DETAILED ACTION
This action is responsive to the application filed on 05/12/2023. Claims 1, 3-5 are pending in the case. Claims 1, 3-5 are independent claims. Claim 2 is cancelled.
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 and 3-5 is rejected under 35 U.S.C. 101 because the claim are directed to an abstract idea without significantly more.
Claim 5 is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claim is directed to software per se.
Regarding Claim 1/4/5:
Under step 1, Claim 1 is directed to a device which is directed to a machine, one of the statutory categories.
Under step 1, Claim 4 is directed to a method which is directed to a process, one of the statutory categories.
Under step 1, Claim 5 is directed to a program for causing a computer to execute which is directed to a software per se, which is not one of the statutory categories.
Under Step 2A Prong 1, the claim recites the following limitations which are considered mental evaluations:
performs, on the feature amount data, principal component analysis in a sample space that is a collection of the plurality of feature amounts of a set of values for each of the plurality of the samples of the feature amounts;
determines whether there is distortion in a distribution of a principal component obtained by the principal component analysis in the sample space;
selects a feature amount from among the plurality of the feature amounts based on a principal component determined to have no distortion in the distribution among the principal components obtained by the principal component analysis performed by the principal component analysis.
Each of these amount to mental evaluation because they describe evaluations and observations of abstract data which can be performed in the mind.
Under step 2A Prong 2, The claim recites the following additional element(s):
feature amount data acquisition unit … a principal component analysis unit… a distortion determination unit… and a feature amount selection unit (which amounts to descriptions which makes use of or applies the abstract idea because under 2106.05(f)(1) “the claim fails to recite details of how a solution to a problem is accomplished”)
acquires feature amount data including a set of values of a plurality of feature amounts for a sample, for each of a plurality of the samples (that amounts to adding insignificant extra-solution activity to the judicial exception, because the limitation describe mere data gathering. See MPEP 2106.05(g))
Therefore the claim is directed to a judicial exception.
Further, additional element acquires feature amount data including a set of values of a plurality of feature amounts for a sample, for each of a plurality of the samples is well understood, routine, and conventional activity because it amounts to “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i))
Under step 2B, the recited additional elements when considered alone or in combination neither integrates the abstract idea into a practical application nor provides significantly more than the abstract idea itself.
Regarding Claim 3
The rejection of claim 1 is incorporated and further:
Under Step 2A Prong 1, the claim recites the following limitations which are considered mental evaluation:
selects a feature amount having a large distance from an origin of the sample space for a principal component determined to have no distortion in the distribution.
Each of these amount to mental evaluation because they describe evaluations and observations of abstract data which can be performed in the mind.
Under step 2A Prong 2, The claim recites the following additional element(s):
The feature amount selection device according to The feature amount selection device according to wherein the feature amount selection unit (which amounts to descriptions which makes use of or applies the abstract idea because under 2106.05(f)(1) “the claim fails to recite details of how a solution to a problem is accomplished”)
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a feature amount data acquisition unit”, ” a principal component analysis unit”, “a distortion determination unit”, “a feature amount selection” in claim 1 and 3.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 and 3-5 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 term “distortion” in “determines whether there is distortion in a distribution” and “a principal component determined to have no distortion” in claim 1, 4, and 5 is a relative term which renders the claim indefinite. The term “distortion” is 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.
Indeed, specification paragraphs 0073 and 0086 describe various conditions which indicate “no distortion” however, the specification points out “the present invention is not limited to” such embodiments.
Further, The term “large” in “selects a feature amount having a large distance” in claim 3 is a relative term which renders the claim indefinite. The term “large” is 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.
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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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, 3-5 is/are rejected under 35 U.S.C. 35 U.S.C. 102(a)(1) as being anticipated by Hubert et al. “Robust PCA for skewed data and its outlier map”
Regarding claim 1/4/5,
Hubert teaches, A feature amount selection device comprising:…a feature amount data acquisition unit that acquires feature amount data including a set of values of a plurality of feature amounts for a sample, for each of a plurality of the samples…[claim 4] A feature amount selection method…[claim 5] … program for causing a computer to execute (pg 1 Introduction “Principal component analysis is one of the best known techniques of multivariate statistics. It is a dimension reduction technique which transforms the data to a smaller set of variables while retaining as much information as possible… When given a data matrix X with n observations and p variables, the PCs ti are linear combinations of the data” PCA begins with a set of data in a matrix corresponding to acquisition of data as claimed, which amounts to sets of n observations and p variables which are the set of values of features of samples as claimed. Further Section 5 Simulations, describes performing the method/system on a computer) a principal component analysis unit that performs, on the feature amount data, principal component analysis in a sample space that is a collection of the plurality of feature amounts of a set of values for each of the plurality of the samples of the feature amounts; (pg 1 “Principal component analysis…Classical PCA (CPCA) uses the classical sample covariance matrix for this… The robustPCA technique we will focus on is called ROBPCA” pg 2 “Perform a singular value decomposition to restrict the observations to the space they span… Reduce the dimension by projecting all data on the k-dimensional subspace V0 spanned by the first k eigenvectors of the robust covariance estimator” projecting data onto a k dimensional subspace from the singular value decomposition corresponds to perform principle component analysis in a sample space on a collection of claimed features having sets of values.) a distortion determination unit that determines whether there is distortion in a distribution of a principal component obtained by the principal component analysis in the sample space ( pg 2 “For each observation, compute its orthogonal distance…
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… with xiˆ the projection of xi on the subspace V0 …We then obtain an improved robust subspace estimate V1 as the subspace spanned by the k dominant eigenvectors of Σ, which is the covariance matrix of all observations xi….Compute a robust center and covariance matrix” The distance of between the data and the projected data is the distortion according to the distribution of components defined by the subspace V0. A robust subspace is a measure of distortion because it describes components resistant to the effects of outliers) and a feature amount selection unit that selects a feature amount from among the plurality of the feature amounts based on a principal component determined to have no distortion in the distribution among the principal components obtained by the principal component analysis performed by the principal component analysis. ( pg 2 “We then obtain an improved robust subspace estimate V1 as the subspace spanned by the k dominant eigenvectors of Σ, which is the covariance matrix of all observations xi for which OD(0) i ≤ COD… The cutoff value cOD is difficult to determine because the distribution of the orthogonal distances is not known exactly… Next, we project all data points on the subspace V1.” The subspace v1 is defined as the set of observations satisfying the cutoff value. Such a subspace is defined by components considered to have no distortion. The final projected data points in such a subspace are the selected features based on those components.)
Regarding claim 3
Hubert teaches, selects a feature amount having a large distance from an origin of the sample space for a principal component determined to have no distortion in the distribution. ( pg 2 “For each observation, compute its orthogonal distance…
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…We then obtain an improved robust subspace estimate V1 as the subspace spanned by the k dominant eigenvectors of Σ, which is the covariance matrix of all observations xi for which OD(0) i ≤ COD” all features which have a non-zero orthogonal distance have a large distance from an origin yet have no distortion as they satisfy the cutoff value.)
Conclusion
Prior art not relied upon:
Hubert et al “ROBPCA: A New Approach to Robust Principal
Component Analysis” further describes details related to robust PCA.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHNATHAN R GERMICK whose telephone number is (571)272-8363. The examiner can normally be reached M-F 9:30-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached on 571-272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.R.G./
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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