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
Application 17/637,251 filed on 02/22/2022, is a 371 of PCT/KR2020/01 1052, filed on 08/19/2020, and claims priority to REPUBLIC OF KOREA 10-2019-0101069 filed on 08/19/2019.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/26/2026 has been entered.
Response to Amendment
This office action is in response to amendments submitted on 03/26/2026 wherein claims 1-9 and 11-19 are pending and ready for examination. Claim 10 was previously canceled.
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. The claims recite an abstract idea as discussed below. This abstract idea is not integrated into a practical application for the reasons discussed below. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons discussed below.
Step 1 of the 2019 Guidance requires the examiner to determine if the claims are to one of the statutory categories of invention. Applied to the present application, the claims belong to one of the statutory classes of a process or product as a computer implemented method or a computer system/product.
Step 2A of the 2019 Guidance is divided into two Prongs. Prong 1 requires the examiner to determine if the claims recite an abstract idea, and further requires that the abstract idea belong to one of three enumerated groupings: mathematical concepts, mental processes, and certain methods of organizing human activity.
Claim 1 is copied below, with the limitations belonging to an abstract idea being underlined.
A training indicators optimizing apparatus implemented using a combination of hardware and software, comprising:
a memory configured to store computer-readable instructions; and
one or more processors configured to execute the computer-readable instructions to:
determine a base dataset on environmental measurement data;
determine and extract dynamic features for the constructed base dataset through multi-resolution wavelet analysis and a dimensionality reduction technique;
determine potential environmental driving forces dominating fluctuation of the environmental measurement data based on the extracted dynamic features and select a key feature group in response to the evaluation result; and
process the selected key feature group and the environmental measurement data as inputs to build along-short term memory network model using the key feature group as input features, pre-quantify a plurality of training indicators based on a time-frequency domain of the selected key feature group, and adjust a plurality of training indicators corresponding to an environmental prediction model in accordance with the pre-quantified values to optimize training performance of the environmental prediction model.
Claim 14 is copied below, with the limitations belonging to an abstract idea being underlined.
A method of optimizing training indicators, the method comprising executing, by one or more processors of a training indicators optimizing apparatus, computer-readable instructions stored in a memory to:
determining, by the one or more processors, a base dataset on environmental measurement data;
determining and extracting, by the one or more processors, dynamic features for the constructed base dataset through multi-resolution wavelet analysis and a dimensionality reduction technique;
determining, by the one or more processors, potential environmental driving forces dominating fluctuation of the environmental measurement data based on the extracted dynamic features from the key feature group selector and selecting a key feature group in response to the evaluation result; and
by the one or more processors, the selected key feature group and the environmental measurement data as inputs to build a long-short term memory network model using the key feature group as input features, pre-quantify a plurality of training indicators based on a time-frequency domain of the selected key feature group, and adjust a plurality of training indicators of an environmental prediction model in accordance with the pre-quantified values to optimize training performance of the environmental prediction model.
Claim 19 is copied below, with the limitations belonging to an abstract idea being underlined.
A training indicators optimizing apparatus implemented using a combination of hardware and software, comprising:
a memory configured to store computer-readable instructions; and
one or more processors configured to execute the computer-readable instructions to:
determine a base dataset on environmental measurement data;
determine and extract dynamic features for the constructed base dataset through multi-resolution wavelet analysis and a dimensionality reduction technique;
determine a multi-resolution correlation between potential environmental driving forces and the environmental measurement data and, in doing so, perform correlation determination that reflects time delay and phase change between the potential environmental driving forces and the observed data and select a maximum correlation scale between the potential environmental driving forces and the observed data based on the performed correlation determination result, and
select a key feature group based on the selected maximum correlation scale, and process the selected key feature group and the environmental measurement data as inputs to build along-short term memory network model using the key feature group as input features, pre-quantify a plurality of training indicators based on a time-frequency domain of the selected key feature group, and adjust the plurality of training indicators of an environmental prediction model in accordance with the pre-quantified values to optimize training performance of the environmental prediction model.
The limitations underlined can be considered to describe a series of mathematical concepts where “determine,” “build,” “pre-quantify,” and “adjust” may include a series of calculations leading to one or more numerical results or answers, obtained by a sequence of mathematical operations on numbers. The lack of a specific equation in the claim merely points out that the claim would monopolize all possible appropriate equations/two-group significance tests for accomplishing this purpose in all possible systems. These steps recited by the claim therefore amount to a series of mental and/or mathematical steps, making these limitations amount to an abstract idea.
Regarding the underlined limitation “determine/determining a base dataset on environmental measurement data,” (claims 1, 14, and 19), it is an abstract idea as it is a set of programming routines and patterns for constructing a dataset. It is an algorithm or program which is a mathematical routine.
Regarding the underlined limitation “determine/determining and extract/extracting dynamic features for the constructed base dataset through multi-resolution wavelet analysis and a dimensionality reduction technique” (claims 1, 14, and 19), it is an abstract idea as it is a set of programming routines and patterns for determining and extract dynamic features using a well known multi-resolution wavelet analysis and well-known dimensionality reduction technique. It is an algorithm or program which is a mathematical routine.
Regarding the underlined limitation “determine/determining potential environmental driving forces dominating fluctuation of the environmental measurement data based on the extracted dynamic features (“from the key feature group selector” (claim 14)) and select/selecting a key feature group in response to the evaluation result,” (claim 1 and 14) it is an abstract idea as it is a set of programming routines and patterns for determining driving forces based on extracted dynamic features and selecting a key feature group. Therefore they are algorithms or programs which are mathematical routines.
Regarding the underlined limitation “determine a multi-resolution correlation between potential environmental driving forces and the environmental measurement data and, in doing so, perform correlation determination that reflects time delay and phase change between the potential environmental driving forces and the observed data and select a maximum correlation scale between the potential environmental driving forces and the observed data based on the performed correlation determination result,” (claim 19) it is an abstract idea as it is a set of programming routines and patterns for determining a correlation by performing correlation determination and selecting a maximum correlation scale based on the results of the correlation determination therefore it is an algorithm or program which is a mathematical routine.
Regarding the underlined limitation “process/processing the selected key feature group and the environmental measurement data as inputs to build a long-short term memory network model using the key feature group as input features, pre-quantify a plurality of training indicators based on a time-frequency domain of the selected key feature group, and adjust a plurality of training indicators corresponding to an environmental prediction model in accordance with the pre-quantified values to optimize training performance of the environmental prediction model” (claim 1, 14, and 19), it is an abstract idea as it is a set of programming routines and patterns for processing data and building a well known long-short term memory network model, pre-quantifying training indicators, and adjusting training indicators to a prediction model with the pre-quantified values optimizing the training performance of the prediction model. Therefore, it is an algorithm or program which is a mathematical routine.
In summary, the highlighted steps in the claims above therefore recite an abstract idea at Prong 1 of the 101 analysis.
The additional elements in the claim have been left in normal font. This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claims 1, 14, and 19 recite memory for storage and one or more processors. The specification on page 46 line 9-page 47 line 1 supports a general purpose computer which would include a processor and memory. Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data and data processing devices to apply the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2b of the 2019 Guidance requires the examiner to determine whether the additional elements cause the claim to amount to significantly more than the abstract idea itself. The considerations for this particular claim are essentially the same as the considerations for Prong 2 of Step 2a, and the same analysis leads to the conclusion that the claim does not amount to significantly more than the abstract idea.
The claims do not integrate the abstract idea into a practical application. Various considerations are used to determine whether the additional elements are sufficient to integrate the abstract idea into a practical application. The claims does not recite a particular machine applying or being used by the abstract idea. The claims do not effect a real-world transformation or reduction of any particular article to a different state or thing. (Manipulating data from one form to another or obtaining a mathematical answer using input data does not qualify as a transformation in the sense of Prong 2.)
The claims do not contain additional elements which describe the functioning of a computer, or which describe a particular technology or technical field, being improved by the use of the abstract idea. (This is understood in the sense of the claimed invention from Diamond v Diehr, in which the claim as a whole recited a complete rubber-curing process including a rubber-molding press, a timer, a temperature sensor adjacent the mold cavity, and the steps of closing and opening the press, in which the recited use of a mathematical calculation served to improve that particular technology by providing a better estimate of the time when curing was complete. Here, the claim does not recite carrying out any comparable particular technological process.) In all of these respects, the claim fails to recite additional elements which might possibly integrate the claim into a particular practical application. Instead, based on the above considerations, the claim would tend to monopolize the abstract idea itself, rather than integrate the abstract idea into a practical application.
Therefore, claims 1, 14, and 19 are rejected under 35 U.S.C. 101 as directed to an abstract idea without significantly more.
Dependent claims 2-9, 11-13, and 15-18 are similarly ineligible. The dependent claims merely add limitations which further detail or limit the abstract idea with limitations such as:
“the environmental measurement data comprises hydrological-environmental time series data measured in real time, the hydrological-environmental time series data comprising at least one environmental data of hydrometeorological data, river water level data, groundwater level data, water quality data, temperature data, electrical conductivity (EC) data, isotope ratio data, soil gas data and fine dust data” (claim 2),
“interpolates missing data for each time domain resolution or time observation interval for the arranged base dataset, noise-filters data of the interpolated base dataset, and standardizes and normalizes the noise-filtered results” (claim 3),
“derives wavelet energy distribution data on a time-frequency domain through the multi-resolution wavelet analysis according to a time domain resolution for the constructed base dataset and selects potential environmental drivers (PEDs) by applying the dimensionality reduction technique to the derived wavelet energy distribution data,” (claim 4),
“extracts variation features according to a time change for each time domain resolution of the selected PEDs and extracts and quantify the dynamic features based on the extracted variation features” (claim 5),
“the dimensionality reduction technique comprises at least one technique of principal/independent component analysis (PCA/ICA), time series factor analysis (TSFA), empirical mode decomposition (EMD) and multi-resolution state-space model (MRSSM)” (claim 6),
“determines a multi-resolution correlation between potential environmental driving force of the PEDs and the environmental measurement data and, in doing so, performs correlation determination that reflects time delay and phase change between the potential environmental driving force and the observed data and selects a maximum correlation scale between the potential environmental driving force and the observed data based on the performed correlation determination result” (claim 7),
“determine the potential environmental driving force using a correlation between a wavelet energy ratio between the potential environmental driving forces and the observed data and the selected maximum correlation scale, determine relative contribution by processing linear coupling between a binding energy ratio of the selected maximum correlation scale and an explanatory power index of a dimensionality reduction model, and selects the key feature group based on the evaluated relative contribution” (claim 8),
“build a pre-tuned long-short term memory (LSTM) network that is trained using at least one of the PEDs and the key feature group, and verify the potential environmental driving force using the pre-tuned LSTM network” (claim 9),
“select at least one predictive model based on residual verification, based on complex model verification indicators of observations measured from the environmental measurement data and values predicted from the long-short term memory network model, and multi- resolution analysis of the residuals, and quantify the plural pre-quantified training indicators based on one predictive model of the selected predictive models or a combined prediction model of two or more predictive models of the selected predictive models” (claim 11),
“determine an optimal training indicator model using at least two training indicators of the plural quantified training indicators” (claim 12),
“the plural training indicators comprise (at least one of (claim 18)) a training period (T), a minibatch size (mbs), the number of hidden layers (HL) and the number of optimal epochs (E)” (claim 13 and 18).
“deriving wavelet energy distribution data on a time-frequency domain through the multi-resolution wavelet analysis according to a time domain resolution for the constructed base dataset and selecting potential environmental drivers (PEDs) by applying the dimensionality reduction technique to the derived wavelet energy distribution data; and extracting variation features according to a time change for each time domain resolution of the selected PEDs and extracting and quantifying the dynamic features based on the extracted variation features” (claim 15),
“performing correlation determination reflecting time delay and phase change between the potential environmental driving force and the observed data and selecting a maximum correlation scale between the potential environmental driving force and the observed data based on the performed correlation determination result; and identifying the potential environmental driving forces using a correlation between a wavelet energy ratio between the potential environmental driving force and the observed data and the selected maximum correlation scale, evaluating a relative contribution by processing linear coupling between a binding energy ratio of the selected maximum correlation scale and an explanatory power index of the dimensionality reduction model, and selecting the key feature group based on the evaluated relative contribution” (claim 16),
“selecting at least one predictive model based on residual verification, based on complex model verification indicators of observations measured from the environmental measurement data and values predicted from the long-short term memory network model, and multi-resolution analysis of the residuals, and post-quantifying the plural pre- quantified training indicators based on one predictive model of the selected predictive models or a combined prediction model of two or more predictive models of the selected predictive models; and constructing an optimal training indicator model using at least two training indicators of the plural quantified training indicators to optimize the plurality of training indicators for use in the environmental prediction model” (claim 17).
which do not help to integrate the claim into a practical application or make it significant more than the abstract idea (which is recited in slightly more detail, but not in enough detail to be considered to narrow the claim to a particular practical application itself).
Claims 3-9 and 11-12 recite one or more processors. The specification on page 46 line 9-page 47 line 1 supports a general purpose computer including a processor which is only recited as a tool for performing steps of the abstract idea, such as data processing devices to apply the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Considering all the limitations individually and in combination, the claimed additional elements do not show any inventive concept to applying algorithms such as improving the performance of a computer or any technology, and do not meaningfully limit the performance of the application.
Response to Arguments
Applicant’s arguments (remarks) filed on 03/26/2026 have been fully considered.
Regarding Claim Rejections 35 U.S.C. § 112(b) page 9-10 of Applicant’s remarks, Examiner acknowledges Applicant’s arguments and due to Applicant’s arguments and changes made to the claims the 35 U.S.C. § 112(b) have been withdrawn.
Regarding Rejections under 35 U.S.C. § 101 page 10-12 of Applicant’s remarks. Examiner acknowledges Applicant’s arguments and agrees the steps of the claims “cannot practically be performed in the human mind” (remarks page 10) however, the steps are a set of programming routines and patterns therefore are algorithms or programs which are mathematical routines. Moreover, the claims do not recite an improvement in a computer related technology or an improvement in the way a computer operates. Therefore the claim language amounts to no more than an algorithm run on a generic computer to determine a result. The claim language needs to disclose more than a mathematical algorithm run on a generic computer it is just applying new data to a known algorithm which is supported by Recentive Analytics, Inc. v. Fox Corp., Case No. 2023-2437 (Fed. Cir. Apr. 18, 2025) wherein the mere use of machine learning to complete a specific task does not automatically quality the claim as non-abstract under Step 2A Prong 1, stating “the claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans
with greater speed and efficiency than could previously be achieved” (id. p. 15)
Applicant's arguments concerning the specification are not germane as according to MPEP 2145 VI, "Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims." Also stated in MPEP 2111.01 II "Though understanding the claim language may be aided by explanation contained in the written description, it is important not to import into claim limitations that are not part of the claim" (1st paragraph). Additionally, MPEP 2111 states that "an examiner must construe claim terms in the broadest reasonable manner during prosecution as is reasonable allowed in an effort to establish a clear record of what applicant intends to claim" (4th paragraph). Additionally, the claim language must include more than mere instructions to perform the method on a generic computer or machinery to qualify as an improvement to an existing technology (MPEP § 2106.05(f).)
The new limitations give little detail as to the adjusting. They broadly and generically recite building a model and selecting input without saying how the building or selecting is done (please see Longitude Licensing Ltd v. Google LLC (Fed Cir, 2024-1202, 4/30/2025)) therefore the proposed amendments do not overcome the § 101 rejection. Additionally, Applicant's arguments are not in keeping with the claims as no sensors and real time data collections are recited in the claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Denise R Karavias whose telephone number is (469)295-9152. The examiner can normally be reached 7:00 - 3:00 M-F.
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/DENISE R KARAVIAS/Examiner, Art Unit 2857
/ARLEEN M VAZQUEZ/Supervisory Patent Examiner, Art Unit 2857
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