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 .
DETAILED ACTION
The action is in response to claims dated 6/26/2023
Claims pending in the case: 1-20
Claim Rejections - 35 USC § 103
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, 3-8, 10-14, 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wu (US 20220366318) in view of Liu (Adaptive and Efficient GPU Time Sharing for Hyperparameter Tuning in Cloud – please refer to attached for claim mapping).
Regarding Claim 1, Wu teaches, A method of training machine learning models comprising:
identifying, via at least one processor, a plurality of configurations for the machine learning models, wherein each configuration indicates a machine learning model and a corresponding technique to determine parameters for the machine learning model (Wu: Fig. 2 [28-29]: identify hyperparameter combinations; learned parameters of the ML models are based on the hyperparameters (technique));
evaluating, via the at least one processor, the plurality of configurations by training the machine learning model of the plurality of configurations according to the parameters determined by the corresponding technique (Wu: Fig. 2 [28-29]: evaluate by training);
monitoring, via the at least one processor, performance of the machine learning models of the plurality of configurations (Wu: Fig. 2 [38]: monitor model performance); and
adjusting, via the at least one processor, resources used for evaluating at least one configuration based on the performance of the machine learning model for the at least one configuration relative to the priority of the machine learning models of others of the plurality of configurations (Wu: [37]: adjust resources based on priority);
However, Wu does not specifically teach,
based on the priority of performance of the machine learning model
Liu teaches, adjusting resources based on the performance of the machine learning model (Liu: Pg. 1 col 2 section 1 [2], Pg. 2 col 1 [1-3], Pg. 4 col 1 section 2.1 [2]: adjust resource based on accuracy/performance during hyperparameter tuning);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Wu and Liu because the combination would enable adjust resources used based not only on priority but also on performance. One of ordinary skill in the art would have been motivated to combine the teachings because the combination would provide an option to adjust resources adaptively according to accuracy at run time (see Liu Pg. 1 col 1 [1]).
Regarding claim 3, Wu and Liu teach the invention as claimed in claim 1 above and, wherein adjusting the resources for evaluating the at least one configuration comprises: terminating the evaluation of the at least one configuration based on the at least one configuration having a machine learning model with lesser performance relative to the performance of the machine learning models of others of the plurality of configurations (Liu: Pg. 2 col 2 section 1 [3], Pg. 4 col 2 [1]: low accuracy terminated).
Regarding claim 4, Wu and Liu teach the invention as claimed in claim 1 above and, wherein monitoring performance of the machine learning models comprises:
pausing evaluation of the plurality of configurations at an intermediate portion of the evaluation; and producing a report for the performance of the machine learning models of the plurality configurations (Wu: [38]: generate performance report after training (pause)) (Liu: Pg. 3 col 1, Pg. 5 section 2.3 [1]: approaches using checkpoints pauses at checkpoints to save report files).
Regarding claim 5, Wu and Liu teach the invention as claimed in claim 4 above and, wherein adjusting the resources for evaluating the at least one configuration comprises:
allocating additional resources to resume the evaluation of the at least one configuration based on the at least one configuration having a machine learning model with greater performance relative to the performance of the machine learning models of others of the plurality of configurations (Wu: [37]: adjust resources based on priority) (Liu: Pg. 3 col 1 [4], Pg. 2 col 1 [1-3], Pg. 4 col 1 section 2.1 [2]: adjust resource based on accuracy/performance during hyperparameter tuning).
Regarding claim 6, Wu and Liu teach the invention as claimed in claim 1 above and, further comprising:
identifying one or more configurations with a machine learning model providing greater performance relative to performance of machine learning models of others of the plurality of configurations (Wu: Fig. 4 [38, 40]: select based on performance) (Liu: Pg. 4 col 1 section 2.1 [1]: select the highest performer set).
Regarding claim 7, Wu and Liu teach the invention as claimed in claim 1 above and, further comprising:
controlling, via the at least one processor, the evaluation of a configuration based on defining a search space for the evaluation of the configuration comprising a set of machine learning hyperparameters and value ranges for the set of machine learning hyperparameters (Wu: [32, 37]: define search space) (Liu Pg. 2 col section 1 [1]: search space).
Regarding Claim(s) 8, 10-13, this/these claim(s) is/are similar in scope as claim(s) 1, 4-7, respectively. Therefore, this/these claim(s) is/are rejected under the same rationale.
Regarding Claim(s) 14, 16-120, this/these claim(s) is/are similar in scope as claim(s) 1, 3-7 respectively. Therefore, this/these claim(s) is/are rejected under the same rationale.
Claim(s) 2, 9, 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wu (US 20220366318) and Liu (Adaptive and Efficient GPU Time Sharing for Hyperparameter Tuning in Cloud – please refer to attached for claim mapping) in view of Worth (US 20240289639).
Regarding claim 2, Wu and Liu teach the invention as claimed in claim 1 above and, wherein the machine learning model of one or more configurations includes a … learning agent and the corresponding technique includes a hyperparameter optimization technique, … (Wu: Fig. 2 [38]: monitor model performance; tune the hyperparameters (learning agent) ) (Liu: Pg. 2 col 2 [3]: iterations of training );
However, Wu and Liu do not specifically teach, configurations includes a reinforcement learning agent;
and wherein the one or more configurations further indicate an environment for the reinforcement learning agent;
Worth teaches, configurations includes a reinforcement learning agent (Worth: Fig. 2a, [41-42, 72]: RL agent using performance);
and wherein the one or more configurations further indicate an environment for the reinforcement learning agent (Worth: Fig. 2a, [43, 47]: RL agent using information on environment);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Wu, Liu and Worth because the combination would enable using an agent in the hyperparameter tuning process. One of ordinary skill in the art would have been motivated to combine the teachings because the combination would provide an option to “automatically adapt a behavior of a learning agent to optimize and/or improve performance in an environment” (see Worth [2-3]).
Regarding Claim(s) 9 and 15, this/these claim(s) is/are similar in scope as claim(s) 2. Therefore, this/these claim(s) is/are rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure in attached 892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANDRITA BRAHMACHARI whose telephone number is (571)272-9735. The examiner can normally be reached Monday to Friday, 11 am to 8 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 571 272 4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Mandrita Brahmachari/Primary Examiner, Art Unit 2144
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