DETAILED ACTION
This Action is responsive to Claims filed 05/26/2026.
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 .
Status of the Claims
Claims 1-3, 5-8, and 11-20 have been amended. Claims 1-20 are currently pending.
Response to Amendment
The amendment(s) to Claim 18 have overcome the Objection to improper form.
The Examiner acknowledges the Applicant’s decision to maintain the dependency of Claims 19 and 20. The Objections to informalities have been withdrawn.
Response to Arguments
Applicant's arguments filed 05/26/2026 regarding the 35 U.S.C. 101 Rejection of claims 1-17 and 19-20 have been fully considered but they are not persuasive.
The Examiner respectfully reminds the Applicant that the quantity of data manipulated does not preclude a limitation from being interpretable as an abstract idea mental process step (MPEP 2106.05(a)). As presently drafted, the newly-amended independent claims recite a generic “receiving…” step (data transmittal), a generic “training…” step, and a series of algorithmic data determination steps interpretable as abstract idea mental process steps (a mini-batch may be selected by a human mind, network architectures may be selected by a human mind, “obtaining…” a loss function results is interpretable as performing the loss calculation and determining the result, and updating weights based on said calculation may be performed by a human mind).
The Examiner respectfully reminds the Applicant that the fact pattern in Desjardins contains an additional element, reciting specific structure or implementation, that can be directly tied back to recitation of the additional element in the Specification, concurrently with how the additional element realizes a specific improvement. On the other hand, the mere “receiving…” and “training…” steps are recited highly generally, and the Examiner submits, in their current form, the specific improvement alleged by the Applicant is a direct result of the abstract idea mental process steps. Per MPEP 2106.05(a), the specific improvement cannot come from the abstract idea(s). See the updated 35 U.S.C. 101 Rejection below.
Applicant's arguments filed 05/26/2026 regarding the 35 U.S.C. 103 Rejection(s) of claims 1-17 and 19-20 have been fully considered but they are not persuasive.
The Applicant argues, on Page 18, alleged differences between the cited reference Li and the instant Application. While the Examiner acknowledges the distinction the Applicant draws, the Examiner submits, in its current form, Claims 1 and 14 do not explicitly detail the distinction the Applicant draws. The “obtaining…” step, as presently drafted, does not claim “Particularly, claim 1 requires, for each minibatch, stochastically selecting a plurality of network architectures of the neural network corresponding to a number of training instances in the minibatch. Each training instance is used with a respective one of the selected architectures, and a loss is determined to update the shared weights of the network” as specifically as the Applicant recites in the arguments. The Examiner submits the combination of Pham and Li continues to broadly read on the current verbiage of the claims. See the updated 35 U.S.C. 103 Rejection(s) below.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more; and because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al, 573 U.S. (2014). In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines. (2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.)
Step 1: All Claims
Claims 1-13 recite a method, which falls under the statutory category of a process. Claims 14-18 recite a method, which falls under the statutory category of a process. Claim 19 recites a computer system, which falls under the statutory category of a machine. Claim 20 recites one or more computer readable storage media, which falls under the statutory category of a manufacture.
Step 2A – Prong 1: Claim 1
Claim 1 recites an abstract idea, law of nature, or natural phenomenon. The limitations of “selecting a mini-batch from the plurality of mini-batches;”, “stochastically selecting a plurality of network architectures of the weight-sharing neural network for the selected mini-batch;”, “obtaining a respective loss for each respective instance of training data in the selected mini-batch by applying a respective network architecture from the plurality of network architectures to the respective instance of training data;”, and “and updating shared weights of the weight-sharing neural network based on the respective loss for each respective instance of training data in the selected mini -batch.” under the broadest reasonable interpretation, cover a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper.
Selecting mini-batches of instances is practically performed within the human mind or with the aid of pen and paper. Selecting architectures is practically performed within the human mind or with the aid of pen and paper. Obtaining a loss and function results is interpretable as performing the loss calculation and determining the result. Updating weights based on said calculation is practically performed within the human mind or with the aid of pen and paper.
Step 2A – Prong 2: Claim 1
The additional elements of claim 1 do not integrate the abstract idea into a judicial exception. The claim recites the additional elements “A method”, “a mini-batch”, and “data” are recognized as generic computer components recited at a high level of generality. Although they have and execute instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(2) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application).
The additional elements of “neural network”, “training data”, “network architecture”, and “shared weights” are recognized as non-generic computer components, but are recited at a high level of generality and are found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
The additional elements recited in the limitations of “receiving a training data set for a task, the training data set being grouped into a plurality of mini-batches, each mini-batch in the plurality of mini-batches including a plurality of instances of training data;” are found to be pre- or post- extra solution activity or data gathering steps (See MPEP 2016.05(g)).
The additional elements recited in the limitations “training a weight-sharing neural network with stochastic architectures” and “training the weight-sharing neural network to perform the task by way of a plurality of iterations of a training procedure, each respective iteration in the plurality of iterations including:” are found to be mere instructions to apply the abstract idea(s) the dataset(s) (see MPEP 2106.05(f) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application).
Step 2B: Claim 1
The only limitation on the performance of the described method is a limitation reciting “A method”, “a mini-batch”, and “data” These elements are insufficient to transform a judicial exception to a patentable invention because the recited elements are considered insignificant extra-solution activity (generic computer system, processing resources, links the judicial exception to a particular, respective, technological environment). The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components; mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (see MPEP 2106.05(f)).
The additional elements of “neural network”, “training data”, “network architecture”, and “shared weights” are recognized as non-generic computer components, but are recited at a high level of generality and are found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
The additional elements recited in the limitations of “receiving a training data set for a task, the training data set being grouped into a plurality of mini-batches, each mini-batch in the plurality of mini-batches including a plurality of instances of training data;” are found to be well-understood, routine, or conventional activity (See MPEP 2106.05(d(II)(i)).
The additional elements recited in the limitations “training a weight-sharing neural network with stochastic architectures” and “training the weight-sharing neural network to perform the task by way of a plurality of iterations of a training procedure, each respective iteration in the plurality of iterations including:” are found to be mere instructions to apply the abstract idea (See MPEP 2106.05(f) indicating mere instructions to apply an abstract idea does not recite significantly more).
Taken alone or in ordered combination, these additional elements do not amount to significantly more than the above-identified abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
For the reasons above, claim 1 is rejected as being directed to non-patentable subject matter under §101.
Step 2A – Prong 1: Claim 14
Claim 14 recites an abstract idea, law of nature, or natural phenomenon. The limitations of “randomly selecting a plurality of network architectures of the weight-sharing neural network;”, “inferring respective output data, based on the input data, using each of the selected plurality of network architectures respectively;”, and “and obtaining a final inference data based on the respective output data inferred using each of the selected plurality of network architectures.” under the broadest reasonable interpretation, cover a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper.
Selecting architectures is practically performed within the human mind or with the aid of pen and paper. Inferring output data is practically performed within the human mind or with the aid of pen and paper. Obtaining a final result based on the inferred data is practically performed within the human mind or with the aid of pen and paper.
Step 2A – Prong 2: Claim 14
The additional elements of claim 14 do not integrate the abstract idea into a judicial exception. The claim recites the additional elements “A method” and “data” are recognized as generic computer components recited at a high level of generality. Although they have and execute instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(2) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application).
The additional elements of “neural network”, “training data”, “network architecture”, and “shared weights” are recognized as non-generic computer components, but are recited at a high level of generality and are found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
The additional element “receiving an input data;” is found to be a mere pre- post-extra-solution activity or data gathering step (See MPEP 2106.05(g)).
Step 2B: Claim 14
The only limitation on the performance of the described method is a limitation reciting “A method” and “data” These elements are insufficient to transform a judicial exception to a patentable invention because the recited elements are considered insignificant extra-solution activity (generic computer system, processing resources, links the judicial exception to a particular, respective, technological environment). The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components; mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (see MPEP 2106.05(f)).
The additional elements of “neural network”, “training data”, “network architecture”, and “shared weights” are recognized as non-generic computer components, but are recited at a high level of generality and are found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
The additional element “receiving an input data;” is found to be well-understood, routine, and conventional activity (See MPEP 2016.05(d)(II)(i)).
Taken alone or in ordered combination, these additional elements do not amount to significantly more than the above-identified abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
For the reasons above, claim 14 is rejected as being directed to non-patentable subject matter under §101.
Dependent Claims:
Claim 2 recites additional elements recognized as non-generic computer components, but are recited at a high level of generality and found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
Claim 3 recites additional elements recognized as non-generic computer components, but are recited at a high level of generality and found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
Claim 4 recites additional elements recognized as non-generic computer components, but are recited at a high level of generality and found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
Claim 5 recites abstract idea mental process steps “calculating…” and “updating…”
Claim 6 recites additional elements recognized as non-generic computer components, but are recited at a high level of generality and found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)). Claim 6 recites an abstract idea mental process step “updating…”
Claim 7 recites additional elements recognized as non-generic computer components, but are recited at a high level of generality and found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
Claim 8 recites additional elements recognized as non-generic computer components, but are recited at a high level of generality and found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
Claim 9 recites additional elements recognized as non-generic computer components, but are recited at a high level of generality and found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
Claim 10 recites abstract idea mental process steps “calculating…” and “updating…”
Claim 11 recites additional elements recognized as non-generic computer components, but are recited at a high level of generality and found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
Claim 12 recites abstract idea mental process step “repeating…”
Claim 13 recites abstract idea mental process step “wherein the plurality of iterations continue…”
Claim 15 recites additional elements recognized as non-generic computer components, but are recited at a high level of generality and found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
Claim 16 recites additional elements recognized as non-generic computer components, but are recited at a high level of generality and found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
Claim 17 recites additional elements recognized as non-generic computer components, but are recited at a high level of generality and found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
Claim 18 recites similar limitations to Claim 1; therefore, it is similarly rejected.
Claim 19 recites generic computer components applying the abstract idea mental process steps of Claim 1.
Claim 20 recites generic computer components applying the abstract idea mental process steps of Claim 1.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-8 and 10-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pham et al. (Efficient Neural Architecture Search via Parameter Sharing, 2018), hereinafter Pham, and Li et al. (Random Search and Reproducibility for Neural Architecture Search, 2019), hereinafter Li.
In regards to claim 1: The present invention claims: “A method for training a weight-sharing neural network…the method comprising:” Pham teaches “In ENAS, there are two sets of learnable parameters: the parameters of the controller LSTM…and the shared parameters of the child models” (Page 3, left column)
“receiving a training data set for a task, the training data set being grouped into a plurality of mini-batches, each mini-batch in the plurality of mini-batches including a plurality of instances of training data;” Pham teaches “The first phase trains !, the shared parameters of the child models, on a whole pass through the training data set. For our Penn Treebank experiments, ω is trained for about 400 steps, each on a minibatch of 64 examples, where the gradient… is computed using back-propagation through time, truncated at 35 time steps.” (Page 2, Section 2.2).
“training the weight-sharing neural network to perform the task by way of a plurality of iterations of a training procedure, each respective iteration in the plurality of iterations including:” Pham teaches “Central to the idea of ENAS is the observation that all of the graphs which NAS ends up iterating over can be viewed as sub-graphs of a larger graph. In other words, we can represent NAS’s search space using a single directed acyclic graph (DAG).” (Page 1), see also Figure 1 for an indication of iterative training.
“selecting a mini-batch from the plurality of mini-batches;” Pham teaches “The first phase trains…the shared parameters of the child models, on a whole pass through the training data set. For our Penn Treebank experiments…is trained for about 400 steps, each on a minibatch of 64 examples” (Page 3, left column).
“obtaining a respective loss for each respective instance of training data in the selected mini-batch by applying a respective network architecture from the plurality of network architectures to the respective instance of training data;” Pham teaches “In this step, we fix the controller’s policy…and perform stochastic gradient descent (SGD)…to minimize the expected loss function... Here, L(m; w) is the standard cross-entropy loss, computed on a minibatch of training data, with a model m sampled from _(m; [theta]).”(page 3, left column).
“and updating shared weights of the weight-sharing neural network based on the respective loss for each respective instance of training data in the selected mini-batch.” Pham teaches “Nevertheless – and this is perhaps surprising – we find that M = 1 works just fine, i.e. we can update [the shared parameters of the child models] using the gradient from any single model m sampled from _(m; _). As mentioned, we train [the shared parameters of the child models] during a entire pass through the training data.” (page 3, left column).
Pham fails to explicitly teach “…with stochastic architectures,” and “stochastically selecting a plurality of network architectures of the weight-sharing neural network for the selected mini-batch;” However, Li, in a similar field of endeavor, teaches “In order to combine random search with weight-sharing, we simply use randomly sampled architectures to train the shared weights. Shared weights are updated by selecting a single architecture for a given minibatch and updating the shared weights by back-propagating through the network with only the edges and operations as indicated by the architecture activated. Hence, the number of architectures used to update the shared weights is equivalent to the total number of minibatch training iterations.” (Page 7).
Li teaches “Leveraging these observations, we evaluate both random search with early-stopping and a novel random search with weight-sharing algorithm on two standard NAS benchmarks—PTB and CIFAR-10. Our results show that random search with early-stopping is a competitive NAS baseline, e.g., it performs at least as well as ENAS [41], a leading NAS method, on both benchmarks. Additionally, random search with weight-sharing outperforms random search with early-stopping, achieving a state-of-the-art NAS result on PTB and a highly competitive result on CIFAR-10.” (Abstract, Page 1). It would have been obvious to one of ordinary skill in the art at the time of the Applicant’s filing to leverage known benefits of a system such as Li’s to randomly or stochastically search architectures when combining with elements of Pham’s ENAS.
In regards to claim 2: The present invention claims: “wherein the weight-sharing neural network comprises a set of nodes and a set of edges, each of the nodes representing at least one operation, each of the edges connecting two of the nodes, each network architecture of the weight-sharing neural network being represented as a directed graph of nodes connected by edges.” Pham teaches “Intuitively, ENAS’s DAG is the superposition of all possible child models in a search space of NAS, where the nodes represent the local computations and the edges represent the flow of information. The local computations at each node have their own parameters, which are used only when the particular computation is activated.” (Pages 1-2 and Figure 2).
In regards to claim 3: The present invention claims: “wherein the shared weights of the weight-sharing neural network comprises at least part of operations of the nodes.” See Pham Section 2.2 (Page 3) and “The shared parameters of the child models ! are trained using SGD with a learning rate of 20.0, decayed by a factor of 0.96 after every epoch starting at epoch 15, for a total of 150 epochs.” (Page 5).
In regards to claim 4: The present invention claims: “wherein the at least part of comprises convolution operations.” Sections 2.3 and 2.4 of Pham pertain directly to performing convolutional operations.
In regards to claim 5: The present invention claims: “wherein updating the shared weights of the weight-sharing neural network based on the loss for each instance of the selected mini-batch further comprises: calculating gradients for the shared weights of the weight-sharing neural network by back-propagating mean loss of the loss for each instance of the selected mini-hatch along the selected plurality of network architectures respectively or by back-propagating the loss for each instance of the selected mini-batch along a corresponding one of the selected plurality of network architectures respectively;” Pham teaches “The first phase trains w, the shared parameters of the child models, on a whole pass through the training data set. For our Penn Treebank experiments, w is trained for about 400 steps, each on a minibatch of 64 examples, where the gradient V is computed using back-propagation through time, truncated at 35 time steps. Meanwhile, for CIFAR-10, ! is trained on 45, 000 training images, separated into minibatches of size 128, where r! is computed using standard back-propagation.” (Page 3, left column)
“and updating the shared weights of the weight-sharing neural network by using an accumulation or average of the gradients for each of the shared weights.” The section of Pham proceeding the above citation on Page 3 (Training the shared parameters w of the child models.) teaches the shard parameters being updated.
In regards to claim 6: The present invention claims: “wherein the weight-sharing neural network further comprises architecture specific weights for each network architecture of the weight-sharing neural network, and the method further comprises: updating the architecture specific weights for each of the selected plurality of network architectures based on the loss for each instance of the selected mini-batch.” Li teaches “Shared weights are updated by selecting a single architecture for a given minibatch and updating the shared weights by back-propagating through the network with only the edges and operations as indicated by the architecture activated. Hence, the number of architectures used to update the shared weights is equivalent to the total number of minibatch training iterations.” (Page 7)
In regards to claim 7: Claim 7 recites similar limitations to Claim 2, therefore both claims are similarly rejected.
In regards to claim 8: Claim 8 recites similar limitations to Claims 3 and/or 4, therefore both claims are similarly rejected.
In regards to claim 10: Claim 10 recites similar limitations to Claim 5, therefore both claims are similarly rejected.
In regards to claim 11: The present invention claims: “wherein the weight-sharing neural network comprises a main chain which comprises the set of nodes connected in series by edges, each network architecture of the weight-sharing neural network comprises the main chain.” See Pham Figures 1-5 and Li Figure 2 for a DAG comprising nodes that the architecture(s) are a part of.
In regards to claim 12: The present invention claims: “wherein the plurality of iterations continues until all of the plurality of mini-batches have been selected for one time.” Li teaches “(1)Training epochs. Increasing the number of training epochs while keeping all other parameters the same increases the total number of minibatch updates and hence, the number of architectures used to update the shared weights. Intuitively, training with more architectures should help the shared weights generalize better to what are likely unseen architectures in the evaluation step. Unsurprisingly, more epochs increase the computational time required for architecture search.
(2) Batch size. Decreasing the batch size while keeping all other parameters the same also increases the number of minibatch updates but at the cost of noisier gradient update. Hence, we expect reducing the batch size to have a similar effect as increasing the number of training epochs but may necessitate adjusting other meta-hyperparameters to account for the noisier gradient update. Intuitively, more minibatch updates increase the computational time required for architecture search.” (Pages 7-8), which broadly reads on training for each mini-batch at least once.
In regards to claim 13: The present invention claims: “wherein the plurality of iteration continue until a convergence condition is met.” Pham teaches “The main contribution of this work is to improve the efficiency of NAS by forcing all child models to share weights to eschew training each child model from scratch to convergence.” (Introduction) and “To prevent premature convergence, we also use a tanh constant of 2.5 and a temperature of 5.0 for the sampling logits…” (Page 5). Section 3.3 also goes into training the architectures until convergence.
In regards to claim 14: The present invention claims: “A method for inferencing by using a weight-sharing neural network, comprising: receiving an input data; randomly selecting a plurality of network architectures of the weight-sharing neural network; inferring respective output data, based on the input data, using each of the selected plurality of network architectures respectively; and obtaining a final inference data based on the respective output data inferred using each of the selected plurality of network architectures.” See above how a combination of Pham and Li reads on a weight-sharing network that randomly selects architectures. Pham teaches “Third, as shown in Figure 6, the output of our ENAS cell is an average of 6 nodes. This behavior is similar to that of Mixture of Contexts (MoC) (Yang et al., 2018). Not only does ENAS independently discover MoC, but it also learns to balance between i) the number of contexts to mix, which increases the model’s expressiveness, and ii) the depth of the recurrent cell, which learns more complex transformations (Zilly et al., 2017).” (Page 6 and Figure 6).
In regards to claims 15-18: Claims 15-18 recite similar limitations to those found in claims 1-6, therefore both sets of claims are similarly rejected.
In regards to claim 19: Claim 19 merely recites a computer system performing the limitations of Claim 1, therefore both claims are similarly rejected.
In regards to claim 20: Claim 20 merely recites a computer readable storage media with instructions for performing the limitations of Claim 1, therefore both claims are similarly rejected.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pham and Li as applied to claim 1 above, and further in view of Xie et al. (SNAS: STOCHASTIC NEURAL ARCHITECTURE SEARCH, published Apr. 2020), hereinafter Xie.
In regards to claim 9: The present invention claims: “wherein the part of operations comprises batch normalization (BN) operations.” While both Pham and Li make reference to “normal” or “normalization” (Pham, Page 6, right column and Li, Page 5, Section 2.2), the combination fails to explicitly teach the limitations of Claim 9. However, Xie, in a similar field of endeavor, teaches “We employ the following techniques in our experiments: centrally padding the training images to 40 x 40 and then randomly cropping them back to 32 x 32; randomly flipping the training images horizontally; normalizing the training and validation images by subtracting the channel mean and dividing by the channel standard deviation.” (Page 16).
Pham (Section 3.2), Li (Page 5, Section 2.2), and Xie (Page 16) make reference to “normal” or “normalization” operations in the context of performing benchmarking with CIFAR-10. It would have been obvious to one of ordinary skill in the art at the time of the Applicant’s filing to leverage known methods to achieve known outcomes with the use of batch normalization operations in a system combining aspects of Pham, Li, and Xie.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 GRIFFIN T BEAN whose telephone number is (703)756-1473. The examiner can normally be reached M - F 7:30 - 4:30.
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/GRIFFIN TANNER BEAN/Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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