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 .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on December 4, 2025 has been entered.
Claims 1-9 are pending. Claims 1-9 are amended. Claim 10 is cancelled.
Response to Arguments
Referring to the 35 USC 112(b) rejection of claim 10, Applicant’s cancellation of the claim is acknowledged. As such, the 35 USC 112(b) rejection of the claim is withdrawn.
Referring to the 35 USC 101 rejection of claims 1-9, as amended, Applicant argues that the claims integrate the abstract idea into a practical application because the claims are directed to the technical field of machine learning programming. Specifically, the claims have been amended to recite ‘executing a plurality of machine learning programs to train a plurality of machine learning models corresponding to the plurality of machine learning programs using training data and to measure a plurality of prediction accuracies corresponding to the plurality of machine learning models using test data. However, Examiner respectfully disagrees. The executing of the machine learning programs to perform the mental step of training machine learning models using training data and the mathematical calculation of measuring prediction accuracies of the machine learning models step is merely linking the implementation of these abstract ideas to the technological environment of the execution of machine learning programs in order to achieve these steps.
As such, the claims do not recite significantly more than the judicial exception and remain rejected under 35 USC 101 and further as addressed below.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 8 and 9 recite executing a plurality of machine learning programs to train a plurality of machine learning models corresponding to the plurality of machine learning programs using training data and to measure a plurality of prediction accuracies corresponding to the plurality of machine learning models using test data; executing hierarchical clustering to classify the plurality of machine learning programs into two or more clusters based on features associated with description of each of the plurality of machine learning programs and each of the plurality of prediction accuracies, wherein the hierarchical clustering generates a plurality of clustering results each of which differs in a number of clusters; calculating, for each cluster of the two or more clusters in each result of the plurality of clustering results, a first evaluation value based on an index value for reusability of two or more machine learning programs included in the each cluster and two or more prediction accuracies corresponding to the two or more machine learning programs; calculating, for the each clustering result, a second evaluation value based on two or more first evaluation values corresponding to the two or more clusters in the each clustering result; and outputting, based on a plurality of second evaluation values respectively corresponding to the plurality of clustering results, one clustering result amongst the plurality of clustering results.
The limitations of training a plurality of machine learning models corresponding to the plurality of machine learning programs using training data, executing hierarchical clustering to classify the plurality of machine learning programs into two or more clusters.. and ..generates a plurality of clustering results..”, and outputting a clustering result, as drafted, are processes that, under their broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “by a processor” (in claim 8) nothing in the claim elements precludes the steps from practically being performed in the mind. For example, but for the “by a processor” language (claim 8), “training a plurality of machine learning models corresponding to the plurality of machine learning programs using training data”, “executing hierarchical clustering to classify the plurality of machine learning programs into two or more clusters..and ..generates a plurality of clustering results..” and “outputting a clustering result” in the context of these claims encompasses a user mentally performing the training, clustering and outputting steps.
Furthermore, the measuring of a plurality of prediction accuracies corresponding to the plurality of machine learning models using test data and the calculating of first and second evaluation values encompasses mathematical calculations but for the recitation of generic computer components such as the processor (claim 8). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitations in the mind or as mathematical calculations but for the recitation of generic computer components, then it falls within the “Mental Processes” and “Mathematical calculations” groupings of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional step of executing a plurality of machine learning programs in order to perform the steps of training of the machine learning models and measuring the plurality of prediction accuracies of the machine learning models.
The executing of the machine learning programs is recited at a high level of generality (i.e. as generic machine learning programs that are executed to perform the mental step of training machine learning models and the mathematical calculation of measuring the prediction accuracies of the machine learning models using test data) and is considered generally linking the judicial exception (i.e. the training of the machine learning models and measuring of prediction accuracies of the models) to a technological environment wherein a plurality of machine learning programs are executed to achieve these abstract ideas.
The combination of this additional step is no more than mere instructions to apply the exception using generic computer components (i.e. the processor). As such, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The executing of the machine learning programs does not add significantly more to the abstract ideas of performing the training and measuring steps and is merely generally linking the judicial exception to a technological environment, similar to Parker v. Flook ("Flook established that limiting an abstract idea to one field of use or adding token postsolution components did not make the concept patentable", see Bilski v. Kappos, 561 U.S. 593, 612, 95 USPQ2d 1001, 1010 (2010)).
Mere instructions to apply an exception using a generic computer component such as a generic processor cannot provide an inventive concept. The claims are not patent eligible.
Claims 2-4 and 6 depend from claim 1 and thus include all the limitations of claim 1, therefore claims 2-4 and 6 recite the same abstract ideas of "mental process" and “mathematical calculations”.
Claims 2-4 and 6 furthermore recite: that the hierarchical clustering includes correcting a distance between different machine learning programs in terms of the features by using a predication accuracy of each of the different machine learning programs, and classifying the plurality of machine learning programs based on the corrected distance (claim 2); that the corrected distance is inversely proportional to the predication accuracy (claim 3); that the outputting includes skipping the calculating of the second evaluation value for other clustering results than the plurality of clustering results amongst a plurality of pattern candidates, each of which differs in the number of clusters, based on a relationship between the number of clusters and the second evaluation values (claim 4); and the hierarchical clustering generates a tree structure including a plurality of hierarchical tiers, and the outputting includes selecting one hierarchical tier amongst the plurality of hierarchical tiers (claim 6), which are mental steps that can also be performed in the human mind.
The claims do not include any additional elements that would integrate the judicial exception into a practical application or teach significantly more than the judicial exception. Claims 2-4 and 6 are therefore not patent eligible.
Claims 5 and 7 depend from claims 4 and 1 and thus includes all the limitations of claims 4 and 1, therefore claims 5 and 7 recite the same abstract ideas of "mental process" and “mathematical calculations”.
Claims 5 and 7 furthermore recite: that the second evaluation values are calculated in descending or ascending order of the number of clusters, and the outputting includes detecting a peak in the second evaluation values and skipping, upon the detecting of the peak, the calculating of the second evaluation value for the other clustering results that follow the peak in the descending or ascending order (claim 5); and that the calculating of the first evaluation value includes calculating, for the each cluster, the first evaluation value based on a number of machine learning programs included in the each cluster, a cohesion degree corresponding to variance of the features of the two or more machine learning programs, a mean of the two or more prediction accuracies, and variance of the two or more prediction accuracies (claim 7), which are mathematical calculations.
The claims do not include any additional elements that would integrate the judicial exception into a practical application or teach significantly more than the judicial exception. As such, claims 5 and 7 are not patent eligible.
To expedite a complete examination of the instant application, the claims
rejected under 35 U.S.C. 101 (nonstatutory) above are further rejected as set forth
below in anticipation of applicant amending these claims to place them within the four statutory categories of the invention.
Novel and/or Non-obvious Subject Matter
Claims 1-9 were found to be novel and/or nonobvious for at least the reasons stated in the Non-final Office Action dated March 3, 2025.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHERYL M SHECHTMAN whose telephone number is (571)272-4018. The examiner can normally be reached on M-F: 10am-6:30pm.
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CHERYL M SHECHTMANPatent Examiner
Art Unit 2164
/C.M.S/
/MARK E HERSHLEY/Primary Examiner, Art Unit 2164