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 communication is in response to Amendment filed on April 27, 2026.
Claims 1-6, 8 and 9 are pending. Claims 1, 8 and 9 are amended. Claims 7 and 10 are cancelled.
Response to Arguments
Referring to the 35 USC 101 rejection of claims 1-6, 8 and 9, as amended, Applicant argues that the claims integrate the abstract idea into a practical application because the executing of the machine learning programs to train machine learning models and to measure prediction accuracies is connected to evaluating and outputting clusters of machine learning programs in order to streamline creation of software components from sample programs. However, Examiner respectfully disagrees.
The claims do not recite any details of the streamline creation of software components and how it is achieved through the training of the machine learning programs or the prediction accuracy measurements. As such, the executing of the machine learning programs to train machine learning models and measure prediction accuracies is merely using the machine learning programs as generic computer software upon which to implement the training and measuring steps without reciting a practical application.
As such, the claims remain rejected under 35 USC 101 and further in view of the new grounds of rejection.
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 1-6, 8 and 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 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 two or more prediction accuracies corresponding to the two or more machine learning programs, and variance of the two or more prediction accuracies;
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.
Step 1: The claims as a whole fall within one or more statutory categories.
Step 2A prong 1: At least claims 1, 8 and 9 recite limitations that are abstract ideas.
The limitations “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 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 two or more prediction accuracies corresponding to the two or more machine learning programs, and variance of the two or more prediction accuracies” and “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” are mental steps. One can mentally determine the claimed values given certain criteria. Thus, the claimed limitations can be performed by the human mind.
The limitation “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” is a mental step. One can mentally cluster data into groups based on certain clustering criteria. Thus, the claimed limitations can be performed by the human mind.
Step 2A prong 2:
Claims 1, 8 and 9 recite the limitation “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”. This limitation is an additional element and is applying the judicial exception to implement it using generic machine learning programs to train machine learning models with training data and to measure prediction accuracies of the machine learning models with test data without disclosing the details as to how the training is accomplished other than to input data into the models and outputting prediction accuracies of the models. Furthermore the executing step does not describe how the training model is improved in order to streamline creation of software components from sample programs as described in the specification. See MPEP 2106.05(f). As such, the ‘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’ does not provide integration into a practical application.
The limitation “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” is an additional element and is mere output recited at a high level of generality and is considered insignificant extra-solution activity as ‘selecting information for display' as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Furthermore, Claims 1, 8 and 9 recites the following additional elements “a computer”, “a processor”, memory” and “information processing apparatus”, note that these recited additional elements are a high-level recitation of generic hardware and software computer components to perform the mental process and applied on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Step 2B: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more.
As explained with respect to Step 2A, Prong Two, the additional element of “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” is at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
With respect to the “outputting” limitation identified as insignificant extra-solution activity above, when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more.
Therefore, the claims as a whole do not change this conclusion and the claims are ineligible.
Claims 2-6 depend from claim 1 and thus include all the limitations of claim 1, therefore claims 2-6 recite the same abstract ideas of "mental process".
Claims 2-6 furthermore recite:
(claim 2): 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 3): that the corrected distance is inversely proportional to the predication accuracy;
(claim 4): 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; and
(claim 5): 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 6): 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.
Step 1: Claims 2-6 as a whole fall within one or more statutory categories.
Step 2A prong 1: Claims 2-6 recite limitations that are abstract ideas.
The limitations of “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” and “the corrected distance is inversely proportional to the predication accuracy” in claims 2 and 3 are mental steps. One can mentally correct a distance metric and categorize data based on the distance metric. Thus the claimed limitations can be performed in the mind.
The limitations “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” and “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” in claims 4 and 5 are also mental steps. One can device to not perform a calculation based on certain criteria and can choose to order the results of a calculation, as well as determining a peak or highest value in a set of values. Thus the claimed limitations can be performed in the mind.
The limitation “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” in claim 6 is a mental step. One can generate a tree structure using pen and paper and mentally select a tier within the tree structure. Thus the claimed limitation can be performed in the mind.
Step 2A prong 2: Claims 2-6 do not recite any additional elements that would integrate the judicial exception into a practical application.
Step 2B: Claims 2-6 do not recite any additional elements that would provide significantly more than the judicial exception.
Therefore, claims 2-6 as a whole are ineligible.
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, as previously presented, 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Srinivasan et al (US 2022/0364478) directed to: training of machine learning models based on parameters such as mean average, variance and statistical parameters and wherein the prediction of a machine learning model is used for selection of stored data to be captured and prediction accuracy is determined for each of multiple machine learning models based on a validation dataset [para 9, 15-17, 21; Fig 12 and related portions of specification];
Debnath et al (US 2024/0119349) directed to: optimizing training of machine learning models [Fig 2-5 and related portions of specification].
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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 Mon-Fri: 8am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amy Ng can be reached on 571-270-1698. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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CHERYL M SHECHTMANPatent Examiner
Art Unit 2164
/C.M.S//AMY NG/Supervisory Patent Examiner, Art Unit 2164
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