layer of the machine learning model; (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
re-training the machine learning model based on the modified one or more parameters and the combined first prediction output and the second prediction output; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of re-training the model based on output (see MPEP 2106.05(g)).)
via the re-trained machine learning model (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
execute the desired dependent task using the generated vector representation of the forecast climate data for the future time period (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 1 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 1 do not provide significantly more than the abstract idea itself, taken alone and in combination because
A computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by one or more processors uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
train a machine learning model is the well understood, routine, and conventional activity of training a model (Yan et al. US 2019/0367019 A1, paragraph 0049, “well-known, neural networks or other machine learning systems can be trained to produce configured output based on training data provided to the neural network or other machine learning system in a training phase.”)
receiving climate data comprising a plurality of spatial components and a plurality of temporal components is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
By executing a first machine learning layer of the machine learning model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
By executing a second machine learning layer of the machine learning model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
By executing a third machine learning layer of the machine learning model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
re-training the machine learning model based on the combined first prediction output and the second prediction output is the well understood, routine, and conventional activity of retraining a model based on output (Ni et al. US 2022/0245448 A1, paragraph 0020, “Conventionally, it is determined whether to retrain a machine learning model by testing the accuracy of the model”).
via the re-trained machine learning model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
execute the desired dependent task using the generated vector representation of the forecast climate data for the future time period uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 1 is subject-matter ineligible.
Regarding Claim 2:
Subject Matter Eligibility Analysis Step 1:
Claim 2 recites the same computer program product as claim 1 and is thus a machine one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 2 recites the same abstract ideas as in claim 1. Therefore, Claim 2 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 2 recites
wherein the plurality of spatial components comprise a plurality of geographic locations and the plurality of temporal components comprise a plurality of time periods (This element does not integrate the abstract idea into a practical application because recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).)
Therefore, Claim 2 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of Claim 2 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the plurality of spatial components comprise a plurality of geographic locations and the plurality of temporal components comprise a plurality of time periods specifies a particular technological environment to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(h)).
Therefore, Claim 2 is subject-matter ineligible.
Regarding Claim 3:
Subject Matter Eligibility Analysis Step 1:
Claim 3 recites the same computer program product as claim 2 and is thus a machine one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 3 recites
wherein the vector representation comprises one or more d-dimensional vector representations of the climate data at the plurality of geographic locations, wherein d is an integer (this limitation could encompass a human mentally making the vector representation comprise climate data at the plurality of geographic locations).
Therefore, Claim 3 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 3 has no additional elements that would integrate the abstract idea into a practical application. Therefore, Claim 3 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since Claim 3 does not recite any additional elements to provide significantly more than the abstract idea itself, taken alone and in combination, Claim 3 is subject-matter ineligible.
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 1:
Claim 4 recites the same computer program product as claim 1 and is thus a machine one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 4 recites the same abstract ideas as claim 1. Therefore, Claim 4 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 4 further recites additional element of
wherein the machine learning model comprises a transformer-based neural network. (This element does not integrate the abstract idea into a practical application because recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).)
Therefore, Claim 4 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of Claim 4 do not provide significantly more than the abstract
idea itself, taken alone and in combination because
wherein the machine learning model comprises a transformer-based neural network, specifies a particular technological environment to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(h)).
Therefore, Claim 4 is subject-matter ineligible.
Regarding Claim 5:
Subject Matter Eligibility Analysis Step 1:
Claim 5 recites the same computer program product as claim 1 and is thus a machine, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 5 recites the same abstract ideas as claim 1. Therefore, Claim 5 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 5 further recites additional elements of
positional embedding is used in connection with the training of the machine learning model to capture positional characteristics in climate data. (This element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).
Therefore, Claim 5 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of Claim 5 do not provide significantly more than the abstract
idea itself, taken alone and in combination, because
positional embedding is used in connection with the training of the machine learning model to capture positional characteristics in climate data uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, Claim 5 is subject-matter ineligible.
Regarding Claim 6:
Subject Matter Eligibility Analysis Step 1:
Claim 6 recites the same computer program product as claim 5 and is thus a machine, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 6 recites the same abstract ideas as claim 5. Therefore, Claim 6 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 6 recites
wherein the location specific embedding is based on location information for one or more locations associated with the climate data. (This element does not integrate the abstract idea into a practical application because recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).)
Therefore, Claim 6 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of Claim 6 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the location specific embedding is based on location information for one or more locations associated with the climate data, specifies a particular technological environment to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(h)).
Therefore, Claim 6 is subject-matter ineligible.
Regarding Claim 7:
Subject Matter Eligibility Analysis Step 1:
Claim 7 recites the same computer program product as claim 5 and is thus a machine, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 7 recites the same abstract ideas as in claim 5. Therefore, Claim 7 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 7 recites
wherein the data specific embedding and the positional characteristics further comprise climate zone information for one or more climate zones associated with the climate data. (This element does not integrate the abstract idea into a practical application because recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).)
Therefore, Claim 7 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of Claim 2 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the data specific embedding and the positional characteristics further comprise climate zone information for one or more climate zones associated with the climate data, specifies a particular technological environment to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(h)).
Therefore, Claim 7 is subject-matter ineligible.
Regarding Claim 8:
Subject Matter Eligibility Analysis Step 1:
Claim 8 recites the same computer program product as claim 1 and is thus a machine, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 8 recites the same abstract ideas as claim 1. Therefore, Claim 8 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 8 further recites additional elements of
seasonality embedding is used in connection with the training of the machine learning model to capture temporal trend characteristics of the climate data. (This element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, Claim 8 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of Claim 8 do not provide significantly more than the abstract idea itself, taken alone and in combination because
seasonality embedding is used in connection with the training of the machine learning model to capture temporal trend characteristics of the climate data uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, Claim 8 is subject-matter ineligible.
Regarding Claim 9:
Subject Matter Eligibility Analysis Step 1:
Claim 9 recites the same computer program product as claim 1 and is thus a machine, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 9 recites the same abstract ideas as claim 1. Therefore, Claim 9 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 9 further recites additional elements of
climate attribute embedding is used in connection with the training of the machine learning model to capture one or more latent space representations of the climate data. (This element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, Claim 9 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of Claim 9 do not provide significantly more than the abstract idea itself, taken alone and in combination because
climate attribute embedding is used in connection with the training of the machine learning model to capture one or more latent space representations of the climate data uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, Claim 9 is subject-matter ineligible.
Regarding Claim 10:
Subject Matter Eligibility Analysis Step 1:
Claim 10 recites the same computer program product as claim 1 and is thus a machine, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 10 recites the same abstract ideas as claim 1. Therefore, Claim 10 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 10 further recites additional elements of
wherein the program instructions further cause the one or processing to fine-tune the machine learning model (This limitation does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).
fine-tune the machine learning model. (This element does not integrate the abstract idea into a practical application because it recites the insignificant, extra-solution activity of fine-tuning that only adjusts the parameters of a model during training (see MPEP 2106.05(g)).
perform one or more enterprise specific forecasting tasks. (This element does not integrate the abstract idea into a practical application because recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).)
Therefore, Claim 10 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of Claim 10 do not provide significantly more than the abstract
idea itself, taken alone and in combination, because
wherein the program instructions further cause the one or processing to fine-tune the machine learning model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f))
fine-tune the machine learning model. is the well understood, routine, and conventional activity of fine-tuning a model (Sha, US 2023/0087777 A1, “A conventional model flow typically includes the generation of training and testing data, the training of a deep neural network-based model, and the fine tuning of the hyperparameters of the deep neural network-based model.”)
perform one or more enterprise specific forecasting tasks specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)).
Therefore, Claim 10 is subject-matter ineligible.
Regarding Claim 11:
Subject Matter Eligibility Analysis Step 1:
Claim 11 recites the same computer program product as in claim 1 and is thus a machine, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 11 recites
wherein the plurality of spatial components and the plurality of temporal components comprise different granularities (this limitation could encompass a human mentally making the spatial and temporal components have different granularities).
Therefore, Claim 11 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 11 has no additional elements that would integrate the abstract idea into a practical application. Therefore, Claim 11 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since Claim 11 does not recite any additional elements to provide significantly more than the abstract idea itself, taken alone and in combination, Claim 11 is subject-matter ineligible.
Regarding Claim 12:
Subject Matter Eligibility Analysis Step 1:
Claim 12 recites the same computer program product as in claim 1 and is thus a machine, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 12 recites the same abstract ideas as claim 1. Therefore, Claim 12 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 12 recites
wherein the climate data further comprises one or more climate attributes. This element does not integrate the abstract idea into a practical application because recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).
Therefore, Claim 12 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of Claim 12 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the climate data further comprises one or more climate attributes, specifies a particular technological environment to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(h)).
Therefore, Claim 12 is subject-matter ineligible.
Regarding Claim 13:
Subject Matter Eligibility Analysis Step 1:
Claim 13 recites the same computer program product as claim 1 and is thus a machine, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 13 recites
learn a latent representation of the masked portion of the climate data by leveraging one or more adjacent un-masked portions of the climate data (this limitation could encompass a human mentally learning a latent representation).
Therefore, Claim 13 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 13 further recites additional elements of
wherein the program instructions further cause the one or more processors. This element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).
Therefore, Claim 13 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of Claim 13 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the program instructions further cause the one or more processors, uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, Claim 13 is subject-matter ineligible.
Regarding Claim 14:
Subject Matter Eligibility Analysis Step 1:
Claim 14 recites the same computer program product as claim 1 and is thus a machine, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 14 recites
learning the latent representation of the masked portion of the climate data (this limitation could encompass a human mentally learning the latent representation) and minimize a loss function which accounts for one or more constraints (this limitation could encompass a human mentally minimizing a loss function).
Therefore, Claim 14 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 14 further recites an additional element of
the program instructions cause the one or more processors to minimize a loss function. (This element does not integrate the abstract idea into a practical application because it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)). )
Therefore, Claim 14 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of Claim 14 do not provide significantly more than the abstract idea itself, taken alone and in combination because
the program instructions cause the one or more processors to minimize a loss function, uses a computer as a tool to perform the abstract idea cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, Claim 14 is subject-matter ineligible.
Regarding Claim 15:
Subject Matter Eligibility Analysis Step 1:
Claim 15 recites the same computer program product as claim 1 and is thus a machine, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 15 recites the same abstract idea as in claim 1. Therefore, Claim 15 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 15 further recites additional elements of
wherein the plurality of temporal components comprise a plurality of timestamps (This element does not integrate the abstract idea into a practical application because recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).)
wherein the program instructions further cause the one or more processors to use the machine learning model. (This element does not integrate the abstract idea into a practical application it recites a generic computer on which to perform the abstract idea, e.g., “apply it on a computer” (see MPEP 2106.05(f)).)
predict climate associated with a timestamp following a last timestamp of the plurality of timestamps (This element does not integrate the abstract idea into a practical application because recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).)
Therefore, Claim 15 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of Claim 15 do not provide significantly more than the abstract
idea itself, taken alone and in combination, because
wherein the plurality of temporal components comprise a plurality of timestamps specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)).
wherein the program instructions further cause the one or more processors to use the machine learning model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
predict climate associated with a timestamp following a last timestamp of the plurality of timestamps specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)).
Therefore, Claim 15 is subject-matter ineligible.
Regarding Claim 16:
Subject Matter Eligibility Analysis Step 1:
Claim 16 recites a computer program product comprising a non-transitory computer readable storage medium and is thus a product, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 16 recites
generating a plurality of pairs of climate data points from the climate data, wherein in a pair of climate data points of the plurality of pairs of climate data points, a first climate data point comprises climate attributes of a first half of a given time period and a second climate data point comprises climate attributes of a second half of the given time period (This limitation is a mental process as it encompasses a human mentally generating pairs of climate data points.)
embedding the plurality of pairs of climate data points with a positional embedding, a seasonality embedding and a climate attribute embedding, the positional embedding further comprising a location specific embedding and a data specific embedding (This limitation is a mental process as it encompasses a human mentally embedding the pairs of data.)
randomly masking a portion of the plurality of pairs of climate data points (This limitation is a mental process as it encompasses a human mentally masking some of the pairs of data.)
predicting climate attributes of the masked portion of the plurality of pairs of climate data points to generate a first prediction output; (This limitation is a mental process as it encompasses a human mentally predicting attributes of the data and generating a prediction.)
randomly replacing a portion of the plurality of pairs of climate data points with random climate data points and, (This limitation is a mental process as it encompasses a human mentally replacing some pairs of data.)
and predicting whether a given climate data point of a given pair of climate data points of the plurality of pairs of climate data points is a randomly replaced climate data point of the random climate data points to generate a second prediction output; (This limitation is a mental process as it encompasses a human mentally predicting whether a data point is a random data point and generating a prediction.)
combining the first prediction output and the second prediction output to forecast climate data for a future time period (This limitation is a mental process as it encompasses a human mentally combining the first output and the second output)
modifying one or more parameters of the trained machine learning model based on a desired dependent task of the machine learning model (This limitation is a mental process as it encompasses a human mentally modifying parameters.)
generating… a vector representation of the forecast climate data for the future time period. (This limitation is a mental process as it encompasses a human mentally creating a vector representation of the forecast data.)
Therefore, claim 16 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 16 further recites additional elements of
training a machine learning model (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of training a model (see MPEP 2106.05(g)).)
receiving climate data comprising a plurality of spatial components and a plurality of temporal components (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
By executing a first machine learning layer (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
By executing a second machine learning layer (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
By executing a third machine learning layer of the machine learning model; (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
re-training the machine learning model based on the modified one or more parameters and the combined first prediction output and the second prediction output; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of re-training the model based on output (see MPEP 2106.05(g)).)
via the re-trained machine learning model (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
executing the desired dependent task using the generated vector representation of the forecast climate data for the future time period (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
wherein the computer implemented method is performed by at least one processing device comprising a processor coupled to a memory when executing program code (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 16 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 16 do not provide significantly more than the abstract idea itself, taken alone and in combination because
training a machine learning model is the well understood, routine, and conventional activity of training a model (Yan et al. US 2019/0367019 A1, paragraph 0049, “well-known, neural networks or other machine learning systems can be trained to produce configured output based on training data provided to the neural network or other machine learning system in a training phase.”)
receiving climate data comprising a plurality of spatial components and a plurality of temporal components is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
By executing a first machine learning layer uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
By executing a second machine learning layer uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
By executing a third machine learning layer of the machine learning model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
re-training the machine learning model based on the combined first prediction output and the second prediction output is the well understood, routine, and conventional activity of retraining a model based on output (Ni et al. US 2022/0245448 A1, paragraph 0020, “Conventionally, it is determined whether to retrain a machine learning model by testing the accuracy of the model”).
via the re-trained machine learning model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
executing the desired dependent task using the generated vector representation of the forecast climate data for the future time period uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
wherein the computer implemented method is performed by at least one processing device comprising a processor coupled to a memory when executing program code uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 16 is subject-matter ineligible.
Regarding claim 17, claim 17 recites substantially similar limitations to claim 5, and is therefore rejected under the same analysis.
Regarding claim 18, claim 18 recites substantially similar limitations to claim 13, and is therefore rejected under the same analysis.
Regarding Claim 19:
Subject Matter Eligibility Analysis Step 1:
Claim 19 recites a computer program product comprising a non-transitory computer readable storage medium and is thus a product, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 19 recites
generating a plurality of pairs of climate data points from the climate data, wherein in a pair of climate data points of the plurality of pairs of climate data points, a first climate data point comprises climate attributes of a first half of a given time period and a second climate data point comprises climate attributes of a second half of the given time period (This limitation is a mental process as it encompasses a human mentally generating pairs of climate data points.)
embedding the plurality of pairs of climate data points with a positional embedding, a seasonality embedding and a climate attribute embedding, the positional embedding further comprising a location specific embedding and a data specific embedding (This limitation is a mental process as it encompasses a human mentally embedding the pairs of data.)
randomly masking a portion of the plurality of pairs of climate data points (This limitation is a mental process as it encompasses a human mentally masking some of the pairs of data.)
predicting climate attributes of the masked portion of the plurality of pairs of climate data points to generate a first prediction output; (This limitation is a mental process as it encompasses a human mentally predicting attributes of the data and generating a prediction.)
randomly replacing a portion of the plurality of pairs of climate data points with random climate data points and, (This limitation is a mental process as it encompasses a human mentally replacing some pairs of data.)
and predicting whether a given climate data point of a given pair of climate data points of the plurality of pairs of climate data points is a randomly replaced climate data point of the random climate data points to generate a second prediction output; (This limitation is a mental process as it encompasses a human mentally predicting whether a data point is a random data point and generating a prediction.)
combining the first prediction output and the second prediction output to forecast climate data for a future time period (This limitation is a mental process as it encompasses a human mentally combining the first output and the second output)
modify one or more parameters of the trained machine learning model based on a desired dependent task of the machine learning model (This limitation is a mental process as it encompasses a human mentally modifying parameters.)
generate… a vector representation of the forecast climate data for the future time period. (This limitation is a mental process as it encompasses a human mentally creating a vector representation of the forecast data.)
Therefore, claim 19 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 19 further recites additional elements of
An apparatus comprising: at least one processing device comprising a processor coupled to a memory, the at least one processing device, when executing program code, is configured to: train a machine learning model (This element does not integrate the abstract idea into a practical application because it generic computing components on which to perform the abstract idea (see MPEP 2106.05(f)).)
train a machine learning model (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of training a model (see MPEP 2106.05(g)).)
receiving climate data comprising a plurality of spatial components and a plurality of temporal components (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
by executing a first machine learning layer (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
by executing a second machine learning layer (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
by executing a third machine learning layer of the machine learning model; (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
re-training the machine learning model based on the modified one or more parameters and the combined first prediction output and the second prediction output; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of re-training the model based on output (see MPEP 2106.05(g)).)
via the re-trained machine learning model (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
execute the desired dependent task using the generated vector representation of the forecast climate data for the future time period (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 19 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 19 do not provide significantly more than the abstract idea itself, taken alone and in combination because
An apparatus comprising: at least one processing device comprising a processor coupled to a memory, the at least one processing device, when executing program code, is configured to: train a machine learning model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
train a machine learning model is the well understood, routine, and conventional activity of training a model (Yan et al. US 2019/0367019 A1, paragraph 0049, “well-known, neural networks or other machine learning systems can be trained to produce configured output based on training data provided to the neural network or other machine learning system in a training phase.”)
receiving climate data comprising a plurality of spatial components and a plurality of temporal components is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
By executing a first machine learning layer uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
By executing a second machine learning layer uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
By executing a third machine learning layer of the machine learning model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
re-training the machine learning model based on the combined first prediction output and the second prediction output is the well understood, routine, and conventional activity of retraining a model based on output (Ni et al. US 2022/0245448 A1, paragraph 0020, “Conventionally, it is determined whether to retrain a machine learning model by testing the accuracy of the model”).
via the re-trained machine learning model uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
execute the desired dependent task using the generated vector representation of the forecast climate data for the future time period uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 19 is subject-matter ineligible.
Regarding claim 20, claim 20 recites substantially similar limitations to claim 13, and is therefore rejected under the same analysis.
Allowable Subject Matter
Claims 1-20 would be allowable over the prior art of record if the 101 rejections are overcome in light of the instant amendments.
Specifically, regarding Claim 1, “generating a plurality of pairs of climate data points from the climate data, wherein in a pair of climate data points of the plurality of pairs of climate data points, a first climate data point comprises climate attributes of a first half of a given time period and a second climate data point comprises climate attributes of a second half of the given time period;”, “randomly masking a portion of the plurality of pairs of climate data points and, by executing a first machine learning layer, and predicting climate attributes of the masked portion of the plurality of pairs of climate data points to generate a first prediction output”, “randomly replacing a portion of the plurality of pairs of climate data points with random climate data points and, by executing a second machine learning layer, and predicting whether a given climate data point of a given pair of climate data points of the plurality of pairs of climate data points is a randomly climate data point of the random climate data points to generate a second prediction output;”, “and combining the first prediction output and the second prediction output to forecast climate data for a future time period by executing a third machine learning layer of the machine learning model;”, and “modify one or more parameters of the trained machine learning model based on a desired dependent task of the machine learning model and re-training the machine learning model, wherein the training is based on the combined first prediction output and the second prediction output at least in part on the masked portion of the climate data;” in conjunction with the other limitations of the claims are not taught by the prior art of record.
The closet prior art of record is Malizia et al. (US 2022/0261928 A1) (hereafter referred to as Malizia), Grigsby et al. (“Long-Range Transformers for Dynamic Spatiotemporal Forecasting”) (hereafter referred to as Grigsby), Devlin et al. (“BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”) (hereafter referred to as Devlin), and Kim et al. (US 2022/0146707 A1) (hereafter referred to as Kim).
Malizia discloses training a machine learning model and receiving climate data in the form of temporal and spatial components (Malizia, page 29, paragraph 0003). Malizia also discloses generating a vector representation (Malizia, page 34, paragraph 0085) and masking a portion of data (Malizia, page 37, paragraph 0140). Malizia fails to disclose generating pairs of climate data points wherein a first climate data point comprises climate attributes of a first half of a given time period and a second climate data point comprises climate attributes of a second half of the given time period, embedding the data, randomly masking a plurality of pairs of climate data, randomly replacing a portion of the plurality of pairs of climate data points with random climate data points, combining predictions, modifying parameters, and retraining the model.
Grigsby discloses climate data (Grigsby, page 6, 1st column, 2nd paragraph) embedding the data with a positional embedding (Grigsby, page 3, 1st column, 2nd paragraph- last paragraph) a seasonality embedding (Grigsby, page 3, 2nd column, 1st paragraph), and a climate attribute embedding (Grigsby, page 3, 2nd column, 2nd paragraph). Grigsby fails to disclose a plurality of climate data points, wherein a first climate data point comprises climate attributes of a first half of a given time period and a second climate data point comprises climate attributes of a second half of the given time period, randomly masking a plurality of pairs of climate data, randomly replacing a portion of the plurality of pairs of climate data points with random climate data points, combining predictions, modifying parameters, and retraining the model.
Devlin discloses randomly masking and randomly replacing data (Devlin, page 4, 2nd column, 2nd paragraph). Devlin also discloses modifying parameters of the model on a desired dependent task of the machine learning model (Devlin, page 3, 1st column, 3rd paragraph). Devlin does not disclose, climate data, predicting climate attributes of the masked data by executing a first layer, predicting whether a climate data point is a random climate data point by executing a second layer, combining the predictions, and retraining the model.
Kim discloses climate data (Kim, page 1, abstract), modifying parameters and retraining the model (Kim, page 7, paragraph 0025). Kim does not disclose pairs of climate data points from the climate data, wherein a first climate data point comprises climate attributes of a first half of a given time period and a second climate data point comprises climate attributes of a second half of the given time period, embedding the data, masking the data, replacing the data, and combining predictions.
Therefore, the prior art of record does not disclose claim 1 as a whole. Claims 2-15 are allowable at least due to their dependencies on claim 1 if the 101 rejections are overcome.
Claim 16 recites substantially similar limitations as claim 1 and is therefore allowable under the same rationale if the 101 rejections are overcome. Claims 17-18 are allowable at least due to their dependencies on claim 16 if the 101 rejections are overcome.
Claim 19 recites substantially similar limitations as claim 1 and is therefore allowable under the same rationale if the 101 rejections are overcome. Claim 20 is allowable at least due to its dependency on claim 19 if the 101 rejections are overcome.
Response to Arguments
On page 8, Applicant argues:
For example, in formulating the§ 101 rejection of claim 1, the Examiner alleges that claim 1 is directed to an abstract idea at Step 1 of the Alice/Mayo analysis framework, corresponding to Step 2A of the USPTO analysis framework. Applicant respectfully submits that claim 1 "when read as a whole" is instead directed to "a specific means or method that improves the relevant technology." See Contour IP Holding LLC v. GoPro, Inc., 2024 U.S. App. LEXIS 22825 (Fed. Cir. 2024), citing McRO, Inc., v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314 (Fed. Cir. 2016).
The Examiner alleges with regard to Step 2A of the USPTO analysis framework that claim 1 is directed to an abstract idea because it allegedly recites "steps which could be reasonably performed in the mind, with the aid of pen and paper," and does not integrate the abstract idea into a practical application.
This is believed to be an incorrect interpretation of claim 1, particularly in view of the recent decision in Ex Parte Desjardins et al., No. 2024-000567 (PTAB Appeals Review Panel, September 26, 2025), which states that "[c]ategorically excluding AI innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology," and further states that improvements to how a machine learning model itself operates, including training of a machine learning model, represent improvements to computer functionality.
Accordingly, even if one assumes for purposes of argument only that claim 1 could somehow be construed as reciting an abstract idea, such claims are not directed to an abstract idea for reasons similar to those set forth in the above-cited Ex Parte Desjardins decision, as claim 1 clearly integrates any such abstract idea into a practical application that provides improvements in computer technology.
Regarding the Applicant’s argument that claim 1 is not directed to an abstract idea, Examiner respectfully disagrees. Specifically, Examiner notes that claims that require a computer may still recite a mental process (MPEP 2106.04(a)(2)(III)(C)). Examiner further notes that any integration into a practical application comes from the additional elements (MPEP 2106.04 (II)(A)(2)). In claim 1, the additional elements do not integrate the judicial exception into a practical application because the additional elements recite generic computing components on which to perform the abstract idea (see MPEP 2106.05(f))), recite insignificant extra-solution activity of training a model (see MPEP 2106.05(g))), recite insignificant extra-solution activity of data gathering (see MPEP 2106.05(g))), amount to mere “apply it on a computer” (see MPEP 2106.05(f))), and recite insignificant extra-solution activity of re-training the model based on output (see MPEP 2106.05(g))).
On page 9, Applicant argues:
In view of the above portions of the July 2024 Updated Guidance, Applicant submits that claim 1 cannot reasonably be said to be directed to mental processes as alleged, at least in part because the claim explicitly incorporates AI-related recitations that cannot practically be performed in the human mind.
A recent USPTO Memorandum, issued August 4, 2025 by Deputy Commissioner for Patents Charles Kim to Technology Centers 2100, 2600 and 3600 (hereinafter "Kim
Memorandum"), emphasizes the above points. For example, the Kim Memorandum at page 2 states with regard to the mental process grouping that "Examiners are reminded not to expand this grouping in a manner that encompasses claim limitations that cannot practically be performed in the human mind."
Regarding the Applicant’s argument that claim 1 is not directed to mental processes, Examiner respectfully disagrees. Specifically, Examiner notes that the August 4 memo has not changed the analysis of claims under 101. Examiner further notes that generating pairs of climate data points from the climate data, is a mental process as it encompasses a human mentally generating pairs of climate data points. Embedding the plurality of pairs of climate data points with a positional embedding, a seasonality embedding and a climate attribute embedding, is a mental process as it encompasses a human mentally embedding the pairs of data. Randomly masking a portion of the plurality of pairs of climate data points is a mental process as it encompasses a human mentally masking some of the pairs of data. Predicting climate attributes of the masked portion of the plurality of pairs of climate data points to generate a first prediction output is a mental process as it encompasses a human mentally predicting attributes of the data and generating a prediction. Randomly replacing a portion of the plurality of pairs of climate data points with random climate data points and is a mental process as it encompasses a human mentally replacing some pairs of data. Predicting whether a given climate data point of a given pair of climate data points of the plurality of pairs of climate data points is a randomly replaced climate data point of the random climate data points to generate a second prediction output is a mental process as it encompasses a human mentally predicting whether a data point is a random data point and generating a prediction. Combining the first prediction output and the second prediction output to forecast climate data for a future time period is a mental process as it encompasses a human mentally combining the first output and the second output. Modifying one or more parameters of the trained machine learning model based on a desired dependent task of the machine learning model is a mental process as it encompasses a human mentally modifying parameters. Generating a vector representation of the forecast climate data for the future time period is a mental process as it encompasses a human mentally creating a vector representation of the forecast data.
On pages 9-11, Applicant argues:
The Examiner further alleges with regard to Step 2B of the above USPTO analysis framework that claim 1 does not include additional elements that are sufficient to amount to significantly more than the alleged abstract idea. However, claim 1 clearly recites an arrangement providing an improvement in computer technology. See the specification at, for example, paragraphs [0017], [0018] and [0057]-[0059]:
…..
The July 2024 Updated Guidance provides as follows with regard to AI inventions such as that recited in claim 1, with emphasis supplied and citations omitted:
A key point of distinction to be made for AI inventions is between a claim that
reflects an improvement to a computer or other technology described in the
specification (which is eligible) and a claim in which the additional elements
amount to no more than (1) a recitation of the words "apply it" ( or an equivalent)
or are no more than instructions to implement a judicial exception on a computer,
or (2) a general linking of the use of a judicial exception to a particular
technological environment or field of use (which is ineligible) .... AI inventions
may provide a particular way to achieve a desired outcome when they claim, for
example, a specific application of AI to a particular technological field (i.e., a
particular solution to a problem). In these situations, the claim is not merely to the
idea of a solution or outcome and amounts to more than merely "applying" the
judicial exception or generally linking the judicial exception to a field of use or
technological environment. In other words, the claim reflects an improvement in a
computer or other technology.
Independent claim 1 is directed to an AI invention that provides a particular solution to an important challenge in the technological field of machine learning, namely, the challenge of modifying one or more parameters of a trained machine learning model based on a desired dependent task of the machine learning model and re-training the machine learning model-based on the modified one or more parameters and combined first prediction output and the second prediction output, and generating, via the re-trained machine learning model, a vector representation of the forecast climate data for the future time period, and executing the desired dependent task using the generated vector representation of the forecast climate data for the future time period. Accordingly, even if one assumes for purposes of argument only that the claim could somehow be construed as reciting an abstract idea, it clearly integrates any such abstract idea into a practical application that provides an improvement in computer technology.
Regarding the Applicant’s argument that claim 1 provides an improvement, Examiner respectfully disagrees. Specifically, Examiner respectfully notes that paragraphs 0017-0018, and 0057-0059 provide a bare assertion of an improvement and does not provide the detail necessary to be apparent to a person of ordinary skill in the art (MPEP 2106.04(d)(1)). Examiner further notes that any integration into a practical application comes from the additional elements (MPEP 2106.04 (II)(A)(2)). In claim 1, the additional elements do not integrate the judicial exception into a practical application because the additional elements recite generic computing components on which to perform the abstract idea (see MPEP 2106.05(f))), recite insignificant extra-solution activity of training a model (see MPEP 2106.05(g))), recite insignificant extra-solution activity of data gathering (see MPEP 2106.05(g))), amount to mere “apply it on a computer” (see MPEP 2106.05(f))), and recite insignificant extra-solution activity of re-training the model based on output (see MPEP 2106.05(g))).
Examiner additionally notes that the additional elements in claim 1 do not provide significantly more than the judicial exception because the additional elements use a computer as a tool to perform the abstract idea (see MPEP 2106.05(f)), are the well understood, routine, and conventional activity of training a model (Yan et al. US 2019/0367019 A1, paragraph 0049, “well-known, neural networks or other machine learning systems can be trained to produce configured output based on training data provided to the neural network or other machine learning system in a training phase.”), are the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)), and are the well understood, routine, and conventional activity of retraining a model based on output (Ni et al. US 2022/0245448 A1, paragraph 0020, “Conventionally, it is determined whether to retrain a machine learning model by testing the accuracy of the model”) and cannot provide significantly more.
On page 13, Applicant argues:
Similar amendments which are similarly supported have been made to the other independent claims 16 and 19.
Accordingly, Applicant respectfully requests withdrawal of the rejection under §101.
The dependent claims are believed to be patentable at least by virtue of their respective dependencies from the independent claims. Moreover, one or more of the dependent claims are also believed to recite separately patentable subject matter.
Regarding the Applicant’s argument that the dependent claims are allowable at least due in part to their dependency on the independent claims, the Examiner respectfully disagrees and notes the instant rejections and response to arguments regarding the independent claims above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Webersinke et al. (“ClimateBert: A Pretrained Language Model for Climate-Related Text”) describes methods to read and classify weather patterns from text using a transformer based neural network.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAITLYN R HAEFNER whose telephone number is (571)272-1429. The examiner can normally be reached Monday - Thursday: 7:15 am - 5:15 pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle Bechtold can be reached on (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/K.R.H./ Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148