DETAILED ACTION
This action is in response to the initial filing filed on December 26, 2022, Claim 1-5 have been examined this application.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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 .
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 1-5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Step 1: Claims 1-5 is/are drawn to method (i.e., a process). As such, claims 1-5 is/are drawn to one of the statutory categories of invention (Step 1: YES).
Under Step 2A Prong 1, the claims are analyzed to determine whether the claims recite any judicial exceptions including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity such as a fundamental economic practice, or mental processes).
Representative Claim 1: A bearings-only target tracking method based on pseudo-linear maximum correlation entropy Kalman filtering, comprising the following steps: Si, initializing a noise variance and a state transition matrix, initializing an initial position state X0|0 of a target, and selecting a Gaussian kernel width a and a convergence determination coefficient et; S2, linearizing a bearings-only observation equation by using a pseudo-linear method, calculating a prior estimated value X k|k-1 and a prior covariance matrix X k|k-1 of the target to be tracked, at which time a sensor obtains angle information of the target, calculating a weighted value of the prior estimation X k|k-1 and the angle information according to an unfixed-point iteration formula of a maximum correlation entropy, then updating a posterior estimated value X k|k-1, calculating a deviation of pseudo-linear Kalman filtering, and instantly compensating on the posterior estimated value to obtain more accurate target tracking information; and S3, when an update of the posterior estimated value satisfies the determination coefficient et, stopping updating, calculating a posterior covariance matrix, and starting the next round of iteration.
(Examiner notes: The underlined claim terms above are interpreted as additional elements beyond the abstract idea and are further analyzed under Step 2A - Prong Two)
Under their broadest reasonable interpretation, the steps of: estimating parameters, tracking a target, using observations, calculating deviations, and determining coefficients (i.e., mathematical relationships), then it also falls within the “Mental Processes” subject matter grouping of abstract ideas.
Further, the steps of estimating parameters, observing signal, obtaining errors, and estimating accuracy (i.e., one or more concepts performed in the human mind, such as one or more observations, evaluations, judgments, opinions), then it also falls within the “Mathematical concepts” subject matter grouping of abstract ideas.
Dependent Claims 2-5 further narrow the abstract idea by applying correlations, mean square calculations and iterative algorithms (i.e., one or more concepts performed in the human mind, such as one or more observations, evaluations, judgments, opinions), then it also falls within the “Mental Processes” and is an abstract idea and then it also falls within the “Mathematical concepts” subject matter grouping of abstract ideas and then also falls within the “Mathematical concepts” subject matter grouping of abstract ideas.
Independent claim(s) 1 recite/describe nearly identical steps (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and this/these claim(s) is/are therefore determined to recite an abstract idea under the same analysis.
As such, the Examiner concludes that claim 1 recites an abstract idea (Step 2A – Prong One: YES).
Under Step 2A Prong 2 the claims are analyzed to determine whether the claims recite additional elements that integrate the judicial exception into a practical application.
Step 2A - Prong Two: In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “addition element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
The requirement to execute the claimed steps/functions using “Kalman filter,” “matrix,” and “target position,” etc. (Claims 1) is/are equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer.
Similarly, the limitations of applying “Kalman filter,” “matrix,” and “target position,” etc. (Independent Claim(s) 1, and dependent claims 2-5 are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components in a vehicle. This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Further, the additional limitations beyond the abstract idea identified above, serves merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, it/they serve(s) to limit the application of the abstract idea to computerized environments (e.g., processing, receiving, estimating, operating, Doppler, and smoothing, etc. steps performed by a predictive model, machine learning algorithms, a communication interface, a memory, a processor, a computational device etc.). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
The recited additional element(s) of A bearings-only target tracking method based on pseudo-linear maximum correlation entropy Kalman filtering, comprising the following steps: Si, initializing a noise variance and a state transition matrix, initializing an initial position state X0|0 of a target, and selecting a Gaussian kernel width a and a convergence determination coefficient et; S2, linearizing a bearings-only observation equation by using a pseudo-linear method, calculating a prior estimated value X k|k-1 and a prior covariance matrix X k|k-1 of the target to be tracked, at which time a sensor obtains angle information of the target, calculating a weighted value of the prior estimation X k|k-1 and the angle information according to an unfixed-point iteration formula of a maximum correlation entropy, then updating a posterior estimated value X k|k-1, calculating a deviation of pseudo-linear Kalman filtering, and instantly compensating on the posterior estimated value to obtain more accurate target tracking information; and S3, when an update of the posterior estimated value satisfies the determination coefficient et, stopping updating, calculating a posterior covariance matrix, and starting the next round of iteration.
(Claim(s) 1), additionally and/or alternatively simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea). This/these limitation(s) do/does not impose any meaningful limits on practicing the abstract idea, and therefore do/does not integrate the abstract idea into a practical application. (See MPEP 2106.05(g)).
Dependent claim 2-5, fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A – Prong two: NO).
Step 2B: In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for an "inventive concept." An "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole-amounts to significantly more than the judicial exception itself.
As discussed above in “Step 2A – Prong 2”, the identified additional elements in independent claim(s) 1, and dependent claims 2-5 are equivalent to adding the words “apply it” on a generic computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
The recited additional element(s) of covariance and correlation calculations (Claim(s) 1), additionally and/or alternatively simply append insignificant extra-solution activity to the judicial exception, (e.g., mere pre-solution activity, such as data gathering, in conjunction with an abstract idea) i.e. estimating noise and entropy (i.e. obtaining data) is similar to “Receiving or transmitting data over a network, e.g., using the Internet to gather data”, is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here) (See MPEP 2106.05(d) (II)).
This conclusion is based on a factual determination. Applicant’s own disclosure at [specification 0005] acknowledges that “research on bearings-only multi-target tracking is very extensive and includes changing tracking methods,” (i.e. conventional nature of tracking an object in the background section as well known in the art). This additional element therefore do not ensure the claim amounts to significantly more than the abstract idea.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer or/and append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, and/or simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.
The dependent claims 2-5 fail to include any additional elements. In other words, each of the limitations/elements recited in respective independent claims is/are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim).
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Therefore, claims 1-5 are not eligible subject matter under 35 USC 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.
Claims 1-5 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al (Remote Sensing, 2021) in view of Chen et al (IEEE, 2015).
Regarding Claim 1, Huang teaches a bearings-only target tracking method based on pseudo-linear maximum correlation entropy Kalman filtering, comprising the following steps [page 3, first paragraph for target is moving under the constant velocity]:
S1, initializing a noise variance and a state transition matrix [page 3, 2nd and 3rd paragraph with equations (2) and (3) for processing noise a covariance matrix],
S2, linearizing a bearings-only observation equation by using a pseudo-linear method [page 4, first 3 paragraphs and equations (6) and (7) for calculating pseudo-linear measurements for noise and bearings],
calculating a prior estimated value Xk|k-1 and a prior covariance matrix P k|k-1 of the target to be tracked, at which time a sensor obtains angle information of the target [page 4, Steps 1-2 and equations (9) and (10)],
then updating a posterior estimated value X k|k-1, calculating a deviation of pseudo-linear Kalman filtering [page 4, last paragraph for determining Kalman gain, with page 5, second 2.2.2 with equations (16) and (17) for determining error, noise, and bias calculations],
and instantly compensating on the posterior estimated value to obtain more accurate target tracking information [page 5, 2nd paragraph and equations (16) and (17) for we can compensate the bias the bias can be reduced].
Huang fails to explicitly teach initializing an initial position state x0|0 of a target, and selecting a Gaussian kernel width a and a convergence determination coefficient et; calculating a weighted value of the prior estimation X k|k-1 and the angle information according to an unfixed-point iteration formula of a maximum correlation entropy, and S3, when an update of the posterior estimated value satisfies the determination coefficient et, stopping updating, calculating a posterior covariance matrix, and starting the next round of iteration.
Chen has a maximum correntropy Kalman filter (abstract) and teaches initializing an initial position state x0|0 of a target [page 4, left column, 2nd paragraph for having an initial estimate with x0|0],
and selecting a Gaussian kernel width a and a convergence determination coefficient et [page 4, left column, 2nd paragraph for choosing a proper kernel bandwidth and small positive number e];
calculating a weighted value of the prior estimation X k|k-1 and the angle information according to an unfixed-point iteration formula of a maximum correlation entropy [page 3, right column, last two paragraph and equations (23-25) for having an optimal solution with a fixed-point equation and iterative algorithm],
and S3, when an update of the posterior estimated value satisfies the determination coefficient et, stopping updating, calculating a posterior covariance matrix, and starting the next round of iteration [page 7, left column, Tables IV and V for using iteration numbers for different variable values].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the bearing-only tracking techniques, as disclosed by Huang, further including the correntropy Kalman filter calculations as taught by Chen for the purpose converge to the optimal solution in just one or two iterations (Chen, page 6, left column, last paragraph).
Regarding Claim 2, Huang fails to explicitly teach for the noise variance and state transition matrix in S1 [page 2, left column, 2-4th paragraph and equations 2-3 for using correntropy with a Gaussian Kernal],
qx and qy are power spectral densities of noise in X axis and Y axis, and T is an iteration time interval [page 2, left column, 2-4th paragraph and equations 2-3 for using correntropy with a Gaussian Kernal].
Huang fails to explicitly teach the convergence determination coefficient e1 is set to be a positive number less than one ten thousandth; correlation entropy is described as generalized similarity between two random variables; for variables with joint distribution functions: where k(-,) represents a scale-invariant Mercer kernel, the scale-invariant Mercer kernel is adopted as a Gaussian kernel, and the formula of the Gaussian kernel is: k(x, y) = Gs (x - y) = exp (_- where the Gaussian kernel width - > 0 is set; because a joint probability distribution function Fx is unknown, N samples are used to estimate the correlation entropy fHxy between two variables; Taylor expansion is conducted on the correlation entropy formula: the correlation entropy is a weighted sum of even moments of errors; and because the correlation entropy contains the information of high moments of errors, maximum correlation entropy Kalman filtering has better performance in dealing with non-Gaussian noise.
Chen has a maximum correntropy Kalman filter (abstract) and teaches convergence determination coefficient e1 is set to be a positive number less than one ten thousandth [page 7, left column, Tables IV and V for using iteration numbers for different variable values];
correlation entropy is described as generalized similarity between two random variables [page 2, left column first 4 paragraph and equations 1-3 for using estimating the correntropy as a weighted sum],
for variables with joint distribution functions: where k(.,.) represents a scale-invariant Mercer kernel, the scale-invariant Mercer kernel is adopted as a Gaussian kernel, and the formula of the Gaussian kernel is: k(x, y) = G s(x - y) = exp (-(x-y)2/2 s2) where the Gaussian kernel width s > 0 is set [page 2, left column first 4 paragraph and equations 1-3 for using estimating the correntropy as a weighted sum];
because a joint probability distribution function Fx is unknown, N samples are used to estimate the correlation entropy fHxy between two variables [page 2, left column first 4 paragraph and equations 1-3 for using estimating the correntropy as a weighted sum],
Taylor expansion is conducted on the correlation entropy formula [page 2, left column first 4 paragraph and equations 4 for using estimating the correntropy as a weighted sum]:
the correlation entropy is a weighted sum of even moments of errors [page 2, left column, last two paragraph for the correntropy is a weighted sum of all even order moments of the random variable];
and because the correlation entropy contains the information of high moments of errors, maximum correlation entropy Kalman filtering has better performance in dealing with non-Gaussian noise [page 3, left column, first paragraph for using a derived Kalman filter perform much better in non-Gaussian noise environments, since correntropy contains second and higher order moments of the error].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the bearing-only tracking techniques, as disclosed by Huang, further including the correntropy Kalman filter calculations as taught by Chen for the purpose converge to perform much better in non-Gaussian noise environments (Chen, page 3, left column, first paragraph).
Regarding Claim 3, Huang teaches S2 comprises the following sub-steps: S201, firstly, giving a bearings-only target positioning model as follows, where xk is a velocity state at a target position [page 3, last three paragraphs and equations 1, 4-5 for Gaussian random vector with mean and covariance matrix],
is a sensor observation angle, and ek is measurement noise [page 7, Section 2.3.1 for measuring noise];
where f(xx) = tan-1(pyk - Sy,k/px,k - sx,k) is a nonlinear equation, and using pseudo-linear estimation, a linear form of the observation equation is expressed as [page 4, first two paragraphs and equations 7-8 for using a noise independent process with the Kalman filter]:
is defined as a vector from the sensor to the target, and symbol represents an Euclidean norm [page 5, left column, Section C, first two paragraph for calculating the vector norm];
and pseudo-linear noise Ilk is defined as therefore, by a pseudo-linear method, the bearings-only target model is converted into [page 4, first two paragraphs and equations 7 for sine and cosine calculations],
S202, calculating the prior estimated value X k|k-1 and the prior covariance matrix PkIk-1 of the target to be tracked with the following calculation mode [page 4, 4th paragraph and step 1 with equations (8) for doing pseudo-linear calculation]:
where X k|k-1 represents the position and velocity of the target to be tracked at the last moment, that is, posterior estimation calculated by an algorithm at the last moment, and the prior estimated value at the current moment is obtained by multiplying X k|k-1 by a target state transition matrix A at the current moment [page 4, last paragraph for corresponding prediction error covariance matrix with Kalman gain];
the covariance matrix refers to a mean square matrix of a state estimation error [page 4, last paragraph].
Huang fails to explicitly teach the covariance matrix is an identity matrix at the initial moment, and the prior estimated value of target estimation is random, which will converge to a target position with the iteration of the algorithm; and S203, updating the posterior estimated value X k|k-1,t according to the unfixed-point iteration formula of a maximum correlation entropy: where Kk is after the posterior estimation X k|k-1 is calculated, deviation compensation is conducted.
Chen has a maximum correntropy Kalman filter (abstract) and teaches the covariance matrix is an identity matrix at the initial moment, and the prior estimated value of target estimation is random, which will converge to a target position with the iteration of the algorithm [page 4, left column, equation 28 for computing fixed point iterations];
and S203, updating the posterior estimated value X k|k-1,t according to the unfixed-point iteration formula of a maximum correlation entropy [page 4, left column, equation 29-34 for using Kalman filter and fixed point iterations for estimates using the bandwidth as a key parameter],
where Kk is after the posterior estimation X k|k-1 is calculated, deviation compensation is conducted [page 4, left column, equation 29-34 for using Kalman filter and fixed point iterations for estimates using the bandwidth as a key parameter].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the bearing-only tracking techniques, as disclosed by Huang, further including the correntropy Kalman filter calculations as taught by Chen for the purpose to ensure a fast optimal solution (Chen, page 4, right column, first paragraph).
Regarding Claim 4, Huang teaches the deviation compensation process comprises: giving a posterior estimation form of pseudo-linear maximum correlation entropy Kalman filtering [page 4, last 3 paragraphs and equations 11-12 for estimating covariance and Kalman gain]:
according to matrix inversion lemma,: the above formula changes to: after algebraic operation, the error representation of a real value and the estimation is obtained, which comprises three parts: Xk - Xk M + Bk + rk, where although Mk contains an error from the estimation at the last moment, no estimation deviation will be generated in pseudo-linear Kalman filtering [page 5, first two paragraphs and equations 17-19 for processing noise and bias estimates];
Bk is a deviation caused by the correlation between the observation matrix Hk and process noise Wk1, the process noise Wk_1 is so small that it is directly ignored, and rk is a deviation of correlation between the observation matrix Hk and pseudo-linear observation noise ik [page 5, first two paragraphs and equations 17-19 for processing noise and bias estimates];
rk plays an important role in biased estimation, and can make up for the deviation caused by reduction [page 5, firs paragraph for compensating so the bias can be reduced];
and after the update of XkIk,t, rk is compensated on XkIk,t according to the following formula where Xkiit represents the posterior estimated value after compensation [page 3, equations 20-21 for determining the second order statistics for the error signal].
Regarding Claim 5, Huang teaches S3 comprises after obtaining Xklkt by compensating XkIk,t, comparing xkIkt obtained by current updating with the last iteration value Xklk,t-1 [page 3, right column, and equations 20-21 for determining the second order statistics for the error signal].
Huang fails to explicitly teach and if a result is less than what satisfies the determination coefficient ct, stopping this round of unfixed-point iteration and calculating the posterior covariance matrix Pk returning to Si to start a new round of iteration.
Chen has a maximum correntropy Kalman filter (abstract) and teaches and if a result is less than what satisfies the determination coefficient ct, stopping this round of unfixed-point iteration and calculating the posterior covariance matrix Pk returning to Si to start a new round of iteration [page 4, left column, last two paragraphs for updating the posterior covariance matrix].
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the applicant’s invention for modifying the bearing-only tracking techniques, as disclosed by Huang, further including the correntropy Kalman filter calculations as taught by Chen for the purpose to ensure a fast optimal solution (Chen, page 4, right column, first paragraph).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Nguyen et al (IEEE, 2017) has a bias-compensated pseudo-linear Kalman Filter to construct instrumental variable vectors with desired properties.
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/SAMARINA MAKHDOOM/
Examiner, Art Unit 3648
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