DETAILED ACTION
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claims 1 and 11
Claim 1 recites “A method…” which is a series of steps and are therefore a process. Claim 11 recites “One or more non-transitory computer-readable media…” which is a manufacture.
Independent claims 1 and 11 recite limitations of:
generating…
training…
Claims 1 and 11 recite the limitations of “generating…” and “training…” which are processes that, under its broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting a processor, a computer, a non-transitory computer-readable media; nothing in the claim elements preclude the step from practically being performed in a human mind.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls withing the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgement, and opinion).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The processor, a computer, and a non-transitory computer-readable media are recited at a high level of generality (i.e. as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (see MPEP 2106.05(f)). The claims are directed to an abstract idea.
The Dependent claims 2-10 and 12-20 recite additional limitations of:
generating…
counting…
summing…
Claims 2-10 and 12-20 recite the limitations of “generating…”, “counting…”, and “summing…” which are processes that, under its broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting a processor, a computer, a non-transitory computer-readable media; nothing in the claim elements preclude the step from practically being performed in a human mind.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls withing the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgement, and opinion).
The judicial exception is not integrated into a practical application. Claims 2-10 and 12-20 recite no additional elements.
The processor, a computer, a non-transitory computer-readable media are recited at a high level of generality (i.e. as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (see MPEP 2106.05(f)). The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Brueckner et al. U.S. Patent No. 10,318,882
An indication of a data source to be used to train a linear prediction model is obtained. The model is to generate predictions using respective parameters assigned to a plurality of features derived from observation records of the data source. The parameter values are stored in a parameter vector. During a particular learning iteration of the training phase of the model, one or more features for which parameters are to be added to the parameter vector are identified. In response to a triggering condition, parameters for one or more features are removed from the parameter vector based on an analysis of relative contributions of the features represented in the parameter vector to the model's predictions. After the parameters are removed, at least one parameter is added to the parameter vector.
Leskovec et al. U.S. Patent No. 11,783,175
Systems and methods for efficiently training a machine learning model are presented. More particularly, using information regarding the relevant neighborhoods of target nodes within a body of training data, the training data can be organized such that the initial state of the training data is relatively easy for a machine learning model to differentiate. Once trained on the initial training data, the training data is then updated such that differentiating between a matching and a non-matching node is more difficult. Indeed, by iteratively updating the difficulty of the training data and then training the machine learning model on the updated training data, the speed that the machine learning model reaches a desired level of accuracy is significantly improved, resulting in reduced time and effort in training the machine learning model.
Moharrer et al. U.S. Patent No. 11,868,854
Herein are techniques that train regressor(s) to predict how effective would a machine learning model (MLM) be if trained with new hyperparameters and/or dataset. In an embodiment, for each training dataset, a computer derives, from the dataset, values for dataset metafeatures. The computer performs, for each hyperparameters configuration (HC) of a MLM, including landmark HCs: configuring the MLM based on the HC, training the MLM based on the dataset, and obtaining an empirical quality score that indicates how effective was said training the MLM when configured with the HC. A performance tuple is generated that contains: the HC, the values for the dataset metafeatures, the empirical quality score and, for each landmark configuration, the empirical quality score of the landmark configuration and/or the landmark configuration itself. Based on the performance tuples, a regressor is trained to predict an estimated quality score based on a given dataset and a given HC.
Gunes et al. U.S. Publication No. 2019/0370684
A computing device selects a feature set and hyperparameters for a machine learning model to predict a value for a characteristic in a scoring dataset. A number of training model iterations is determined. A unique evaluation pair is selected for each iteration that indicates a feature set selected from feature sets and a hyperparameter configuration selected from hyperparameter configurations. A machine learning model is trained using each unique evaluation pair. Each trained machine learning model is validated to compute a performance measure value. An estimation model is trained with the feature set, the hyperparameter configuration, and the performance measure value computed for unique evaluation pair. The trained estimation model is executed to compute the performance measure value for each unique evaluation pair. A final feature set and a final hyperparameter configuration are selected based on the computed performance measure value.
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/KRIS E MACKES/Primary Examiner, Art Unit 2153