DETAILED ACTION
Final Rejection
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 .
Response to Amendment
Applicant’s amendments, filed 12/01/2025 to claims are accepted. In this amendment, Claim 1 has been amended.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim limitation “a module for generating, calculating, optimization training module, a module for early warning (as cited in claims 7), multilayer perceptron module(as cited in claims 10, 15)” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description discloses the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function based on [0021, where it discloses such modules are program instructions and stored in the memory, and the processor calls the program instructions stored in the memory to enable the device to implement the steps of the method according to any one of the first aspect. See [0035]-[0037] of current discloser PgPub.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-19 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Each of claims1-19 falls within one of the four statutory categories. See MPEP § 2106.03. For example, each of claims 1-7 fall within category of process; each of claim 8-19 falls within category of machine, i.e., a “concrete thing, consisting of parts, or of certain devices and combination of devices.” Digitech, 758 F.3d at 1348–49, 111 USPQ2d at 1719 (quoting Burr v. Duryee, 68 U.S. 531, 570, 17 L. Ed. 650, 657 (1863));
Regarding Claims 1-6
Step 2A – Prong 1
Exemplary claim 1 is directed to an abstract idea of predicting a sea surface temperature.
The abstract idea is set forth or described by the following italicized limitations:
1. A tropical instability wave early warning method based on temporal-spatial cross-scale attention fusion, comprising the following steps:
up-sampling and down-sampling temporal-spatial data of sea surface temperatures by convolutional and deconvolutional networks based on two-dimensional sea surface temperature images at all moments and all positions to generate multi-scale spatial data;
inputting the multi-scale spatial data into corresponding branch networks to calculate feature maps under corresponding scales, and calculating a regularization loss;
performing cross-scale spatial map fusion on the multi-scale feature maps by a bilateral local attention mechanism, generating a global feature description map,
calculating a prediction loss by the global feature description map, and combining the prediction loss and the regularization loss for optimization training of neural networks; and
predicting a sea surface temperature at a moment T based on the optimally trained neural networks, selecting data at K moments before the moment T and inputting the data into the optimally trained neural networks,
outputting a predicted value of tropical instability waves by the optimally trained neural networks, and drawing a temporal-spatial image of the tropical instability waves by associating the predicted value with coordinates, and
automatically generating an early-warning output by computing a tropical instability wave index from the predicted value, comparing the tropical instability wave index with a predetermined threshold, and providing a georeferenced temporal- spatial image and an alert signal when the threshold is crossed, so as to achieve early warning of the tropical instability waves.
The italicized limitations above represent a combination of mathematical concepts (i.e., a process that can be performed by mathematical relationships or rules or idea) and a mental step (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment). Therefore, the italicized limitations fall within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 Revised Patent Subject Matter Eligibility Guidance.
For example, the limitations “calculate feature maps [..]; calculating a regularization; performing cross-scale spatial map [..];calculating a prediction loss […]; predicting a sea surface temperature […]; outputting a predicted value [..] ; automatically generating an early-warning output[..]” are mathematical concepts (i.e., a process that can be performed by mathematical relationships or rules or idea)
For example, the limitation “selecting data at K moments [..]; drawing a temporal-spatial image [..]” are combination of mathematical concepts (i.e., a process that can be performed by mathematical relationships or rules or idea) and a mental step (i.e., a process that can be performed by can be performed mentally and/or with pen and paper or a mental judgment), see 2106.04(a)(2). Limitations are considered together as a single abstract idea for further analysis. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)).
Step 2A – Prong 2
Claims 1 does not include additional elements (when considered individually, as an ordered combination, and/or within the claim as a whole) that are sufficient to integrate the abstract idea into a practical application.
For example, first additional first element is “up-sampling and down-sampling temporal-spatial data of sea surface temperatures by convolutional and deconvolutional networks based on two-dimensional sea surface temperature images at all moments and all positions to generate multi-scale spatial data; inputting the multi-scale spatial data into corresponding branch networks; inputting the data into the optimally trained neural networks; selecting data at K moments before the moment T and inputting the data into the optimally trained neural networks ” to be performed, at least in-part, these additional elements appear to only add insignificant extra-solution activity (e.g., data gathering) and only generally link the abstract idea to a particular field. Therefore, this element individually or as a whole does not provide a practical application. See MPEP 2106.05(g)
The 2nd additional element is “optimally trained neural networks”. This element amounts to mere use of a generic computer system with high level of generality, which is well understood routine and conventional (see background of current discloser and IDS and PTO 892) and this element individually does not provide a practical application. In view of the above, the “additional element” individually or combine does not provide a practical application of the abstract idea. see MPEP 2106.05(d).
In view of the above, the three “additional elements” individually do not provide a practical application of the abstract idea. Furthermore, the “additional elements” in combination amount to a plurality of generic component with software, where such computers and software amount to mere instructions to implement the abstract idea on a computer(s) and/or mere use of a generic computer component(s) as a tool to perform the abstract idea. Therefore, these elements in combination do not provide a practical application. The combination of additional elements does no more than generally link the use of the abstract idea to a particular technological environment, and for this additional reason, the combination of additional elements does not provide a practical application of the abstract idea.
.
Step 2B
Claims1 does not include additional elements, when considered individually and as an ordered combination, that are sufficient to amount to significantly more than the abstract idea. The reasons for reaching this conclusion are substantially the same as the reasons given above in § Step 2A – Prong 2. For brevity only, those reasons are not repeated in this section. See MPEP §§ 2106.05(g) and MPEP §§2106.05(II).
.
Dependent Claims 3-6
Dependent claims 3-6 fail to cure this deficiency of independent claim 1 (set forth above) and are rejected accordingly. Particularly, claims 2-6 recite limitations that represent (in addition to the limitations already noted above) either the abstract idea or an additional element that is merely extra-solution activity, mere use of instructions and/or generic computer component(s) as a tool to implement the abstract idea, and/or merely limits the abstract idea to a particular technological environment.
For example, the limitations of Claims 3-6 are a combination of mathematical steps with mental steps.
Dependent Claims 2 and 10
Regarding claims 2 and 10, claim contain a language which is patent eligible.
Claims 7-9, 11-19 Claims 7-9, 11-19 contains language similar to claims 1, 3-6 as discussed in the preceding paragraphs, and for reasons similar to those discussed above, claims 7-9 and 11-19 are also rejected under 35 U.S.C. § 101(abstract idea).
.
.
Allowable Subject Matter
Claims 1 and 7-9 are allowable if overcome current 101 rejection.
The following is a statement of reasons for the indication of allowable subject matter: The subject matter of claims 1 and 7-9 are allowable because the closest prior art (see attached PTO-892) fails to disclose or render obvious the limitations of “
“up-sampling and down-sampling temporal-spatial data of sea surface temperatures by convolutional and deconvolutional networks based on two-dimensional sea surface temperature images at all moments and all positions to generate multi-scale spatial data; inputting the multi-scale spatial data into corresponding branch networks to calculate feature maps under corresponding scales, and calculating a regularization loss; performing cross-scale spatial map fusion on the multi-scale feature maps by a bilateral local attention mechanism, generating a global feature description map, calculating a prediction loss by the global feature description map, and combining the prediction loss and the regularization loss for optimization training of neural networks; and predicting a sea surface temperature at a moment T based on the optimally trained neural networks, selecting data at K moments before the moment T and inputting the data into the optimally trained neural networks, outputting a predicted value of tropical instability waves by the optimally trained neural networks, and drawing a temporal-spatial image of the tropical instability waves by associating the predicted value with coordinates, so as to achieve early warning of the tropical instability waves.”.
Response to Argument
Applicant’s arguments with respect 101 rejection, specially claim 1, the applicant did not agree with it, see pages 12-16.the Applicant argus that “claim 1 is directed to a concrete, processor-implemented technical method for early warning of tropical instability waves based on temporal-spatial cross-scale attention fusion, not to a disembodied mathematical formula or mental process.”, “independent claim 1 is directed to a specific processor-implemented neural-network-based early-warning technique for tropical instability waves, rather than to an abstract mental process or pure mathematical relationships. The same reasoning applies to the other independent and dependent claims, which further refine this same concrete technical implementation.”, “The claimed method therefore transforms large-scale SST data into actionable TIW early-warning information used in real-world decision-making, rather than merely performing calculations for their own sake” and as such claim is paten eligible.
In response, the Examiner respectfully disagree because current 101 rejection based on 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (examples 47-49). outputting the result from the” optimally trained neural networks” using “up-sampling and down-sampling temporal-spatial data of sea surface temperatures by convolutional and deconvolutional networks based on two-dimensional sea surface temperature images at all moments and all positions to generate multi-scale spatial data; inputting the multi-scale spatial data into corresponding branch networks; inputting the data into the optimally trained neural networks; selecting data at K moments before the moment T and inputting the data into the optimally trained neural networks and optimally trained neural networks ”are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, this limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
Furthermore, reciting “using the optimally trained neural networks; herein the neural networks is specifically configured for calculate feature maps [..]; calculating a regularization; performing cross-scale spatial map [..];calculating a prediction loss […]; predicting a sea surface temperature […]; outputting a predicted value [..] ; automatically generating an early-warning output[..]”; “selecting data at K moments [..]; drawing a temporal-spatial image [..] ” provide nothing more than mere instructions to implement an abstract idea on a generic computer component with high level of generality and “optimally trained neural networks, up-sampling and down-sampling temporal-spatial data of sea surface temperatures by convolutional and deconvolutional networks based on two-dimensional sea surface temperature images at all moments and all positions to generate multi-scale spatial data; inputting the multi-scale spatial data into corresponding branch networks; inputting the data into the optimally trained neural networks; selecting data at K moments before the moment T and inputting the data into the optimally trained neural networks and optimally trained neural networks”, this element amounts to mere use of data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05. In view of the above “additional elements” individually does not provide a practical application of the abstract idea. See, MPEP §§2106.05(a) . See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. The judicial exception of “predicting a sea surface temperature” and “to generate output result” is performed “using the trained ANN.” The trained ANN is used to generally apply the abstract idea without placing any limits on how the trained ANN functions. Rather, these limitations only recite the outcome of “detecting one or more status” and “analyzing it” and do not include any details about how the “detecting” and “analyzing” are accomplished. See MPEP 2106.05(f). As such 101 rejection is maintained.
Applicant’s arguments with respect to claim(s) 112(f) have been considered but they are not persuasive. Regarding applicant’s 112(f) arguments, examiner notes that as neither of “If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph” have been made, the interpretation is maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
a) Gaitan et al. (US 11,537,889) disclose methods and systems for training a neural network (NN)-based climate forecasting model on a pre-processed multi-model ensemble of global climate simulation data from a plurality of global climate simulation models (GCMs), are disclosed. The methods and systems perform steps of determining a common spatial scale and a common temporal scale for the multi-model ensemble of global climate simulation data; spatially re-gridding the multi-model ensemble to the common spatial scale; temporally homogenizing the multi-model ensemble to the common temporal scale; augmenting the spatially re-gridded, temporally homogenized multi-model ensemble with synthetic simulation data generated from the spatially re-gridded, temporally homogenized multi-model ensemble; and training the NN-based climate forecasting model using the spatially re-gridded, temporally homogenized, and augmented multi-model ensemble of global climate simulation data. Embodiments of the present invention enable accurate climate forecasting without the need to run new dynamical global climate simulations on supercomputers.
b)Tocornal et al. (US 10, 871,594) disclose Methods and systems for generating a neural network (NN)-based climate forecasting model are disclosed. The methods and systems perform steps of generating a multi-model ensemble of global climate simulation data by combining simulation data from at least two global climate simulation models; pre-processing the multi-model ensemble of global climate simulation data, where the pre-processing comprises at least one action of spatial re-gridding, temporal homogenization, and data augmentation; training the NN-based climate forecasting model on the pre-processed multi-model ensemble of global climate simulation data; and validating the NN-based climate forecasting model using observational historical climate data. Embodiments of the present invention enable accurate climate forecasting without the need to run new dynamical global climate simulations on supercomputers. Also disclosed are benefits of the new methods, and alternative embodiments of implementation.
c) Liang et al. (US 2003/0225715) disclose A neural network learning process provides a trained network that has good generalization ability for even highly nonlinear dynamic systems, and is trained with approximations of a signal obtained, each at a different respective resolution, using wavelet transformation. Approximations are used in order from low to high. The trained neural network is used to predict values. In a preferred embodiment of the invention, the trained neural network is used in predicting network traffic patterns.
d) Cook (US 2022/0343221) disclose Extended-range predictions of severe weather (to monthly and even yearly timescales) do not exist at this time. Sparrow and Mercer (2015) used El Niño Southern Oscillation and geopotential height variability, stepwise multivariate linear regression, and support vector regression to diagnose predictability of U.S. winter tornado seasons. Nath et al. (2015) also developed and described a model using neural networks to predict seasonal tropical cyclone activity over the North Indian Ocean. “Feature intensification”, if intensity fluctuation of a tropical cyclone within the next 24 hours is desired, 24-hour intensity fluctuations can be derived from prior observations of tropical cyclone intensity and stored in an array by the machine learning service 102 for use in later steps.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD K ISLAM whose telephone number is (571)270-0328. The examiner can normally be reached M-F 9:00 a.m. - 5:00 p.m..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A Turner can be reached at 571-272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMMAD K ISLAM/Primary Examiner, Art Unit 2857