DETAILED ACTION
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.
Claim(s) 1 - 7 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) limitations such as “embedding the latent space learning into the feature selection model” and “adding a graph Laplacian regularization term into the feature selection model” are groupings of terms directed to a mathematical concept. This judicial exception is not integrated into a practical application because data gather steps required to use for correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1 - 7 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Mahadevan et al (US 2022/0137930, hereafter Mahadevan).
As per claim 1, Mahadevan discloses an unsupervised feature selection method based on latent space learning and manifold constraints, comprising: S1, inputting an original data matrix to obtain a feature selection model; S2, embedding latent space learning into the feature selection model to obtain a feature selection model with the latent space learning; S3, adding a graph Laplacian regularization term into the feature selection model with the latent space learning to obtain an objective function; S4, solving the objective function by adopting an alternative iterative optimization strategy; S5, sequencing each feature in the original matrix, and selecting the first k features to obtain an optimal feature subset (¶ 37, 80, 80, 90,and 92).
As per claim 2, Mahadevan discloses the unsupervised feature selection method based on latent space learning and manifold constraints of claim 1, wherein, the feature selection model with the latent space learning obtained in step S2 is represented as:
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wherein, V E Rnxc represents a latent space matrix of n data, and c represents the number of potential factors; X e Rnxd represents the original data matrix, and d represents a data feature dimension; W e Rdxc represents a transform coefficient matrix, and A represents an adjacency matrix; VT represents a transposed matrix of V; F represents a Frobenius norm; a and β represent parameters that balance latent space learning and potential space feature selection (¶ 37, 80, 80, 90, and 92).
As per claim 3, Mahadevan discloses the unsupervised feature selection method based on latent space learning and manifold constraints of claim 2, wherein step S2 specifically comprises: S21, decomposing the adjacency matrix A into a latent space matrix V and a transposed matrix V T of the latent space matrix V through a symmetrical non-negative matrix decomposition model; wherein the product of V and VT in a low dimensional potential space is represented as:
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S22, performing feature matrix transform on data in the latent space matrix V, and modeling the transformed data through a multiple linear regression model, represented as:
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wherein, W e Rdxc represents a transform coefficient matrix;
S23, adding a l2,1 norm regularization term to the transform coefficient matrix W, represented as:
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S24,embeddinglatentspacelearning into the feature selection model to obtain a feature selection model with the latent space learning (¶ 37, 80, 80, 90,and 92).
As per claim 4, Mahadevan discloses the unsupervised feature selection method based on latent space learning and manifold constraints of claim 2, wherein the objective function obtained in step S3 is represented as:
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wherein, 7 represents an equilibrium local manifold geometry regularization coefficient; L represents a Laplacian matrix, L = D - S; D represents a diagonal matrix, D1=S1,S represents a similarity matrix of similarity between pairs of measured data instances, represented as:
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wherein, Nk(xi) represents a set of xi nearest neighbors; 0 represents a width parameter; xi e Rd represents each row in the original data matrix X E Rnxd sample; xj; represents each column in the original data matrix X E Rnxd sample.
Allowable Subject Matter
Claim(s) 5 - 7 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHIKAODILI E ANYIKIRE whose telephone number is (571)270-1445. The examiner can normally be reached 8 am - 4:30 pm.
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/CHIKAODILI E ANYIKIRE/Primary Examiner, Art Unit 2487
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