DETAILED ACTION
1. This office action is in response to application 18/753,566 filed on 6/25/2024. Claims 1-20 are pending in this office action.
Notice of Pre-AIA or AIA Status
2. 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 § 103
3. 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 (i.e., changing from AIA to pre-AIA ) 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 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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0121886 (hereinafter Jahjah) in view of US 2024/0406477 (hereinafter Dela Rose).
As for claim 1 Jahjah discloses: A method, comprising: receiving, by at least one computing device (See paragraph 0025 note devices), a plurality of datasets (See paragraph 0038 note featurized datasets are generated) ; executing, by the at least one computing device , a plurality of machine-learning models on each of the plurality of datasets (See paragraphs 0034-0041 note the system is trained on data). Jahjah does not explicitly disclose: generating, by the at least one computing device, for each of the plurality of datasets, a label identifying a best performing one of the plurality of machine-learning models, the best performing one of the plurality of machine-learning models being evaluated based on performance evaluations derived from executing the plurality of the machine-learning models on a same one of the plurality of datasets; executing, by the at least one computing device, a predetermined dataset profiler to extract a set of profiles from each of the plurality of datasets; associating, by the at least one computing device, the label with the set of profiles of the same one of the plurality of datasets for each of the plurality of datasets to form a plurality of label- associated sets of profiles nor generating, by the at least one computing device, a meta dataset from the plurality of label- associated sets of profiles; and selecting, by the at least one computing device running a predetermined estimating machine-learning model on the meta dataset, one of the plurality of the machine-learning models as a trained machine-learning model. Dela Rose however discloses: generating, by the at least one computing device, for each of the plurality of datasets, a label identifying a best performing one of the plurality of machine-learning models, the best performing one of the plurality of machine-learning models being evaluated based on performance evaluations derived from executing the plurality of the machine-learning models on a same one of the plurality of datasets (See paragraph 0108 and 0154 note training data including labels are generated) and executing, by the at least one computing device, a predetermined dataset profiler to extract a set of profiles from each of the plurality of datasets; associating, by the at least one computing device, the label with the set of profiles of the same one of the plurality of datasets for each of the plurality of datasets to form a plurality of label- associated sets of profiles (See paragraph 0054) and generating, by the at least one computing device, a meta dataset from the plurality of label- associated sets of profiles; and selecting, by the at least one computing device running a predetermined estimating machine-learning model on the meta dataset, one of the plurality of the machine-learning models as a trained machine-learning model (See paragraph 0218 note the system estimates performance). It would have been obvious to an artisan of ordinary skill in the pertinent at the time the instantly claimed invention was filed to have incorporated the teaching of Dela Rose into the system of Jahjah. The modification would have been obvious because the two references are concerned with the solution to problem of data organization (See Jahjah and Dela Rose abstract), therefore there is an implicit motivation to combine these references (i.e. motivation from the references themselves). In other words, the ordinary skilled artisan, during his/her quest for a solution to the cited problem, would look to the cited references at the time the invention was made. Consequently, the ordinary skilled artisan would have been motivated to combine the cited references since Dela Rose’s teaching would enable users of the Jahjah system to more efficient processing.
As for claim 2 the rejection of claim 1 is incorporated and further Dela Rose discloses: further comprising generating a machine-learning pipeline comprising the executing the plurality of machine-learning models on the plurality of datasets to generate the labels, extracting dataset profiles, generating a meta dataset from the labels and profiles, and running the estimating machine-learning model on the meta dataset (See figure 11 and paragraphs 0212 and 0221 note the pipeline is the learned process to generate).
As for claim 3 the rejection of claim 1 is incorporated and further Jahjah discloses: wherein the plurality of datasets comprises user provided real tabular datasets (See paragraph 0087 note tabular form).
As for claim 4 the rejection of claim 3 is incorporated and further Jahjah discloses: wherein each of the real tabular datasets comprise a target column as a first column thereof (See paragraph 0027 note rows and columns).
As for claim 5 the rejection of claim 1 is incorporated and further Jahjah discloses: wherein the plurality of datasets comprises a plurality of tabular datasets synthesized with one or more user input parameters (See paragraph 0002 note the system synchronized datasets with current data).
As for claim 6 the rejection of claim 5 is incorporated and further Jahjah discloses: wherein the one or more user inputted parameters comprise bounds on a number of rows in the tabular dataset and a number of features in the tabular dataset (See paragraph 0052 note the thresholds represent the bound).
As for claim 7 the rejection of claim 1 is incorporated and further Dela Rose discloses: wherein the performance evaluations comprise a quantitative metric selected from the group consisting of F1 score, root mean squared error (RMSE), accuracy, area under a receiver operating characteristic curve (AUC-ROC), mean absolute error (MAE) and any combination of thereof (See paragraph 0115 note similarity scores).
As for claim 8 the rejection of claim 1 is incorporated and further Dela Rose discloses: wherein the dataset profiler is configured to extract a profile selected from the group consisting of a number of observations in the dataset, a feature count, a class ratio, a percentage of duplicate records, a percent of features that have binary data, and any combination thereof (See paragraphs 0222-0224 note duplicates are calculated and removed).
As for claim 9 the rejection of claim 1 is incorporated and further Dela Rose discloses: wherein the predetermined estimating machine-learning model is a gradient boosted tree model (See paragraphs 0216 and 0219).
As for claim 10 the rejection of claim 1 is incorporated and further Dela Rose discloses: wherein the selected one of the plurality of the machine- learning models is a best performing one of the plurality of the machine-learning models on the meta dataset (See paragraphs 0216 and 0219).
Claims 11-19 are system claims substantially corresponding to the method of claims 1-10 and are thus rejected for the same reasons as set forth in the rejection of claims 1-10.
Claim 20 is a system claims substantially corresponding to the method of claim 1 and are thus rejected for the same reasons as set forth in the rejection of claim 1.
Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIYAH STONE HARPER whose telephone number is (571)272-0759. The examiner can normally be reached on Monday-Friday 10:00 am - 6:00 pm.
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/Eliyah S. Harper/Primary Examiner, Art Unit 2166 August 30, 2026
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