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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 1 is a method claim thus it falls into one of the four categories of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding independent claim 1, following limitations recite a judicial exception:
“altering, for each feature of features of a population to be fused and based on a marginal measurement uncertainty distribution corresponding to a feature of the features and the marginal measurement uncertainty distribution accounting for uncertainty in measuring the feature, a marginal feature distribution of the feature resulting in respective marginal feature distributions that account for measurement uncertainty”
[Mental Process] – altering a marginal feature distribution that account for measurement uncertainty requires to layout the distributions and compare each feature to uncertainty measurements which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“altering, based on a measurement uncertainty covariance of the features, a feature covariance of the features resulting in a covariance that jointly accounts for feature covariance and measurement uncertainty covariance”
[Mental Process] – altering a feature covariance of the features requires to layout both covariances to compare and analyze to account the covariance the both which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“generating, based on the covariance that jointly accounts for feature covariance and measurement uncertainty covariance and the respective marginal feature distributions that account for measurement uncertainty, a joint density function that accounts for feature and measurement uncertainty”
[Mental Process] – generating a joint density function that accounts the both feature and measurement uncertainty requires to layout the joint covariance and the respective marginal feature distributions to compare and analyze to account the covariance the both which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“classifying feature values of the features based on the joint density function that accounts for feature and measurement uncertainty”
[Mental Process] – classifying feature values of the features based on the density requires to compare and analyze the function which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 1 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 2
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 2 is a dependent claim of 1, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 2, following limitations recite a judicial exception:
“altering the marginal feature distribution includes determining, for each point of points in the marginal feature distribution, a weighted sum”
[Mathematical Calculation] – determining a weighted sum of each point requires mathematical computation which recites to an abstract idea.
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 2 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 3
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 3 is a dependent claim of 2, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 3, following limitations recite a judicial exception:
“weights of the weighted sum are measurement uncertainty values in the marginal measurement uncertainty distribution”
[Mathematical Concept] – pointing out that weights of the weighted sum are measurement uncertainty values in the marginal measurement uncertainty ‘distribution” that recites Mathematical (Statistical) Concept, which recites to an abstract
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 3 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 4
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 4 is a dependent claim of 3, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 4, following limitations recite a judicial exception:
“the weights are constrained to a neighborhood of a corresponding point of the points”
[Mathematical Concept] – constraining mathematical values is mathematical concept of restricting local which recites to an abstract
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 4 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 5
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 5 is a dependent claim of 1, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 5, following limitations recite a judicial exception:
“generating the joint density function that accounts for feature values and measurement uncertainty includes using a copula”
[Mathematical Concept] – generating the joint density function using copula recites to Mathematical Concept of copula which recites to an abstract idea
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 5 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 6
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 6 is a dependent claim of 1, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 6, following limitations recite a judicial exception:
“the marginal measurement uncertainty distribution is Gaussian”
[Mathematical Concept] – pointing out that the distribution is Gaussian simply refers to Mathematical Concept which recites to an abstract idea
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 6 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 7
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 7 is a dependent claim of 1, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 7, following limitations recite a judicial exception:
“classifying includes classifying as part of an automatic target recognition (ATR) operation”
[Mathematical Calculation] – automatic target recognition operation is a mathematical algorithm to classify targets that goes through Mathematical Calculations which recites to an abstract idea
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 7 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 8
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 8 is a dependent claim of 1, thus it falls within the same category of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding dependent claim 8, following limitations recite a judicial exception:
“the marginal measurement uncertainty distributions and marginal feature distributions are one-dimensional”
[Mathematical Concept] – pointing out that the distributions are one-dimensional simply refers to Mathematical Concept which recites to an abstract idea
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
The claim 8 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea.
Regarding Claim 9
Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03
Claim 9 is a system claim thus it falls into one of the four categories of statutory subject matter.
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding independent claim 9, following limitations recite a judicial exception:
“altering, for each feature of features of a population to be fused and based on a marginal measurement uncertainty distribution corresponding to a feature of the features and the marginal measurement uncertainty distribution accounting for uncertainty in measuring the feature, a marginal feature distribution of the feature resulting in respective marginal feature distributions that account for measurement uncertainty”
[Mental Process] – altering a marginal feature distribution that account for measurement uncertainty requires to layout the distributions and compare each feature to uncertainty measurements which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“altering, based on a measurement uncertainty covariance of the features, a feature covariance of the features resulting in a covariance that jointly accounts for feature covariance and measurement uncertainty covariance”
[Mental Process] – altering a feature covariance of the features requires to layout both covariances to compare and analyze to account the covariance the both which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“generating, based on the covariance that jointly accounts for feature covariance and measurement uncertainty covariance and the respective marginal feature distributions that account for measurement uncertainty, a joint density function that accounts for feature and measurement uncertainty”
[Mental Process] – generating a joint density function that accounts the both feature and measurement uncertainty requires to layout the joint covariance and the respective marginal feature distributions to compare and analyze to account the covariance the both which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
“classifying feature values of the features based on the joint density function that accounts for feature and measurement uncertainty”
[Mental Process] – classifying feature values of the features based on the density requires to compare and analyze the function which involves observations, evaluations, judgments, and opinions that is capable of being performed in the human mind with the assistance of paper and pen
Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception?
Regarding Claim 9, the claim recites additional elements of
“processing circuitry”
The processing circuitry is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
“a memory including instructions that, when executed by the processing circuitry, causes the processing circuitry to perform operations”
The memory is recited at a high level of generality and is merely adding words “apply it” to the judicial exception. (see MPEP 2106.05(f))
[Even when viewed in combination, the additional elements do no more than automate the mental processes that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.]
Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea?
The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible.
As explained above, the additional elements [1,2] are considered merely computer components that are just to store and execute code-based instructions which are considered a mere instruction to apply an exception and amount to storing and receiving information in memory, which is well-understood, routine, conventional activity (See MPEP 2106.05(d), subsection II). This limitation remains a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional element represents a mere instruction to apply an exception, which cannot provide an inventive concept.
Regarding Claims 10-16
Claims 10-16 have similar limitations of Claims 2-8, respectively. For the reasons described above with respect to Claims 2-8, these judicial exceptions are not meaningfully integrated into a practical application, or significantly more than the abstract ideas. The claims do not provide anything more than the abstract ideas of mental processes and mathematical calculations that are practically capable of being performed with the assistance of pen and paper. Therefore, Claims 10-16 also recite abstract ideas that do not integrate into a practical application or amount to significantly more than judicial exception, and thus are rejected under U.S.C. 101.
Regarding Claims 17-20
Claims 17-20 have similar limitations of Claims 1-4, respectively. For the reasons described above with respect to Claims 1-4, these judicial exceptions are not meaningfully integrated into a practical application, or significantly more than the abstract ideas. The claims do not provide anything more than the abstract ideas of mental processes and mathematical calculations that are practically capable of being performed with the assistance of pen and paper. Therefore, Claims 17-20 also recite abstract ideas that do not integrate into a practical application or amount to significantly more than judicial exception, and thus are rejected under U.S.C. 101.
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.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dunik et al. (Dunik), Non-Patent Literature listed in IDS filed on May 17 2023, “Copula-based Convolution for Fast Point-Mass Prediction”, Published on March 2022, 10 Pages, in view of Campbell et al. (Campbell), US Patent Application No. US-10,304,001-B2, Published in May 28, 2019.
As to independent Claim 1,
Dunik teaches a method comprising:
altering, for each feature of features of a population to be fused and based on a marginal measurement uncertainty distribution corresponding to a feature of the features and the marginal measurement uncertainty distribution accounting for uncertainty in measuring the feature, a marginal feature distribution of the feature resulting in respective marginal feature distributions that account for measurement uncertainty (Dunik, Pg2, Left Column, Section2, Paragraph1, Lines9-12, "Particular realizations of the state and measurement noises wk and vk are unknown , but their PDF(probability density function)s, i.e., the state noise PDF p(wk) and the measurement noise PDF p(vk) , are supposed to be known",
Pg2, Left Column, Paragraph2, Lines1-3, "The proposed fast PMF prediction step decomposes the standard convolution in nx-dimensional space into nx scalar convolutions",
Pg5, Algorithm1, Equation25,
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Pg6, Algorithm2, Equation34,
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, wherein Dunik explicitly discloses altering each marginal feature distribution based on a corresponding marginal uncertainty distribution to generate updated marginal distributions that account for uncertainty. Specifically, Dunik teaches the corresponding marginal feature distribution as Dunik’s individual filtering marginal PDF
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calculated for each feature/state dimension. Dunik also teaches the corresponding marginal measurement uncertainty distribution as the marginal transition density P(xk+1,l|xk,l), which directly incorporates the measurement and system noise PDF p(wk) or p(vk) along feature dimension l. Finally, Dunik shows the corresponding altering which results in marginal feature distributions that account for measurement uncertainty as in Equation 34. Dunik computes the predictive marginal PDF
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by performing a scalar convolution (weighted sum) between each marginal feature density
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and the marginal uncertainty PDF P(xk+1,l|xk,l). Because Dunik’s scalar marginal convolution explicitly transforms each feature’s marginal distribution using its corresponding marginal noise/uncertainty PDF, rendering it functionally equivalent to the claimed invention);
altering, based on a measurement uncertainty covariance of the features, a feature covariance of the features resulting in a covariance that jointly accounts for feature covariance and measurement uncertainty covariance (Dunik, Pg5, Algorithm1, Equation27,
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Pg6, Algorithm2, Equation36,
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, wherein Dunik explicitly calculates the feature covariance part from the grid points and directly adds the measurement noise covariance matrix, cov[wk] (the corresponding measurement uncertainty covariance) to it in Equation36 (the corresponding altering feature covariance), where you can simplify the equation 36 as PPMD,k+1|k = [Feature Covariance Part] + [Noise Covariance Part]. The resulting output PPMD,k+1|k, is precisely the joint covariance matrix that accounts for both feature correlations and measurement uncertainty. Dunik then converts this matrix into the correlation matrix Ck+1|k in Equation27 to feed directly into the Copula, rendering it functionally equivalent to the claimed invention of combining two covariance matrix, the feature covariance and the measurement uncertainty covariance);
generating, based on the covariance that jointly accounts for feature covariance and measurement uncertainty covariance and the respective marginal feature distributions that account for measurement uncertainty, a joint density function that accounts for feature and measurement uncertainty (Dunik, Pg5, Algorithm1, Step5, “Based on the predictive marginal PDFs (25) and approximate copula (26), compute, w.r.t. (16)-(18), the approximate predictive PDF according to”,
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, Pg6, Algorithm2, Step4&5(Equation38,39), “4. Compute the correlation matrix Ck +1 | k (27) on the basis of (35), (36) and evaluate the copula ck +1 | k (21), (26) at the new grid”, “Considering the predictive PMD marginals (33), copula (37), and the predictive PDF approximation (29), the predictive PMD can be written as”,
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, wherein Dunik explicitly discloses generating a joint probability density function (joint PDF) that accounts for both feature values and measurement uncertainty based on the joint covariance matrix and the uncertainty-adjusted marginal feature distribution. Specifically, Dunik takes the joint correlation/covariance matrix Ck +1 | k derived from PPMD,k+1|k (which combines feature spread and noise/uncertainty covariance cov[wk],the corresponding measurement uncertainty covariance) to compute a copula density function
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(the corresponding jointly accounting covariance), and the predictive marginal feature distributions p(xk+1,l | zk ) that account for measurement uncertainty. As shown in Equation29 and 39, Dunik constructs the full joint predictive density function
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by multiplying the copula density function
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with the product of the individual uncertainty-adjusted marginal feature distributions
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. Dunik synthesizes the overall joint PDF using the joint covariance-derived copula and the updated marginal feature distributions, rendering it functionally equivalent to the claimed invention.)
Dunik teaches about generating the joint density function that accounts feature and measurement uncertainty, but does not the following limitations, but from the same field of endeavor Campbell teaches classifying feature values of the features based on the joint density function that accounts for feature and measurement uncertainty (Campbell, Pg13, Column6, Lines1-2, Bayes Classifier,
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Pg6, Column6, Line55, "The goal is an estimate of p(T|z1,2,…k) at each look k"
Pg20, Column19, Lines34-35, "obtain an estimate of the target type from the first conditional probability"
Pg18, Column15, Lines28-30, "Finally, p(T|z1,z2,...k) is provided to the decision rule 314 in order to obtain an estimate of the target type"
Pg21, Column22, Claim7, "The target identification system of claim 1 wherein the estimate is obtained using a Bayes classifier", wherein Campbell explicitly states that the calculated posterior probability density p(T|z1,2,...k) is fed into the decision rule to obtain the final target type estimate. Furthermore, Campbell explicitly claim obtaining a target type estimate using a Bayes classifier based on this conditional probability, rendering it functionally equivalent to the claimed invention once that conditional probability is the one generated by Dunik)
Dunik and Campbell are analogous to the claimed invention as they are from the same field of endeavor of state estimation and target tracking/identification using multi-dimensional sensor data. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the fast copula-based convolution algorithm of Dunik, which decomposes high-dimensional joint density functions into 1D marginals and a copula matrix to reduce computational complexity from exponential to linear, with the target identification framework of Campbell, which marginalizes high-dimensional target states and feature variables to calculate a target type posterior. The motivation is as recited by Campbell (Campbell, Pg11, Column1, Lines8-16, "It is often desirable that sensing systems be able to exploit the data they collect for interesting and relevant information.... Target detection and identification is a fundamental problem in many applications..." and Pg11, Column1, Lines26-28, "Decision methods that provide a minimum decision error rate require knowledge of the posterior distribution of target type (p(T|E))") such that Dunik discloses a highly efficient algorithm for calculating and updating the joint probability density function of target states and feature measurements, but focuses primarily on the filtering and state estimation phase. As mentioned by Campbell, sensing and tracking systems are designed to exploit such collected state data for downstream target detection and identification to perform automated target type classification with minimum decision error rates.
As to dependent Claim 2,
The combination of Dunik and Campbell teaches, as mentioned above, all the limitations of Claim 1. It teaches about overall architecture of identifying and classifying objects or features using the joint density function that is made with a copula inserted with two inputs, the covariance accounting both feature and measure uncertainty covariances and the respective marginal feature distributions that account the measurement uncertainty.
Dunik further teaches the method of claim 1, wherein
altering the marginal feature distribution includes determining, for each point of points in the marginal feature distribution, a weighted sum (Dunik, Pg6, Algorithm2, Equation34,
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Pg3, Equation14,
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Pg4, Right Column, Section3.4, Second Bullet, "nx scalar convolutions, meaning nxN2pe evaluations of the scalar transition PDFs", wherein Dunik discloses a fast copula-based convolution framework for point-mass filters which a multi-dimensional state feature space is decomposed into low-dimensional/scalar marginal probability density functions defined over discrete sets of grid points,
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. Dunik also explicitly calculates the updated point-mass density value at each individual grid point jl of the l-th marginal feature distribution via discrete summation in Equation34. Because Dunik's scalar marginal convolution in Equation34 calculates the altered density value at each individual point jl of a marginal feature distribution as a weighted sum of neighboring points using uncertainty weights, rendering it functionally equivalent to the claimed invention.)
As to dependent Claim 3,
The combination of Dunik and Campbell teaches, as mentioned above, all the limitations of Claim 2. It teaches about calculating a weighted sum of neighboring points using uncertainty weights by finding altered density value at each point of a marginal feature distribution.
Dunik further teaches the method of claim 2, wherein
weights of the weighted sum are measurement uncertainty values in the marginal measurement uncertainty distribution (Dunik, Pg6, Algorithm2, Equation34,
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Pg2, Left Column, Section2, Paragraph1, Lines9-12, "Particular realizations of the state and measurement noises wk and vk are unknown , but their PDFs, i.e., the state noise PDF p(wk) and the measurement noise PDF p(vk) , are supposed to be known"
Pg2, Right Column, Equation3,
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, wherein Dunik explicitly multiplies each marginal feature probability
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by the weighting factor
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inside the discrete summation of Equation34. Dunik also shows p(xk+1,l | xk,l) which represents the l-th marginal transition probability density function derived directly from the system state/measurement noise distribution p(wk,l). Thus, the weighting factors evaluating
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are precisely the individual uncertainty density values sampled from the marginal uncertainty distribution. Because Dunik's scalar marginal summation formula, Eq34, directly applies discrete values from the marginal noise/uncertainty PDF as the weighting factors in calculating the weighted sum, rendering it functionally equivalent to the claimed invention.)
As to dependent Claim 4,
The combination of Dunik and Campbell teaches, as mentioned above, all the limitations of Claim 3. It teaches about the marginal measurement uncertainty distribution have measurement uncertainty values of weights.
Dunik further teaches the method of claim 3, wherein
the weights are constrained to a neighborhood of a corresponding point of the points (Dunik, Pg3, Left Column, Second Bullet, "
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defines a (hyper-)rectangular neighborhood of a grid point",
Pg3, Left Column, Third Bullet,
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Pg6, Algorithm2, Step1 and 2's Equation33,
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,
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, wherein Dunik defines a local rectangular neighborhood
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for each grid point
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and utilizes a selection function
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that bounds evaluations strictly within the interval
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. When evaluating the marginal convolutions, the integration and uncertainty weighting coefficients are constrained to this specified neighborhood interval surrounding each point. Because Dunik's neighborhood interval and selection function S perform the exact step of constraining weights to a neighborhood of a corresponding point, rendering it functionally equivalent to the claimed invention.)
As to dependent Claim 5,
The combination of Dunik and Campbell teaches, as mentioned above, all the limitations of Claim 1. It teaches about overall architecture of identifying and classifying objects or features using the joint density function that is made with a copula inserted with two inputs, the covariance accounting both feature and measure uncertainty covariances and the respective marginal feature distributions that account the measurement uncertainty.
Dunik further teaches the method of claim 1, wherein
generating the joint density function that accounts for feature values and measurement uncertainty includes using a copula (Dunik, Pg1, Abstract, Lines5-6, "The copula-based convolution decomposes the joint conditional density into the marginal densities (allowing efficient prediction) and an easy-to-calculate copula density function"
Pg4, Left Column, Section3.1, Lines3-6,"The theory enables the PDF of the random variable to be split into (i) marginal PDFs describing particular elements of the random variable and (ii) copula characterizing unitless correlation of the elements"
Pg5, Algorithm1, Step5, "Based on the predictive marginal PDFs (25) and approximate copula (26), compute, w.r.t. (16)–(18), the approximate predictive PDF"
Pg6, Algorithm2, Step5, "Considering the predictive PMD marginals (33), copula (37), and the predictive PDF approximation (29), the predictive PMD can be written as Equation38,39", wherein Dunik teaches a framework that constructs the full multi-dimensional joint predictive PDF by combining individual marginal PDFs with an approximate copula density function. The final joint density function accounting for both state features and noise/measurement uncertainties is explicitly calculated as the product of the predictive marginal distributions and the copula function, rendering it functionally equivalent to the claimed invention.)
As to dependent Claim 6,
The combination of Dunik and Campbell teaches, as mentioned above, all the limitations of Claim 1. It teaches about overall architecture of identifying and classifying objects or features using the joint density function that is made with a copula inserted with two inputs, the covariance accounting both feature and measure uncertainty covariances and the respective marginal feature distributions that account the measurement uncertainty.
Dunik further teaches the method of claim 1, wherein
the marginal measurement uncertainty distribution is Gaussian (Dunik, Pg7, Section 4.1, Lines2-3, "The PDFs are constructed as the sum of two Gaussian terms"
Pg7, Equation43, "where Onx is a vector of zeros of indicated dimension ...",
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Pg8, Left Column, Lines5-7, "wk is the state noise characterizing the measurement uncertainty of the shift vector, which is given by the normal PDF", wherein Dunik models system noise and measurement uncertainties using normal Gaussian probability density function, ‘N’ symbol defined in Eq43, rendering it functionally equivalent to the claimed invention.)
As to dependent Claim 7,
The combination of Dunik and Campbell teaches, as mentioned above, all the limitations of Claim 1. It teaches about overall architecture of identifying and classifying objects or features using the joint density function that is made with a copula inserted with two inputs, the covariance accounting both feature and measure uncertainty covariances and the respective marginal feature distributions that account the measurement uncertainty.
However, Dunik does not teach the following limitation but from the same field of endeavor, Campbell teaches the method of claim 1, wherein
classifying includes classifying as part of an automatic target recognition (ATR) operation (Campbell, Pg11, Column1, Lines14-19, "Target detection and identification is a fundamental problem in many applications, such as in hyperspectral imaging, computer-aided diagnosis, geophysics, Raman spectroscopy and flying object identification. Under limited conditions, this task has been automated by machine learning algorithms"
Pg13, Column6, Lines1-2, Bayes Classifier,
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Pg18, Column15, Lines28-30, "Finally, p(T|z1,z2,...k) is provided to the decision rule 314 in order to obtain an estimate of the target type"
Pg21, Column22, Claim7, "The target identification system of claim 1 wherein the estimate is obtained using a Bayes classifier", wherein Campbell teaches an automated system and Bayes optimal estimator for identifying/classifying target types (e.g., flying vehicles or ground targets) from sensor feature measurements (the corresponding ATR system). Also, the generated joint density / probability function is fed into a decision rule or Bayes classifier to automatically estimate and classify the target type, rendering it functionally equivalent to the claimed invention.)
As to dependent Claim 8,
The combination of Dunik and Campbell teaches, as mentioned above, all the limitations of Claim 1. It teaches about overall architecture of identifying and classifying objects or features using the joint density function that is made with a copula inserted with two inputs, the covariance accounting both feature and measure uncertainty covariances and the respective marginal feature distributions that account the measurement uncertainty.
Dunik further teaches the method of claim 1, wherein
the marginal measurement uncertainty distributions and marginal feature distributions are one-dimensional (Dunik, Pg2, Left Column, Paragraph2, Lines1-3, "The proposed fast PMF(point-mass filter) prediction step decomposes the standard convolution in nx-dimensional space into nx scalar convolutions"
Pg4, Section3.4, Right Column, Lines5-9, "Consider, for example, a diagonal model (3) with nx,l = 1 , ∀ l, i.e., L = nx . Then, the following calculations have to be performed to get the approximate predictive PMD; nx scalar convolutions (33), meaning nxN2pe evaluations of the scalar transition PDFs"
Pg9, Right Column, Lines4-5, "In this case, the FCC(fast copula-based convolution) reduces the convolution in two-dimensional state-space into two scalar convolutions", wherein Dunik teaches a fast copula-based convolution framework that decomposes a multi-dimensional state space in to scalar (the corresponding one-dimensional) partial state vectors where nx,l = 1 for all dimensions l. Dunik explicitly details decomposing multi-dimensional feature and uncertainty distributions into nx one-dimensional (scalar) marginal feature distributions and evaluating nx one dimensional (scalar) uncertainty convolutions, rendering it functionally equivalent to the claimed invention.)
As to independent Claim 9,
it is a system claim that contains similar limitations of Claim 1 and thus rejected under the same rationale.
As to dependent Claim 10,
it is a system claim that contains similar limitations of Claim 2 and thus rejected under the same rationale.
As to dependent Claim 11,
it is a system claim that contains similar limitations of Claim 3 and thus rejected under the same rationale.
As to dependent Claim 12,
it is a system claim that contains similar limitations of Claim 4 and thus rejected under the same rationale.
As to dependent Claim 13,
it is a system claim that contains similar limitations of Claim 5 and thus rejected under the same rationale.
As to dependent Claim 14,
it is a system claim that contains similar limitations of Claim 6 and thus rejected under the same rationale.
As to dependent Claim 15,
it is a system claim that contains similar limitations of Claim 7 and thus rejected under the same rationale.
As to dependent Claim 16,
it is a system claim that contains similar limitations of Claim 8 and thus rejected under the same rationale.
As to independent Claim 17,
it is a non-transitory computer-readable claim that contains similar limitations of Claim 1 and thus rejected under the same rationale.
As to dependent Claim 18,
it is a non-transitory computer-readable claim that contains similar limitations of Claim 2 and thus rejected under the same rationale.
As to dependent Claim 19,
it is a non-transitory computer-readable claim that contains similar limitations of Claim 3 and thus rejected under the same rationale.
As to dependent Claim 20,
it is a non-transitory computer-readable claim that contains similar limitations of Claim 4 and thus rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pham et al., Non-Patent Literature, “SEPARABLE BILATERAL FILTERING FOR FAST VIDEO PREPROCESSING”, Published in 2005, IEEE, 4 Pages
Karmaker et al., Non-Patent Literature, “Image denoising with Weighted Orientation-Matched Filters (WORM)”, Published in 2018, IEEE, 6 Pages
Karine et al., Non-Patent Literature, “AIRCRAFT TARGET RECOGNITION USING COPULA JOINT STATISTICAL MODEL AND SPARSE REPRESENTATION BASED CLASSIFICATION”, Published in 2018, IEEE, 4 Pages
Qu et al., Chinese Patent No. CN-115720177-A, Published in February 2023
Wang et al., Chinese Patent No. CN-115374867-A, Published in November, 2022
Huang et al., Chinese Patent No. CN-113688531-A, Published in November, 2021
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/DONG YOON JUNG/Examiner, Art Unit 2145
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145
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