DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is responsive to pending claims 1-20 filed 10/6/2023.
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) 8-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 8 recites a computer-readable medium containing instructions for performing the recited steps. However, Specs 0086, 0094 contemplates encoding on carrier waves or wireless media communication. Hence, the claims are non-statutory as they are directed to transitory computer media such as carrier waves. Dependent claims 9-14 are rejected for the same reasons.
Claim(s) 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The 35 U.S.C. 101 subject matter eligibility analysis first asks whether the claim is directed to one of the four statutory categories (Step 1). It next asks whether the claim is directed to an abstract idea (Step 2A), via Prong 1, whether an abstract idea (e.g., mathematical concept, mental process, certain methods of organizing human activity) is recited, and Prong 2, whether it is integrated into a practical application. It finally asks whether the claim as a whole includes additional elements that amount to significantly more than the judicial exception (Step 2B). See MPEP 2106.
STEP 1: The claims falls within one of the four statutory categories:
Claims 1-7, 15-20 are statutory as being directed to methods and physical systems, respectively. Claims 8-14 are not statutory, as described above; however, analysis will continue below.
STEP 2A PRONG 1: The claims recite a judicial exception:
The claims are directed to a mathematical concept:
Claim 1 is directed processing a mathematical vector through dimensional reductions, determining a similarity, such as via ranking, and aggregating the reduced samples. However, all these are mathematical operations.
Claim 2-3 are directed to using k-NN mathematical algorithm for the clustering step, and hence, continues to be a mathematical concept.
Claim 4 is directed to incorporating randomness to he dimension reduction step, which is a mathematical concept.
Claim 5 is directed to using a threshold during the selecting step, but the use of thresholds is a mathematical concept.
Claim 6 is directed to using a distance function during the selecting step, but distance functions are mathematical concepts.
STEP 2A PRONG 2: The claims do not integrate the exception into a practical application:
In claim 7, the additional elements include the application to software code as an input to the method. However, this amounts to general incorporation into a particular technical field and does not meaningfully limit the practice of the abstract idea and hence does not constitute an integration into a practical application.
The additional elements include, in claims 8 and 15, the recitation of a computer-medium to perform the above process. However, this is mere instructions to implement the mathematical concept in a general purpose computing environment and hence does not comprise an integration into a practical application.
STEP 2B: The claim as a whole do not include additional elements that amount to significantly more than the abstract idea:
In claim 7, the additional elements include the application to software code as an input to the method. However, this the application of classifiers to computer code is well-understood, routine and conventional (WURC) in the field of classification and hence does not constitute significantly more.
The additional elements include, in claim 8 and 15, the recitation of a computer-medium to perform the above process. However, the use of computer is well-understood, routine and conventional (WURC) in the field of classification and hence does not constitute significantly more.
Claim Rejections - 35 USC § 102
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.
Claim(s) 1-6, 8-13, 15-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cannings ("Random-projection ensemble classification", published 2017).
For claim 1, Cannings disclose: a method, comprising:
obtaining a first feature vector of a sample (p.961-962 gives overview of the proposed random-projection ensemble classifier, see p.961¶2: drawing random projections uniformly (i.e., based on Haar measure), splitting into groups, training classifiers on the projections (see §4 for possible classifiers), and within each group keeping the best performing projection, hence, receiving various feature vectors for training and inference for the classifiers, with §6.1-6.2 disclosing training and inference on synthetic and real data);
processing the first feature vector through a plurality of dimensionality reduction processes, wherein each of the plurality of dimensionality reduction processes generates a respective second feature vector, each of the second feature vectors has a smaller dimension than a dimension of the first feature vector (§2 p.963 ¶4-5 (“We no define …”) gives formalization of ensemble randomly selected classifiers A_1 … A_B1 of projection dimension d<p (¶3), the projections generating reduced dimension second feature vectors, with §3 ¶1, eq.7 formalizing selection of the best (lowest error) projection with respect to a classifier for the final ensemble);
for each of the second feature vectors, determining an intermediate set of similarity samples (§4 gives overview of various possible classifiers for operation on the various projections, with §4.3 disclosing a knn classifier that would consider the top k of a similarity or distance metric); and
aggregating the intermediate sets of similarity samples to generate an output set of similarity samples (ibid: votes of the nearest neighbors are aggregated in order to generate an output for each projection).
For claim 2, Cannings discloses the method of claim 1, as described above. Cannings further discloses: wherein the intermediate set of similarity samples is determined by using a k-nearest neighbors (k-NN) algorithm (§4.3).
For claim 3, Cannings discloses the method of claim 2, as described above. Cannings further discloses: wherein the k-NN algorithm is an exact k-NN algorithm or an approximate k-NN algorithm (§4.3 describes exact kNN algorithm).
For claim 4, Cannings discloses the method of claim 2, as described above. Cannings further discloses: wherein each of the plurality of dimensionality reduction processes is generated by using a random process (§3 ¶1, §2 p.963 ¶4).
For claim 5, Cannings discloses the method of claim 1, as described above. Cannings further discloses: wherein the aggregating the intermediate sets of similarity samples comprises:
selecting similarity samples that are included in more than a preset threshold number of intermediate sets (§4.3: each sample is included in at least one set).
For claim 6, Cannings discloses the method of claim 1, as described above. Cannings further discloses: wherein the aggregating the intermediate sets of similarity samples comprises:
selecting similarity samples based on a normalized distance with the respective second feature vector (§4.3 shows norm distance used for selection).
Claims 8-13, 15-20 disclose computer media and systems analogous to the above methods and are hence likewise rejected.
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.
Claim(s) 7, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Cannings ("Random-projection ensemble classification", published 2017) in view of Copty (US 20210357207 A1).
For claim 7, Cannings discloses the method of claim 1, as described above. Cannings does not discloses: wherein the sample is a software code.
Copty discloses: wherein the sample is a software code (fig.1, 0014 discloses classifying software code).
It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the method of Cannings by incorporating the software code classification of Copty. Both concern the art of machine learning classification, and the incorporation would have, according to Copty, better detect code features, such as vulnerabilities (0012).
Claim(s) 14 disclose computer media analogous to the above methods and are hence likewise rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Chang (US 11250039 B1) discloses the use of random projections for query processing, see fig.4.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIANG LI whose telephone number is (303)297-4263. The examiner can normally be reached Mon-Fri 9-12p, 3-11p MT (11-2p, 5-1a ET).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Jennifer Welch can be reached on (571)272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LIANG LI/
Primary examiner AU 2143
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