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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 22-27, 29-34, and 36-41 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 8-10, 15-17, and 20 of U.S. Patent No. 12,229,642 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the US patent discloses all the claim limitations of the independent claims on the instant application.
Claims 22-27, 29-34, and 36-41 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 1-3, 8-10, 15-17, and 20 of to US Patent 12,229,642 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the US Patent discloses all the claim limitations of the independent claims 22, 29, and 36. For example claim 1 of the US Patent 12,229,642 B2 discloses obtaining a request to tune one more hyperparameters of a machine learning model, wherein the request includes a machine learning task to be executed; selecting a range of values for a particular hyperparameter; performing tuning wherein each individual value of the range of the values for a hyperparameter is used by machine learning model; evaluating the results of tuning operations to wherein the results of using the individual values of the range of values for a hyperparameter are compared and proved the respective candidate values for one or more hyperparameters. This discloses the limitations of claim 22 of the instant application. The only difference being that claim 22 of instant application states the system access a knowledgebase to identify the range of vales for a hyperparameter based on similarity of hyperparameters and the tuning operation is iterative and is based on an optimization criterion and results of the range of values of the hyper-parameter are explored iteratively. However, this limitations are already implied in the claim 1 of the US Patent. Claim 1 of the patent discloses the range of values for a hyperparameter being selected by the service wherein it cites “based on the one or more hyper-parameters, selecting by the service, respective ranges of values applicable for individual ones of the one or more hyper- parameters to determine respective candidate values for the one or more hyper-parameters from the respective ranges of values that optimize the one or more hyper-parameters, wherein the respective ranges of values are selected by the service based, at least in part, on:”, this means that the range of value is selected by the service based on the hyperparameter. As it is selecting values it must be stored in a system or knowledge based already and its making the hyperparameter or task to a hyperparameter in the system and selecting corresponding range of values, thus is based on similarity and accessing a knowledgebase. In the US Patent it states the evaluating respective results of the one or more tuning operations for the one or more hyperparameters based on a comparison of the results for respective candidate values out of the ranges of values for the individual hyperparameters. This means individual values of the range of values for a hyperparameter are tested and compared, thus range of values are iteratively explored. Also claim 2 of the US Patent discloses the optimization criterion wherein it states “2. The computer-implemented method as recited in claim 1, wherein the request comprises an indication that the service is to identify an acceptance criterion for terminating tuning of the one or more hyper-parameters, and wherein the one or more tuning operations are terminated after determining that an acceptance criterion identified by the service has been satisfied.”. Thus Claim 1 and 2 of US Patent 12,229,642 anticipates all the limitations of claim 22 of the instant application. In claim 1 of the US Patent 12,229,642 B2, it states a request to tune one or more hyperparameter of a machine learning model that performs one or more task. As a request is being made to tune a machine learning model that performs a task, the task is already known as the model that does task is selected or requested to be tuned. Also claim 3 of US Patent 12,229,642 B2 states the request has indication of resource limits, mean it has indicated what resource to use and what its limits are. In addition, claim 2 of the US Patent 12,229,642 B2 also discloses the claims 23, 26 and 27 of the instant application as respective values are selected by system, providing candidate hyper-parameter values and training and executing a machine learning mode. As such the instant application claims are found to be anticipated by US Patent 12,229,642 B2. See the chart before for further details.
Instant Application – 17/810,554
US Patent – 12,229,642
22. A computer-implemented method, comprising:
obtaining, via one or more programmatic interfaces at a service of a cloud computing environment: a request to tune at least a particular hyper- parameter of a machine learning task; and
responsive to the request to tune:
accessing a knowledge base at the service to identify a range of respective values to be explored for the particular hyper-parameter, wherein the range is identified based on similarity of the particular hyperparameter to hyperparameters of machine learning tasks in the knowledgebase;
executing, at the service, tuning operations in which a range of respective values of the particular hyper-parameter are iteratively explored for the machine learning task;
determining, based on an optimization criterion and results of the tuning operations executed to iteratively explore the range of respective values of the particular hyper-parameter, at least one value of the range of respective values of the particular; and
providing, via the one or more programmatic interfaces, an indication of the determined at least one value of respective values of the particular hyper-parameter.
23. The computer-implemented method as recited in claim 22, further comprising: selecting, by the service, a first value of the range of respective values of the particular hyper-parameter, without receiving an indication of the first value via the one or more programmatic interfaces.
26. The computer-implemented method as recited in claim 22, wherein the result provided via the one or more programmatic interfaces comprises a plurality of candidate values identified for the particular hyper-parameter in the one or more tuning operations.
27. The computer-implemented method as recited in claim 22, wherein the machine learning task comprises one or more of: (a) training a machine learning model or (b) executing a trained machine learning model.
1. A computer-implemented method, comprising:
obtaining, via one or more programmatic interfaces at a service of a cloud computing environment, a request to tune one or more hyper-parameters of a machine learning model which performs one or more machine learning tasks, wherein tuning the one or more hyper-parameters of the machine learning model affect the result of the one or more machine learning tasks;
based on the one or more hyper-parameters, selecting by the service, respective ranges of values applicable for individual ones of the one or more hyper- parameters to determine respective candidate values for the one or more hyper-parameters from the respective ranges of values that optimize the one or more hyper-parameters, wherein the respective ranges of values are selected by the service based, at least in part, on:
one or more respective ranges of values of the service for one or more previously obtained requests to tune one or more hyper-parameters of one or more machine learning models;
performing one or more tuning operations in which the respective ranges of values for individual ones of the one or more hyper-parameters are utilized for the machine learning model;
evaluating respective results of the one or more tuning operations to generate the according to the evaluation; and
providing, via the one or more programmatic interfaces, the respective candidate values for the one or more hyper-parameters.
2. The computer-implemented method as recited in claim 1, wherein the request comprises an indication that the service is to identify an acceptance criterion for terminating tuning of the one or more hyper-parameters, and wherein the one or more tuning operations are terminated after determining that an acceptance criterion identified by the service has been satisfied.
24. The computer-implemented method as recited in claim 22, further comprising: obtaining, at the service via the one or more programmatic interfaces, an indication of a criterion for terminating execution of the tuning operations; and terminating, by the service, execution of the tuning operations based at least in part on the criterion.
22. The computer-implemented method as recited in claim 21, wherein the request comprises an indication that the service is to identify an acceptance criterion for terminating tuning of the one or more hyper-parameters, and wherein the one or more tuning operations are terminated after determining that an acceptance criterion identified by the service has been satisfied
25. The computer-implemented method as recited in claim 22, further comprising: determining, by the service, at least one value of the range of respective values of the particular hyper-parameter based at least in part on a size of an input data set of the machine learning task.
40. The one or more non-transitory computer-accessible storage media as recited in claim 35, wherein the one or more hyper-parameters comprise a bin size to be used for quantile binning.
Claims 22-23 and 26-27 of the instant application correspond to US Patent 12,229,642 B2 claims 1-2.
Claim 24 of the instant application corresponds to US Patent 12,229,642 B2 claim 2.
Claim 25 of the instant application corresponds to US Patent 12,229,642 B2 claim 1
Claims 29-30 and 33-34 of the instant application correspond to US Patent 12,229,642 B2 claims 8-9.
Claim 31 of the instant application corresponds to US Patent 12,229,642 B2 claim 9.
Claim 32 of the instant application corresponds to US Patent 12,229,642 B2 claim 20.
Claims 36-37 and 40-41 of the instant application correspond to US Patent 12,229,642 B2 claim 15-16.
Claim 38 of the instant application corresponds to US Patent 12,229,642 B2 claim 16.
Claim 39 of the instant application corresponds to US Patent 12,229,642 B2 claim 20.
Response to Arguments
Applicant's arguments filed 27 January 2026 with respect to claim(s) 22-41 have been considered are persuasive. Also, the applicant argues the that double patent rejection be withdrawn or held in abeyance until allowable subject matter is indicate. The examiner understands the applicant stance but maintains the rejection for double patenting.
Conclusion
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/PAULINHO E SMITH/ Primary Examiner, Art Unit 2127
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