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. Claims 1-5 are presented in the case. Priority Acknowledgment is made of applicant's claim for foreign priority based on application CN2022100599845 filed in China on 0 1 / 19 /20 22 . Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b ) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the appl icant regards as his invention. Claims 1-5 are FILLIN "Enter claim indentification information" \* MERGEFORMAT rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The term s “ small sample high-dimensional data ” , “ guaranteeing maximum transmission and minimum redundancy ”, “ high-precision modeling ” and “ a high-dimensional an industrial ” in claim 1 are a relative term which renders the claim indefinite. The latter may also be a drafting error and should be recited as “ a high-dimensional industrial ” . The term s “ small sample high-dimensional data ” , “ guaranteeing maximum transmission and minimum redundancy ”, “ high- precision modeling ” and “ a high-dimensional an industrial ” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. These can be replaced with thresholds or value ranges or removed for a more concrete interpretation. Claim 3 also recites the terms “ considering that a dimension of A is much higher than a dimension ”, “ minimizing redundant ” and “ an information maximization selection mechanism ” which need to be explained if they are part of the formula listed. O ne of ordinary skill in the art would not be reasonably apprised of the scope of the invention . Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claim 1-5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”) Claim 1 has the following abstract idea analysis. Step 1 : The claim is directed to “a method”. The claim is directed to the statutory categories accordingly. Step 2A Prong 1 : claims recite the abstract idea limitations of "obtaining a weight matrix with a Moore-Penrose pseudo-inverse". The limitation includes mathematical concepts see MPEP § 2106.04(a)(2)) where it cites "the phrase “calculating the force of the object by multiplying its mass by its acceleration” is using a textual replacement for the particular equation ". The specification also provides example formula for weight matrix and Moore-Penrose pseudo -inverse (See USPGPUB ¶67). See USPTO 2024 example 47 where an ANN was deemed eligible. That claim did not recite a mathematical concept whereas here the claim itself recites a mathematical concept. Thus, these steps are an abstract idea in the “mathematical concept”. Other sections of the claims such as "constructing the feature mapping layer", “constructing the latent feature extraction layer", " constructing the feature incremental layer", "training the feature incremental layer based on an extracted latent feature", "constructing the incremental learning layer", "verifying the soft sensor model" and “using a soft sensor on DXN emission" are advanced processes, too generic or high level to be listed as a judicial exception given the available descriptions and MPEP comparisons. Step 2A Prong 2: The judicial exceptions recited in these claims are not integrated into a practical application. Merely invoking "soft sensor method for dioxin (DXN) emission", "random forest", "training", or "using a soft sensor" does not yield eligibility. It appears the claims and model used do not recite an actual effect to the plant or its emissions yet. Claims are still in line with mathematical concepts such as claim 1 are not specific to a practical application. The additional elements as such are processors and instructions which do not include specialized hardware. See MPEP § 2106.05(f). Claim 1 do es not include a particular field but even doing so may not be sufficient to overcome the abstract idea rejection. Merely applying an model to a field or data without an advancement in the new field or new hardware is ineligible. MPEP § 2106.05(h). Step 2B : The claims do not contain significantly more than their judicial exceptions. Soft sensor, DXN emissions and MSWI are in their standard forms in the field. These additional elements are well-understood, routine, and conventional activity, see MPEP 2106.05(d)(II). Claims lacks any particular "how" or algorithm for a solution in a field in a novel way. Claims require more specificity on processes that would be incapable of simple mathematics, mental processes or use more substantial structure than conventional devices such as non-textbook implementations. Regarding claims 2-5 merely narrow the previously recited abstract idea limitations with more abstract concepts and/or routine fundamental processes. For the reasons described above with respect to claim 1 this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. Abstract idea steps 1, 2A prong 1 and 2 remain the same as independent analysis above. See specification for more practical application concepts as none are seen in claims 2-5. A certain type of data used may only be applying concepts to a field of use. With respect to step 2B The claims disclose similar limitations described for the independent claims above and do not provide anything significantly more than mathematical or mental concepts. Claims 2-5 recite the additional elements of "setting original data as {X, y}, wherein XϵRN Raw ×M represents original input data, NRaw represents a number of the original data, M represents a dimension of the original input data, is sourced from six different stages in the MSWI process, and is collected and stored in a distributed control system (DCS) in seconds, and yϵRN Raw ×1 represents an output truth of a DXN emission concentration, and is sourced from an emission DXN measurement sample obtained through an off-line measurement method; describing a modeling process of the feature mapping layer by taking a nth hybrid forest group of feature mapping layer as an example: obtaining J training subsets of a hybrid forest group model by performing Bootstrap and random subspace (RSM) sampling on {X,y} as follows: {XBootstrapn,j,yBootstrapn,j}j=1J=φnFML(ϕnFML((X,y),PBootstrap))(1) wherein XBootstrap n,j and yBootstrap n,j represent an input and an output of a jth training subset respectively, ϕn FML( ⋅ ) and φn FML( ⋅ ) represent Bootstrap sampling and RSM sampling of the nth hybrid forest group in the feature mapping layer respectively, and PBootstrap represents a Bootstrap sampling probability; training a hybrid forest algorithm comprising J decision trees based on Bootstrap {BBootstrap n,j, yBootstrap n,j}j=1 J, wherein a jth decision tree of the nth hybrid forest group in the feature mapping layer is expressed as follows: fn,jDT(·)=∑l=1LclI(xBootstrapn,j ∈ Rl),l=1,2,…,L(2) wherein L represents a number of leaf nodes of the decision tree, I( ⋅ ) represents an indicator function, and cl is computed through recursive splitting; expressing a splitting loss function Ωi( ⋅ ) of the decision tree in the RF as: Ωi(s,v)=min([yL-E[yL]]+[yR-E[yR]])=min(∑xBootstrapn,j ∈ RL(yLi-cL)2+∑xBootstrapn,j ∈ RR(yRi-cR)2)(3) wherein Ωi(s,v) represents a value V of a sth feature value taken as a loss function value of a splitting criterion, yL represents a truth vector of a DXN emission concentration at a left leaf node, E[yl] represents a mathematical expectation of yL, yR represents a truth vector of a DXN emission concentration at a right leaf node, E[yR] represents a mathematical expectation of yR, yL i represents a ith DXN emission concentration truth at the left leaf node, yR i represents a i DXN emission concentration truth at the right leaf node, cL represents a predicted output of the DXN emission concentration at the left leaf node, and cR represents a predicted output of the DXN emission concentration at the right leaf node; splitting, by minimizing Ωi(s,v), a training set (XBootstrap n,j,yBootstrap n,j) into two tree nodes as follows: min{Ωi(s,v)}i=1NRaw×M→Treenodesplitting{RLNL×MRRNR×M(4) wherein RL N L ×M and RR N R ×M represent sample sets comprised in a left tree node and a right tree node after division respectively, and NL and NR represents a number of samples in RL N L ×M and RR N R ×M respectively; expressing predicted output values and cL RF and cR RF of the DXN emission concentration at a current left tree node and a current right tree node as sample truth expectations as follows: {cLRF=E[yL],yL ∈ RLNL×McRRF=E[yR],yR ∈ RRNR×M(5) wherein yL and yR represent truth vectors of the DXN emission concentration in RL N L ×M and RR N R ×M, and E[yL] and E[yR] represent mathematical expectations of yL and yR; different from the RF, expressing a splitting loss function in the CRF in a completely random selection mode as follows: rand{(s,v)}i=1NRaw×M→Treenodesplitting{RLNL×MRRNR×M(6) wherein rand{(s,v)i}i=1 N Raw×M represents that a value v of a sth feature is completely randomly selected as a split point; expressing predicted output values cL CRF and cR CRF of the DXN emission concentration at a left tree node and a right tree node that are randomly split as sample truth expectations as follows: cLCRF=E[yL],yL ∈ RLNL×McRCRF=E[yR],yR ∈ RRNR×M(7) expressing, through the above process, the nth hybrid forest group fn FML( ⋅ ) as follows: fnFML(·)={fn,RFFML(·),fn,CRFFML(·)}(8) wherein fn,RF FML( ⋅ ) represents a nth random forest, and fn,CRF FML( ⋅ ) represents a nth completely random forest; expressing a nth mapped feature Zn as follows: Zn=fnFML(X)={fn,RFFML(X),fn,CRFFML(X)}=[(c1,ln,RF,c1,ln,RF),…,(cnRaw,ln,RF,cnRaw,ln,RF),…,(cNRaw,ln,RF,cNRaw,ln,RF)](9) wherein (c1,l n,RF, c1,l n,RF) represents a mapped feature, obtained through the nth hybrid forest group, of a first sample of original input data from six different stages in the MSWI process, (cn Raw ,l n,RF,cn Raw ,l n,RF) represents a mapped feature, obtained through the nth hybrid forest group, of a nRawth sample of original input data from six different stages in the MSWI process, and (cn Raw ,l n,RF,cn Raw ,l n,RF) represents a mapped feature, obtained through the nth hybrid forest group, of a NRaw th sample of original input data from six different stages in the MSWI process; and expressing an output of the feature mapping layer as: ZN=(Z1,Z2,…,ZN) ∈ RNRaw×2N(10) wherein Z1 represents a first mapped feature, Z2 represents a second mapped feature, ZN represents a Nth mapped feature, and a mapped feature matrix ZN comprises NRaw samples and a 2N dimensional feature. obtaining and expressing a fully connected hybrid matrix A by combining original input data X from six different stages in the MSWI process with a mapped feature matrix ZN as follows: A=[X ❘ ZN] ∈ RNRaw×(M+2N)(11) wherein A comprises NRaw samples and a (M+2N) dimensional feature; considering that a dimension of A is much higher than a dimension of the original data, minimizing redundant information in A through principal component analysis (PCA), and computing a correlation matrix R of A as follows: R=1NRaw-1ATA ∈ R(M+2N)×(M+2N)(12) obtaining (M+2N) feature values and corresponding feature vectors by performing singular value decomposition on R as follows: R=U(M+2N)∑(M+2N)V(M+2N)(13) wherein U(M+2N) represents a (M+2N) order orthogonal matrix, Σ(M+2N) represents a (M+2N) order diagonal matrix, and V(M+2) represents a (M+2N) order orthogonal matrix; ∑(M+2N)=[σ1 ⋱ σ(M+2N)](14) wherein σ1>σ2> . . . >σ(M+2N) represents eigenvalues arranged in a descending order; determining a number of final principal components according to a set latent feature contribution threshold η, η=∑q=1QPCAσq/∑q=1(M+2N)σq(15) wherein a number of latent features is QPCA<<(M+2N); based on QPCA determined latent features above, obtaining a feature vector matrix VQ PCA (that is, a projection matrix of A) corresponding to a eigenvalue set {σq=}q=1 Q PCA , minimizing redundant information by performing feature projection on A, and recording an obtained latent feature as XPCA, that is, XPCA=AVIPCA ∈ RNRaw×MPCA(16) wherein VQ PCA ϵR(M+2N)×Q PCA represents a eigenvector of front QPCA latent features; computing a mutual information value IMI between a selected latent feature XPCA and a truth yϵRN Raw ×1 as follows: IMI(XPCA,y)=∑q=1QPCAp(xqPCA,y)log2p(xqPCA,y)p(xqPCA),p(y)(17) wherein p(xq PCA,y) represents a joint probability distribution of a qth latent feature xq PCA and a DXN emission concentration truth y, p(xq PCA) represents a marginal probability distribution of the 9th latent feature xq PCA, and p(y) represents a marginal probability distribution of the DXN emission concentration truth y; guaranteeing a correlation between the selected latent feature and the truth through an information maximization selection mechanism, and expressing the correlation as: {IqMI}q=1QPCA ⟶ IqMI≥ζ{IqMI}q=1QPCAMI;(18) wherein {Iq MI}q=1 Q represents a mutual information value between QPCA latent features xq PCA and the truth y, ζ represents an information maximization threshold, and {IqMI}q=1QPCAMI represents latent features having a greatest correlation with information of the DXN emission concentration truth y; and obtaining a new data set {X′,y}ϵRN Raw ×(Q PCA MI +1) comprising QPCA MI latent features, and setting a dimension MPCA NI=QPCA MI after extraction. obtaining a J training subset of a hybrid forest algorithm by performing Bootstrap sampling and RSM sampling on the new data set {X′,y} as follows: {XBootstrap′k,j,yBootstrapk,j}j=1J=φkFEL(ϕkFEL({X′,y},PBootstrap))(19) wherein X′Bootstrap k,j and yBootstrap k,j represent an input and an output of the jth training subset, X′ and y represent an input and an output of a new training set respectively, ϕk FEL( ⋅ ) represents Bootstrap sampling on a kth hybrid forest group, and φk FEL( ⋅ ) represents RSM sampling on the kth hybrid forest group; taking construction of a jth RF in the kth hybrid forest group as an example as follows: {XBootstrap′k,j,yBootstrapk,j} ⟶ Ωj(s,v)fk,jDT-RF(·)=∑l=1LclI(xBootstrapk,j ∈ Rl),l=1,2,...,L(20) wherein fk,j DT-RF( ⋅ ) represents a jth decision tree of the RF in the kth hybrid forest group fDT-RF ( ⋅ ) in the feature incremental layer; L represents a number of leaf nodes of the decision tree; and cl is computed through recursive splitting with specific process formulas of (3)-(5); obtaining an RF model in the kth hybrid forest group in the feature incremental layer and expressing the RF model as follows: fk,RFFEL(·)={fk,jDT-REF(·)}j=1J(21) similarly taking construction of a jth CRF in the kth hybrid forest group as an example as follows: {XBootstrap′k,j,yBootstrapk,j} ⟶ randj(s,v)fk,jDT-CRF(·)=∑l=1LclI(xBootstrapk,j ∈ Rl),l=1,2,…,L(22) wherein fk,j DT-CRF( ⋅ ) represents a jth decision tree of the CRF in the kth hybrid forest group in the feature incremental layer; and cl is computed through recursive splitting with specific process formulas of (6)-(7); obtaining a CRF model in the kth hybrid forest group in the feature incremental layer and expressing the CRF model as follows: fk,CRFFEL(·)={fk,jDT-CRF(·)}j=1J(23) obtaining the kth hybrid forest group fk FEL( ⋅ ) through the above process, and expressing a kth enhanced feature as follows: Hk=fkFEL(X′)=[fk,RFFEL(X′),fk,CRFFEL(X′)]=[(c1,lk,RF,c1,lk,CRF),…,(cnRaw,lk,RF,cnRaw,lk,RF),…,(cNRaw,lk,RF,cNRaw,lk,CRF)](24) wherein (c1,l n,RF, c1,l n,RF) represents enhanced mapping on a first sample in new data through the kth hybrid forest group, (cn Raw ,l k,RF,cn Raw ,l k,RF) represents enhanced mapping on a nRawth sample in the new data through the kth hybrid forest group, and (cN Raw ,l k,RF,cN Raw ,l k,RF) represents enhanced mapping on a NRawth sample in the new data through the kth hybrid forest group; expressing an output Hk of the feature incremental layer as follows: HK=[H1,H2,…,HK] ∈ RNRaw×2K(25) wherein H1 represents a first enhanced feature, H2 represents a second enhanced feature, and HK represents a Kth enhanced feature; expressing a BHFR model without considering the incremental learning strategy as follows: Y=GKWK=[Z1,Z2,…,ZN ❘ H1,H2,…HK]WK(26) wherein GK represents a combination of outputs from the feature mapping layer and the feature incremental layer, that is GK=[ZN|HK], and comprises NRaw samples and a (2N+2K) dimensional feature; and WK represents a weight of each of the feature mapping layer and the feature incremental layer relative to the output layer, and is computed as follows: WK=(λI+[GK]TGK)-1[GK]TY(27) wherein I represents a unit matrix, and λ represents a regularization term coefficient; and accordingly, expressing a pseudo-inverse computation of GK as: [GK]†=(λI+[GK]TG)-1[GK]T=[ZN ❘ HK]†.(28) obtaining a training subset of the hybrid forest algorithm by performing Bootstrap sampling and RSM sampling on the new data set {X′,y} in a process as follows: {XBootstrap′p,j,yBootstrapp,j}j=1J=φpILL(ϕpILL{{X′,y},PBootstrap})(29) wherein Bootstrap X′Bootstrap p,j and yBootstrap p,j represent the input and the output of the jth training subset of the hybrid forest algorithm, X′ and y represent the input and the output of the new training set respectively, and ϕi ILL( ⋅ ) and φp ILL( ⋅ ) represent Bootstrap sampling and sampling of a pth hybrid forest group in the incremental learning layer; construction decision trees fp,RF ILL( ⋅ ) and fp,CRF ILL( ⋅ ) in the pth hybrid forest group in the same process (not repeated herein) as the feature mapping layer and the feature incremental layer; under the condition that one hybrid forest group is added, expressing outputs GK+1 of the feature mapping layer, the feature incremental layer and the incremental learning layer as follows: GK+1=[GK ❘ f1FEL(X′)]=[GK ❘ {f1,RFFEL(X′),f1,CRFFEL(X′)}]=[GK ❘ [(c1,l1,RF,c1,l1,CRF),…,(cNRaw,l1,RF,cNRaw,l1,CRF)]](30) wherein Gk=[Zn|Hk] comprises NRaw samples and a (2N+2K) dimensional feature, and GK+1 comprises NRaw samples and a (2N+2K+2J) dimensional feature; recursively updating a Moore-Penrose inverse matrix of GK+1 as follows: BT={[C]†,ifC≠0[1+DTD]-1DT[Gk]†,ifC=0(31) wherein a matrix C and a matrix D are computed as follows: C=HK+1-GKD(32) D=[GK]†f1ILL(XNew)(33) expressing a recursive formula of the Moore-Penrose inverse matrix of GK+1 as follows: [GK+1]†=[[GK]†-DBTBT](34) computing an updating matrix WK+1 of a weight of the feature mapping layer, the feature incremental layer, the incremental learning layer relative to the output layer as follows: WK+1=[WK-DBTYBTY]whereinWK=(λI+[GK]TGK)-1[GK]TY;(35) implementing rapid incremental learning due to the fact that a pseudo-inverse matrix of the hybrid forest group in the incremental learning layer is merely required to be computed according to a pseudo-inverse updating strategy above; implementing adaptive incremental learning according to a convergence degree of a training error; determining a number P of hybrid forest groups of incremental learning by defining a convergence threshold of the error as θCon, and expressing accordingly an incremental learning training error of the BHFR model as follows: ℓ=limp→∞ ❘ 1N((GK+pWK+p-y)2-(GK+p+1WK+p+1-y)2) ❘ ≤θConS.T.θCon≥0(36) wherein represent a training error value between a p+1 hybrid forest group and a p hybrid forest group of incremental learning, and √{square root over ((GK+pWK+p−y)2)} and √{square root over ((GK+p+1WK+p+1−y)2)} represent training errors of a BHFR model comprising P hybrid forest groups and a BHFR model comprising p+1 hybrid forest groups; and expressing a predicted output Ŷ of the BHFR soft sensor model as follows: Y^=GK+PWK+P.(37)" These elements are more abstract concepts, generic applications to a field of use or well-understood, routine, conventional activity (see MPEP § 2106.05(d) and can't be simply appended to qualify as significantly more or being a practical application. What type of application, or structure of components beyond generic machine learning is still unknown for these claims. Therefore claims 2-5 also recite abstract ideas that do not integrate into a practical application or amount to significantly more than the judicial exception, and 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 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 of this title, 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. Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Roverso (US 20100325071 A1) in view of Zheng et al. (US 20200202182 A1 hereinafter Zheng) , Sapir et al. (US 20070112716 A1 hereinafter Sapir) and Ong et al. (US 20140257063 A1 hereinafter Ong) As to independent claim 1 , Roverso teaches a broad hybrid forest regression (BHFR)-based soft sensor method for dioxin (DXN) emission in a municipal solid waste incineration (MSWI) process, comprising: [virtual sensor (soft sensor) for gas prediction including carbon ¶6, ¶41-42 "a virtual sensor suitable for gas sensing by a combination of empirical modelling with ensemble modelling"] based on a broad learning system (BLS) framework, constructing a BHFR soft sensor model for small sample high-dimensional data by replacing a neuron with a non-differential base learner, [constructs an ensemble of models (replaces neurons) for a virtual sensor ¶41-42 combines/updates models (replaces) ¶35 including trees and other models (non-differential) ¶24 " Regression trees" ] S5, verifying the soft sensor model by using a high-dimensional an industrial process DXN data set; and [historical measurement data for training model from a combustion process (DXN) ¶66 "trained using empirical data (ED) from the combustion process (CP). In an embodiment of the invention the empirical data are historical measurement data from the combustion process (CP) where the virtual sensor system (VS) is arranged"] S6, using a soft sensor on DXN emission in the MSWI process with the soft sensor model constructed in steps S1-S5. [uses models to estimate gas (emission) in combustion process ¶68, ¶65 "ensemble based virtual sensor system (VS) for the estimation of an amount of a gas (G) resulting from a combustion process (CP) comprises two or more empirical models"] Roverso does not specifically teach wherein the BHFR soft sensor model comprises a feature mapping layer, a latent feature extraction layer, a feature incremental layer and an incremental learning layer, and the method specifically comprises: S1, constructing the feature mapping layer, and mapping a high-dimensional feature by constructing a hybrid forest group composed of a random forest (RF) and a completely random forest (CRF); and S3, constructing the feature incremental layer, and further enhancing a feature representation capacity by training the feature incremental layer based on an extracted latent feature; However, Zheng teaches wherein the BHFR soft sensor model comprises a feature mapping layer, a latent feature extraction layer, a feature incremental layer and an incremental learning layer, and the method specifically comprises: [layers ¶27-28, feature extraction and processing ¶63] S1, constructing the feature mapping layer, and mapping a high-dimensional feature by constructing a hybrid forest group composed of a random forest (RF) and a completely random forest (CRF);[layers and ensemble including random forest and complete-random tree forest ¶28 "Each level of decision tree forest set can include different classifications of forests (such as a random forest and a complete-random tree forest), to improve network diversity."] S3, constructing the feature incremental layer, and further enhancing a feature representation capacity by training the feature incremental layer based on an extracted latent feature; [concatenates features from a current level (extracted) for the next level (incremental layer) ¶7-9 " training base classifiers in a first-level decision tree forest set based on the second dimension feature, concatenating an output feature of a current-level decision tree forest set with the second dimension feature, and training base classifiers in a next-level decision tree forest set by using a concatenated feature"] Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the gas sensing virtual sensor by Roverso by incorporating the wherein the BHFR soft sensor model comprises a feature mapping layer, a latent feature extraction layer, a feature incremental layer and an incremental learning layer, and the method specifically comprises: S1, constructing the feature mapping layer, and mapping a high-dimensional feature by constructing a hybrid forest group composed of a random forest (RF) and a completely random forest (CRF); and S3, constructing the feature incremental layer, and further enhancing a feature representation capacity by training the feature incremental layer based on an extracted latent feature disclosed by Zheng because both techniques address the same field of machine learning and by incorporating Zheng into Roverso improves models prediction ability and network diversity with deeper networks and tiers of training techniques [ Zheng ¶ 28, ¶ 33 ] Roverso and Zheng do not specifically teach S2, constructing the latent feature extraction layer, extracting a latent feature from a feature space of a fully connected hybrid matrix according to a contribution rate, guaranteeing maximum transmission and minimum redundancy of potential valuable information based on an information measurement criterion, and reducing model complexity and computation consumption; However, Sapir teaches S2, constructing the latent feature extraction layer, extracting a latent feature from a feature space of a fully connected hybrid matrix according to a contribution rate, guaranteeing maximum transmission and minimum redundancy of potential valuable information based on an information measurement criterion, and reducing model complexity and computation consumption; [extracts (selects) and removes (reduces complexity/consumption) features based on metrics including contribution ¶11-12 " The importance of each of the n features may be evaluated according to a metric of feature contribution in order to produce a contribution value for each of the features. A feature may be removed from the set of n features based on the contribution values"] Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the prediction model by Roverso and Zheng by incorporating the S2, constructing the latent feature extraction layer, extracting a latent feature from a feature space of a fully connected hybrid matrix according to a contribution rate, guaranteeing maximum transmission and minimum redundancy of potential valuable information based on an information measurement criterion, and reducing model complexity and computation consumption disclosed by Sapir because all techniques address the same field of machine learning and by incorporating Sapir into Roverso and Zheng enhances prediction ability with improved selection of features while reducing processing resources needed [Sapir ¶ 38 ] . Roverso , Zheng and Sapir do not specifically teach S4, constructing the incremental learning layer based on an incremental learning strategy, obtaining a weight matrix with a Moore-Penrose pseudo-inverse, and implementing high-precision modeling of the BHFR soft sensor model; However, Ong teaches S4, constructing the incremental learning layer based on an incremental learning strategy, obtaining a weight matrix with a Moore-Penrose pseudo-inverse, and implementing high-precision modeling of the BHFR soft sensor model; [Moore-Penrose of weights in model ¶244-246 "output weights can be estimated as .beta.=H.sup..dagger.T, where H.sup..dagger. is the Moore-Penrose generalized inverse of the hidden layer output matrix"] Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the prediction model by Roverso, Zheng and Sapir by incorporating the S4, constructing the incremental learning layer based on an incremental learning strategy, obtaining a weight matrix with a Moore-Penrose pseudo-inverse, and implementing high-precision modeling of the BHFR soft sensor model disclosed by Ong because all techniques address the same field of machine learning and by incorporating Ong into Roverso, Zheng and Sapir better assesses a models sensitivity and specificity for reports achieving a solid evaluation of accuracy [Ong ¶ 332, ¶ 349]. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action. Zhu et al. (US 20200260960 A1) teaches calculating a weight matrix and Moore-Penrose solution (see ¶77) It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)). Any inquiry concerning this communication or earlier communications from the examiner should be directed to Beau Spratt whose telephone number is 571 272 9919. The examiner can normally be reached 8:30am to 5:00pm (PST) . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch can be reached at 571 272 7212. The fax phone number for the organization where this application or proceeding is assigned is 571 483 7388. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. 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