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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on October 25, 2023 and May 05, 2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP2023-044642, filed on 03/20/2023.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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. Below is a claim-by-claim analysis.
Claim 1, 18, 20
Step 1: Recites a system (claim 1), a method (claim 18) and a non-transitory computer readable medium. Therefore, they are directed to the statutory categories of invention.
Step 2A Prong 1: The claim recites:
calculate a forecasting value of a target variable that is a forecasting target;
calculate a first forecasting residual amount that is a deviation of the forecasting value by using second learning data;
These are generic and basic mathematical calculations that can be done in one’s head.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The remaining limitations of the claim are directed to insignificant extra-solution activity (“input first learning data including…”) or generic to the point of not being significantly more than the judicial exception (“construct a second model for predicting…”).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis mirrors
the analysis of step 2A prong 2.
Claim 2, 19
Step 1: Recites a system (claim 2), a method (claim 19). Therefore, they are directed to the statutory categories of invention.
Step 2A Prong 1: The claim recites the abstract idea it inherits from the claim it depends on.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The remaining limitations of the claim are directed to additional components of the insignificant extra-solution activity (“select one or two more first models…”, “the first learning data is input to the…”).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis mirrors
the analysis of step 2A prong 2.
Claim 3
Step 1: Recites a system (claim 3). Therefore, it is directed to the statutory categories of invention.
Step 2A Prong 1: The claim recites the abstract idea it inherits from the claim it depends on.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The remaining limitations of the claim are directed to additional components of the insignificant extra-solution activity (“…calculated based on the first learning data…”) or the abstract idea (“the first forecasting residual amount is calculated based on the forecasting value calculated…”).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis mirrors
the analysis of step 2A prong 2.
Claim 4
Step 1: Recites a system (claim 3). Therefore, it is directed to the statutory categories of invention.
Step 2A Prong 1: The claim recites the abstract idea it inherits from the claim it depends on. It also recites:
calculate a contribution amount with respect to the forecasting value for each of the two or more first models
Considered a generic mathematical calculation that is considered an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The remaining limitations of the claim are directed to additional components of the abstract idea (“the first forecasting residual amount is calculated based on a target variable…”).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis mirrors
the analysis of step 2A prong 2.
Claim 5
Step 1: Recites a system (claim 5). Therefore, it is directed to the statutory categories of invention.
Step 2A Prong 1: The claim recites the abstract idea it inherits from the claim it depends on.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The remaining limitations of the claim are directed to additional components of the abstract idea (“the first forecasting residual amount is calculated by subtracting each value…”).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis mirrors
the analysis of step 2A prong 2.
Claim 6
Step 1: Recites a system (claim 6). Therefore, it is directed to the statutory categories of invention.
Step 2A Prong 1: The claim recites the abstract idea it inherits from the claim it depends on.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The remaining limitations of the claim are directed to additional components of the insignificant extra-solution activity (“the time-series data is divided into a plurality of characteristic sections…”) or the abstract idea (“a contribution amount of the first model is calculated for each…”, “the first forecasting residual amount is calculated for each of the two or more first models…”).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis mirrors
the analysis of step 2A prong 2.
Claim 7
Step 1: Recites a system (claim 7). Therefore, it is directed to the statutory categories of invention.
Step 2A Prong 1: The claim recites the abstract idea it inherits from the claim it depends on. It also recites:
correlation coefficient is calculated between the forecasting value calculated by the first model and the target variable for each of the characteristic sections, and the contribution amount is calculated based on the calculated correlation coefficient
Considered a mathematical calculation, and thus an abstract idea.Step 2A Prong 2: There are no remaining limitations in the claim.
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis mirrors
the analysis of step 2A prong 2.
Claim 8
Step 1: Recites a system (claim 8). Therefore, it is directed to the statutory categories of invention.
Step 2A Prong 1: The claim recites the abstract idea it inherits from the claim it depends on.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The remaining limitations of the claim are directed to additional components of the abstract idea (“contribution amount is increased as the correlation coefficient increases…”).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis mirrors
the analysis of step 2A prong 2.
Claim 9
Step 1: Recites a system (claim 9). Therefore, it is directed to the statutory categories of invention.
Step 2A Prong 1: The claim recites the abstract idea it inherits from the claim it depends on. It also includes:
predict the target variable based on the learned second model
Prediction is considered a type of abstract idea that can be done in one’s mind.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The remaining limitations are considered merely linking the cited judicial exception(s) (“perform a model learning process”).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis mirrors
the analysis of step 2A prong 2.
Claim 10
Step 1: Recites a system (claim 10). Therefore, it is directed to the statutory categories of invention.
Step 2A Prong 1: The claim recites the abstract idea it inherits from the claim it depends on
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The remaining limitations of the claim are directed to additional components of the insignificant extra-solution activity (“input first forecasting data corresponding to the first learning…”, “input second forecasting data corresponding to the second learning data…”) or the abstract idea (“calculate a forecasting vale of the target variable…”, “calculates, by the second model, a second forecasting residual amount…”).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis mirrors
the analysis of step 2A prong 2.
Claim 11
Step 1: Recites a system (claim 10). Therefore, it is directed to the statutory categories of invention.
Step 2A Prong 1: The claim recites the abstract idea it inherits from the claim it depends on.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The remaining limitations of the claim are directed to additional components of the abstract idea (“the forecasting value of the target variable is calculated based on the first forecasting data corresponding to each of the two or more first models”).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis mirrors
the analysis of step 2A prong 2.
Claim 12
Step 1: Recites a system (claim 10). Therefore, it is directed to the statutory categories of invention.
Step 2A Prong 1: The claim recites the abstract idea it inherits from the claim it depends on. It also recites:
identify the first forecasting data into a plurality of characteristic sections
Identification in this context can be considered classification, which is an abstract idea that can be done in one’s mind
extract a contribution amount of the first model for each of the plurality of characteristic sections
Can be interpreted as gauging the accuracy of the prediction over each section, which can be considered as a mental process that can be done in one’s mind
target variable is forecasted for each of the two or more first models based on the contribution amount of the first model for each of the characteristic sections
A forecast is a type of prediction, which is an abstract idea that can be done in one’s mind
Step 2A Prong 2: The judicial exception is not integrated into a practical application. There are no further limitations in the claim.
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis mirrors
the analysis of step 2A prong 2.
Claims 13-16
Step 1: Recites a system. Therefore, they are directed to the statutory categories of invention.
Step 2A Prong 1: The claim recites the abstract idea it inherits from the claim it depends on.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. All four claims generically and generally link the judicial exception to a field of use (dam inflow, wind power, river flow, weather data), which is not significantly enough.
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis mirrors
the analysis of step 2A prong 2.
Claim 17
Step 1: Recites a system. Therefore, they are directed to the statutory categories of invention.
Step 2A Prong 1: The claim recites the abstract idea it inherits from the claim it depends on.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The limitations of the claim are directed to additional components of the insignificant extra-solution activity (“store the divided first learning data”, “store the divided second learning data”), which are then applied by the judicial exception (“the forecasting value of the target variable is calculated based on the stored first learning data”,” the first forecasting residual amount is calculated by using the stored second learning data”).
Step 2B: The claim does not contain significantly more than the judicial exception. The analysis mirrors
the analysis of step 2A prong 2.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 9-11, 17-20 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Takezawa et al. (“US 20050096758”).
Regarding claim 1, Takezawa teaches an information processing apparatus (Abstract) comprising processing circuitry, the processing circuitry configured to:
input first learning data (Paragraph 33-34) including time-series data (Figure 71) to a first model (Paragraph 34) and calculate a forecasting value of a target variable that is a forecasting target (Paragraph 36, “…subsequently the prediction apparatus receives a value x at a target point for prediction (step 6) and calculates prediction values M1(x)…”)
calculate a first forecasting residual amount that is a deviation of the forecasting value (Paragraph 38) by using second learning data (Paragraph 34)2
construct a second model for predicting the first forecasting residual amount by machine learning based on the second learning data and the first forecasting residual amount (Paragraph 34, “The prediction apparatus then creates models P1, P2, . . . , PQ by using the verification data (steps 4 to 5). As explained later, these models P1, P2, . . . , PQ are used to predict absolute values of errors of prediction values”)
Regarding claim 2, Takezawa teaches an information processing apparatus as claimed in claim 1, wherein the processing circuitry is further configured to:
select one or two or more first models (Paragraph 91) from a plurality of the first models (Paragraph 34, “…prediction models M1, M2, . . . , MQ…”),
wherein the first learning data is input to the selected one or two or more first models (Figure 1) to calculate the forecasting value of the target variable (see claim 1 analysis)
Regarding claim 3, Takezawa teaches an information processing apparatus as claimed in claim 2, wherein when two or more first models is selected, the forecasting value of the target variable is calculated based on the first learning data corresponding to each of the two or more first models (Figure 23)
Regarding claim 9, Takezawa teaches an information processing apparatus as claimed in claim 3, wherein the processing circuitry is further configured to:
perform a model learning process4 including inputting the first learning data, calculating the first forecasting residual amount, and constructing the second model (see claim 1 analysis)
predict the target variable based on the learned second model (Figure 15)
Regarding claim 10, Takezawa teaches an information processing apparatus as claimed in claim 9, wherein the processing circuitry is further configured to,
input first forecasting data (Figure 76) corresponding to the first learning data to the first model and calculates a forecasting value of the target variable by the first model (see claim 1-2 analysis);
input second forecasting data corresponding to the second learning data to the second model and calculates, by the second model, a second forecasting residual amount that is a deviation between the target variable and the calculated forecasting value (see claim 1-2 analysis);
predict the target variable based on the calculated forecasting value and the calculated second forecasting residual amount (see claim 1-2 analysis)
Regarding claim 11, Takezawa teaches an information processing apparatus as claimed in claim 10, wherein when the two or more first models are selected, the forecasting value of the target variable is calculated based on the first forecasting data corresponding to each of the two or more first models (see claim 3 analysis).
Regarding claim 17, Takezawa teaches an information processing apparatus as claimed in claim 10, wherein the processing circuitry is further configured to:
divide the learning data into the first learning data and the second learning data (Paragraph 33);
store the divided first learning data; and store the divided second learning data (Paragraph 43), wherein the forecasting value of the target variable is calculated based on the stored first learning data (Paragraph 43), the first forecasting residual amount is calculated by using the stored second learning data (Paragraph 43-46), and the second model is constructed by machine learning based on the stored second learning data and the first forecasting residual amount (Paragraph 47)
Claim 18 is a method claim corresponding to the system claim 1 and is rejected for the same
reasons as given in the rejection of that claim.
Claim 19 is a method claim corresponding to the system claim 2 and is rejected for the same
reasons as given in the rejection of that claim.
Claim 20 is a non-transitory computer readable medium claim corresponding to the system claim 1 and is rejected for the same reasons as given in the rejection of that claim.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Takezawa et al. (“US 20050096758”), in view of Sato et al. (“WO 2021250838”).
Regarding claim 4, Takezawa teaches the information processing apparatus as claimed in claim 3, and two or more first models (see claim 2 analysis). Takezawa fails to teach the calculation of a contribution amount with respect to the forecasting value, wherein the first forecasting residual amount is calculated based on a target variable included in the second learning data and a value obtained by multiplying the contribution amount corresponding to the forecasting value calculated by each of the two or more first models.
However, Sato teaches calculate a contribution amount with respect to the forecasting value (Page 3, Paragraph 5, “reliability7 α is calculated based on the time-series observation value… and the time-series residual) …, wherein the first forecasting residual amount is calculated based on a target variable included in the second learning data (Page 1, Abstract, “calculates a time-series residual, which is a difference between the time-series observation values and the time-series prediction values”) and a value obtained by multiplying the contribution amount corresponding to the forecasting value calculated (Page 4, Paragraph 2, “The residual regression unit 50 multiplies the reliability… by the basic prediction value… to obtain the modified basic prediction result) …8.
Takezawa and Sato are considered analogous to the invention because all are directed to forecasting and prediction apparatuses. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Takezawa to incorporate the teachings of Sato, and include a means of calculating the contribution amount of the models. Doing so reduces the spread of possible deterioration in the main prediction model (see Page 6 Paragraph 4 of Sato).
Regarding claim 5, Takezawa teaches the information processing apparatus as claimed in claim 4, and two or more first models (see claim 2 analysis). Takezawa fails to teach the first forecasting residual amount is calculated by subtracting each value obtained by multiplying the contribution amount corresponding to the forecasting value calculated… from the target variable included in the second learning data.
However, Sato teaches the first forecasting residual amount is calculated by subtracting each value obtained by multiplying the contribution amount corresponding to the forecasting value calculated (Page 1, Abstract, “a corrected time-series residual obtained by subtracting the corrected basic prediction result9 from the time-series observation values”)…10
Takezawa and Sato are considered analogous to the invention because all are directed to forecasting and prediction apparatuses. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Takezawa to incorporate the teachings of Sato, and include a means of calculating the contribution amount of the models. Doing so reduces the spread of possible deterioration in the main prediction model (see Page 6 Paragraph 4 of Sato).
Claims 6-8, 12 are rejected under 35 U.S.C. 103 as being unpatentable over Takezawa et al. (“US 20050096758”), in view of Sato et al. (“WO 2021250838”) and in further view of Paul et al. (“US 20220335271”).
Regarding claim 6, Takezawa teaches the information processing apparatus as claimed in claim 4, and two or more first models (see claim 2 analysis). Takezawa fails to teach the remaining limitations of the claim. However, Sato teaches a contribution amount of the first model is calculated,…11 and the first forecasting residual amount is calculated…12 based on the contribution amount of the first model (see claim 4 analysis). Sato fails to teach the further limitations of the claim. However, Paul teaches the time-series data (Paragraph 34) is divided into a plurality of characteristic sections (Paragraph 52, “data grouping device 3 may divide the variable into the data groups according to previous knowledge13)…. .14
Takezawa, Sato and Paul are considered analogous to the invention because all are directed to forecasting and prediction apparatuses. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Takezawa to incorporate the teachings of Paul, and divide the time-series data in characteristic sections. Doing so can reduce prediction error over peak values (see Paragraph 3 of Paul).
Regarding claim 7, Takezawa fails to teach the further limitations of the claim. However, Paul teaches a correlation coefficient15 is calculated between the forecasting value calculated by the first model and the target variable (Paragraph 63, “…calculates the evaluation score of the prediction model, based on the difference between the actual value and the predicted value of the sample”) for each of the characteristic sections (Paragraph 65, “…for each of combinations between one or more pieces of data grouping and one or more model architectures, and the evaluation score is calculated”). Paul fails to teach the contribution amount is calculated based on the calculated correlation coefficient. However, Sato teaches the contribution amount is calculated based on the calculated correlation coefficient (see claim 4 analyses16).
Takezawa, Sato and Paul are considered analogous to the invention because all are directed to forecasting and prediction apparatuses. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Takezawa to incorporate the teachings of Sato and Paul, and evaluate the accuracy of the prediction over the divided time-series data. Doing so can reduce prediction error over peak values (see Paragraph 3 of Paul).
Regarding claim 8, Takezawa fails to teach the further limitations of the claim. Sato teaches the contribution amount is increased as the correlation coefficient increases (see claim 4-7 analysis)17
Takezawa, Sato and Paul are considered analogous to the invention because all are directed to forecasting and prediction apparatuses. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Takezawa to incorporate the teachings of Sato and Paul, and adjust model contribution based on its prediction ability. Doing so can reduce prediction error over peak values (see Paragraph 3 of Paul).
Regarding claim 12, Takezawa teaches two or more first models (see claim 2 analysis). Takezawa fails to teach the further limitations of the claim. However, Sato teaches to extract a contribution amount of the first model…18 wherein the target variable is forecasted…19 based on the contribution amount of the first model… 20 (see claim 6 analysis). Sato fails to teach the further limitations of the claim. However, Paul teaches to identify the first forecasting data into a plurality of characteristic sections (see claim 6 analysis).
Takezawa, Sato and Paul are considered analogous to the invention because all are directed to forecasting and prediction apparatuses. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Takezawa to incorporate the teachings of Paul, and divide the time-series data in characteristic sections as well as include a means of calculating the contribution amount of the models. Doing so can reduce prediction error over peak values (see Paragraph 3 of Paul), and reduce the spread of possible deterioration in the main prediction model (see Page 6 Paragraph 4 of Sato).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Takezawa et al. (“US 20050096758”), in view of Luo et al. (“CN 112541839”, 2021), and in further view of Usutani et al. (“JP 2008185489”).
Regarding claim 13, Takezawa teaches the information processing apparatus as claimed in claim 10 (see claim 1 analysis). Takezawa fails to teach the target variable is a dam inflow amount, a tank model and a snow melting model are selected as the first model, and a dam outflow amount of the tank model and a dam outflow amount of the snow melting model are calculated.
However, Luo teaches the target variable is a dam inflow amount (Page 2, Paragraph 1, “invention relates to reservoir warehousing flow prediction”), a tank model… as the first selected model (Page 2, Paragraph 3, “a mathematical/physical model is usually designed to simulate the dynamics of the inflow/outflow”)…, and a dam outflow amount of the tank model and a dam outflow of the snow melting21…are calculated (Page 1, Paragraph 1, “…predicting the reservoir flow of the future multi-step reservoir according to the fusion result by the multi-layer sensing machine to obtain the reservoir flow predicted value”).
Luo fails to disclose the further limitations of the claim. However, Usutani teaches a snow melting model… as the first model (Page 1, Paragraph 1, “…This snow melt runoff prediction system…”), … and the snow melting model are[is] calculated (Page 1, Paragraph 1, “…a snow melt calculation section 320 calculates the water amount that flows down on a snow cover layer…”)
Takezawa, Luo and Usutani are considered analogous to the invention because all are directed to forecasting and prediction apparatuses. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Takezawa to incorporate the teachings of Luo and Usutani, and use the system for dam flow and snow melting rate forecasting. Doing so allows for more accurate dam flow forecasting (see Page 1, Paragraph 1 of Luo), and snow melting prediction (Page 19, Paragraph 1 of Usutani).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Takezawa et al. (“US 20050096758”), in view of Herzog (“US 20170206452”).
Regarding claim 14, Takezawa teaches the information processing apparatus as claimed in claim 10 (see claim 1 analysis). Takezawa fails to disclose the further limitations of the claim. However, Herzog teaches the target variable as a wind power generation amount, the first model is a power generation model of a wind turbine (Paragraph 29, “…a model may be configured to forecast the electrical power output of a wind turbine”), and a power generation amount of the power generation model of the wind turbine is calculated based on a wind speed (Paragraph 29, “event data may include meteorological forecast data22”) and a power generation curve (Paragraph 29, “…By overlying event data with time series data, the modeling system 108 may generate a forecast of total power output”23).
Takezawa and Suzuki are considered analogous to the invention because all are directed to forecasting and prediction apparatuses. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Takezawa to incorporate the teachings of Herzog, and take into account asynchronous and intermittent event data. Doing so allows for more accurate predictions in real world conditions (see Paragraph 1 of Herzog).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Takezawa et al. (“US 20050096758”), in view of Hu et al. (“CN 114201928”, 2022).
Regarding claim 15, Takezawa teaches the information processing apparatus as claimed in claim 10 (see claim 1 analysis). Takezawa fails to disclose the further limitations of the claim. However, Hu teaches the target variable is a river flow rate (Page 1, Paragraph 1, Abstract24), the first model is a hydrological model (Page 1, Paragraph 1, Abstract, “constructing a two-dimensional hydrodynamic model”25), and an outflow amount of the hydrological model is calculated (Page 3, Paragraph 1, “calculating the predicted flow field through26 the two-dimensional hydrodynamic model, so as to realize the simulation prediction.”).
Takezawa and Hu are considered analogous to the invention because all are directed to forecasting and prediction apparatuses. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Takezawa to incorporate the teachings of Suzuki, and use the system for river flow rate forecasting. Doing so allows for stronger more robust and efficient emergency response and decision management (see Page 1, Abstract of Hu).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Takezawa et al. (“US 20050096758”), in view of Michalakes (“HPC for Weather Forecasting”, 2020).
Regarding claim 16, Takezawa teaches the information processing apparatus as claimed in claim 10 (see claim 1 analysis). Takezawa fails to disclose the further limitations of the claim. However, Michalakes teaches the target variable is weather data (Page 297, Paragraph 1, “Numerical weather prediction”), weather research and forecasting (WRF) model (Page 308, Paragraph 1, “global version of the NCAR Weather Research and Forecast (WRF) model [41] is an overset mesh scheme comprised of two Cartesian meshes…”) and a computational fluid dynamics (CFD) model (Page 312, Paragraph 1, “Finite element and spectral element methods, used widely in aerospace and other applications of computational fluid dynamics, are being applied to weather modeling”) are selected as the first model, and weather data of the WRF model and weather data of the CFD model are calculated (Page 312, Paragraph 2, “Six dynamical cores from development teams in the USA were evaluated… NCAR’s MPAS…”27)
Takezawa and Michalakes are considered analogous to the invention because all are directed to forecasting and prediction apparatuses. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Takezawa to incorporate the teachings of Michalakes, and include the high-performance computing techniques present. Doing so allows for forecasting predictions that are more accurate (see Page 298, Paragraphs 1 -2, Figure 1 of Michalakes).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEWOS MESFIN whose telephone number is (571)270-0782. The examiner can normally be reached Monday-Friday 8am-5pm.
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, Cesar Paula, can be reached at (571) 272-4128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEWOS MESFIN/Examiner, Art Unit 2145
/CHAU T NGUYEN/Primary Examiner, Art Unit 2145
1 Examples of time-series data present
2 Second learning data understood as verification/testing data
3 The Data Storing unit stores all the various learning data, and the lines extending from the unit, as well as the label of Data specification can be interpreted as corresponding to the model they are feeding into
4 Interpreted similarly to model construction under BRI
5 Second model (Model P) used to make final prediction value
6 Time-series data for the means of prediction can be interpreted as forecasting data
7 Reliability can be interpreted as contribution amount, under BRI, given that it adjusts how much the prediction model contributes to the prediction
8 The claim language includes “for each of the two or more first models”, which isn’t taught by Sato/Paul. Those limitations are present in Takezawa (see above) and can be combined to cover the statement as a whole.
9 The basic prediction result is calculated by the residual regression unit, which multiplies the contribution amount (see claim 4 analysis)
10 See footnote 8
11 The claim language includes “for each of the characteristic sections”, which isn’t taught by Sato. Those limitations are present in Paul and can be combined to cover the statement as a whole.
12 See footnote 8
13 Previous knowledge can be interpreted as baseline for determining characteristic
14 See footnote 11
15 A value of prediction ability, as indicated by the evaluation score, can be interpreted as a correlation coefficient
16 Claim 4 teaches the calculation of a contribution amount, which is based on how well the prediction lines up with the actual value (Page 3, Paragraph 5-6 of Sato), which is equivalent to the evaluation score, and thus is taught by the combination of Sato and Paul
17 This claim is the logical conclusion of the above claims. If contribution amount (reliability in Sato) is tied to correlation amount (evaluation score in Paul), then given its construction, contribution increases as the correlation does.
18 See footnote 11
19 See footnote 8
20 See footnote 11
21 Luo doesn’t teach a snow melting model. It does, however it cites snow melting as one of many external factors of dam/reservoir flow (Page 1, Paragraph 2 of Luo). Thus, one can combine the snow melting model taught in Usutani, and incorporate its model to account for said factors in the system of dam flow taught in Luo.
22 Meteorological data typically includes wind speed and direction data
23 The previous time series data is understood as a historical record of power output, and thus acts as an empirical power generation curve
24 River flow field can function as river flow
25 The hydrodynamic model can function as a hydrological model
26 The word through lets us know we are calculating the outflow of the model as well
27 While it is considered with evaluation, evaluation implies testing the forecasting capabilities, and thus it follows that the weather data was calculated
Prosecution Insights