CTFR 18/139,016 CTFR 94446 Detailed Action Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claims 1-20 are pending for examination. Claims 1, 13, and 20 are independent. Response to Amendment The office action is responsive to the amendments filed on 03/31/2026. As directed by the amendments claims 1, 13, and 20 are amended. Response to Arguments 07-37 Applicant's arguments filed 03/31/2026 have been fully considered but they are not fully persuasive. Applicant arguments regarding 35 U.S.C. § 101: At Step 2A Prong 1, the claims as amended do not recite a mental process. The claims recite starting multiple training tracks that each train a candidate MLM using the same training data and different training settings (e.g., sets of hyperparameters such as learning rates, regularization constants, and parameters of gradient descent, as described in Specification at par. [0065]), evaluating a candidate MLM mid-training (which involves analyzing computational metrics such as loss functions, accuracy metrics, and parameter statistics generated during the training process), placing a training track on a stopped status indicating that training is ceased based at least in part on a processing cost associated with the further training while the candidate MLM is retained in a pool of MLMs from which final MLMs are selected, continuing other training tracks, and selecting final MLMs. The claims further require causing the final MLMs to perform processing of inference data on cloud-based hardware resources, which is an inherently computational operation. These operations involve computationally intensive ML model training, mid-training evaluation, and managing distinct track statuses with candidate pools-none of which can practically be performed in the human mind. At Step 2A Prong 2, even if the claims recite an abstract idea, the claims as a whole integrate any such exception into a practical application by improving the technological process of ML model training. As described in Specification, the multitrack training with early stopping "enables a computing system, on one hand, to explore a significant region of training parameters space while, on the other hand, applying most of the available computational resources to training and selecting the most promising MLM candidates." (Specification, par. [0027].) The amended claims reflect this improvement: they recite a specific technical approach where multiple training tracks are started (each exploring a different region of the hyperparameter space, making this a systematic computational search that cannot be replicated mentally), evaluated mid-training, and placed on a stopped status that retains the partially-trained MLM in a candidate pool while freeing computational resources for continuing other tracks. This is not merely "apply it" on a generic computer-it is a specific resource-optimization technique for ML training that improves computational efficiency. USPTO Example 47, Claim 3 was found eligible because the claim reflected a technical improvement described in the specification (improving network security). Similarly here, the claims reflect the disclosed improvement to ML training efficiency. The claims as amended also require causing the selected final MLMs to perform processing of inference data on cloud-based hardware resources, which ties the entire multi-track training process to a concrete, tangible technological outcome-the deployment and use of the trained model for inference. The training steps (starting TTs, continuing TTs) are not mere instructions to apply an abstract idea-they are the core technological operations being improved by the claimed method. The claims do not merely recite the idea of training ML models on a generic computer; they recite a specific multi-track training architecture with stopped/active status management and candidate pooling that provides a concrete improvement to how ML training is performed. Examiner response: Examiner respectfully disagrees, under broadest reasonable interpretation, the claim does not recite specific training steps/settings (e.g., sets of hyperparameters such as learning rates, regularization constants, and parameters of gradient descent, as described in Specification at par. [0065]), and specific evaluating steps (which involves analyzing computational metrics such as loss functions, accuracy metrics, and parameter statistics generated during the training process), instead training and evaluating are broadly described. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. Performing processing of inference data is addressed as an additional step (i.e. computer function - See MPEP 2106.05(f)) in steps 2A Prong 2 & 2B. The steps for performing an evaluation and selection are understood to be a recitation of a mental process. Examiner respectfully disagrees, with applicant’s arguments regarding (Specification, par. [0027].). MPEP 2106.04(d)(1) states the specification must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. Example 47 claim 3 describes a different improvement compared to applicants claims and it is unclear how both claims relate to one another. The claims broadly describe performing a generic training step according to a status. The status information is understood to be field of use (See MPEP 2106.05(h)). Applicants claimed improvement is describing an improvement to an abstract idea and not to an improvement to a computer or technical field. MPEP 2106.05(a) says an improvement in the abstract idea itself is not an improvement in technology. The claim limitations are a combination of mental steps under step 2A Prong 1, and additional elements under steps 2A Prong 2 & 2B as detailed in the updated 101 rejection below. Applicant arguments regarding 35 U.S.C. § 103: Applicant’s arguments with respect to claim(s) have been considered but are moot because the new ground of rejection. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 According to the first part of the analysis, in the instant case, claims 1-12 are directed to a method, claims 13-19 are directed to a system, and claim 20 is directed to a processor. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter). Regarding Claim 1: 2A Prong 1: A method comprising: performing, using the processing device , a first evaluation of a first candidate MLM of the plurality of candidate MLMs prior to completion of a corresponding first TT of the plurality of TTs; (This step for evaluating a MLM is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).) responsive to the first evaluation, placing the first TT on an inactive status, wherein the inactive status comprises a stopped status indicat ing that further training of the first candidate MLM is to be ceased based at least in part on a processing cost associated with the further training, (This step for placing an inactive status is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., judgment/evaluation).) responsive to conclusion of the plurality of TTs, selecting, as the one or more final MLMs, at least one of: the first candidate MLM, or a second candidate MLM of a corresponding second TT of the plurality of TTs; (This step for selecting a final MLM is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., judgment/evaluation).) 2A Prong 2 : This judicial exception is not integrated into a practical application. Additional elements: using the processing device (The processing device is understood to be a generic computer element - See MPEP 2106.05(f).) starting, using a processing device, a plurality of training tracks (TTs), wherein a respective candidate machine learning model (MLM ) of a plurality of candidate MLMs is trained during at least one TT of the plurality of TTs using a same training data and a respective set of training settings of a plurality of sets of training settings; (Training a machine learning model is understood as mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f).)) wherein the inactive status comprises a stopped status indicat ing that further training of the first candidate MLM is to be ceased based at least in part on a processing cost associated with the further training, wherein the inactive status indicates that the first candidate MLM is included in a pool of MLMs from which one or more final MLMs are selected; (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the inactive status - See MPEP 2106.05(h).) continuing at least a second TT of the plurality of TTs using the training data; (Training a machine learning model is understood as mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f).)) and causing the one or more final MLMs to perform processing, on cloud-based hardware resources, of inference data. (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic MLM as a tool to perform the abstract idea (e.g., inference/predicting) - see MPEP 2106.05(f).) The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are generic computer functions in combination of field of use that are implemented to perform the disclosed abstract idea above. 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: using the processing device (The processing device is understood to be a generic computer element - See MPEP 2106.05(f).) starting, using a processing device, a plurality of training tracks (TTs), wherein a respective candidate machine learning model (MLM ) of a plurality of candidate MLMs is trained during at least one TT of the plurality of TTs using a same training data and a respective set of training settings of a plurality of sets of training settings; (Training a machine learning model is understood as mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f).)) wherein the inactive status comprises a stopped status indicat ing that further training of the first candidate MLM is to be ceased based at least in part on a processing cost associated with the further training, wherein the inactive status indicates that the first candidate MLM is included in a pool of MLMs from which one or more final MLMs are selected; (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the inactive status - See MPEP 2106.05(h).) continuing at least a second TT of the plurality of TTs using the training data; (Training a machine learning model is understood as mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f).)) and causing the one or more final MLMs to perform processing, on cloud-based hardware resources, of inference data. (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic MLM as a tool to perform the abstract idea (e.g., inference/predicting) - see MPEP 2106.05(f).) The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are generic computer functions in combination with field of use that are implemented to perform the disclosed abstract idea above. Regarding Claim 13 : see the rejection of claim 1 above. Same rationale applies. 2A Prong 2 & 2B: The claim recites another additional element “A system comprising: a processing device to:” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f)) Regarding Claim 20 : see the rejection of claim 1 above. Same rationale applies. 2A Prong 2 & 2B: The claim recites another additional element “A processor comprising processing circuitry to perform operations comprising:” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f)) Regarding Claims 2 and 14 : 2A Prong 1 : The method of claim 1, wherein placing the first TT on the inactive status comprises placing the first TT on a stopped list indicating that the first candidate MLM is included into a pool of MLMs from which the one or more final MLMs are selected. (This step is practically performable in the human mind with the aid of pen and paper and is understood to be a recitation of a mental process (i.e., judgment/evaluation).) 2A Prong 2 & 2B : The claim does not recite any additional elements. Regarding Claim 3 : 2A Prong 1 : The method of claim 2, wherein placing the first TT on the stopped list is responsive to the first evaluation determining that an improvement of the first candidate MLM over one or more training epochs is below a threshold value. (This step is practically performable in the human mind with the aid of pen and paper and is understood to be a recitation of a mental process (i.e., judgment/evaluation).) 2A Prong 2 & 2B : The claim does not recite any additional elements. Regarding Claims 4 and 15 : 2A Prong 1 : The method of claim 1, wherein placing the first TT on the inactive status comprises placing the first TT on an eliminated list indicating that the first candidate MLM is excluded from a pool of MLMs from which the one or more final MLMs are selected. (This step is practically performable in the human mind with the aid of pen and paper and is understood to be a recitation of a mental process (i.e., judgment/evaluation).) 2A Prong 2 & 2B : The claim does not recite any additional elements. Regarding Claim 5 : 2A Prong 1 : The method of claim 4, wherein placing the first TT on the eliminated list is responsive to the first evaluation determining that an accuracy corresponding to the first candidate MLM is below at least one of: an accuracy corresponding to the second candidate MLM, or an accuracy corresponding to a third candidate MLM of the plurality of candidate MLMs. (This step is practically performable in the human mind with the aid of pen and paper and is understood to be a recitation of a mental process (i.e., judgment/evaluation).) 2A Prong 2 & 2B : The claim does not recite any additional elements. Regarding Claim 6 : 2A Prong 1 : The claim does not recite any Abstract idea. 2A Prong 2 & 2B : The method of claim 5, wherein the accuracy corresponding to the first candidate MLM is below the accuracy corresponding to the second candidate MLM or the accuracy corresponding to the third candidate MLM by at least a threshold amount. (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the accuracy - See MPEP 2106.05(h).) Regarding Claims 7 and 16 : 2A Prong 1 : The method of claim 1, wherein performing the first evaluation of the first candidate MLM comprises: comparing an improvement of the first candidate MLM over one or more training epochs to a processing cost of training of the first candidate MLM over the one or more training epochs. (This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., judgment/evaluation).) 2A Prong 2 & 2B : The claim does not recite any additional elements. Regarding Claims 8 and 17 : 2A Prong 1 : The method of claim 1, wherein performing the first evaluation comprises evaluating a change of statistics of parameters of the first candidate MLM over one or more training epochs. (This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., judgment/evaluation).) 2A Prong 2 & 2B : The claim does not recite any additional elements. Regarding Claims 9 and 18 : 2A Prong 1 : The method of claim 1, wherein continuing the second TT is responsive to the first evaluation determining that an improvement of the second candidate MLM over one or more training epochs is above a threshold value. (This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., judgment).) 2A Prong 2 & 2B : The claim does not recite any additional elements. Regarding Claim 10 : 2A Prong 1 : The method of claim 1, further comprising: performing a second evaluation of the second candidate MLM and a third candidate MLM of the plurality of candidate MLMs; (This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., judgment).) and responsive to the second evaluation, placing at least one of the second TT or a third TT of the plurality of TTs on the inactive status, (This step is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., judgment).) 2A Prong 2 & 2B : wherein the third MLM is trained during the third TT. (Training a machine learning model is understood as mere instructions to implement an abstract idea on a computer - see MPEP 2106.05(f).)) Regarding Claim 11 : 2A Prong 1 : The claim does not recite any Abstract idea. 2A Prong 2 : The method of claim 1, wherein starting the plurality of TTs is responsive to receiving, from a remote computing device, an identification of the MLM and an identification of the training data for training of the MLM. (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).) 2B : The method of claim 1, wherein starting the plurality of TTs is responsive to receiving, from a remote computing device, an identification of the MLM and an identification of the training data for training of the MLM. (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i)))) Regarding Claim 12 : 2A Prong 1 : The claim does not recite any Abstract idea. 2A Prong 2 & 2B : The method of claim 1, wherein at least two TTs of the plurality of TTs are executed in parallel. ( This step is adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)) Regarding Claim 19 : 2A Prong 1 : The claim does not recite any Abstract idea. 2A Prong 2 & 2B : The system of claim 13, wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system implemented using an edge device; a system for generating or presenting at least one of augmented reality content, virtual reality content, or mixed reality content; a system implemented using a robot; a system for performing conversational Al operations; a system for generating synthetic data; a system for performing one or more operations using a language model; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources. (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the system - See MPEP 2106.05(h).) Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claim (s) 1, 8-13, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Heinrich et al. (US 20200226461 A1, hereinafter "Heinrich") in view of Wu et al. (US 20220366318 A1, hereinafter "Wu") and Li et al. ("A System for Massively Parallel Hyperparameter Tuning", hereinafter "Li") . Regarding Claim 1 Heinrich discloses: A method comprising: starting, using a processing device ([Para 0013, 0019, and Fig 1-2] disclose a processor device.) , a plurality of training tracks (TTs), wherein a respective candidate machine learning model ( MLM ) of a plurality of candidate MLMs is trained during at least one TT of the plurality of TTs using a same training data and a respective set of training settings of a plurality of sets of training settings; ([Para 0023-0024, 0034-0041, 0053-0055, and Fig 2-4] describes searching a hyperparameter space associated with machine learning models 210-212 by using a different set of hyperparameters 206-208 to train each machine learning model.) performing, using the processing device, a first evaluation of a first candidate MLM of the plurality of candidate MLMs prior to completion of a corresponding first TT of the plurality of TTs; ([Para 0025, 0034, 0055, and Fig 2-4] describes evaluating, performance metrics, eviction rate, phase counts, and phase completion times of machine learning models (e.g. machine learning model 210).) responsive to the first evaluation, placing the first TT on an inactive status, wherein the inactive status comprises a stopped status indicat ing that further training of the first candidate MLM is to be ceased based at least in part on a processing cost associated with the further training ([Para 0028, 0034-0041, 0053-0055, and Fig 2-4] describes stopping training of a machine learning model (e.g. machine learning model 210) responsive to the evaluating, performance metrics, eviction rate, phase counts, and phase completion times of machine learning models.) , wherein the inactive status indicates that the first candidate MLM is included in a pool of MLMs from which one or more final MLMs are selected ; continuing at least a second TT of the plurality of TTs using the training data; ( [Para 0028, 0034-0041, 0053-0055, Fig 2-4] describes other machine learning models (i.e. second TT) continuing a training phase.) responsive to conclusion of the plurality of TTs ([Para 0026, 0039-0040, 0055, and Fig 2-5] describes completing training.) , selecting, as the one or more final MLMs, at least one of: the first candidate MLM, or a second candidate MLM of a corresponding second TT of the plurality of TTs; ([Para 0103-0104, 0039-0040, 0055, and Fig 2-5] describes selecting the one or more of models to be included.) Heinrich does not explicitly disclose: using a same training data; and causing the one or more final MLMs to perform processing, on cloud-based hardware resources, of inference data. However, Wu discloses in the same field of endeavor: starting, using a processing device, a plurality of training tracks (TTs), wherein a respective candidate MLM of a plurality of candidate MLMs is trained during at least one TT of the plurality of TTs using a same training data and a respective set of training settings of a plurality of sets of training settings ([Para 0024-0030 and Fig 1-4] describes training machine learning models using the same training data 152 and different values for the hyperparameters.) ; responsive to conclusion of the plurality of TTs, selecting, as one or more final MLMs, at least one of: the first candidate MLM, or a second candidate MLM of a corresponding second TT of the plurality of TTs ([Para 0020, 0040 and Fig 1-4] describes selecting a one of the trained models based on performance.) . causing the one or more final MLMs to perform processing, on cloud-based hardware resources ([Para 0021 and Fig 1-4] describe a could environment.) , of inference data. ([Para 0039-0040, 0027 and Fig 1-4] describes generating one or more predictions using the selected one of the trained machine learning models.) It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of Hyperparameter tuning disclosed by Wu into the method of Early stopping disclosed by Heinrich to train models with the same training data. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of Hyperparameter tuning disclosed by Wu as all the references are in the field of machine learning. A person of ordinary skill of the art would have been motivated to perform the combination for being able to fairly evaluate and select models trained with the same data. Heinrich in view of Wu does not explicitly disclose: wherein the inactive status comprises a stopped status indicat ing that further training of the first candidate MLM is to be ceased based at least in part on a processing cost associated with the further training, wherein the inactive status indicates that the first candidate MLM is included in a pool of MLMs from which one or more final MLMs are selected; However, Li discloses in the same field of endeavor: responsive to the first evaluation, placing the first TT on an inactive status, wherein the inactive status comprises a stopped status indicat ing that further training of the first candidate MLM is to be ceased based at least in part on a processing cost associated with the further training, wherein the inactive status indicates that the first candidate MLM is included in a pool of MLMs from which one or more final MLMs are selected; ([Section 3.1-3.2 and Algorithm 1-2] describes early-stopping s (i.e. inactive status comprising a stopped status) based on an amount of resource (i.e. processing cost) and returns the top performing configurations (i.e. candidate MLM).) It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the hyperparameter optimization algorithm disclosed by Li into the method of Heinrich in view of Wu to have an inactive status indicate a first candidate MLM included in a pool of MLMs from which one or more final MLMs are selected. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of hyperparameter optimization algorithm disclosed by Li as all the references are in the field of machine learning. A person of ordinary skill of the art would have been motivated to perform the combination for being able to further optimize large scale hyperparameter tuning. Regarding Claim 13 Heinrich in view of Wu and Li discloses: A system comprising: a processing device ([Para 0013, 0019, and Fig 1-2] Heinrich, discloses a processor device.) to: (Claim 13 is a system claim that corresponds to claim 1 and the rest of the limitations are rejected on the same ground) Regarding Claim 20 Heinrich in view of Wu and Li discloses: A processor comprising processing circuitry to perform operations ([Para 0013, 0019, and Fig 1-2] Heinrich, discloses a processing circuitry.) comprising: (Claim 20 is a processing circuitry claim that corresponds to claim 1 and the rest of the limitations are rejected on the same ground) Regarding Claim 8 : Heinrich in view of Wu and Li discloses: The method of claim 1, wherein performing the first evaluation comprises evaluating a change of statistics of parameters of the first candidate MLM over one or more training epochs. ([Para 0001, 0029-0032, and Fig 2-4], Heinrich) Regarding Claim 9 : Heinrich in view of Wu and Li discloses: The method of claim 1, wherein continuing the second TT is responsive to the first evaluation determining that an improvement of the second candidate MLM over one or more training epochs is above a threshold value ([Para 0037-0038, 0046, 0053, and Fig 2-4], Heinrich) . Regarding Claim 10 : Heinrich in view of Wu and Li discloses: The method of claim 1, further comprising: performing a second evaluation of the second candidate MLM and a third candidate MLM of the plurality of candidate MLMs; and responsive to the second evaluation, placing at least one of the second TT or a third TT of the plurality of TTs on the inactive status, wherein the third MLM is trained during the third TT ([Para 0028, 0034-0041, 0053-0055, and Fig 2-4], Heinrich describes evaluating a plurality of machine learning models.) . Regarding Claim 11 : Heinrich in view of Wu and Li discloses: The method of claim 1, wherein starting the plurality of TTs is responsive to receiving, from a remote computing device, an identification of the MLM and an identification of the training data for training of the MLM. ([Para 0006-0009, 0022-0024, 0030], Wu describes a user device requesting optimization of one or more hyperparameters of a machine learning model.) Regarding Claim 12 : Heinrich in view of Wu and Li discloses: The method of claim 1, wherein at least two TTs of the plurality of TTs are executed in parallel. ([Para 0008-0009, 0018, 0027, 0061 Fig 2-3, and Fig 6-7] Heinrich discloses training executed in parallel.) Regarding Claim 17 (Claim 17 recites analogous limitations to claim 8 and therefore is rejected on the same ground as claim 8.) Regarding Claim 18 (Claim 18 recites analogous limitations to claim 9 and therefore is rejected on the same ground as claim 9.) Regarding Claim 19 : Heinrich in view of Wu and Li discloses: The system of claim 13, wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system implemented using an edge device; a system for generating or presenting at least one of augmented reality content, virtual reality content, or mixed reality content; a system implemented using a robot; a system for performing conversational Al operations; a system for generating synthetic data; a system for performing one or more operations using a language model; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources. ([Para 0020-0020, Fig 1-2, and Fig 5-8] Heinrich discloses systems.) 07-21-aia AIA Claim (s) 2-3 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Heinrich in view of Wu, Li, and Gupta et al. (US 11869490 B1, hereinafter "Gupta") . Regarding Claim 2 : Heinrich in view of Wu and Li discloses: The method of claim 1, wherein placing the first TT on the inactive status comprises placing the first TT on a stopped list indicating that the first candidate MLM is included into a pool of MLMs from which the one or more final MLMs are selected ([Para 0020, 0030, 0040, and Fig 1-3], Wu describes storing and selecting models.) . Heinrich in view of Wu and Li does not explicitly disclose: placing the first TT on a stopped list; However, Gupta discloses in the same field of endeavor: wherein placing the first TT on the inactive status comprises placing the first TT on a stopped list indicating that the first candidate MLM is included into a pool of MLMs ([Col 5 lines 38-49, Col 6 lines 1-5, Col 36 lines 25-39, Fig 1B and Fig 7] describes storing models that satisfy stopping criteria.) It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the method for a stopping criteria disclosed by Gupta into the method of Hyperparameter tuning disclosed by Wu into the method of Early stopping disclosed by Heinrich to store stopped models. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of a stopping process disclosed by Gupta as all the references are in the field of machine learning. A person of ordinary skill of the art would have been motivated to perform the combination for being able to reference and process the stored models in the future. Regarding Claim 3: Heinrich in view of Wu, Li, and Gupta discloses: The method of claim 2, wherein placing the first TT on the stopped list is responsive to the first evaluation determining that an improvement of the first candidate MLM over one or more training epochs is below a threshold value. ([Para 0038, 0053, and Fig 2-4], Heinrich) Regarding Claim 14 (Claim 14 recites analogous limitations to claim 2 and therefore is rejected on the same ground as claim 2.) 07-21-aia AIA Claim (s) 4-6 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Heinrich in view of Wu, Li, and Konishi et al. (US 20210056420 A1, hereinafter "Konishi") . Regarding Claim 4 : Heinrich in view of Wu and Li discloses: The method of claim 1, wherein placing the first TT on the inactive status comprises placing the first TT on an eliminated list indicating that the first candidate MLM is excluded from a pool of MLMs from which the one or more final MLMs are selected ([Para 0020, 0030, 0040, and Fig 1-3], Wu describes storing and selecting final models.) . Heinrich in view of Wu and Li does not explicitly disclose: placing the first TT on an eliminated list indicating that the first candidate MLM is excluded from a pool of MLMs; However, Konishi discloses in the same field of endeavor: placing the first TT on an eliminated list indicating that the first candidate MLM is excluded from a pool of MLMs ([Para 0046-0048, 0056, 0060, 0068, 0103 and Fig 4] discloses excluded models.) ; It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the method for an evaluation list disclosed by Konishi into the method of Hyperparameter tuning disclosed by Wu into the method of Early stopping disclosed by Heinrich to exclude models. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of an evaluation list disclosed by Konishi as all the references are in the field of machine learning. A person of ordinary skill of the art would have been motivated to perform the combination for being able to narrow a selection of models. Regarding Claim 5 : Heinrich in view of Wu, Li, and Konishi discloses: The method of claim 4, wherein placing the first TT on the eliminated list is responsive to the first evaluation determining that an accuracy corresponding to the first candidate MLM is below at least one of: an accuracy corresponding to the second candidate MLM, or an accuracy corresponding to a third candidate MLM of the plurality of candidate MLMs. ([Para 0038-0040, and Fig 1-3], Wu describes selecting the highest performing model]) Regarding Claim 6 : Heinrich in view of Wu, Li, and Konishi discloses: The method of claim 5, wherein the accuracy corresponding to the first candidate MLM is below the accuracy corresponding to the second candidate MLM or the accuracy corresponding to the third candidate MLM by at least a threshold amount. ([Para 0038-0040, and Fig 1-3], Wu describes selecting the highest performing model ] ) Regarding Claim 15 (Claim 15 recites analogous limitations to claim 4 and therefore is rejected on the same ground as claim 4.) 07-21-aia AIA Claim (s) 7 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Heinrich in view of Wu, Li, and Huang et al. (US 20200279187 A1, hereinafter "Huang") . Regarding Claim 7: Heinrich in view of Wu and Li discloses: The method of claim 1, wherein performing the first evaluation of the first candidate MLM comprises: Heinrich in view of Wu and Li does not explicitly disclose: comparing an improvement of the first candidate MLM over one or more training epochs to a processing cost of training of the first candidate MLM over the one or more training epochs. However, Huang discloses in the same field of endeavor: comparing an improvement of the first candidate MLM over one or more training epochs to a processing cost of training of the first candidate MLM over the one or more training epochs. ([Para 0026-0027, 0035-0038 and Fig 2-3] describes a cost to performance ratio calculation.) It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the method for Hyperparameter tuning disclosed by Huang into the method of Hyperparameter tuning disclosed by Wu into the method of Early stopping disclosed by Heinrich to compare improvement with processing cost. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of Hyperparameter tuning disclosed by Huang as all the references are in the field of machine learning. A person of ordinary skill of the art would have been motivated to perform the combination for being able to utilize models without exceeding computation resources. Regarding Claim 16 (Claim 16 recites analogous limitations to claim 7 and therefore is rejected on the same ground as claim 7.) Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Unterthiner (US 20210256422 A1) describes early stopping for training a machine learning model . THIS ACTION IS MADE FINAL. 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 TEWODROS E MENGISTU whose telephone number is (571)270-7714. The examiner can normally be reached Mon-Fri 9:30-5:30. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ABDULLAH KAWSAR can be reached at (571)270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /TEWODROS E MENGISTU/Examiner, Art Unit 2127 Application/Control Number: 18/139,016 Page 2 Art Unit: 2127 Application/Control Number: 18/139,016 Page 3 Art Unit: 2127 Application/Control Number: 18/139,016 Page 4 Art Unit: 2127 Application/Control Number: 18/139,016 Page 5 Art Unit: 2127 Application/Control Number: 18/139,016 Page 6 Art Unit: 2127 Application/Control Number: 18/139,016 Page 7 Art Unit: 2127 Application/Control Number: 18/139,016 Page 8 Art Unit: 2127 Application/Control Number: 18/139,016 Page 9 Art Unit: 2127 Application/Control Number: 18/139,016 Page 10 Art Unit: 2127 Application/Control Number: 18/139,016 Page 11 Art Unit: 2127 Application/Control Number: 18/139,016 Page 12 Art Unit: 2127 Application/Control Number: 18/139,016 Page 13 Art Unit: 2127 Application/Control Number: 18/139,016 Page 14 Art Unit: 2127 Application/Control Number: 18/139,016 Page 15 Art Unit: 2127 Application/Control Number: 18/139,016 Page 16 Art Unit: 2127 Application/Control Number: 18/139,016 Page 17 Art Unit: 2127 Application/Control Number: 18/139,016 Page 18 Art Unit: 2127 Application/Control Number: 18/139,016 Page 19 Art Unit: 2127 Application/Control Number: 18/139,016 Page 20 Art Unit: 2127 Application/Control Number: 18/139,016 Page 21 Art Unit: 2127 Application/Control Number: 18/139,016 Page 22 Art Unit: 2127 Application/Control Number: 18/139,016 Page 23 Art Unit: 2127 Application/Control Number: 18/139,016 Page 24 Art Unit: 2127 Application/Control Number: 18/139,016 Page 25 Art Unit: 2127 Application/Control Number: 18/139,016 Page 26 Art Unit: 2127 Application/Control Number: 18/139,016 Page 27 Art Unit: 2127 Application/Control Number: 18/139,016 Page 28 Art Unit: 2127 Application/Control Number: 18/139,016 Page 29 Art Unit: 2127 Application/Control Number: 18/139,016 Page 30 Art Unit: 2127
Prosecution Insights