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 .
DETAILED ACTION
The following NON-FINAL Office Action is in response to application 18/124,426 filed on 3/21/2023. This communication is the first action on the merits.
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Drawings
The drawings were received on 03/21/2023. These drawings are acceptable.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. A subject matter eligibility analysis is set forth below. See MPEP 2106.
Specifically, representative Claim 1 recites:
A method comprising:
generating, by one or more processing elements, a correntropy matrix using a training set of data samples and a testing set of data samples;
generating, by the one or more processing elements, a functional Wiener filter based at least in part on the correntropy matrix;
receiving, by the one or more processing elements, continuous time series data;
generating, by the one or more processing elements, projected continuous time series data by projecting the continuous time series data to a reproducing kernel Hilbert space associated with a Gaussian kernel;
generating, by the one or more processing elements, an estimated signal associated with the continuous time series data by applying the functional Wiener filter to the projected continuous time series data; and
initiating, by the one or more processing elements, performance of one or more postfiltering actions based at least in part on the estimated signal.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements.”
Similar limitations comprise the abstract idea of Claim 11 and Claim 21, which performs the method of Claim 1.
Under Step 1 of the analysis, claim 1 belongs to a statutory category, namely it is a method claim. Likewise, claim 11 is a apparatus claim, and claim 21 is a non-transitory computer readable storage medium.
Under Step 2A, prong 1: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
In the instant case, claim 1 is found to recite at least one judicial exception (i.e. abstract idea), that being a Mental Process and a Mathematical Concept. This can be seen in the claim limitations of “generating a correntropy matrix using a training set of data samples and a testing set of data samples”, “generating a functional Wiener filter based at least in part on the correntropy matrix”, “generating projected continuous time series data by projecting the continuous time series data to a reproducing kernel Hilbert space associated with a Gaussian kernel”, and “generating an estimated signal associated with the continuous time series data by applying the functional Wiener filter to the projected continuous time series data” which is the judicial exception of a mental process because these limitations are merely data observations, evaluations, and/or judgements in order to generate an estimated signal associated with continuous time series data by applying a functional Wiener filter based on a correntropy matrix and projection into a reproducing kernel Hilbert space associated with a Gaussian kernel and is capable of being performed mentally and/or with the aid of pen and paper. Additionally, the aforementioned limitations recite mathematical calculations, e.g. see Spec. [0049]-[0087] describing the use of a correntropy functional defined within a reproducing kernel Hilbert space (RKHS) and computation of a functional Wiener filter solution using kernel-based operations, including Gaussian kernel evaluations and estimation for nonlinear filtering and prediction of continuous time series data.
Similar limitations comprise the abstract ideas of Claim 11 and Claim 21.
Step 2A, prong 2 of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
In addition to the abstract ideas recited in claim 1, the claimed method recites additional elements including “receiving, by the one or more processing elements, continuous time series data”, and “initiating, by the one or more processing elements, performance of one or more postfiltering actions based at least in part on the estimated signal” however these elements are found to be data gathering and output steps, which are recited at a high level of generality, and thus merely amount to “insignificant extra-solution” activity(ies). See MPEP 2106.05(g) “Insignificant Extra-Solution Activity,”. Furthermore, the claim recites that the steps, e.g. “receiving”, are performed “by the one or more processing elements” however this is found to be equivalent to adding the words “apply it” and mere instructions to apply a judicial exception on a general purpose computer does not integrate the abstract idea into a practical application. See MPEP 2106.05(f).
Apparatus claim 11 and non-transitory computer readable storage medium claim 21 recites the same additional elements as claim 1.
The generic data gathering, processing, and output steps, are recited at such a high level of generality (e.g. using “processing element”) that it represents no more than mere instructions to apply the judicial exceptions on a computer. It can also be viewed as nothing more than an attempt to generally link the use of the judicial exceptions to the technological environment of a computer. Noting MPEP 2106.04(d)(I): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) ("The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point")”.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. No specific practical application is associated with the claimed system. For instance, the estimated signal generated by applying the functional Wiener filter to the projected continuous time series data is used to initiate one or more post-filtering actions, including nonlinear filtering, signal prediction, and signal denoising operations performed on continuous time series data.
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to a general purpose computer system that attempts to apply the abstract idea in a technological environment, limiting the abstract idea to a particular field of use, and/or merely performs insignificant extra-solution activit(ies) (claims 1, 11 and 21). Such insignificant extra-solution activity, e.g. data gathering and output, when re-evaluated under Step 2B is further found to be well-understood, routine, and conventional as evidenced by MPEP 2106.05(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, and electronically scanning or extracting data from a physical document).
Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that claim 1, as well as claim 11 and 21, amount to significantly more than the abstract idea.
With regards to the dependent claims, claims 2-10 and 12-20, merely further expand upon the algorithm/abstract idea and do not set forth further additional elements that integrate the recited abstract idea into a practical application or amount to significantly more. Therefore, these claims are found ineligible for the reasons described for claims 1, 11 and 21. Specifically:
With respect to dependent claims 2-4 and 12-14 specifically, the claims further recite configuring the functional Wiener filter with a correntropy function and generating the correntropy matrix based on measuring equality in probability distribution between training data samples and testing data samples. These limitations merely add additional mathematical analysis and statistical evaluation to the abstract signal processing algorithm described above. Such limitations further define how the mathematical calculations are performed but do not recite any additional elements that improve the functioning of a computer or any technology. Rather, these claims merely refine or narrow the underlying abstract idea through additional mathematical steps and therefore fail to integrate the recited abstract idea into a practical application or amount to significantly more. See MPEP 2106.05(g).
With respect to dependent claims 5-8 and 15-18 specifically, the claims further recite defining the correntropy matrix within a reproducing kernel Hilbert space, configuring a correntropy kernel based on a number of lags, generating cross-correlation functionals, and specifying invariance with respect to different numbers of samples. These limitations merely provide additional mathematical structure and further data analysis within the abstract filtering process. Such limitations amount to nothing more than additional mathematical concepts applied to collected data and do not integrate the abstract idea into a practical application or amount to significantly more. See MPEP 2106.05(g).
With respect to dependent claims 9-10 and 19-20 specifically, the claims further recite that the estimated signal comprises a predicted portion and/or a denoised portion of the continuous time series data. These limitations merely characterize the results of the abstract calculations described above. Outputting or defining the form of the result constitutes insignificant extra-solution activity and does not integrate the abstract idea into a practical application. See MPEP 2106.05(g).
Accordingly, for the reasons above and those discussed in relation to independent claims 1, 11, and 21, the dependent claims are insufficient to integrate the claimed abstract idea into a practical application or amount to significantly more.
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 1-21 are rejected under 35 U.S.C. 103 as being unpatentable over US 20080293372 A1, Jose et al (hereinafter Jose) in view of US 20160242690 A1, Austin et Al (hereinafter Austin).
Regarding Claim 1, 11, and 21, Jose discloses a method comprising:
generating, by the one or more processing elements (Jose, [0047] FIG. 1 is a schematic illustration of a nonlinear correntropy-based filter 100, according to one embodiment of the invention. The nonlinear correntropy-based filter 100 illustratively comprises a signal preprocessor 102 for receiving a signal input, and a processing unit 104 for linear filtering the processed signal input), a functional Wiener filter based at least in part on the correntropy matrix (Jose, [0035] another aspect of the invention is a nonlinear Wiener filter based on the correntropy function already described. According to this aspect of the invention, for an input .phi.(x(n)) to a Wiener structure, (L+1) being the order of the filter and .phi. being a function defined such that E[k(x.sub.i-x.sub.j)]=E[.phi.(x.sub.i), .phi.(x.sub.j)], the following composite vector is generated using L lags of .phi.(x(n)), [0039] assuming ergodicity, the expected value E{.} can be approximated by the time average. Accordingly, .OMEGA. = V - 1 1 N k = 1 N d ( k ) .PHI. ( k ) , ( 9 ) ##EQU00011## where V.sup.-1 represents the inverse of the correntropy matrix and N is the number of samples in the window of calculation, the output, therefore, is y ( n ) = .PHI. T ( n ) .OMEGA. = .PHI. T ( n ) V - 1 1 N k = 1 N d ( k ) .PHI. ( k ) = 1 N k = 1 N j = 0 L i = 0 L .phi. ( n - i ) a ij .phi. ( k - j ) d ( k ) = 1 N k = 1 N d ( k ) j = 0 L i = 0 L a ij { .phi. ( n - i ) .phi. ( k - j ) } = 1 N k = 1 N d ( k ) j = 0 L i = 0 L a ij { .phi. ( n - i ) .phi. ( k - j ) } .apprxeq. 1 N k = 1 N { d ( k ) j = 0 L i = 0 L a ij K ( x ( n - i ) , x ( k - j ) ) } ( 10 ) ##EQU00012## where a.sub.ij is the ij.sup.th element of V.sup.-1 the final expression is obtained by approximating {.phi.(n-i).phi.(k-j)} by K(x(n-i),x(k-j)), which holds good on an average sense. Equation 10 shows the calculation that needs to be done to compute the Wiener filter based on correntropy);
receiving, by the one or more processing elements, continuous time series data (Jose, [0048] the correntropy filter output y(n)=.PHI..sub.T(n).OMEGA. is computed by averaging over the data set the product of the desired signal samples with the Gaussian kernel of the input at the defined lags and weighted by the corresponding entries of the inverse of the correntropy matrix);
generating, by the one or more processing elements, projected continuous time series data by projecting the continuous time series data to a reproducing kernel Hilbert space associated with a Gaussian kernel (Jose, [0029] The correntropy is a positive function that defines a unique reproducing kernel Hilbert space that is especially appropriate for statistical signal processing. According to one aspect of the invention, the samples xi of an input time series are mapped to a nonlinear space by .phi.(x.sub.i), where…the brackets denoting the inner product operation. When the Gaussian kernel is utilized, the input signal x(t) is transformed to the surface of a sphere of radius [0027] where E[] is the expected value operator, and k, a is kernel function that obeys the Mercer conditions. The kernel function, k, can be, for example, the Gaussian function);
generating, by the one or more processing elements, an estimated signal associated with the continuous time series data (Jose, [0048] the correntropy filter output y(n)=.PHI..sub.T(n).OMEGA. is computed by averaging over the data set the product of the desired signal samples with the Gaussian kernel of the input at the defined lags and weighted by the corresponding entries of the inverse of the correntropy matrix, as explained in equation 11) by applying the functional Wiener filter to the projected continuous time series data (Jose, [0049] Still referring to FIG. 1, the nonlinear correntropy-based filter 100, in one application, extends the Wiener filter in context of the statistical filtering problem. In particular, the filtered signal output y(n) generated by the nonlinear correntropy-based filter 100 is optionally supplied to a summer 106, to which a desired response d(n) is also supplied. The difference between the desired response d(n) and the filtered signal output y(n) provides an estimation error); and
initiating, by the one or more processing elements, performance of one or more postfiltering actions based at least in part on the estimated signal (Jose, [0049] extends the Wiener filter in context of the statistical filtering problem. In particular, the filtered signal output y(n) generated by the nonlinear correntropy-based filter 100 is optionally supplied to a summer 106, to which a desired response d(n) is also supplied. The difference between the desired response d(n) and the filtered signal output y(n) provides an estimation error, [0050] The system 200 comprises a nonlinear correntropy-based filter 202 and a plant 204 that is to be identified. Both the nonlinear correntropy-based filter 202 and the plant 204 are driven by the same input to the system 200. The filtered output generated by the nonlinear correntropy-based filter 202, based on the input, is supplied to a summer 206 along with the plant response to the same system input...extends the Wiener filter in context of the statistical filtering problem. In particular, the filtered signal output y(n) generated by the nonlinear correntropy-based filter 100 is optionally supplied to a summer 106, to which a desired response d(n) is also supplied. The difference between the desired response d(n) and the filtered signal output y(n) provides an estimation error).
Jose does not disclose generating, by one or more processing elements, a correntropy matrix using a training set of data samples and a testing set of data samples.
However, Austin teaches generating, by one or more processing elements, a correntropy matrix using a training set of data samples and a testing set of data samples (Austin, [0347] In the classification setting, the dataset is divided into the training and testing set and only the training set is used to fit the factors of the spatiotemporal model, [0057] Testing and evaluation of the disclosed system was performed. Different stages of processing on LFPs under two unknown conditions were investigated for disambiguating conditions from phasic descriptors. The two conditions were separable using the multiscale processing analysis, [0058] It was demonstrated how discriminative methods can be used to extract shift-invariant spatio-temporal features from multichannel local field potentials (LFPs). The discrimination comes from the fact that the spatio-temporal component filters are learned on a training segment from only one condition. Thus, the resulting filters efficiently describe only this one condition. Then the entire recording under both conditions is decomposed using these component filters, [0059] The short-term energy for each decomposition (i.e., the component energy) can used as the descriptor at temporal scales a magnitude longer than the component filters. The statistics of the component energy in the training set is used to learn a projection to a two-dimensional state in which the states for the two conditions are maximally separated).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Jose and Austin’s teaching because Jose teaches a correntropy based nonlinear Wiener filter for statistical signal processing, while Austin teaches generating correntropy matrices using explicitly defined training and testing sets for evaluating and improving signal classification and modeling accuracy. One of ordinary skill in the art would have been motivated to incorporate Austin’s training and testing correntropy matrix generation into Jose’s Wiener filtering using correntropy framework in order to enable performance evaluation and enhance robustness to noise and non-gaussian disturbances.
Regarding Claim 2 and 12, Jose in view of Austin discloses the method of claim 1, wherein the functional Wiener filter is configured with a correntropy function (Jose, [0035] Another aspect of the invention is a nonlinear Wiener filter based on the correntropy function already described. According to this aspect of the invention, for an input .phi.(x(n)) to a Wiener structure, (L+1) being the order of the filter and .phi. being a function defined such that E[k(x.sub.i-x.sub.j)]=E[.phi.(x.sub.i), .phi.(x.sub.j)]).
Regarding Claim 3 and 13, Jose in view of Austin discloses the method of claim 2, wherein the correntropy function is configured to measure equality in probability distributions across different lags of the continuous time series data (Jose, [0029] The correntropy is a positive function that defines a unique reproducing kernel Hilbert space that is especially appropriate for statistical signal processing. According to one aspect of the invention, the samples xi of an input time series are mapped to a nonlinear space by .phi.(x.sub.i), where k(x.sub.i,x.sub.j)=<.phi.(x.sub.1),.phi.((x.sub.j)>, the brackets denoting the inner product operation. When the Gaussian kernel is utilized, the input signal x(t) is transformed to the surface of a sphere of radius… therefore, correntropy estimates the average cosine of the angle between two points separated by a lag on the sphere, [0031] The relationship with information theoretic learning is apparent from the following. The mean of the correntropy estimate of a random process x.sub.k over the lag is V ^ [ m ] = 1 2 N - 1 m = - N + 1 N - 1 1 N - m n = m N - 1 .kappa. ( x n - x n - m ), [0032] which equals the entropy of the random variable x estimated with Parzen windows, [0033] where, as will be readily understood by one of ordinary skill in the art, entropy provides a measure of randomness, and the Parzen windows correspond to methods of estimating the probability density function of a random variable).
Regarding Claim 4 and 14, Jose in view of Austin teaches the method of claim 3, wherein the correntropy matrix is generated based at least in part on estimating the equality in probability distributions between the training set and a data sample of the testing set (Austin, Fig 16A and 16B, [0167] The algorithm uses a probabilistic formulation with the Kullback-Leibler divergence as the cost function. Specifically, all of the pairwise Euclidean distances in both spaces, original and latent, can be transformed to densities that represent the probability of points i and j being in the same neighborhood, [0058] It was demonstrated how discriminative methods can be used to extract shift-invariant spatio-temporal features from multichannel local field potentials (LFPs). The discrimination comes from the fact that the spatio-temporal component filters are learned on a training segment from only one condition. Thus, the resulting filters efficiently describe only this one condition. Then the entire recording under both conditions is decomposed using these component filters [0059] The short-term energy for each decomposition (i.e., the component energy) can used as the descriptor at temporal scales a magnitude longer than the component filters. The statistics of the component energy in the training set is used to learn a projection to a two-dimensional state in which the states for the two conditions are maximally separated).
Before the effective filing date of the claimed invention, It would have been obvious to one of ordinary skill in the art to combine Jose and Austin’s teaching because Jose teaches correntropy based filtering and statistical signal processing techniques, while Austin teaches estimating similarities and differences between probability distributions of training and testing datasets using probability techniques. A person of ordinary skill in the art would have recognized that applying Austin’s probability distribution comparison techniques to Jose’s correntropy matrix generation would allow the correntropy matrix to be generated would allow the matrix to be generated based on similarities between training data and testing data samples therefore improving robustness and separation.
Regarding Claim 5 and 15, Jose in view of Austin discloses the method of claim 1, wherein the correntropy matrix is defined within a reproducing kernel Hilbert space associated with a correntropy kernel (Jose, [0028] It will be apparent from the discussion herein that other functions can be used in lieu of the Gaussian function of equation (2). Indeed, the correntropy defined in equation (1) can be based on any other kernel function obeying the Mercer conditions as well. As will be readily appreciated by one of ordinary skill in the art, in accordance with the Mercer conditions k is both symmetric and positive definite. [0029] The correntropy is a positive function that defines a unique reproducing kernel Hilbert space that is especially appropriate for statistical signal processing. According to one aspect of the invention, the samples xi of an input time series are mapped to a nonlinear space by .phi.(x.sub.i), where k(x.sub.i,x.sub.j)=<.phi.(x.sub.1),.phi.((x.sub.j)>, the brackets denoting the inner product operation. When the Gaussian kernel is utilized, the input signal x(t) is transformed to the surface of a sphere of radius).
Regarding Claim 6 and 16, Jose in view of Austin discloses the method of claim 5, wherein the correntropy kernel comprises a dimension based at least in part on a number of lags (Jose, [0029] The correntropy is a positive function that defines a unique reproducing kernel Hilbert space that is especially appropriate for statistical signal processing. According to one aspect of the invention, the samples xi of an input time series are mapped to a nonlinear space by .phi.(x.sub.i), where k(x.sub.i,x.sub.j)=<.phi.(x.sub.1),.phi.((x.sub.j)>, the brackets denoting the inner product operation. When the Gaussian kernel is utilized, the input signal x(t) is transformed to the surface of a sphere of radius .sigma. 2 .pi. ##EQU00002##
in kernel space. Therefore, correntropy estimates the average cosine of the angle between two points separated by a lag on the sphere. [0030] Correntropy for discrete, strictly stationary and ergodic random processes can be estimated as V ^ ( m ) = 1 N - m + 1 i = 0 N k ( x i - x i - m )).
Regarding Claim 7 and 17, Jose in view of Austin discloses the method of claim 6, wherein generating the functional Wiener filter (Jose, [0035] Another aspect of the invention is a nonlinear Wiener filter based on the correntropy function already described. According to this aspect of the invention, for an input .phi.(x(n)) to a Wiener structure, (L+1) being the order of the filter and .phi. being a function defined such that E[k(x.sub.i-x.sub.j)]=E[.phi.(x.sub.i), .phi.(x.sub.j)], the following composite vector is generated using L lags of .phi.(x(n)) comprises generating an estimation of a cross-correlation functional based at least in part on the number of lags (Jose, [0029] The correntropy is a positive function that defines a unique reproducing kernel Hilbert space that is especially appropriate for statistical signal processing. According to one aspect of the invention, the samples xi of an input time series are mapped to a nonlinear space by .phi.(x.sub.i), where k(x.sub.i,x.sub.j)=<.phi.(x.sub.1),.phi.((x.sub.j)>, the brackets denoting the inner product operation. When the Gaussian kernel is utilized, the input signal x(t) is transformed to the surface of a sphere of radius 1 .sigma. 2 .pi. ##EQU00002## in kernel space. Therefore, correntropy estimates the average cosine of the angle between two points separated by a lag on the sphere).
Regarding Claim 8 and 18, Jose in view of Austin discloses the method of claim 1, wherein the correntropy matrix is configured to be invariant for different numbers of samples for the continuous time series data (Jose, [0030] Correntropy for discrete, strictly stationary and ergodic random processes can be estimated as V ^ ( m ) = 1 N - m + 1 i = 0 N k ( x i - x i - m )).
Regarding Claim 9 and 19, Jose in view of Austin discloses the method of claim 1, wherein the estimated signal comprises a predicted portion of the continuous time series data (Jose, [0053] yet another application of the nonlinear correntropy-based filter is prediction. FIG. 4 provides a schematic illustration of a system 400 for generating predictions using the nonlinear correntropy-based filter. As shown, a random signal is supplied through a delay 402 to the nonlinear correntropy filter 404. The random signal is also supplied directly to a summer 406, as is the filtered output generated by the nonlinear correntropy-based filter 404. According to this arrangement, the nonlinear correntropy-based filter 404 provides a prediction of the present value of the random signal, the prediction being best in terms of a predefined criterion. The present value of the random signal represents the desired response of the nonlinear correntropy-based filter 404, while past values of the random signal supply inputs).
Regarding Claim 10 and 20, Jose in view of Austin discloses the method of claim 1, wherein the estimated signal comprises a denoised portion of the continuous time series data (Jose, [0055] Still another application of the nonlinear correntropy-based filter is interference cancellation. A system 500 using a nonlinear correntropy-based filter 502 is schematically illustrated in FIG. 5. A primary signal is supplied to a summer 502, as is the output of the nonlinear correntropy-based filter 502 in the system 500. The primary signal is the desired response for the nonlinear correntropy filter 502. The output of the nonlinear correntropy-based filter 502 is based on a reference signal input. The reference signal can be derived from one or more sensors, which are positioned such that the information-bearing signal component is weak or otherwise difficult to determine. The system 500 is used to cancel unknown interference in the primary signal so as to enhance detection of the information content. The cancellation afforded by the nonlinear correntropy-based filter 502 is optimized according to a predefined criterion).
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to
applicant’s disclose:
-US 8233873 B2, describing systems and methods for nonlinear statistical signal processing using correntropy based filtering. The reference discloses receiving input signals, applying filters based on a correntropy statistic, and generating estimated output signals. The reference further describe computing estimated signal outputs using kernel based formulations and applying statistical decision processes based on the estimated signal values.
-US 20220076114 A1, describing systems and methods for modular based machine learning, including continual learning techniques. The reference further describes learning parameter distributions, applying constraint based optimization techniques, and using stored data or memory mechanism to support adaptive training across multiple tasks.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IBRAHIM NAGI SHOHATEE whose telephone number is (571)272-6612. The examiner can normally be reached 8am-5pm.
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/IBRAHIM NAGI SHOHATEE/Examiner, Art Unit 2857
/SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857