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 .
Response to Arguments
Applicant’s arguments, see pgs. 11-19, filed January 2, 2026, with respect to the claim objections and rejection(s) of claim(s) 1-25 under 35 U.S.C. 112(b), 35 U.S.C. 101, and 35 U.S.C. 103 have been fully considered and are discussed below.
Applicant argues on pg. 11, regarding the claim objections presented in the previous office action, that:
“Applicant herein amends claim 10 in accordance with the Examiner’s suggestion. As to claim 24, the Examiner states that a multiple dependent claim cannot depend from any other multiple dependent claim. However, claim 24 depends on any one of claims 23 to 23, and none of claims 21 to 23 is multiple dependent claim. Thus, this objection appears to be a mistake.”
In response, the examiner agrees and finds the arguments persuasive. Therefore the claim objections presented in the previous office action are withdrawn.
Applicant argues on pgs. 11-13, regarding the 35 U.S.C. 112(b) rejections presented in the previous office action, that:
“In view of the above, withdrawal of the outstanding rejections is respectfully requested.”
In response, the examiner finds the arguments persuasive and agrees. Therefore the 35 U.S.C. 112(b) rejections presented in the previous office action are withdrawn.”
Applicant argues on pgs. 13-15, regarding the 35 U.S.C. 101 rejection presented in the previous office action, that:
“It is submitted that improvement of computer functions or improvement of a computer technology is achieved by the present application.
More specifically, in the present application, paragraph [0002] of the specification describes “Therefore, it is desirable to be able to predict the occurrence of turbulence on an airline route in a short time period, with a low computational load, and with high accuracy.”
Moreover, paragraph [0005] describes “The method of using a simple model with an exact solution affects the accuracy of turbulence prediction, while the method of analyzing with a model close to the exact solution still causes the problem of increasing the computational load. Therefore, there is a demand for a system that predicts the occurrence of turbulence based on new data.”
The present subject matter takes advantage of “the turbulence prediction pattern data” regarding arbitrary meteorological parameters including the distributions of values of meteorological data regarding arbitrary meteorological parameters at a turbulence occurrence zone where a turbulence has occurred in the past.
"The turbulence prediction pattern data" consist of actual past data (a fact data where a turbulence is occurred at a certain location and a past time) when and where a turbulence occurred, regarding meteorological parameters ( e.g. wind speed, wind shear, air temperature, air temperature gradient, air density, air density gradient, infrared radiance temperature and so on).
Also, the present subject matter takes advantage of "the determination-purposed meteorological data." "The determination-purposed meteorological data" consist of data regarding meteorological parameters about prediction areas at present or in the future when an occurrence of the turbulence is unknown. The chosen meteorological parameters between "the turbulence prediction pattern data" and "the determination-purposed meteorological data" are common parameters.
By the comparison calculation between "the turbulence prediction pattern data" as past data and "the determination-purposed meteorological data" as present or future data in a common area, it is determined whether a turbulence highly occurs at the high similarity portion or a turbulence does not occurs at the low similarity portion.
Since the above-mentioned comparison calculation does not perform complicated analysis of air flow behaviors above the ground and does not need complicated and large amount of data like topography parameter of ground like mountain and sea and circumstance meteorological parameters, the load of the calculation can be dramatically reduced rather than the conventional weather prediction methods.
Furthermore, since the complicated calculation with large amount of data is not necessary, the present subject matter can reduce uncertainties of air behaviors in physics to achieve the highly preciseness of the prediction of the occurrence of a turbulence rather than the conventional weather prediction methods.
Moreover, of course, the present subject matter uses NO direct measurement.
As described above, the present subject matter achieves drastic improvement by means of a fact data where a turbulence is occurred at a certain location and a past time and a comparison between the fact data and present or future data with the dramatically reduced calculation load compared to the conventional weather prediction methods.”
In response the examiner finds the arguments not persuasive and respectfully disagrees. A claim reciting a judicial exception is not directed to the judicial exception if it also recites additional elements demonstrating that the claim as a whole integrates the exception into a practical application. One way to demonstrate such an integration is when the claimed invention improves the functioning of a computer or improves another technology of technical field. The application or use of the judicial exception in this manner meaningfully limits the claim by going beyond generally linking the use of the judicial exception to a particular technological environment, and thus transforms a claim into patent-eligible subject matter. Such claims are eligible at Step 2A because they are not “directed to” the recited judicial exception. The courts have not provided an explicit test for this consideration, but have instead illustrated how it is evaluated in numerous decisions. See MPEP 2106.04(d)(1).
First, the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the details necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.
The applicant has explicitly provided paras. [0002] and [0005] as indicating an improvement. Para. [0002] is provided in the Background Art of the disclosure and provides a bare assertion in a conclusory manner, which is construed by the examiner as setting forth an improvement without the details necessary to be apparent to a person of ordinary skill in the art. Para. [0005] is provided in the Summary of the disclosure which states “the methods disclosed in PTL 1 and PTL 2 take advantage of a process to obtain solutions from a mathematical model of fluid physical behavior related to the generation of turbulence,.. wherein there is a demand for a system that predicts the occurrence of turbulence based on new idea.” The cited paragraph [0005] is also construed as setting forth an improvement without the details necessary to be apparent to a person of ordinary skill in the art; e.g., a bare assertion. Furthermore, both of these citations appear to mere automation of manual processes and appear to utilize a general-purpose computer to accelerate a process of analysis; e.g., see MPEP 2106.05(a)(I) which provide examples that the courts have indicated as showing and not showing an improvement.
The applicant has also cited limitations without specifically disclosing the related paragraphs of the disclosure. Those limitations include “the turbulence prediction pattern data” and “the determination-purposed meteorological data.” Para. [0014] sets forth the use of the turbulence prediction pattern data as “teacher data,” which is combined with the determination-purposed meteorological data, which is made in advance in a learning process. Para. [0016] then sets forth the determination-purposed meteorological data and the turbulence prediction pattern data are compared to image data and subsequently utilize a CNN (Convolutional Neural Network) to calculate the presence or absence of common characteristic features without recognizing the process of extracting characteristic features of images. However, CNNs are known in the art to utilize filters to “slide” over input data to detect relevant features, such as patterns/images, which is not construed as an improvement.
Therefore the first criteria for an improvement has not been met.
Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification.
The examiner notes that although the first criteria has not been met, the second criteria will be evaluated. As disclosed in the arguments above, the paragraphs citing the “the turbulence prediction pattern data” and “the determination-purposed meteorological data” include the use of a CNN for learning. Although a learning process is disclosed in independent claim 21, it is noticeably absent in independent claim 1. Furthermore, neither of claims 1 or 21 provide for the use of a neural network, such as the CNN to perform the functions required by the model, which only appears in dependent claim 15, wherein also a Recurrent Neural Network is only mentioned in claim 16.
Therefore the second criteria for an improvement has not been met.
Applicant argues on pgs. 15-19, regarding the 35 U.S.C. 103 rejection presented in the previous office action, that:
“More specifically, Kim is silent about the aforementioned "the turbulence prediction pattern data" and "the determination-purposed meteorological data."
Applicant submits that the presently amended claim 1 takes advantage of "the turbulence prediction pattern data" regarding arbitrary meteorological parameters including the distributions of values of meteorological data regarding arbitrary meteorological parameters at a turbulence occurrence zone where a turbulence occurred in the past.
"The turbulence prediction pattern data" consist of actual past data (a fact data where a turbulence is occurred at a certain location and a past time) when and where a turbulence occurred, regarding meteorological parameters ( e.g. wind speed, wind shear, air temperature, air temperature gradient, air density, air density gradient, infrared radiance temperature and so on).
Also, the presently amended claim 1 takes advantage of "the determination-purposed meteorological data." "The determination-purposed meteorological data" consist of data regarding meteorological parameters about prediction areas at present or in the future when an occurrence of the turbulence is unknown. The chosen meteorological parameters between "the turbulence prediction pattern data" and "the determination-purposed meteorological data" are common parameters.
By the comparison calculation between "the turbulence prediction pattern data" as past data and "the determination-purposed meteorological data" as present or future data in a common area, it is determined whether a turbulence highly occurs at the high similarity portion or a turbulence does not occurs at the low similarity portion.”
In response, the examiner finds the argument not persuasive and respectfully disagrees. Kim, in the Office Action, as addressing the lack of turbulence information in the wind-optimal route application to produce a predictive model of aviation-scale turbulence guidance. This predictive model utilizes an ensemble of turbulence diagnostics for a wind-optimal route solution. The turbulence diagnostics are computed from numerical weather prediction models or ensembles of NWP models, wherein those ensembles are further clarified. Numerical weather prediction models necessarily make use of distributions of numerical values, which is cited below; examiner notes that ensemble systems necessarily operate on probability distributions, which is also cited below. Probability distributions utilize prior distributions and observation likelihoods, wherein the ensemble computes instances of means, variances, quantiles, and/or probability of exceeding thresholds; e.g., a degree of similarity.
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 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims are evaluated for patent subject matter eligibility under 35 U.S.C. 101 using the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) as follows:
Step 1:
Claims 1-20 are directed to a system and therefore falls within the four statutory categories of subject matter.
Step 2A:
This step asks if the claim is directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea. Step 2A is a two-prong inquiry: in prong 1 it is determined whether a claim recites a judicial exception, and if so, then in prong 2 it is determined if the recited judicial exception is integrated into a practical application of that exception.
Analyzing claim 1 under prong 1 of step 2A, the abstract idea in bold:
A turbulence prediction system comprising a calculation unit and a memory unit storing a plurality of turbulence prediction pattern data regarding arbitrary meteorological parameters, for predicting a possible turbulence-occurrence-zone having a high possibility of occurrence of a turbulence among prediction areas at a determination time point,
wherein each of the plurality of turbulence prediction pattern data regarding arbitrary meteorological parameters is made as a determination-purposed meteorological data consists of distributions of values of meteorological data regarding arbitrary meteorological parameters at a turbulence occurrence zone where a turbulence occurred in the past,
wherein the calculation unit makes determination-purposed meteorological data about the prediction areas at the determination time point as the determination-purposed meteorological data consists of distributions of values of meteorological data regarding the arbitrary meteorological parameters,
wherein the calculation unit performs a calculation to obtain a high similarity part in a comparison between the determination-purposed meteorological data and each of the plurality of turbulence prediction data so that the high similarity part is determined as the possible turbulence-occurrence-zone having a possibility in which a turbulence highly occurs among the prediction areas.
has a scope that encompasses mathematical concepts and mental steps, e.g., mathematical relationships and/or mathematical calculations, and concepts that may be performed in the human mind; e.g., human observation/performable with pen and paper/mere data gathering. Claim 1 discloses storing a plurality of turbulence prediction pattern data regarding arbitrary meteorological parameters, for predicting a possible turbulence-occurrence-zone having a high possibility of occurrence of a turbulence among prediction areas at a determination time point; construed by the examiner as a mental step; e.g., mere data gathering; wherein each of the plurality of turbulence prediction pattern data regarding arbitrary meteorological parameters is made as a determination-purposed meteorological data consists of distributions of values of meteorological data regarding arbitrary meteorological parameters at a turbulence occurrence zone where a turbulence occurred in the past; construed by the examiner as a mental step; e.g., a mental step; e.g., performable with pen and paper; makes determination-purposed meteorological data about the prediction areas at the determination time point as the determination-purposed meteorological data consists of distributions of values of meteorological data regarding the arbitrary meteorological parameters; construed by the examiner as a mental step; e.g., performable with pen and paper; performs a calculation to obtain a high similarity part in a comparison between the determination-purposed meteorological data and each of the plurality of turbulence prediction data so that the high similarity part is determined as the possible turbulence-occurrence-zone having a possibility in which a turbulence highly occurs among the prediction areas; construed as a mathematical concept; e.g., a mathematical relationship. The broadest reasonable interpretation of the abovementioned steps in light of the specification has a scope that encompasses a mathematical relationship between variables or numbers and steps that may be performed in the human mind. It is therefore concluded under prong 1 of step 2A that claim 1 recites a judicial exception in the form of an abstract idea, i.e., mathematical concepts and mental steps. See MPEP 2106.04(a)(2)(A-C) and MPEP 2106.05(f).
In prong 2 of step 2A it is determined whether the recited judicial exception is integrated into a practical application of that exception by: (1) identifying whether there are any additional elements recited in the claim beyond judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application.
Analyzing claim 1 under prong 2 of step 2A, in addition to the abstract ideas described above, claim 1 further recites:
a calculation unit and a memory unit
wherein the calculation unit
wherein the calculation unit
Analyzing these additional elements of claim 1 under prong 2 of step 2A, these additional elements appear to merely recite the use of a generic processor/computer as a tool to implement the abstract idea and/or to perform functions in its ordinary capacity, e.g., receive, store, or transmit data. However, use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer component after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f).
Step 2B:
In step 2B it is determined whether the claim recites additional elements that amount to significantly more than the judicial exception. The additional elements discussed above in connection with prong 2 of step 2A merely represents implementation of the abstract idea using a generic processor/computer and use of a generic processor/computer. However, use of a computer or other machine in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f).
It is therefore concluded under step 2B that claim 1 does not recite additional elements that amount to significantly more than the judicial exception.
Dependent claims 2-20 merely recite further details of the abstract idea of claim 1 and therefore do not represent any additional elements that would integrate the abstract idea into a practical application or represent significantly more than the abstract idea itself.
Step 1:
Claims 21-25 are directed to a method and therefore falls within the four statutory categories of subject matter.
Step 2A:
This step asks if the claim is directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea. Step 2A is a two-prong inquiry: in prong 1 it is determined whether a claim recites a judicial exception, and if so, then in prong 2 it is determined if the recited judicial exception is integrated into a practical application of that exception.
Analyzing claim 21 under prong 1 of step 2A, the abstract idea in bold:
A turbulence prediction method of predicting a possible turbulence-occurrence-zone having a high possibility of occurrence of a turbulence among prediction areas at a determination time point, by a turbulence prediction system comprising a memory unit and a calculation unit,
wherein the turbulence prediction method comprises a learning process and a determination process,
wherein the learning process includes storing a plurality of turbulence prediction pattern data as a determination-purposed meteorological data consists of distributions of values of meteorological data regarding arbitrary meteorological parameters of a turbulence occurrence zone where a turbulence occurred in the past, by the calculation unit,
wherein the determination process includes making determination-purposed meteorological data as the determination-purposed meteorological data consists of distributions of values of meteorological data regarding the arbitrary meteorological parameters in the prediction areas at the determination time point by the calculation unit, and
comparing between each of the determination-purposed meteorological data and the plurality of turbulence prediction pattern data to obtain a high similarity part and determining the high similarity portion in the comparison as the possible turbulence-occurrence-zone having a high possibility of occurrence of a turbulence in an arbitrary area among the prediction areas, by the calculation unit.
has a scope that encompasses mental steps, e.g., concepts that may be performed in the human mind; e.g., human observation/performable with pen and paper/mere data gathering. Claim 21 discloses wherein the turbulence prediction method comprises a learning process and a determination process; wherein the learning process includes storing a plurality of turbulence prediction pattern data as a determination-purposed meteorological data consists of distributions of values of meteorological data regarding arbitrary meteorological parameters of a turbulence occurrence zone where a turbulence occurred in the past; construed as a mental step; e.g., mere data gathering; wherein the determination process includes making determination-purposed meteorological data as the determination-purposed meteorological data consists of distributions of values of meteorological data regarding the arbitrary meteorological parameters in the prediction areas at the determination time point and; construed by the examiner as performable with pen and paper; comparing between each of the determination-purposed meteorological data and the plurality of turbulence prediction pattern data to obtain a high similarity part and determining the high similarity portion in the comparison as the possible turbulence-occurrence-zone having a high possibility of occurrence of a turbulence in an arbitrary area among the prediction areas; construed as a mental step; e.g., performable with pen and paper. The broadest reasonable interpretation of the abovementioned steps in light of the specification has a scope that encompasses steps that may be performed in the human mind. It is therefore concluded under prong 1 of step 2A that claim 21 recites a judicial exception in the form of an abstract idea, i.e., mental steps. See MPEP 2106.04(a)(2)(A-C) and MPEP 2106.05(f).
In prong 2 of step 2A it is determined whether the recited judicial exception is integrated into a practical application of that exception by: (1) identifying whether there are any additional elements recited in the claim beyond judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application.
Analyzing claim 21 under prong 2 of step 2A, in addition to the abstract ideas described above, claim 21 further recites:
a memory unit and a calculation unit;
by the calculation unit,
by the calculation unit,
by the calculation unit.
Analyzing these additional elements of claim 21 under prong 2 of step 2A, these additional elements appear to merely recite the use of a generic processor/computer as a tool to implement the abstract idea and/or to perform functions in its ordinary capacity, e.g., receive, store, or transmit data. However, use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer component after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f).
Step 2B:
In step 2B it is determined whether the claim recites additional elements that amount to significantly more than the judicial exception. The additional elements discussed above in connection with prong 2 of step 2A merely represents implementation of the abstract idea using a generic processor/computer and use of a generic processor/computer. However, use of a computer or other machine in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See MPEP 2106.05(f).
It is therefore concluded under step 2B that claim 21 does not recite additional elements that amount to significantly more than the judicial exception.
Dependent claims 22-25 merely recite further details of the abstract idea of claim 21 and therefore do not represent any additional elements that would integrate the abstract idea into a practical application or represent significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1-4, 10, 14, 17, and 20-24 are rejected under 35 U.S.C. 103 as being unpatentable over Kim, Jung-Hoon & Chan, William & Sridhar, Banavar & Sharman, Robert. (2015). Combined Winds and Turbulence Prediction System for Automated Air-Traffic Management Applications. Journal of Applied Meteorology and Climatology. 54. 10.1175/JAMC-D-14-0216.1, hereinafter Kim, in further view of Tucker et al. (US 12,174,217 B2), hereinafter Tucker.
Regarding claim 1, Kim discloses A turbulence prediction system comprising
a calculation unit and (Kim, e.g., see pg. 768, col. 2, para. [0002] disclosing the model was run using the Pleiades supercomputer at the NASA Ames Research Center; examiner notes that the supercomputer is construed as a calculation unit).
a plurality of turbulence prediction pattern data regarding arbitrary meteorological parameters, for predicting a possible turbulence-occurrence-zone having a high possibility of occurrence of a turbulence among prediction areas at a determination time point, wherein each of the plurality of turbulence prediction pattern data regarding arbitrary meteorological parameters is made as a determination-purposed meteorological data consists of distributions of values of meteorological data regarding arbitrary meteorological parameters at a turbulence occurrence zone where a turbulence occurred in the past, (Kim, e.g., see pg. 767, col. 1 disclosing to address the lack of turbulence information in WOR [wind-optimal routes] applications, a predictive model of aviation-scale turbulence, such as the Graphical Turbulence Guidance (GTG) product in which an ensemble of turbulence diagnostics are computed can be used to modify the WOR solution. The turbulence diagnostics in turn are based on forecasts from a numerical weather prediction (NWP) model or ensemble of NWP models. Here, time-lagged ensemble NWP forecast are used; construed by the examiner as a determination time point; to drive ensembles of turbulence diagnostics to provide probabilistic information about turbulence likelihood; examiner notes that an ensemble of turbulence diagnostics based on forecast from an NWP model is construed as arbitrary meteorological parameters. And to better predict the effects of convection as well as provide better representation of mountain-wave and clear-air turbulence sources, a high-resolution (e-km horizontal grid spacing) NWP model is implemented; construed as a zone having a possibility in which a turbulence highly causes among prediction areas. Further, each computed turbulence diagnostic is scaled to energy dissipation rate (
E
D
R
=
ε
1
3
m
2
3
s
-
1
) as an aircraft-independent atmospheric turbulence metric. EDR is defined as the rate of the turbulent kinetic energy (TKE) transfer from large-to-small-scale eddies. The model-derived EDR metric is consistent with in situ EDR estimates currently available from several fleets of commercial airliners including Boeing Co. B767s, B757s, and B737s, which is convenient for forecast verification; construed by the examiner as based on meteorological data regarding arbitrary meteorological parameters of a zone where a turbulence caused in the past. The in situ EDR metric can be related to traditional turbulence intensity based on pilot-reported categories of “light (LGT),” “moderate (MOD),” and “severe (SEV)” by appropriate considerations of aircraft type and flight conditions; see also pg. 768, col. 1, bullet points 1-2 disclosing 1) A high-resolution NWP forecast model is used to produce 3D meteorological data such a
u
,
v
,
a
n
d
w
wind components; potential temperature
θ
; pressure
p
; humidity; and cloud mixing ratios at a given valid time. Time-lagged ensembles are constructed from the forecast fields for different lead times but valid at the same time. 2) Ten aviation turbulence metrics, each based on combinations of horizontal and/or vertical gradients of 3D meteorological variables from the NWP model, are calculated; see also pg. 767, col. 2, section 2 – pg. 768, col. 1, line 8 disclosing to take into account these many turbulence-generation mechanisms as well as uncertainties in the NWP model forecasts, a combination of several turbulence metrics due to different mechanisms and from different forecasts is essential, and is more reliable than using a single diagnostic or simple rule-of-thumb predictor. In addition, a convection-permitting high-resolution numerical weather prediction model is more useful to capture small-scale turbulent eddies induced by convective activity or other turbulence sources; see also pg. 773, cols. 1-2 disclosing figure 5 shows a snapshot of (Figs. 5a,c) a deterministic ensemble EDR using Eq. (1) and (Figs. 5b,d) a probabilistic forecast for SOG-level turbulence for the two cases; examiner notes that figs. 5a,c illustrating a deterministic ensemble Energy Dissipation Rate [EDR] for Severe-or-Greater-level [SOG] turbulence, wherein an EDR is cited above as a variety of turbulence categories, which is provided as values in a color-coded bar of
m
2
/
3
s
-
1
).
wherein the calculation unit makes determination-purposed meteorological data about the prediction areas at the determination time point as the determination-purposed meteorological data consists of distributions of values of meteorological data regarding the arbitrary meteorological parameters, (Kim, e.g., see rejection as applied above; see also pg. 768, col. 1, bullet points 3-4 disclosing 3) the 10 metrics from different time-lagged forecasts are mapped into a common atmospheric turbulence-scale (EDR scale) based on the assumed lognormal (random) distributions. 4) All EDR-scale metrics are combined to produce both deterministic and probabilistic turbulence forecasts using different weights as a function of turbulence forecasting skill of each metric and are used to modify the WORs; see also pg. 772, section 3. Ensemble of EDR-scale turbulence metrics – pg. 774, col. 1 disclosing the final step combines all DR-scale metrics into deterministic and probabilistic turbulence forecasts. For the deterministic ensemble EDR, 30 EDR-scale metrics are combined into a weighted ensemble mean (e.g., Figs. 5a,c) using different weighting functions of each metric (
W
i
), as follows; see equations (1) and (2); see also fig. 5a,c. Figure 5 shows a snapshot of (Figs. 5a,c) a deterministic ensemble EDR using Eq. (1) and (Figs. 5b,d) a probabilistic forecast for SOG-level turbulence for the two cases. These are averaged over flight levels FL300, FL350, and FL400 using three time-lagged ensemble members of forecast data (1.5-3.5h) valid on 1730 UTC September 2012 (top panels) and on 1830 UTC December 2011 (bottom panels). The results show the deterministic ensemble EDR for larger values (orange shading;
e
d
r
≥
0.22
m
2
3
s
-
1
) mostly agrees well with the observed in situ EDR measurements
≥
0.22
m
2
3
s
-
1
(blue asterisks) in Figs. 5a,c.) .
wherein the calculation unit performs a calculation to obtain a high similarity part in a comparison between the determination-purposed meteorological data and each of the plurality of turbulence prediction pattern data so that the high similarity part is determined as the possible turbulence-occurrence-zone having a possibility in which a turbulence highly occurs among the prediction areas. (Kim, e.g., see rejection as applied above; see also pg. 774, section 3 disclosing the forecast EDR-scaled turbulence diagnostics shown in fig. 3 and 4 and the deterministic ensemble EDR shown in figs. 5a,c are compared with in situ EDR reports to objectively obtain their statistical skill. The forecasting performance skills are calculated using the probability-of-detection “yes” for the
E
D
R
≥
0.22
m
2
3
s
-
1
(PODY) versus “no” for the
E
D
R
≤
0.01
m
2
3
s
-
1
(PODN). This technique has been used for the verification of various turbulence forecasts (e.g., Sharman et al. 2006; Kim et al. 2011). If the forecast value of each EDR-scaled turbulence metric at the nearest grid point to the observed MOG location around
±
30 min (30-min time window) of the valid time is higher (lower) than the in situ EDR, the
Y
f
o
r
Y
o
b
s
(
N
f
o
r
Y
o
b
s
) was counted as shown in Table 1 and Eq. (3):
P
O
D
Y
=
Y
f
o
r
Y
o
b
s
Y
f
o
r
Y
o
b
s
+
N
f
o
r
Y
o
b
s
and
P
O
D
N
=
N
f
o
r
N
o
b
s
Y
f
o
r
N
o
b
s
+
N
f
o
r
N
o
b
s
, and if the forecast EDR value near the null observation is smaller (higher) than the observed in situ EDR, the
N
f
o
r
N
o
b
s
(
Y
f
o
r
N
o
b
s
) was counted. These procedures were applied to a total of 270 turbulence events
≥
0.22
m
2
3
s
-
1
EDR value (MOG EDR) and 55 150 smooth events with
E
D
R
≤
0.01
m
2
3
s
-
1
ON BOTH 7-8 September 2012 and 31 December 2011. This process was repeated through 20 different thresholds that ranged from EDR values of 0 to 1, resulting in 20 PODY and PODN statistics for both the ensemble EDR and EDR-scale turbulence metrics. figures 6a,c,e show example PODY-PODN plots constructed from these 20 threshold values for the DEFWQ/Ri diagnostic for 7-8 September 2012 (Fig. 6a), 31 December 2011 (Fig. 6b), and both cases (Fig. 6c), for various forecast lead times (1.5-5.5 h). Values of AUC are a measure of the forecast performance skill. An AUC = 1 is a perfect forecast [i.e., a turbulence metric can perfectly discriminate all MOG EDR and smooth events and/or a turbulence metric has a perfect forecast for MOG EDR without any false alarm ratios (1-PODN)]. In Fig. 6, the ensemble EDRs have generally higher forecasting performance skill than any of single EDR-scale turbulence metric. This result is the same as in the second case on 31 December 2011. This is consistent with the previous results of turbulence forecasts that the integrated turbulence metrics provide superior forecasting skill than any single turbulence metric, at least in terms of the AUC performance metric. the minimum and maximum AUC values of the final deterministic EDR forecast are 0.77 and 0.9, respectively, which is about a from -9% to +6% difference around the obtained performance (0.85) in Figs. 6c,f.; see also pg. 778, section 5 Summary and conclusion disclosing a simple WOR and three LTAR applications were developed to show the utility of this forecast product for route planning applications. the results shown in Figs. 7 and 8 are summarized in Table 3).
Kim is not relied upon as explicitly disclosing a memory unit storing
However, Tucker further discloses: a memory unit storing. (Tucker, e.g., see fig. 2 illustrating memory (240); see also col. 6, lines 31-45 disclosing the control system (222) of the multifunctional instrument (104) can include various processing and operating components, including but not limited to a processor (236), memory (240), and a communications interface (244). The memory (240) can include solid-state volatile or non-volatile memory, such as flash memory, RAM, DRAM, SDRAM, or the like. the memory (240) can also include various other types of memory or other data storage devices, such as magnetic storage devices, optical storage devices, or the like).
Accordingly, it would be prima facie obvious to one of ordinary skill in the art, at the time the invention was effectively filed, to have modified Kim with Tucker’s memory storing unit for at least the reasons that the processor can execute application programming or instruction stored in the memory, as taught by Tucker; e.g., see col. 6, lines 46-62.
Regarding claim 2, Kim in view of Tucker discloses: A turbulence prediction system according to claim 1,
wherein the arbitrary meteorological parameters are a plurality of meteorological parameters, wherein the plurality of turbulence prediction pattern data are is made for each of the plurality of meteorological parameters, (Kim, e.g., see rejection as applied to claim 1, specifically to pg. 767, col. 1 disclosing the turbulence diagnostics in turn are based on forecasts from a numerical weather prediction model or ensemble of NWP models, which is construed as a plurality of turbulence prediction data; see also pg. 767, col. 1 – col. 2 disclosing each computed turbulence diagnostic is scaled to energy dissipation rate (
E
D
R
=
ε
1
3
m
2
3
s
-
1
) as an aircraft-independent atmospheric turbulence metric. EDR is defined as the rate of the turbulent kinetic energy (TKE) transfer from large-to-small-scale eddies. The large-scale eddies in atmosphere are inherently unstable. These large eddies break up and cascade down to smaller-scale eddies until the viscous dissipation becomes dominant and the TKE is converted to heat; the examiner notes that EDR is calculated as a function of each of TKE, large eddies, small eddies, and heat are all considered components of the plurality of turbulence prediction pattern data and are necessarily a plurality of meteorological parameters; see also pg. 767 – pg. 768, section 2 disclosing to take into account these many turbulence-generation mechanisms as well as uncertainties in the NWP model forecasts, a combination of several turbulence metrics due to different mechanisms and from different forecasts is essential, and is more reliable than using a single diagnostic or simple rule-of-thumb predictor; construed by the examiner as further examples of a plurality of meteorological parameters which are made of the plurality of turbulence prediction pattern data).
wherein the determination-purposed meteorological data are made for each of the plurality
of meteorological parameters, (Kim, e.g., see rejection as applied above; see also eq. (1) disclosing the calculation:
E
n
s
e
m
b
l
e
E
D
R
x
,
y
,
z
=
∑
i
=
1
N
W
i
E
D
R
i
(
x
,
y
,
z
)
, wherein EDR is comprised of TKE, large eddies, small eddies, and heat meteorological parameters; see also pg. 773, col. 1- col. 2 disclosing figure 5 shows a snapshot of (Figs. 5a,c) a deterministic ensemble EDR using Eq. (1); examiner notes the deterministic ensemble EDR; construed as the determination-purposed meteorological data; is explicitly shown in figs. 5a,c).
wherein the calculation to obtain a high similarity part is performed about each of the
plurality of meteorological parameters by a comparison between the determination-purposed
meteorological data and the plurality of turbulence prediction pattern data. (Kim, e.g., see pg. 772, section d disclosing the final step combines all EDR-scale metrics into deterministic and probabilistic turbulence forecasts. AT a given forecast time, we used a total 30 of EDR-scale metrics [i.e., 10 different turbulence metrics from three different NWP forecasts (e.g., 1.5-,2.5-, and 3.5-h forecast data)] for the ensemble EDR forecasts. For the deterministic ensemble EDR, 30 EDR-scale metrics are combined into a weighted ensemble mean (e.g., Figs. 5a,c) using different weighting functions for each metric (
W
i
), as follows; e.g., see eqns. 1-2).
Regarding claim 3, Kim in view of Tucker discloses: A turbulence prediction system according to claim 2,
wherein the plurality of turbulence prediction pattern data about each of the plurality of meteorological parameters are formed as combined turbulence prediction pattern data by allocating the plurality of turbulence prediction pattern data about each of the plurality of meteorological parameters in a predetermined allocation about each of the plurality of meteorological parameters, and see rejection as applied to claim 2, specifically Kim, e.g., eq. (1).
the determination-purposed meteorological data about each of the plurality of meteorological parameters are formed as combined determination-purposed meteorological data by allocating the determination-purposed meteorological data about each of the plurality of meteorological parameters in a same allocation as the predetermined allocation to form combined determination-purposed meteorological data. see rejection as applied above, specifically Kim, e.g., eq. (2); see also pg. 772 disclosing for the deterministic ensemble EDR, 30 EDR-scale metrics are combined into a weighted ensemble mean (e.g., Figs. 5a,c) using different weighting functions of each metric (
W
i
).
Regarding claim 4, Kim in view of Tucker discloses: A turbulence prediction system according to claim 1,
wherein the plurality of turbulence prediction pattern data are made as a plurality of groups including predetermined plural time-points with predetermined time intervals, (Kim, e.g., see rejection as applied to claim 1; see also pg. 771, col. 1 disclosing three different time-lagged ensemble members of weather forecasts (e.g., 1.5, 2.5, and 3.5 h) were used to calculate the turbulence diagnostics each valid time. the 10 turbulence metrics used are the ARW-produced subgrid-scale turbulent kinetic energy (SGS TKE), Frehlich and Sharman’s (2004) EDR (FS EDR), square of total deformation (DEFSQ), absolute value of horizontal divergence (ADIV), square of vertical component of relative vorticity (VORTSQ), absolute value of vertical velocity (ABW), two-dimensional frontogenesis function on pressure coordinates (F2D), Brown turbulence index 1 (Brown 1), nested grid model turbulence index (NGM), and the horizontal temperature gradient (HTG). these diagnostics were then divided by the gradient Richardson number
R
i
g
. Detailed formulations of the diagnostics are provided in appendix A).
wherein the determination-purposed meteorological data is made as data of the arbitrary meteorological parameters including predetermined time-points with predetermined time intervals, (Kim, e.g., see rejection as applied to claim 1, wherein claim 1 specifically discloses on pg. 772, section d disclosing at a given forecast time, we used a total 30 of EDR-scale metrics [i.e., 10 different turbulence metrics from three different NWP forecasts (e.g., 1.5-, 2.5, and 3.5-h forecast data)]; construed as time-points with predetermined time intervals).
wherein the calculation to obtain a high similarity part is performed by a comparison based on a comparison of characteristics focused on time-dependent changes at the predetermined plural time-points, between the determination-purposed meteorological data and each of the data of the plurality of groups including the plurality of turbulence prediction pattern data. (Kim, e.g., see rejection as applied to claim 1; see also fig. 6 illustrating Plots x-y for the PODY and PODN statistics of the (1), (3), (3) DEFSQ/Ri metrics from 1.5-h (purple dashed line), 2.5-h (orange dash-dot-dotted line), 3.5-h (blue dash-dotted line), 4.5-h (green dotted line), and 5.5-h (red long dashed line) forecast data and (b), (d), (f) EDR-scale turbulence metrics (SGS TKE/Ri; purple dashed line, FS EDR/Ri; orange dash-dot-dotted line, DEFSQ/Ri; blue dashed-dotted line, ADIV/Ri; green dotted line, VORTSQ/Ri; red long dashed line) from 2.5-h forecast data, compared with the observed in situ EDR measurements for (top) 7-8 Sep 2012 (middle) 31 Dec 2011, and (bottom) both periods. Those for time-lagged ensemble EDR 1 using 1.5-3.5-h data (blue thick solid line), 2 using 2.5-4.5-h data (red thick solid line), and 3 using 3.5-5.5-h data (black thick solid line) are also depicted in all plots).
Regarding claim 10, Kim in view of Tucker discloses: A turbulence prediction system according to any one of claims 1 to 9,
wherein the determination-purposed meteorological data is weather prediction data. (Kim, e.g., see rejection as applied to claim 1; see also pg. 778, col. 2 disclosing the new turbulence forecasting techniques can create both deterministic and probabilistic turbulence information using a sequence of four procedures. These include high-resolution weather modeling using time-lagged ensembles, calculation of reliable turbulence diagnostics on these grids, mapping of these metrics to an EDR scale, and combining the predictions into a turbulence product; examiner notes a turbulence product of weather is construed as a weather predict data).
Regarding claim 14, Kim in view of Tucker discloses: A turbulence prediction system according to claim 1,
wherein the plurality of turbulence prediction pattern data consists of data acquired over a period of time in the past, (Kim, e.g., see rejection as applied to claim 1; see also pg. 771, section c disclosing we assumed that each model-derived turbulence diagnostic has a lognormal distribution that can be derived from the best-fit function of the log-scale probability density function (PDF) especially for larger values of turbulence diagnostics for longer period of time).
wherein calculation to obtain a high similarity part is based on degree to which at least part of any one of the plurality of turbulence prediction patterns is included in the determination-purposed meteorological data. (Kim, e.g., see rejection as applied above and to claim 1; see also pg. 771 – pg. 772, section c disclosing figures 3 and 4 show an example of nine EDR-scale metrics from a 2.5-h forecast product averaged over three different flight levels of FL300, FL350, and FL400 valid at 1730 UTC 7 September 2012 and at 1830 UTC December 2011, respectively. In general, most of the EDR-scale metrics for relatively larger values are consistent with the turbulence encounters
≥
0.22
m
2
3
s
-
1
values in the observed in situ EDR measurements in commercial flights both near the convective system for the first case (Fig 3.) and over the rocky Mountain regions for the second case (Fig. 4). Relatively lower values of EDR-scale metrics also capture well the smooth areas of the in-flight bumpiness
≤
0.01
m
2
3
s
-
1
values depicted as gray dotted lines over the CONUS; the examiner notes that “based on degree to which at least part of any one of the larger number of turbulence prediction patterns” is construed as a threshold above 0.22
m
2
3
s
-
1
and below 0.01
m
2
3
s
-
1
).
Regarding claim 17, Kim in view of Tucker discloses: A turbulence prediction system according to claim 1,
wherein the turbulence prediction system comprises a turbulence prediction apparatus, wherein the turbulence prediction apparatus includes a memory unit and a calculation unit. (see rejection as applied to claim 1, wherein Kim of Kim in view of Tucker discloses a supercomputer/calculation unit, and Tucker of Kim in view of Tucker discloses multifunctional instrument (104); construed as a turbulence prediction apparatus, and memory unit (240)).
Kim in view of Tucker is not relied upon as explicitly disclosing that the turbulence prediction apparatus is connected to a network.
However, Tucker further discloses: connected to a network (Tucker, e.g., see col. 6, line 64 – col. 7, line 3 disclosing data collected or generated by the sensors of the multifunctional instrument (104) can be stored in the memory (204), presented to the crew of the aircraft (100), or communicated using the communication interface (244) to other systems; construed as a network).
Accordingly, it would be prima facie obvious to one of ordinary skill in the art, at the time the invention was effectively filed, to have modified Kim in view of Tucker’s system with Tucker’s connected to a network for at least the reasons that a network would allow the communication of data regarding safety and turbulence.
Regarding claim 20, Kim in view of Tucker is not relied upon as explicitly disclosing: A turbulence prediction system according to claim 1 7 or 18, wherein the turbulence prediction system comprises a communication unit,
wherein the turbulence prediction system comprises an aircraft,
wherein the communication unit is connected to the network and transmits an output of a prediction result of occurrence of the turbulence to the aircraft.
However, Tucker further discloses: wherein the turbulence prediction system comprises a communication unit, wherein the turbulence prediction system comprises an aircraft, wherein the communication unit is connected to the network and transmits an output of a prediction result of occurrence of the turbulence to the aircraft. (Tucker, e.g., see fig. 1 illustrating multifunctional instrument (104), wherein multifunctional instrument (104) is a component of an aircraft (100); see also fig. 2 illustrating multifunctional instrument (104) comprising communications interface (244); see also col. 6, line 46 – col. 7, line 3 disclosing the processor (236) can execute application programming or instructions stored in the memory (240) for the onboard prediction of aviation safety related weather conditions, and improved flight navigation paths including but not limited to the detection of clear air turbulence along the path of the aircraft (100). Such predictions can be made in connection with wind speed measurements taken by the lidar system (204) along lines of sight other than those within the forward-looking field of regard (108a), such as downward looking field of regard (108b), or an upward looking field of regard (108c). Data collected or generated by the sensors of the multifunctional instrument (104) can be stored in the memory (240), presented to the crew of the aircraft (100), or communicated using the communication interface (244) to other systems, such as aviation safety or navigation related weather information consumers, other aircraft, weather services, or the like).
Accordingly, it would be prima facie obvious to one of ordinary skill in the art, at the time the invention was effectively filed, to have modified Kim in view of Tucker’s system with Tucker’s turbulence prediction system comprises a communication unit, wherein the turbulence prediction system comprises an aircraft, wherein the communication unit is connected to the network and transmits an output of a prediction result of occurrence of the turbulence to the aircraft for at least the reasons that it would be beneficial to apprise the aircraft crew of the results of inclement weather to increase safety.
Regarding claim 21, Claim 21 discloses A turbulence prediction method of predicting a possible turbulence-occurrence-zone having a high possibility of occurrence of a turbulence among prediction areas at a determination time point, by a turbulence prediction system comprising a memory unit and a calculation unit, wherein the turbulence prediction model comprises a learning process and a determination process, wherein the learning process includes storing a plurality of turbulence prediction pattern data as a determination-purposed meteorological data consists of distribution of values of meteorological data regarding arbitrary meteorological parameters of a turbulence occurrence zone where a turbulence occurred in the past, by the calculation unit wherein the determination process includes making determination-purposed meteorological data as the determination-purposed meteorological data consists of distributions of values of meteorological data regarding the arbitrary meteorological parameters in the prediction areas at the determination time point by the calculation unit, and comparing between each of the determination-purposed meteorological data and the plurality of turbulence prediction pattern data to obtain a high similarity part and determining the high similarity portion in the comparison as the possible turbulence-occurrence-zone having a high possibility of occurrence of turbulence in an arbitrary area among the prediction areas, by the calculation unit., and is rejected under 35 U.S.C. 103 as being unpatentable by Kim in view of Tucker for reasons analogous to those set forth in connection with claim 1. Claim 21 is different than claim 1 in the claim recitation disclosing: wherein the turbulence prediction method comprises a learning process, wherein the learning process includes, wherein Tucker discloses (Tucker, e.g., see fig. 4 illustrating stored input data (404); see also step (412) disclosing train a deep learning model with fused data, step (416) disclosing use the deep learning models to predict improved output information, and step (424) use the reinforced learning models to incorporate the fused data and predict an optimized courses of actions; see also steps (420) and (428); see also col. 10, line 48 – col. 11, line 24 disclosing fig. 4 depicts a process for applying deep learning processing to detect turbulence in accordance with embodiments of the present disclosure. the process can be implemented by execution by the processor (236) of the deep learning algorithm (242) stored in memory (240). The process includes receiving and processing inputs from multiple data sources (step 404). These data sources can include inputs from a multifunctional system or instrument (104). Algorithmic input data fusion is then performed (step 408). Data fusion can include correlating turbulence predictions made by execution of the deep learning algorithm (242) based on measurements by the lidar system (204) or other multifunctional instrument (104) sensors with actual turbulence encountered by the aircraft (100)).
Accordingly, it would be prima facie obvious to one of ordinary skill in the art, at the time the invention was effectively filed, to have modified Kim in view of Tucker with Tucker’s turbulence prediction method comprises a learning process, wherein the learning process includes for at least the reasons that by comparing the data used to make the predictions with the actual turbulence measurements, refinements to the model or algorithm (242) to increase the accuracy of the predictions can be made, as taught by Tucker; e.g., see col. 11, lines 30-33.
Regarding claim 22, Claim 22 recites A turbulence prediction method according to claim 21, wherein the arbitrary meteorological parameters are a plurality of meteorological parameters, wherein the plurality of turbulence prediction pattern data are made for each of the plurality of meteorological parameters, wherein the determination-purposed meteorological data is made for each of the plurality of meteorological parameters, wherein the calculation to obtain a high similarity part is performed about each of the plurality of meteorological parameters by a comparison between the determination-purposed meteorological data and the plurality of turbulence prediction pattern data., and is rejected under 35 U.S.C. 103 as being unpatentable by Kim in view of Tucker for reasons analogous to those set forth in connection with claim 2.
Regarding claim 23, Kim in view of Tucker discloses: A turbulence prediction method according to claim 22,
wherein the determination process includes allocating the determination-purposed meteorological data about each of the plurality of meteorological parameters in the predetermined allocation to form combined determination-purposed meteorological data. see rejection as applied to claim 21, specifically Kim, e.g., eqns. (1) and (2).
Kim in view of Tucker is not relied upon as explicitly disclosing: wherein the learning process includes allocating the plurality of turbulence prediction pattern data about each of the plurality of meteorological parameters in a predetermined allocation about each of the plurality of meteorological parameters to form combined turbulence prediction pattern data,
However, Tucker further discloses: wherein the learning process includes allocating the plurality of turbulence prediction pattern data about each of the plurality of meteorological parameters in a predetermined allocation about each of the plurality of meteorological parameters to form combined turbulence prediction pattern data, (Tucker, e.g., see rejection as applied above; see also fig. 4 illustrating a process for applying deep learning processing to detect and predict turbulence and other atmospheric conditions, specifically to steps (408) disclosing algorithmic input data fusion, step (412) disclosing train a deep learning model with fused data, step (416) disclosing use deep learning models to predict improved output information, and step (424) disclosing use the reinforced learning models to incorporate the fused data and predict an optimized courses of actions; see also col. 10, line 48 – col. 12, line 25 disclosing data fusion can include correlating the severity of the predicted turbulence to the severity of the turbulence detected by the aircraft at various altitudes and relative air speeds of turbulence encounters. Other examples of data fusion include correlating external weather and turbulence forecasting data with turbulence predictions made by the multifunction system (104) and/or actual turbulence measurements made by sensors included in the malfunction system (104). Correlations between predictions regarding icing conditions, the presence of organic ash, or other particles in the atmosphere and the conditions actually encountered by the aircraft (100) can be made. Data regarding the polarization of backscattered return laser light (120) and the detection of clouds using the infrared camera (216) are examples of sources of data regarding predictions of such other atmospheric conditions; examiner notes that fig. 4 and steps (408), (412), (416), and (424) provide a predetermined allocation about each of the plurality of meteorological parameters, wherein fusion is construed as the combined turbulence prediction pattern data).
Accordingly, it would be prima facie obvious to one of ordinary skill in the art, at the time the invention was effectively filed, to have modified Kim in view of Tucker’s method with Tucker’s learning process includes allocating the plurality of turbulence prediction pattern data about each of the plurality of meteorological parameters in a predetermined allocation about each of the plurality of meteorological parameters to form combined turbulence prediction pattern data for at least the reasons that fused data can predict and optimize courses of action for an aircraft, as taught by Tucker; e.g., see col. 12, lines 16-31.
Regarding claim 24, Claim 24 recites A turbulence prediction method according to any one of claims 21 to 23, wherein the plurality of turbulence prediction pattern data is made as a plurality of groups including predetermined plural time-points with predetermined time intervals, wherein the determination-purposed meteorological data is made as data of the arbitrary meteorological parameters including predetermined plural time-points with predetermined time intervals, wherein the calculation to obtain a high similarity part is performed by a comparison based on a comparison of characteristics focused on time-dependent changes at the predetermined plural time-points, between the determination-purposed meteorological data and each of the data of the plurality of groups including the plurality of turbulence prediction pattern data., and is rejected under 35 U.S.C. 103 as being unpatentable by Kim in view of Tucker for reasons analogous to those set forth in connection with claim 4.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Tucker in further view of Y. Wang and Y. Li, "Research on Multi-class Weather Classification Algorithm Based on Multi-model Fusion," May 1, 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chongqing, China, 2020, pp. 2251-2255, doi: 10.1109/ITNEC48623.2020.9084786., hereinafter Wang.
Regarding claim 15, Kim in view of Tucker discloses: A turbulence prediction system according to claim 1,
wherein the plurality of turbulence prediction pattern data consists of data acquired over a period of time in the past, (Kim, e.g., see rejection as applied to claim 1; see also pg. 771, section c disclosing we assumed that each model-derived turbulence diagnostic has a lognormal distribution that can be derived from the best-fit function of the log-scale probability density function (PDF) especially for larger values of turbulence diagnostics for longer period of time).
wherein calculation to obtain a high similarity part is performed by weighted ensemble mean so that the plurality of the turbulence prediction pattern data is used as a function of different weightings. (Kim, e.g., see rejection as applied to claim 1, specifically to pg. 772, section d disclosing for the deterministic ensemble EDR, 30 EDR-scale metrics are combined into a weighted ensemble mean (e.g., Figs. 5a,c) using different weighting functions of each metric (
W
i
)).
Kim in view of Tucker is not relied upon as explicitly disclosing: a convolutional neural network and convolutional neural network layer.
However, Wang further discloses: a convolutional neural network and convolutional neural network layer. (Wang, e.g., see pg. 2251, section I disclosing the classification results of these methods have proved the feasibility and superiority of Convolutional Neural Networks (CNN) in weather recognition classification. However, due to the lack of a dataset of multiple types of weather phenomena, it becomes difficult to apply deep learning to the classification of multiple types of weather. In order to efficient identify multiple types of weather phenomena, we propose a method that combines ResNet (Residual Neural Networks) and DenseNet (Dense connected Convolutional Networks), and conducts experiments on our weather dataset; see also pg. 2252, section IV disclosing a CNN mainly includes an input layer, a convolutional layer, a pooling layer, a fully connected layer, and a Softmax layer. Since this paper uses the ResNet and DenseNet network structures evolved from CNN, CNN will not be described too much; see also pg. 2253, section B disclosing as the number of convolutional neural network layers increases, the problem of gradient disappearance and model degradation emerge. In order to ensure more features, the DenseNet network architecture adopts to connect all layers to each other).
Accordingly, it would be prima facie obvious to one of ordinary skill in the art, at the time the invention was effectively filed, to have modified Kim in view of Tucker’s system with Wang’s convolutional neural network and convolutional neural network layer for at least the reasons that it is known to utilize a convolutional neural network for image processing in recognition of two-dimensional shapes utilizing not fully connected neurons and shared weights to generate a feature map that share a set of parameters, as taught by Wang; e.g., see pg. 2252, section IV.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Tucker, in further view of Rahman, Sk Mashfiqur & Pawar, Suraj & San, Omer & Rasheed, Adil & Iliescu, Traian. (2019). A non-intrusive reduced order modeling framework for quasi-geostrophic turbulence. 10.48550/arXiv.1906.11617., hereinafter Rahman.
Regarding claim 16, Kim in view of Tucker discloses: A turbulence prediction system according to claim 4, wherein calculation to obtain a high similarity part is performed by a weighted ensemble mean. (Kim, e.g., see rejection as applied to claim 1, specifically to pg. 772, section d disclosing for the deterministic ensemble EDR, 30 EDR-scale metrics are combined into a weighted ensemble mean (e.g., Figs. 5a,c) using different weighting functions of each metric (
W
i
)).
Kim in view of Tucker is not relied upon as explicitly disclosing: a recurrent neural network.
However, Rahman further discloses: a recurrent neural network. (Rahman, e.g., see pg. 2, col. 2, para. [0002] disclosing we propose a methodology based on long short-term memory (LSTM) recurrent neural networks. Since reduced order modeling of such noisy large-scale systems is comparatively difficult due to instabilities, which results in using a very large number of POD modes to capture the true physics, our main motivation in this study is to utilize the time series prediction capability of LSTM to capture the flow physics with a very few POD modes).
Accordingly, it would be prima facie obvious to one of ordinary skill in the art, at the time the invention was effectively filed, to have modified Kim in view of Tucker’s system with Rahman’s recurrent neural network for at least the reasons that recurrent neural networks is one of widely used neural network architectures which is designed to operate on input information as well as the previously stored observations to predict the dependencies among the temporal data sequences, as taught by Rahman; e.g., see pg. 5, section. IV, col. 1.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Tucker, in further view of Yan et al. (US 10,795,056 B2), hereinafter Yan.
Regarding claim 18, Kim in view of Tucker discloses: A turbulence prediction system according to claim 1, wherein he turbulence prediction system is a supercomputer system see rejection as applied to claim 1.
Kim in view of Tucker is not relied upon as explicitly disclosing: a cloud computing system, and in which the calculation unit and the memory are connected via a network.
However, Tucker further discloses: in which the calculation unit and the memory are connected via a network. (Tucker, e.g., see rejection as applied to claim 1; see also fig. 1 illustrating a plane comprising multifunctional instrument (104); see also fig. 2 illustrating an in-depth view of multifunctional instrument (104), wherein control system (222) of multifunction instrument (104) comprises memory (240), communications interface (244) and processor (236); wherein the examiner construes the processor (236) as the calculation unit and the memory (240) as the memory, which are communicatively coupled to communications interface (244); construed by the examiner as connected via a network).
Accordingly, it would be prima facie obvious to one of ordinary skill in the art, at the time the invention was effectively filed, to have modified Kim in view of Tucker’s system with Tucker’s calculation unit and the memory are connected via a network for at least the reasons that utilizing a microprocessor and a memory in conjunction with a network provides transmission abilities at distance.
Kim in view of Tucker is not relied upon as explicitly disclosing a cloud computing system.
However, Yan further discloses: a cloud computing system. (Yan, e.g., see col. 4, lines 13-57 disclosing fig. 2 is a schematic diagram illustrating an example cloud-based server (200). The cloud-based server (200) may provide infrastructure services, platform services, and software application services).
Accordingly, it would be prima facie obvious to one of ordinary skill in the art, at the time the invention was effectively filed, to have modified Kim in view of Tucker’s system with Yan’s cloud computing for at least the reasons that it is known to utilize a sever to operate one or more image libraries comprising images captured by a data collection device, which may be accessed by a computing device, as taught by Yan; e.g., see col. 3, lines 56-61.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Tucker, in further view of Pyle et al. (US 2012/0185414 A1), hereinafter Pyle.
Regarding claim 19, Kim in view of Tucker is not relied upon as explicitly disclosing: A turbulence prediction system according to claim 17 or 18,
wherein the turbulence prediction system has a terminal connected to the network,
wherein an output from the turbulence prediction system is sent to the terminal.
However, Pyle further discloses: wherein the turbulence prediction system has a terminal connected to the network, wherein an output from the turbulence prediction system is sent to the terminal. (Pyle, e.g., see fig. 17 illustrating user interface displayed on customer PC (1760) as connected to a system bus of the WENDSS System; construed as the turbulence prediction system, and further connected to a web service interface; construed as a network, wherein fig. 17 explicitly illustrates an output to user interface (1760); see also para. [0034] disclosing a wind forecasting system is used to predict a wind event in an area of interest (100) as illustrated in fig. 1; see also para. [0248] disclosing the systems and methods for wind forecasting and/or WPRE predictions are implemented via a software interface provided to the user of the forecasting; see also para. [0269] disclosing WENDSS User Interface (1760) is a web-based user interface that presents system data to the user. Users access the UI using their existing network and associated PCs).
Accordingly, it would be prima facie obvious to one of ordinary skill in the art, at the time the invention was effectively filed, to have modified Kim in view of Tucker’s system with Pyle’s turbulence prediction system has a terminal connected to the network, wherein an output from the turbulence prediction system is sent to the terminal for at least the reasons that it would be beneficial to apprise the user of the results of the prediction system.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US 2016/0293021 A1 to Shipley et al. relates to prediction and warning of transported turbulence in long-haul aircraft operations.
US 2015/0339930 A1 to McCann et al. relates to dynamic aircraft threat controller manager apparatuses, methods and systems.
US 2007/0260366 A1 to Lacaze et al. relates to a method and device for determining the air turbulence likely to be encountered by an aircraft.
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/E.S.V./Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857