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 .
This office action is in response to the claimed invention filed on July 25, 2023, in which claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statement, filed on July 27, 2023 and July 25, 2023, complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609. It has been placed in the application file. The information referred to therein has been considered as to the merits.
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 applicant regards as his invention.
Claims 1-20 are 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 independent claims 1, 12 and 19 recite “by solving an optimizing function”. It is unclear how an optimizing function would solve to perform feature selection of the matrix of the plurality of observations and the plurality of features. The claims also recite “outputting, by the processor set, a class probability prediction based on estimated coefficient parameter values for selected features based on performing the feature selection of the matrix “. It is unclear as to what the applicant means by outputting a class probability prediction based on estimated coefficient parameter values for selected features based on performing the feature selection of the matrix. Applicant is advised to amend the claims to solve such ambiguity set forth above.
Claims 2-11 13-18 and 20 are rejected for incorporating the deficiency of their respective base claims by dependency.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 11-16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al., (hereinafter “Wang”) CN-114565026A in view Narsky et al., (hereinafter “Narsky”) US 11,410,073.
Claim 1, Wang discloses a computer-implemented method, comprising:
receiving, by a processor set, a matrix of a plurality of observations and a plurality of features (see page 2, par. [5], obtaining a plurality of data points to be clustered in the high-dimensional space to obtain a characteristic matrix; using the metric mapping algorithm to embed the feature matrix of the high-dimensional space into the low-dimensional space, obtaining the embedded matrix in the low-dimensional space);
performing, by the processor set, feature selection of the matrix of the plurality of observations and the plurality of features by solving an optimizing function (see page 2, par. [5], selecting the feature corresponding to the maximum plurality of contribution values according to the preset feature number needed to be selected to form a new feature matrix).
However, Wang does disclose the claimed “outputting, by the processor set, a class probability prediction based on estimated coefficient parameter values for selected features based on performing the feature selection of the matrix (see col.1, lines 65-67 and col.2, lines 1-20, performing feature selection, a subset of potential features of the input data be selected as features, which are predictor variables to use in determining values, e.g., quantitative values or categorical values of the desired output of the model, wherein the model is trained such that input of data corresponding to the features results in an output of a prediction., wherein the features are selected based on a determination that a predictive error for the output of the model using a model trained using the features is optimized and provided systems and methods for robust feature selection, wherein a set of weights for features relevant for training a model may be identified using robust feature selection, and a model is trained with an improved predictive accuracy relative to other techniques for feature selection),
wherein the plurality of features are mixed and comprise categorical features, functional features, and continuous features (see col.1, lines 65-67 and col.2, lines 1-3, performing features selection, a subset of potential features of the input data may be selected as features, referred to as “predictor variables” to use in determining values, e.g., quantitative values or categories values of the desired output of the model)”.
Therefore, it would have been obvious to one having ordinary skill in the art before the effective date of the claimed invention to have modified the system of Wang to output a class probability prediction based on estimated coefficient parameter values for selected features, in order to efficiently support adaptive thresholds, thereby ensuring that predicted probabilities match observed frequencies.
Claim 2, the combination of Wang and Narsky discloses the invention as claimed. In addition, Narsky discloses “wherein a number of the features is greater than a number of the observations” (see col.12, lines 23-67).
Claim 3, the combination of Wang and Narsky discloses the invention as claimed. In addition, Narsky discloses the claimed solving the optimization function by minimizing a sum of a logistic loss and a penalty ” (see col.12, lines 23-67).
Claim 4, the combination of Wang and Narsky discloses the invention as claimed. In addition, Narsky discloses the claimed wherein the logistic loss performs classification of the features and predicts probabilities of the features belonging to a predetermined class (col.1, lines 25-41).
Claim 5, the combination of Wang and Narsky discloses the invention as claimed. In addition, Narsky discloses the claimed wherein the penalty performs the feature selection using a plurality of terms and weights ” (see col.12, lines 23-67).
Claim 11, the combination of Wang and Narsky discloses the invention as claimed. In addition, Narsky discloses the claimed wherein the continuous features include any value between a maximum value and a minimum value, the categorical features include discrete values with each discrete value representing a category, and the functional features include longitudinal data and categorical responses (see col.4, lines 48-67).
As to claims 12-16 and 18, claims 12-16 and 18 are computer program product for executing the method of claims 1-5 and 11 above. They are rejected under the same rationale.
As to claim 19, claim 19 is a system for performing the method of claim 1 above. It is rejected under the same rationale.
Claims 6-10, 17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al., (hereinafter “Wang”) CN-114565026A in view Narsky et al., (hereinafter “Narsky”) US 11,410,073 and further in view of Ihara et al., (hereinafter “Ihara”) US 10,268,876.
As to claims 6-10, 17 and 20, the combination of Wang and Narsky discloses the invention as claimed, except for LASSO term and dual augmented lagrangian (DAL) algorithm.
Meanwhile, Ihara discloses LASSO term and dual augmented lagrangian (DAL) algorithm (see col.7, lines 10-60).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective date of the claimed invention to have modified the combined system of Wang and Narsky to LASSO term and dual augmented lagrangian (DAL) algorithm, in order to ensure solution is sparse, thereby solving the resulting problem quickly and reliably.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20170091637 (involved in assaying a test entity for a property, without measuring the property).
US 10417226 (involved in estimating a set of computational resources required to perform the data-mining task over a distributed computing system based at least in part on the set of task parameters, the set of control values, the set of data descriptors, and an availability of the distributed computing system).
CN 115795361 A (involved in collecting a data of each dimension, integrating and pre-processing the data, and obtaining a feature matrix, performing preliminary screening on the features by using the filtering method, filtering out the features that are least relevant to the class label according to a threshold, integrating the feature selection by using logistic regression, linear support vector machine, random forest and XGBoost, performing sub-model training in the classification algorithm by using the filtered feature set, performing combined optimization of the sub-models by using the optimized genetic algorithm, integrating the sub-models in the optimal model combination, and predicting the probability of classification by using the weighted mean method.)
CN 114565026 A (involved in obtaining multiple to-be-clustered data points in a high-dimensional space to obtain a feature matrix. The feature matrix of the high-dimensional space is embedded into a low-dimensional space using a metric mapping algorithm to obtain an embedded matrix in the low-dimensional space. The feature matrix is normalized to obtain a reference feature matrix. A feature vector formed by features in dimensions in the embedded matrix is fit by using the reference feature matrix and the embedded matrix to obtain a sparse coefficient vector of the feature vector. A maximum value of a sparse coefficient in the sparse coefficient vector is selected as a contribution value. The feature corresponding to the maximum contribution value are selected according to a preset feature number required to select to form an adjacent feature matrix. )
CN 114547405 A (involved in solving the data optimization problem by multi-target group intelligent optimization algorithm, and obtaining the optimal feature subset multiple iterations, so as to realize the feature selection of target cost minimization, thus reducing the feature number).
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/JEAN M CORRIELUS/Primary Examiner, Art Unit 2159 August 21, 2026
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