Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This Office Action is in response to the amendment filed on 11/21/2025. Claims 3 and 12 are canceled. Claims 1, 2, 4, and 13 are amended. Claims 1-2, 4-11, and 13 are presently pending and are presented for examination.
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.
Claim(s) 1-2, 4-11, and 13 is/are rejected under 35 U.S.C. 101. Independent claim 1 is directed toward a method. Independent claim(s) 11 along with dependent claims 2, 4-11, and 13 are directed to a statutory category of invention under Step 1.
Under Step 2A, Prong 1, the claims are analyzed to determine whether one or more of the claims recites subject matter that falls within one of the following groups of abstract ideas: (1) mental processes, (2) certain methods of organizing human activity, and/or (3) mathematical concepts. In this case, the independent claim(s) 11 is/are directed to an abstract idea without significantly more. Specifically, the claim(s), under its/their broadest reasonable interpretation(s) cover(s) certain mental processes and mathematical concepts. The language of independent claim 1 is used for illustration:
Method implemented by computer (19) for detecting flight regimes of an aircraft (1) equipped with a monitoring system (2) configured to acquire samples of a number of quantities relating to the flight of the aircraft, comprising the steps of:
-acquiring (202) at least one unknown matrix (TFDMx) including, for each quantity, a corresponding series of samples (sxi[n] - sxNQn]) acquired by the monitoring system (2) during a flight of the aircraft;
- performing (204) smoothing operations of each series of samples (sx1[n]-sxNQ[n]) of the unknown matrix (TFDMx), so as to generate a corresponding smoothed sample series (sx'i[n]- sx'NQ[n]) and so as to determine a corresponding time-continuous approximating function (Fxi(t)) defined by a respective set of coefficients (Cxk,1) and by a plurality of base functions ((k(t)), the smoothed sample series forming an unknown smoothed matrix (TFDMx'); (Performing smoothing on a matrix of data and fitting an approximation function defined by coefficients and base functions are both mathematical calculations and therefore mathematical concepts.);
- on the basis of the base functions (qk(t)), applying (206) to the unknown smoothed matrix (TFDMx') and to the corresponding sets of coefficients (Cxk,1) a classifier trained to generate, for each flight regime among a plurality of flight regimes, a corresponding estimate of the probability that the unknown smoothed matrix (TFDMx') and the corresponding sets of coefficients (Cxk,1) belong to a cluster relative to said flight regime (Using fitted mathematical models and a data matrix to output an estimate of a probability, i.e. a number, is a mathematical calculation and therefore a mathematical concept.); and
identifying (208) a flight regime wherein the aircraft operated during said flight, based on the estimates generated by the classifier (A human could mentally estimate a particular maneuver an aircraft is executing and its corresponding flight state based on e.g. visually watching the aircraft, meaning that this claim recites a mental process.).
As explained above, independent claim 1 recites at least one abstract idea under Step 2A, Prong 1.
Under Step 2A, Prong 2, the claims are analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements such as merely using a computer to implement an abstract idea, adding insignificant extra-solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a "practical application"; see at least MPEP 2106.04(d).
In this case, the mental processes and mathematical concepts are not integrated into a practical application. For example, independent claim 1 and dependent claims 2, 4-11, and 13 recite additional elements. These/this limitation(s) amount to implementing the abstract idea on a computer, add insignificant extra-solution activity, and/or generally link use of the judicial exception to a particular technological environment or field of use; see at least MPEP 2106.04(d). More specifically,
an aircraft (1) equipped with a monitoring system (2)… This limitation amounts to generally linking the use of the abstract idea to a particular technological environment or field of use.
acquiring (202) at least one unknown matrix… found in independent claim(s) 1. This limitation amounts to insignificant extra-solution activity.
Processing system… found in dependent claim(s) 11. This limitation amounts to implementing the abstract idea on a computer.
A non-transitory computer medium readable… found in dependent claim(s) 12. This limitation amounts to implementing the abstract idea on a computer.
Therefore, taken alone, the additional elements do not integrate the abstract idea into a practical application. Furthermore, looking at the additional limitation(s) as an ordered combination or as a whole, the limitations add nothing significant that is not already present when looking at the elements taken individually. Because the additional elements do not integrate the abstract idea into a practical application by imposing meaningful limits on practicing the abstract idea, independent claim(s) 1 and dependent claims 2, 4-11, and 13 is/are directed to an abstract idea.
Under Step 2B, the claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application in Step 2A, Prong Two, the additional element of limiting the use of the idea to one particular environment employs generic computer functions to execute an abstract idea and, therefore, does not add significantly more. Mere instruction to apply an exception using generic computer components and limiting the use of the abstract idea to a particular environment or field of use cannot provide an inventive concept. Additionally, as discussed above, the remaining limitation(s) as recited above, is/are considered insignificant extra-solution activity.
A conclusion that an additional element is insignificant extra-solution activity in Step 2A must be re-evaluated in Step 2B to determine if the element is more than what is well-understood, routine, and conventional in the field. In this case, the additional limitations of a Processing system… and A non-transitory computer medium readable … are well-understood, routine, and conventional activity, because the specification does not provide any indication that the subject matter is/are anything more than conventional computer(s). Additionally, the remaining element(s), has/have been deemed insignificant extra-solution activity by one or more courts; see at least MPEP 2106.05(d) and MPEP 2106.05(g):
acquiring (202) at least one unknown matrix… is considered well-understood, routine, and conventional activity under CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (mere data gathering in conjunction with a law of nature or abstract idea ).
Because the claims fail to recite anything sufficient to amount to significantly more than the judicial exception, independent claim(s) 1 and dependent claims 2, 4-11, and 13 is/are patent ineligible under 35 U.S.C. 101.
Dependent claims 2, 4-11, and 13 have been given the full two-part analysis, including analyzing the additional limitations, both individually and in combination. Dependent claims 2-13, when analyzed both individually and in combination, are also patent ineligible under 35 U.S.C. 101 based on the same analysis as above. The additional limitations recited in the dependent claims fail to establish that the dependent claims are not directed to an abstract idea. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea. Accordingly, claims 2-13 are patent ineligible under 35 U.S.C. 101.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 4-5, 7, 11, and 13 are rejected under 35 USC 103 as being obvious over NPL document “Airborne Sensor Data-Based Unsupervised Recursive Identification for
UAV Flight Phases”, hereinafter “Wang”, in view of NPL document “Smoothing and Differentiation of Data by Simplified Least Squares Procedures”, hereinafter “Savitzky”.
Regarding claim 1, Wang discloses Method implemented by computer (19) for detecting flight regimes of an aircraft (1) equipped with a monitoring system (2) configured to acquire samples of a number of quantities relating to the flight of the aircraft, comprising the steps of (See Abstract, the method detects UAV, i.e. aircraft, flight phases, i.e. flight regimes, using sensor data. Using sensor data for this purpose indicates the aircraft has a monitoring system that acquires samples of quantities related to the aircraft flight. See section IV. Experiment and Discussion, the method is implemented on a computer to test the algorithm.):
- acquiring (202) at least one unknown matrix (TFDMx) including, for each quantity, a corresponding series of samples (sxi[n] - sxNQn]) acquired by the monitoring system (2) during a flight of the aircraft (See page 10734 column 2 paragraph 3-page 10735 column 1 paragraph 3, Gaussian mixture model clustering is used to identify the flight phases. DxN matrices of sensor data, are used to perform the clustering. See page 10736 column 2 paragraph 1-3, DxN matrices of sensor data are the UAV flight data, i.e. are N samples of D quantities collected during flight of the aircraft.);
- performing (204) smoothing operations of each series of samples (sx1[n]-sxNQ[n]) of the unknown matrix (TFDMx), so as to generate a corresponding smoothed sample series (sx'i[n]- sx'NQ[n]) and so as to determine a corresponding approximating function (Fxi(t)) defined by a respective set of coefficients (Cxk,1) and by a plurality of base functions ((k(t)), the smoothed sample series forming an unknown smoothed matrix (TFDMx') (See page 10736 column 2 paragraph 1-3, a moving average filter is used to smooth the flight data after standardization, creating the smoothed matrix. See section II. Methodologies, the flight data is approximated a Gaussian mixture model, which fits several multivariable Gaussian functions, i.e. basis functions, to the data, and determines a weight coefficient for each fitted Gaussian function.);
- on the basis of the base functions (qk(t)), applying (206) to the unknown smoothed matrix (TFDMx') and to the corresponding sets of coefficients (Cxk,1) a classifier trained to generate, for each flight regime among a plurality of flight regimes, a corresponding estimate of the probability that the unknown smoothed matrix (TFDMx') and the corresponding sets of coefficients (Cxk,1) belong to a cluster relative to said flight regime (See section II. Methodologies, especially page 10734 column 2 paragraph 6, the determined Gaussian mixture model is used to determine that the probability that the input data belongs to a given category, i.e. flight regime. The Gaussian mixture model is therefore a classifier for flight regimes. See page 10736 column 2 paragraph 1-3, the smoothed data matrix is used for flight regime identification.); and
- identifying (208) a flight regime wherein the aircraft operated during said flight, based on the estimates generated by the classifier (See page 10737 column 2 paragraph 3-4, after the clustering tasks are complete, labels corresponding to each flight phase are assigned to the aircraft at times at each data point and therefore corresponding time. See page 10737 column 1 paragraph 5-column 2 paragraph 2, the Gaussian mixture model, i.e. the classifier, is used to perform the clustering operation.).
Wang does not explicitly disclose a corresponding time-continuous approximating function.
Savitzky renders obvious a corresponding time-continuous approximating function (See page 1628 column 3 paragraph 6-page 1629 column 3 paragraph 3, the method fits polynomials, which are continuous in the input variables, i.e. are time-continuous approximating functions, to the data. See page 1628 column 1 paragraph 21-column 2 paragraph 1, the data can be smoothed by applying the convolution operator. See page 1638 paragraph 1 and programs 1 and 2, the polynomial fit is presented as a convolution operator and used for smoothing the data. Because the convolution operator applied to the data produces a weighted sum of the coefficients of the fitted polynomial and the data values. Use of the resulting smoothed data points therefore comprises use of the coefficients. It would be obvious to try, with a reasonable chance of success, smoothing the data with the Savitzky-Golay filter.).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for clustering of flight data, including preprocessing the data by smoothing, disclosed by Wang to include use of a least squares filted polynomial filter of Savitzky. One of ordinary skill in the art would have been motivated to make this modification in order to reduce noise in the data, as suggested by Savitzky at page 1630 column 1 paragraph 2.
Regarding claim 2, Wang combined with Savitzky renders obvious the limitations of claim 1. Gafney renders obvious wherein said smoothing operations of each series of samples (sxl[n]-sxNQ[n]) of the unknown matrix (TFDMx) is such that the corresponding smoothed sample series (sx'l[n]-sx'NQ[n]) is formed by values of the corresponding approximating function (Fx1(t)) (See page 1628 column 3 paragraph 6-page 1629 column 3 paragraph 3, the method fits polynomials, which are continuous in the input variables, i.e. are time-continuous approximating functions, to the data. See page 1628 column 1 paragraph 21-column 2 paragraph 1, the data can be smoothed by applying the convolution operator. See page 1638 paragraph 1 and programs 1 and 2, the polynomial fit is presented as a convolution operator and used for smoothing the data, i.e. the coefficients of the fitted model produce the smoothed data. Because the convolution operator applied to the data produces a weighted sum of the coefficients of the fitted polynomial and the data values. Use of the resulting smoothed data points therefore comprises use of the coefficients. It would be obvious to try, with a reasonable chance of success, smoothing the data with the Savitzky-Golay filter.).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for clustering of flight data, including preprocessing the data by smoothing, disclosed by Wang to include use of a least squares filted polynomial filter of Savitzky. One of ordinary skill in the art would have been motivated to make this modification in order to reduce noise in the data, as suggested by Savitzky at page 1630 column 1 paragraph 2.
Regarding claim 4, Wang combined with Savitzky renders obvious the limitations of claim 1. Wang further discloses wherein said classifier has been generated by performing the steps of, for each flight regime of said plurality of flight regimes:
- for each time interval of a plurality of time intervals in which said aircraft (1) or one or more aircrafts other than said aircraft and equipped with respective monitoring systems have operated in the flight regime, acquiring (102) corresponding training matrices (TFDM[j,m]), each of which includes, for each quantity, a corresponding series of training samples (sij[n]) (See Section C Subsection 2) Real Data Experiment, data from real UAV sensors is gathered and flight phases, i.e. regimes, are identified. This means that the aircrafts were operated in the regime. See page 10734 column 2 paragraph 3-page 10735 column 1 paragraph 3, Gaussian mixture model clustering is used to identify the flight phases. DxN matrices of sensor data, are used to perform the clustering. See page 10736 column 2 paragraph 1-3, DxN matrices of sensor data are the UAV flight data, i.e. are N samples of D quantities collected during flight of the aircraft.);
- for each training matrix (TFDM[j,m]), performing (106) smoothing operations of each series of training samples (sij[n]) of the training matrix (TFDM[j,m]) so as to generate a corresponding smoothed series of training samples (s'ij[n]) and so as to determine a corresponding approximating function (Fij(t)) defined by a respective set of coefficients (C,ij) and by said plurality of base functions (pk(t)), the smoothed series of training samples (s'ij[n]) forming a smoothed training matrix (TFDM'[j,m]) (See page 10736 column 2 paragraph 1-3, a moving average filter is used to smooth the flight data after standardization, creating the smoothed matrix. See section II. Methodologies, the flight data is approximated a Gaussian mixture model, which fits several multivariable Gaussian functions, i.e. basis functions, to the data, and determines a weight coefficient for each fitted Gaussian function. See section II. Methodologies, especially page 10734 column 2 paragraph 6, the determined Gaussian mixture model is used to determine that the probability that the input data belongs to a given category, i.e. flight regime. The Gaussian mixture model is therefore a classifier for flight regimes. See page 10736 column 2 paragraph 1-3, the smoothed data matrix is used for flight regime identification.);
- for each smoothed training matrix (TFDM'[j,m]), determining (108,110), for each smoothed series of training samples (s'ij[n]) of the smoothed training matrix (TFDM'[j,m]), a corresponding processed series of training samples (s"ij[n]), which is either equal to the smoothed series of training samples (s'ij[n]) or is equal to a temporal shift of the smoothed series of training samples (s'ij[n]), the processed series of training samples (s"ij[n]) forming a corresponding processed training matrix (TFDM"[j,m]) (See page 10736 column 2 paragraph 1-3, a moving average filter is used to smooth the flight data after standardization, creating the smoothed matrix. The smoothed training matrix is also is a processed series of training samples.);
and wherein the classifier has further been generated by performing the step of:
- training (200) the classifier on the basis of observations including, each, a corresponding processed training matrix (TFDM"[j,m]) and the corresponding sets of coefficients (Ck,ij), so as to identify, for each flight regime of said plurality of flight regimes, the centroid of the corresponding cluster (See page 5 column 1 paragraph 5, the classifier uses the clusters. See Section II. Methodologies, the clusters are defined by the coefficients of the Gaussian functions used in the Gaussian mixture model. See Figs. 3-4 and page 4 column 2 paragraph 5-page 5 column 1 paragraph 4, a hierarchical process uses selected subsets of data to determine the flight phases of each data point. The final layer in the hierarchy of clusters for a specific data point corresponds to the categorization of that data point. The final layer cluster corresponds to a Gaussian function with corresponding covariance matrix and mean vector, i.e. the centroid.).
Savitzky renders obvious a corresponding time-continuous approximating function (See page 1628 column 3 paragraph 6-page 1629 column 3 paragraph 3, the method fits polynomials, which are continuous in the input variables, i.e. are time-continuous approximating functions, to the data. See page 1628 column 1 paragraph 21-column 2 paragraph 1, the data can be smoothed by applying the convolution operator. See page 1638 paragraph 1 and programs 1 and 2, the polynomial fit is presented as a convolution operator and used for smoothing the data. Because the convolution operator applied to the data produces a weighted sum of the coefficients of the fitted polynomial and the data values. Use of the resulting smoothed data points therefore comprises use of the coefficients. It would be obvious to try, with a reasonable chance of success, smoothing the data with the Savitzky-Golay filter.).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for clustering of flight data, including preprocessing the data by smoothing, disclosed by Wang to include use of a least squares filted polynomial filter of Savitzky. One of ordinary skill in the art would have been motivated to make this modification in order to reduce noise in the data, as suggested by Savitzky at page 1630 column 1 paragraph 2.
Regarding claim 5, Wang combined with Savitzky renders obvious the limitations of claim 4. Savitzky renders obvious wherein, for each training matrix (TFDM[j,m]), said smoothing operations of each series of training samples (sij[n]) of the training matrix (TFDM[j,m]) are such that the corresponding smoothed series of training samples (s'ij[n]) is formed by values of the corresponding approximating function (Fij(t)) (See page 1628 column 3 paragraph 6-page 1629 column 3 paragraph 3, the method fits polynomials, which are continuous in the input variables, i.e. are time-continuous approximating functions, to the data. See page 1628 column 1 paragraph 21-column 2 paragraph 1, the data can be smoothed by applying the convolution operator. See page 1638 paragraph 1 and programs 1 and 2, the polynomial fit is presented as a convolution operator and used for smoothing the data, i.e. the coefficients of the fitted model produce the smoothed data. Because the convolution operator applied to the data produces a weighted sum of the coefficients of the fitted polynomial and the data values. Use of the resulting smoothed data points therefore comprises use of the coefficients. It would be obvious to try, with a reasonable chance of success, smoothing the data with the Savitzky-Golay filter.).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for clustering of flight data, including preprocessing the data by smoothing, disclosed by Wang to include use of a least squares filted polynomial filter of Savitzky. One of ordinary skill in the art would have been motivated to make this modification in order to reduce noise in the data, as suggested by Savitzky at page 1630 column 1 paragraph 2.
Regarding claim 7, Wang combined with Savitzky renders obvious the limitations of claim 4. Wang further discloses wherein said step of training (200) the classifier comprises determining, for each observation, respective degrees of membership to the clusters, said method comprising associating (201) to each cluster a corresponding flight regime among said plurality of flight regimes, as a function of the flight regimes to which the observations refer and of the degrees of membership to the clusters of the observations (See page 10737 column 1 paragraph 5, each observation has a probability of membership, i.e. degree of membership, to each cluster. Observations are assigned to clusters by taking the maximum probability of membership cluster. See page 10737 paragraph 1-2, the clusters correspond to flight phase, i.e. regimes.).
Regarding claim 11, Wang combined with Savitzky renders obvious the limitations of claim 1. Wang further discloses Processing system comprising means configured to carry out the method according to claim 1 (See Section IV. Experiment and discussion, the method is tested on sets of simulated and real data. This indicates that the method was implemented on a computer, i.e. a processing system.).
Regarding claim 13, Wang combined with Savitzky renders obvious the limitations of claim 1. Wang further discloses A non-transitory computer medium readable by a computer (19), on which a computer program is stored, said computer program comprising instructions which, when the program is executed by the computer (19), cause the execution of the method according to claim 1 (See Section IV. Experiment and discussion, the method is tested on sets of simulated and real data. This indicates that the method was implemented on a computer as a computer program. Computer programs executed by a computer are inherently stored on computer readable media. General purpose computers store program instructions on non-transitory media.).
Claim 6 is rejected under 35 U.S.C. 103 as being obvious over Wang and Savitzky in view of NPL document “Functional Data Analysis of Amplitude and Phase Variation”, hereinafter “Marron”.
Regarding claim 6, Wang combined with Savitzky renders obvious the limitations of claim 4. Wang combined with Savitzky does not explicitly disclose wherein the classifier has further been generated by performing the step of:
- for each quantity, performing (108) a shift registration procedure of the approximating functions (Fij(t)) relative to the smoothed series of training samples (s'ij[n]) relative to the quantity, so as to determine, for each of said approximating functions (Fij(t)), a corresponding shifted approximating function (F*ij(t)), which is temporally shifted with respect to the corresponding approximating function (Fij(t)) by a corresponding phase shift (Aij);
and wherein, in each processed training matrix (TFDM"[j,m]), each processed series of training samples (s"ij[n]) is obtained by shifting the corresponding smoothed series of training samples (s'ij[n]) by a time equal to the phase shift (Aij) present between the corresponding approximating function (Fij(t)) and the corresponding shifted approximating function (F*ij(t)).
Marron, in same field of endeavor, renders obvious - for each quantity, performing (108) a shift registration procedure of the approximating functions (Fij(t)) relative to the smoothed series of training samples (s'ij[n]) relative to the quantity, so as to determine, for each of said approximating functions (Fij(t)), a corresponding shifted approximating function (F*ij(t)), which is temporally shifted with respect to the corresponding approximating function (Fij(t)) by a corresponding phase shift (Aij) (See Section 2.5 Amplitude/Phase Separation via Equivalence classes, identifying and shifting the phase variations, i.e. performing a shift registration and removing it, can allow better analysis of the shapes of the curves. See Fig. 1, the wine NMR data is discrete gathered data. See Fig. 10, curves fitted to the wine data are shifted. See page 13 column 2 paragraph 4-page 14 column 1 paragraph 1, the curves are shifted by shifting the data, i.e. the training samples are used to determine the shift of the approximating function.);
and wherein, in each processed training matrix (TFDM"[j,m]), each processed series of training samples (s"ij[n]) is obtained by shifting the corresponding smoothed series of training samples (s'ij[n]) by a time equal to the phase shift (Aij) present between the corresponding approximating function (Fij(t)) and the corresponding shifted approximating function (F*ij(t)) (See Section 2.5 Amplitude/Phase Separation via Equivalence classes, identifying and shifting the phase variations, i.e. performing a shift registration and removing it, can allow better analysis of the shapes of the curves. See Fig. 1, the wine NMR data is discrete gathered data with fitted splines. See Fig. 10, curves fitted to the wine data are shifted. See page 13 column 2 paragraph 4-page 14 column 1 paragraph 1, the curves are shifted by shifting the data, i.e. the training samples are used to determine the shift of the approximating function. This means that the training samples were shifted. Splines are piecewise polynomials. This means that the underlying polynomials and therefore the entire curve fit are shifted by the same amount that the input data is shifted.).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for clustering of flight data, including preprocessing the data by smoothing, disclosed by Wang and Savitzky to include removal of phase variation from the data and curves of Marron. One of ordinary skill in the art would have been motivated to make this modification in order to more easily analyze the shape of the data and resulting curves, as suggested by Marron at page 6 column 1 paragraph 3-column 2 paragraph 1.
Claim 8 is rejected under 35 U.S.C. 103 as being obvious over Wang and Savitzky in view of NPL document “An improved kernel regression method based on Taylor expansion”, hereinafter “Zhang”.
Regarding claim 8, Wang combined with Savitzky renders obvious the limitations of claim 1. Wang combined with Savitzky does not explicitly disclose wherein said smoothing operations comprise performing a kernel nearest neighbour smoothing.
Zhang, in the same field of endeavor and solving a related problem, renders obvious wherein said smoothing operations comprise performing a kernel nearest neighbour smoothing (See page 420 paragraph 2, k-nearest neighbor smoothing, i.e. kernel nearest neighbor smoothing, is a standard technique for smoothing. Smoothing can improve prediction ability. It would be obvious to try, with a reasonable chance of success, smoothing with the kernel nearest neighbor smoother before smoothing with the Savitzky-Golay filter.). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for segmenting aircraft data using a clustering algorithm disclosed by Wang and Savitzky to include use of a nearest neighbor smoother of Zhang before smoothing with the Savitzky-Golay filter. One of ordinary skill in the art would have been motivated to make this modification in order to improve the prediction ability of the fitted model, as suggested by Zhang at page 420 paragraph 2.
Claims 9-10 are rejected under 35 U.S.C. 103 as being obvious over Wang and Savitzky in view of NPL document “Fuzzy clustering”, hereinafter “Wikipedia”.
Regarding claim 9, Wang combined with Savitzky renders obvious the limitations of claim 1. Wang combined with Savitzky does not explicitly disclose wherein the classifier is of the fuzzy type.
Wikipedia, in the same field of endeavor, renders obvious wherein the classifier is of the fuzzy type (See sections “Comparison to hard clustering” and “Example”, fuzzy clustering allows data points to have degrees of membership to multiple categories. This allows for more expressive and accurate representation to data points that occur at a transition between two states, as would be observed in flight sensor data when the aircraft switches between maneuvers, i.e. flight regimes.)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for segmenting aircraft data using a clustering algorithm disclosed by Wang combined with Savitzky to include use a fuzzy classifier of Wikipedia. One of ordinary skill in the art would have been motivated to make this modification in order to improve provide better categorization of data points in the transition between flight regimes, as suggested by Wikipedia in sections “Comparison to hard clustering” and “Example”.
Regarding claim 10, Wang combined with Savitzky and Wikipedia renders obvious the limitations of claim 9. Wikipedia further discloses wherein the classifier is a fuzzy C-means classifier (See section “Fuzzy C-means clustering”, Fuzzy C-means clustering, i.e. fuzzy C-means classification, is one of the most widely used fuzzy clustering algorithms.).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system for segmenting aircraft data using a clustering algorithm disclosed by Wang combined with Savitzky to include use a fuzzy C-means classifier of Wikipedia. One of ordinary skill in the art would have been motivated to make this modification in order to improve provide better categorization of data points in the transition between flight regimes, as suggested by Wikipedia in sections “Comparison to hard clustering” and “Example”.
Response to Arguments
(A) Applicant argues “35 U.S.C. §101
Claims 1-11 stand rejected under 35 U.S.C. §101. Applicant respectfully traverses.
Accordingly, reconsideration and withdrawal of the rejection of claims under 35 U.S.C.§101 is respectfully requested.
As explained above, by determining, from a sample matrix (i.e. the unknown matrix) a smoothed matrix (i.e., the unknown smoothed matrix) and time-continuous approximating functions defined by sets of coefficients (referred to a set of base functions) and by applying a classifier to the smoothed matrix and the set of coefficients, i.e. by making the classification depend on the shape over time of the time-continuous approximating functions, it is possible to improve the accuracy of the classification, thereby identifying a flight regime with a better accuracy. That directly and unambiguously derives from the wording of claim 1, which specifies (emphasis added) :
- on the basis of the base functions (<Pk(t)), applying (206) to the unknown smoothed matrix (TFDMx') and to the corresponding sets of coefficients (Cxki) a classifier trained to generate, for each flight regime among a plurality of flight regimes, a corresponding estimate of the probability that the unknown smoothed matrix (TFDMx') and the corresponding sets of coefficients (Cxk.1) belong to a cluster relative to said flight regime; and
- identifying (208) a flight regime wherein the aircraft operated during said flight, based on the estimates generated by the classifier.
Therefore, it is readily apparent that claim 1 integrates into a very specific practical application (i.e., the identification of flight regimes).
In view of the above, claim 1 it is not objectionable under 35 USC§101.”
As to (A), Examiner does not find the argument persuasive. A human could e.g. visually and using reason, estimate the current maneuver, i.e. regime, of an airplane given flight data. Execution of a mental process on a computer is not sufficient to show integration into a practical application.
(B) Applicant argues “35 U.S.C. 102
Claims 1, 4, 7, and 11-13 stand rejected under 35 U.S.C. §102(a)(1) on the grounds of allegedly being anticipated by NPL document "Airborne Sensor Data-Based Unsupervised Recursive Identification for UAV Flight Phases", hereinafter "Wang". Applicant respectfully traverses.
35 U.S.C. 103
Claims 2-3 stand rejected under 35 U.S.C.§103 on the grounds of allegedly being unpatentable over Wang in view of NPL documents "Spatio-temporal clustering methods classification", hereinafter "Tork", and "Unsupervised Dimensionality Reduction for Gaussian Mixture Model", hereinafter "Yang". Applicant respectfully traverses.
Claim 4 stands rejected under 35 U.S.C. § 103 on the grounds of allegedly being unpatentable over Wang in view Yang. Applicant respectfully traverses.
Claim 6 stands rejected under 35 U.S.C. § 103 on the grounds of allegedly being
unpatentable over Wang and Tork in view of NPL document "Data Augmentation Methods for Machine-learning-based Classification of Bio-signals", hereinafter "Sakai". Applicant respectfully traverses.
Claim 8 stands rejected under 35 U.S.C. §103 on the grounds of allegedly being
unpatentable over Wang in view of NPL document "An improved kernel regression method based on Taylor expansion", hereinafter "Zhang". Applicant respectfully traverses.
Claims 9-10 stand rejected under 35 U.S.C. §103 on the grounds of allegedly being
unpatentable over Wang in view of NPL document "Fuzzy clustering", hereinafter "Wikipedia". Applicant respectfully traverses.
Amendments to the claims
Claim 1 has been amended to specify that the approximating function is time-continuous, as mentioned in the original claim 3 (deleted). Claim 2 has been amended accordingly, by deleting the passage specifying that the approximating function is a function of time.
Also claim 4 has been amended so as to specify that also the approximating function determined by performing the smoothing operation on each series of training samples is of the time-continuous type.
Claim 12 has been deleted and claim 13 has been amended so as to specify that the computer medium is non-transitory.
Furthermore, as a minor formal amendment, claim 1 has been amended so as to clarify that the series of samples (sx1[n] - sxNQ[n]) is acquired by the monitoring system during a flight of the aircraft. Finally, a typo has been corrected in claim 1: the passage "applying (206) to the smoothed unknown matrix (TFDMx')" has been changed into "applying (206) to the unknown smoothed matrix (TFDMx') ", to adopt a wording coherent with the preceding part of claim 1.
Objections under 35 USCG 102 and 103
As a preliminary remark, here below is reported the new claim 1 (emphasis added):
1. Method implemented by computer (19) for detecting flight regimes of an aircraft (1) equipped with a monitoring system (2) configured to acquire samples of a number of quantities relating to the flight of the aircraft, comprising the steps of:
- acquiring (202) at least one unknown matrix (TFDMx) including, for each quantity, a corresponding series of samples (sxi[n] - sxNQ[n]) acquired by the monitoring system (2) during a flight of the aircraft;
- performing (204) smoothing operations of each series of samples (sxl[n]-sxNQ[n]) of the unknown matrix (TFDMx), so as to generate a corresponding smoothed sample series (sx'inl-sx'NO n 1) and so as to determine a corresponding time-continuous approximating function (Fxi(t) ) defined by a respective set of coefficients (Cxk.i) and by a plurality of base functions ((k(t)),the smoothed sample series forming an unknown smoothed matrix (TFDMx');
- on the basis of the base functions (<pk(t)), anlying (206) to the unknown smoothed matrix (TFDMx') and to the corresponding sets of coefficients (Cxk.i) a classifier trained to generate, for each flight regime among a plurality of flight regimes, a corresponding estimate of the probability that the unknown smoothed matrix (TFDMx') and the corresponding sets of coefficients (Cxk.i) belong to a cluster relative to said flight regime;and8
- identifying (208) a flight regime wherein the aircraft operated during said flight, based on the estimates generated by the classifier.
This having been said, it is of the utmost importance to notice that, according to the above claim 1:
i) the smoothing operations mentioned in claim 1 refer to basis/kernel smoothing adopted inthe functional data analysis (see as an example [AltContent: rect]ui[AltContent: rect]kipegli[AltContent: rect]asshownas an example in the original Figure 5 and explained in the description from page 9, line 14 to page 11, line 20; in particular, according to claim 1, the smoothing operations amount to determining (among other things) a time-continuous approximating function, which is defined by a respective set of coefficients (Cxk.1) and by a plurality of base functions (<pk(t)), as visually represented here below (note the dependency on time):
Basis/KernelSmoothing[AltContent: rect]
(discretedata->continuum data)
[AltContent: rect]After the set of coefficients and the base functions have been determined, it is possible to determine any value of the approximating function, i.e. the corresponding value at any time instant; equivalently, determining the set of coefficients (Cxk,1) amounts to determining the shape over time of the time-continuous approximating function. As clearly visible above, the time-continuous profile of an approximating function (i.e, the bold continuous line as opposed to the discrete samples) provides for much more information than the set of samples alone. In view of the above, it is readily apparent that the smoothing mentioned in claim 1 has not to be mistaken for a low pass
filtering of a sequence of discrete samples (like in WANG, as explained here below), which merely leads to a smoothed sequence of discrete samples.
ii) The classifier is applied to:
-the smoothed unknown matrix (TFDMx') (i.e., to the smoothed sample series
(sx'1[n]-sx'NQ[n])); and
-the corresponding sets of coefficients (Cxk.1);
therefore the classification depends on the shape over time of the approximating functions (Fx;(t)). In other words, the classification is carried out on an "observation" including the smoothed unknown matrix and the corresponding sets of coefficients, so as to take in consideration not only the discrete samples, but also the shapes over time of the underlying time- continuous approximating functions, which are defined by the corresponding sets of coefficients, thereby improving the accuracy of the classification.
In facts, according to the claim 1, the classifier is applied (as an example) to the following "observation" (taken from the original Figure 12):
[AltContent: rect][AltContent: rect]''"''[AltContent: rect] . 1 2
whichincludesthesmoothedunknownmatrix(TFDMx')andfivesetsofcoefficients(Cxk,1;Cxk,2; Cxk,3; Cxk,4; Cxk,5), these latter defining five corresponding time-continuous approximating functions. Still from another point of view, according to claim 1, the whole observation (matrix10
and sets of coefficients) is classified, as opposed to, as an example, classifying only single rows of the matrix.
This having been said, WANG ("Airborne sensor data-based unsupervised recursive identification for UAV flight phases", IEEE SENSORS JOURNAL, vol.20, no.18, September 15, 2020, of Benkuan Wang et al.) discloses a Gaussian Mixture Model (GMM) clustering to identify flight phases. Furthermore, in order to reduce the identification errors caused by the fluctuations of the flight data, the flight data are pre-processed to achieve data smoothing. However, the smoothing mentioned in WANG has nothing to do with the smoothing according to the present claim 1. In facts, WANG mentions that (column 10736, right-hand column) "the moving average filter is used to smooth the standardized flight data"; put in other words, this smoothing is equivalent to apply a low pass filter to the data (as shown here below, as an example), as opposed to determining the shape over time of a time-continuous approximating function.
[AltContent: rect][AltContent: rect]This having been said, it has to be noted that the GMM clustering is applied to discrete data (i.e., points), as opposed to approximating functions in the time domain. In detail, assuming a number M of classes, GMM provides for representing each class through a corresponding multi- variate Gaussian distribution. In greater detail, referring for the sake of simplicity to only two quantities/parameters (i.e., the features i and j), WANG provides for the following steps.
[AltContent: rect][AltContent: rect]In each of the above two Figures on the left, the points of each monitored feature form a corresponding time sequence of points, which are referred to subsequent time instants; this time instants are the same for both the feature i and the feature j; on the contrary, in the above Figure on the right, the arrangement of the points is no more time-depending. This having been said, according to WANG, a number of multivariate Gaussian distribution equal to the number M of classes is determined, through the so-called "expectation maximization algorithm", as represented here below (please note that the points are not coherent with the example above), wherein the level lines of three Gaussian bivariate distributions are visible.
[AltContent: rect][AltContent: rect]
It is thus readily apparent that the above Gaussian multivariate distribution functions do not have any dependence on time (they cannot have it, because, as clearly visible here above, they are computed based on the spatial arrangement of the points in the two-dimensional space (feature i -feature j)). Therefore, the Gaussian multivariate distribution functions have nothing to do with the time-continuous approximating functions mentioned in claim 1.
In practice, according to WANG, the Gaussian multivariate distribution functions are used to classify each "point", i.e. any single pair of values of (feature i, feature i), irrespective of the time instant to which the pair refers, as shown as an example here below, wherein two points are shown.
[AltContent: rect][AltContent: rect]Without any prejudice to the foregoing analysis, it has further to be noted that the Examiner's statement on page 16, last three lines (i.e., the Gaussian function [mentioned in WANG] is continuous with respect to its input vector x. Using this approximating function with time as a feature of the input vector therefore is a time-continuous function) is not technically acceptable: as explained above, in no way the Gaussian functions of WANG can be changed so as to make them depend from time, because "x" is a "D-dimensional vector with N samples" (see page 10734, right column, first line under Equation (1)).
In view of the above, it is thus readily apparent that the classification mechanism proposed by WANG is completely different from the one of the present invention.
In addition, the Applicant respectfully believes that neither YORK or YANG would render obvious the limitations of:
- determining, for each quantity, a corresponding time-continuous approximating function defined by a respective set of coefficients, referred to a plurality of base functions; and
- applying a classifier not only to a sample matrix, but also to the sets of coefficients.
In facts, TORK merely presents strategies for adapting traditional clustering methods (such as DBSCAN and OPTICS) to the analysis of data that include both spatial coordinates and temporal information, such strategies including: using different distance functions (spatial, temporal, spatio-temporal); introducing the temporal dimension into data; transforming the spatio- temporal data into new objects; and carrying out a progressive clustering. No reference is made to determining time-continuous approximating functions defined by corresponding sets of coefficients and performing a (single, non-progressive) classification based on a sample matrix and the sets of coefficients previously determined.
In that concerns YANG, it addresses the challenge of performing dimensionality reduction
(DR) in conjunction with Gaussian Mixture Models (GMMs) for unsupervised learning tasks such as clustering. In particular, dimensionality reduction and GMM parameter estimation are performed simultaneously. The optimization is carried out using a modified Expectation- Maximization (EM) algorithm, which alternates between estimating the latent variables and updating the model parameters, including the dimensionality reduction matrix. No reference is made to determining time-continuous approximating functions defined by corresponding sets of coefficients and performing a classification based on a sample matrix and the sets of coefficients previously determined.
In view of the above, new claim 1 seems to be not obvious over the cited prior art.”
As to (B), Examiner does not find the argument persuasive. Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20180018570 A1 which relates to change point detection, i.e. segmentation, in time series.
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.
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/AUSTIN ROBERT CHENNAULT/Examiner, Art Unit 3667
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
3/25/26
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