DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is in response to an amendment filed on June 3rd, 2026. Claims 1, 3-6, 8-14, and 16-20 are pending in the current application, with claims 2, 7, and 15 cancelled, and claims 1, 3, 4, 6, 8, 10, 14, 16, and 17 being currently amended therein.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-6, 8-14, and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, Under Step 1 of the Subject Matter Eligibility Test of Products and Processes, the claim is directed towards a machine, which is one of the four statutory categories.
Next, under a Step 2A Prong 1 Analysis, the claim recites the following limitations which interpreted to be, under the broadest reasonable interpretation, abstract ideas:
dividing an initial seed network into a plurality of blocks to form a network search space, wherein the network search space includes a plurality of a candidate neural architectures (mental process, with psychical aid (pen/paper, computer))
defining, for each block in the plurality of blocks, a plurality of sample-based search spaces, wherein each sample-based search space includes a plurality of candidate block configurations, and the plurality of candidate block configurations are determined by determining candidate block configurations that minimize a block- wise knowledge distillation loss (mental process, evaluation)
determining a first set of block configurations that are Pareto optimal block configurations from the plurality of candidate block configurations in the plurality of sample-based search spaces (mental process, evaluation)
determining a plurality of sub-super-net search spaces for each block configuration in the first set of block configurations (mental process, evaluation)
and determining an optimized neural architecture by determining a first trained candidate model of the plurality of trained candidate models that minimizes a knowledge distillation loss of the plurality of trained candidate models. (mental process, evaluation)
Therefore, we have to examine the claim under Step 2A prong 2, which considers the additional elements within the claim. The claim’s additional elements are:
one or more processors
one or more non-transitory computer-readable media that stores instructions
training, using a set of input training data, each of the plurality of sub-super-net search spaces to generate a plurality of trained candidate models
storing results of the neural architecture search into a meta-information database, wherein the meta-information database includes information describing each block in the plurality of blocks of the initial seed network;
and in response to a subsequent neural architecture search involving a second initial seed network divided into a second plurality of blocks, retrieving, from the meta-information database, information describing at least one block of the second plurality of blocks to reduce computational overhead of the subsequent neural architecture search.
The limitations, “one or more processors”, “one or more non-transitory computer-readable media that stores instructions”, and “training, using a set of input training data, each of the plurality of sub-super-net search spaces to generate a plurality of trained candidate models are interpreted to be mere instructions to apply a judicial exception, as it instructs to use one or more processors and media, as tools to perform the abstract idea, and use a set of input training data to train each of the plurality of sub-super-net spaces to then generate a plurality of models. (See MPEP 2106.05(f)) The limitations “storing results of the neural architecture search into a meta-information database, wherein the meta-information database includes information describing each block in the plurality of blocks of the initial seed network”, and “in response to a subsequent neural architecture search involving a second initial seed network divided into a second plurality of blocks, retrieving, from the meta-information database, information describing at least one block of the second plurality of blocks to reduce computational overhead of the subsequent neural architecture search” are considered to be insignificant extra-solution activity. (See MPEP 2106.05(g)) Therefore, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Under a Step 2B analysis, the claim’s additional elements do not amount to significantly
more than the judicial exception as explained above in Step 2A prong 2. Furthermore, the limitations, “storing results of the neural architecture search into a meta-information database, wherein the meta-information database includes information describing each block in the plurality of blocks of the initial seed network”, and “in response to a subsequent neural architecture search involving a second initial seed network divided into a second plurality of blocks, retrieving, from the meta-information database, information describing at least one block of the second plurality of blocks to reduce computational overhead of the subsequent neural architecture search”, are considered to be well-understood routine, and conventional, as it is interpreted to be storing or retrieving information in memory. (See MPEP 2106.05(d)(II)(iv.)) Therefore, the claim is ineligible.
Regarding claim 6, Under Step 1 of the Subject Matter Eligibility Test of Products and Processes, the claim is directed towards a machine, which is one of the four statutory categories.
Next, under a Step 2A Prong 1 Analysis, the claim recites the following limitations which interpreted to be, under the broadest reasonable interpretation, abstract ideas:
determining a network search space that includes a plurality of a candidate neural architectures based on an initial seed network (mental process, with psychical aid (pen/paper, computer))
defining, for each block in the plurality of blocks, a plurality of sample-based search spaces, wherein each sample-based search space includes a plurality of candidate block configurations (mental process, evaluation)
determining a plurality of sub-super-net search spaces for each block configuration in a first set of block configurations of the plurality of candidate block configurations (mental process, evaluation)
and determining an optimized neural architecture by determining a first trained candidate model of the plurality of trained candidate models, wherein the first trained candidate model minimizes a knowledge distillation loss of the plurality of trained candidate models. (mental process, evaluation)
Therefore, we have to examine the claim under Step 2A prong 2, which considers the additional elements within the claim. The claim’s additional elements are:
one or more processors
one or more non-transitory computer-readable media that stores instructions
training, using a set of input training data, each of the plurality of sub-super-net search spaces to generate a plurality of trained candidate models.
storing results of the neural architecture search into a meta-information database, wherein the meta-information database includes information describing each block in the plurality of blocks of the initial seed network;
and in response to a subsequent neural architecture search involving a second initial seed network divided into a second plurality of blocks, retrieving, from the meta-information database, information describing at least one block of the second plurality of blocks to reduce computational overhead of the subsequent neural architecture search.
The limitations, “one or more processors”, “one or more non-transitory computer-readable media that stores instructions”, and “training, using a set of input training data, each of the plurality of sub-super-net search spaces to generate a plurality of trained candidate models are interpreted to be mere instructions to apply a judicial exception, as it instructs to use one or more processors and media, as tools to perform the abstract idea, and use a set of input training data to train each of the plurality of sub-super-net spaces to then generate a plurality of models. (See MPEP 2106.05(f)) The limitations “storing results of the neural architecture search into a meta-information database, wherein the meta-information database includes information describing each block in the plurality of blocks of the initial seed network”, and “in response to a subsequent neural architecture search involving a second initial seed network divided into a second plurality of blocks, retrieving, from the meta-information database, information describing at least one block of the second plurality of blocks to reduce computational overhead of the subsequent neural architecture search” are considered to be insignificant extra-solution activity. (See MPEP 2106.05(g)) Therefore, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Under a Step 2B analysis, the claim’s additional elements do not amount to significantly
more than the judicial exception as explained above in Step 2A prong 2. Furthermore, the limitations, “storing results of the neural architecture search into a meta-information database, wherein the meta-information database includes information describing each block in the plurality of blocks of the initial seed network”, and “in response to a subsequent neural architecture search involving a second initial seed network divided into a second plurality of blocks, retrieving, from the meta-information database, information describing at least one block of the second plurality of blocks to reduce computational overhead of the subsequent neural architecture search”, are considered to be well-understood routine, and conventional, as it is interpreted to be storing or retrieving information in memory. (See MPEP 2106.05(d)(II)(iv.)) Therefore, the claim is ineligible.
Regarding claim 14, Under Step 1 of the Subject Matter Eligibility Test of Products and Processes, the claim is directed towards a machine, which is one of the four statutory categories.
Next, under a Step 2A Prong 1 Analysis, the claim recites the following limitations which interpreted to be, under the broadest reasonable interpretation, abstract ideas:
determining a network search space that includes a plurality of a candidate neural architectures based on an initial seed network (mental process, with psychical aid (pen/paper, computer))
defining, for each block in the plurality of blocks, a plurality of sample-based search spaces, wherein each sample-based search space includes a plurality of candidate block configurations (mental process, evaluation)
determining a plurality of sub-super-net search spaces for each block configuration in a first set of block configurations in the plurality of candidate block configurations (mental process, evaluation)
and determining an optimized neural architecture by determining a first trained candidate model of the plurality of trained candidate models, wherein the first trained candidate model minimizes a knowledge distillation loss of the plurality of trained candidate models. (mental process, evaluation)
Therefore, we have to examine the claim under Step 2A prong 2, which considers the additional elements within the claim. The claim’s additional elements are:
one or more processors
one or more non-transitory computer-readable media that stores instructions
training, using a set of input training data, each of the plurality of sub-super-net search spaces to generate a plurality of trained candidate models.
storing results of the neural architecture search into a meta-information database, wherein the meta-information database includes information describing each block in the plurality of blocks of the initial seed network;
and in response to a subsequent neural architecture search involving a second initial seed network divided into a second plurality of blocks, retrieving, from the meta-information database, information describing at least one block of the second plurality of blocks to reduce computational overhead of the subsequent neural architecture search.
The limitations, “one or more processors”, “one or more non-transitory computer-readable media that stores instructions”, and “training, using a set of input training data, each of the plurality of sub-super-net search spaces to generate a plurality of trained candidate models are interpreted to be mere instructions to apply a judicial exception, as it instructs to use one or more processors and media, as tools to perform the abstract idea, and use a set of input training data to train each of the plurality of sub-super-net spaces to then generate a plurality of models. (See MPEP 2106.05(f)) The limitations “storing results of the neural architecture search into a meta-information database, wherein the meta-information database includes information describing each block in the plurality of blocks of the initial seed network”, and “in response to a subsequent neural architecture search involving a second initial seed network divided into a second plurality of blocks, retrieving, from the meta-information database, information describing at least one block of the second plurality of blocks to reduce computational overhead of the subsequent neural architecture search” are considered to be insignificant extra-solution activity. (See MPEP 2106.05(g)) Therefore, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Under a Step 2B analysis, the claim’s additional elements do not amount to significantly
more than the judicial exception as explained above in Step 2A prong 2. Furthermore, the limitations, “storing results of the neural architecture search into a meta-information database, wherein the meta-information database includes information describing each block in the plurality of blocks of the initial seed network”, and “in response to a subsequent neural architecture search involving a second initial seed network divided into a second plurality of blocks, retrieving, from the meta-information database, information describing at least one block of the second plurality of blocks to reduce computational overhead of the subsequent neural architecture search”, are considered to be well-understood routine, and conventional, as it is interpreted to be storing or retrieving information in memory. (See MPEP 2106.05(d)(II)(iv.)) Therefore, the claim is ineligible.
Regarding claims 3, 8, and 16, the claims recite “the information describing each block includes at least one of an input resolution, an output resolution, a position in the seed network, and a size of each block.” The limitation, as drafted, merely describes the particular technological environment, and field of use, and “generally links” input resolution, output resolution, a position in the seed network, and size of each block, to the information describing each block. (See MPEP 2106.05(h)) Therefore, the claims are rejected on the same basis as claims 1, 6, and 14.
Regarding claims 4, 10, and 17, the claims recite “dividing an initial seed network into a second plurality of blocks, wherein the second initial seed network includes a second plurality of a candidate neural architectures” The limitation, as drafted, is considered to be, under the broadest reasonable interpretation, a “mental process” that can be done with the aid of pen and paper, which is a grouping of abstract idea. Therefore, the claims are rejected on the same basis as claims 1, 6, and 14 respectively.
Regarding claims 5, 11, and 18 the claims recite “the step of retrieving, from the database, information describing at least one block of the second plurality of blocks is performed before defining, for each block in the second plurality of blocks, a second plurality of sample-based search spaces, wherein each sample-based search space in the second plurality of sample-based search spaces includes a second plurality of candidate block configurations, and the second plurality of candidate block configurations are determined by determining candidate block configurations that minimize the block-wise knowledge distillation loss.” The limitations, as drafted, is interpreted to be, under the broadest reasonable interpretation, “mental processes”, which is a grouping of abstract idea. Therefore, the claims are rejected on the same basis as claims 4, 10, and 17.
Regarding claims 12 and 19, the claims recite “determining the first set of block configurations includes determining the first set of block configurations are Pareto optimal block configurations.” The limitation, as drafted, is considered to be, under the broadest reasonable interpretation, a “mental process”, which is a grouping of abstract idea. Therefore the claims are rejected on the same basis as claims 6 and 14.
Regarding claims 13 and 20, the claims recite “training, using a set of input training data, each of the plurality of sub-super-net search spaces to generate the plurality of trained candidate models.” The limitations, as drafted, merely recite instructions to apply a judicial exception, as it instructs to use a set of input training data to train each of the plurality of sub-super-net spaces to then generate a plurality of models. (See MPEP 2106.05(f)) Therefore the claims are rejected on the same basis as claims 6 and 14.
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.
Claims 1, 4, 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (Herein referred to as Li) (Block-wisely Supervised Neural Architecture Search with Knowledge Distillation) (As cited in the IDS) in further view of Ci et al. (Herein referred to as Ci) (Evolving Search Space for Neural Architecture Search) in view of Liu et al. (Herein referred to as Liu) (Block Proposal Neural Architecture Search) and in further view of Lee et al. (Herein referred to as Lee) (RAPID NEURAL ARCHITECTURE SEARCH BY LEARNING TO GENERATE GRAPHS FROM DATASETS)
Regarding claim 1, Li teaches one or more processors; and one or more non-transitory computer-readable media that stores instructions that, when executed by the one or more processors, cause the computing system to implement a neural architecture search (While not explicitly disclosed in Li, one would implicitly need these components to run the NAS with knowledge distillation of Li) by performing the steps of: dividing an initial seed network into a plurality of blocks to form a network search space, wherein the network search space includes a plurality of a candidate neural architectures, (“We consider a network architecture has several blocks, conceptualized as analogous to the ventral visual blocks… As Fig. 1 shows, we find that different blocks of an existing architecture have different knowledge in extracting different patterns of an image”, Figure 1; pg. 2, left column, third paragraph) (Figure 1 shows candidate neural architectures) defining, for each block in the plurality of blocks, a plurality of sample-based search spaces, (“we propose to modularize the large search space of NAS into blocks to ensure that the potential candidate architectures are fully trained; this reduces the representation shift caused by the shared parameters and leads to the correct rating of the candidates.”, pg. 1, Abstract; See also Figure 1) (The search spaces are divided into blocks.) wherein each sample-based search space includes a plurality of candidate block configurations, and the plurality of candidate block configurations are determined by determining candidate block configurations that minimize a block- wise knowledge distillation loss, (“All the three strategy is performed block by block by minimizing the MSE loss between feature maps of student supernet and the teacher”, pg. 7, right column, bottom paragraph) (The MSE loss between teach and student corresponds to a distillation loss.) determining a plurality of sub-super-net search spaces for each block configuration in the first set of block configurations, (“To improve the accuracy of the evaluation, we divide the super-net into blocks of smaller sub-space.”, pg. 3, right column, under “Block-wise NAS.”) training, using a set of input training data, each of the plurality of sub-super-net search spaces to generate a plurality of trained candidate models, (“Illustration of our DNA. The teacher’s previous feature map is used as input for both teacher and student block. Each cell of the supernet is trained independently to mimic the behavior of the corresponding teacher block by minimizing the l2-distance between their output feature maps. The dotted lines indicate randomly sampled paths in a cell… As shown in Figure 2, in each training step, the teacher’s previous feature map is first fed to several cells (as suggested by the solid line), and one of the candidate operations of each layer in the cell is randomly chosen to form a path (as suggested by the dotted line).”, pg. 4, Figure 2; pg. 5, left column, second paragraph) and determining an optimized neural architecture by determining a first trained candidate model of the plurality of trained candidate models that minimizes a knowledge distillation loss of the plurality of trained candidate models. (“We tested two progressive block-wise distillation strategy and compare their effectiveness with ours by experiments. All the three strategy is performed block by block by minimizing the MSE loss between feature maps of student supernet and the teacher.”, pg. 7, right column, under “4.4. Ablation Study”, See also Tables 4 and 5 on pg. 8) (A student model is selected with minimal distillation loss and high accuracy.)
However, Li does not explicitly teach determining a first set of block configurations that are Pareto optimal block configurations from the plurality of candidate block configurations in the plurality of sample-based search spaces, nor storing results of the neural architecture search into a meta-information database, wherein the meta-information database includes information describing each block in the plurality of blocks of the initial seed network; nor in response to a subsequent neural architecture search involving a second initial seed network divided into a second plurality of blocks, retrieving, from the meta-information database, information describing at least one block of the second plurality of blocks to reduce computational overhead of the subsequent neural architecture search.
Ci teaches determining a first set of block configurations that are Pareto optimal block configurations from the plurality of candidate block configurations in the plurality of sample-based search spaces (“During the iterative process, instead of keeping a single architecture as the intermediate result, we combine all architectures on the Pareto front found by a supernet trained with One-Shot [2] method to obtain an optimized search space, which will be inherited to the next round of search.”, pg. 2, left column, bottom paragraph)
Therefore, it would have been considered obvious to one of ordinary skill in the art, prior to the filing date of the current application, to combine the block-wise network architecture of Li, with the Pareto optimization of Ci. One would be motivated to combine the teachings, prior to the filing date of the current application, as Patero optimal architectures help obtain optimal search spaces, as disclosed in Ci (“After Pareto front retrieval, we take the union of operations from all P Pareto-optimal architectures to get the optimized search space Aˆ s. Mathematically, we denote e p l = {opl n |g l n = 1, n ∈ Kl} as the selected operations of l-th layer for the p-th Pareto-optimal architecture ap, and denote Eˆ s l as the optimized search space subset of the layer l in Aˆ s”, pg. 5, right column, under “Aggregation.”)
However, the combination does not teach storing results of the neural architecture search into a meta-information database, wherein the meta-information database includes information describing each block in the plurality of blocks of the initial seed network; nor in response to a subsequent neural architecture search involving a second initial seed network divided into a second plurality of blocks, retrieving, from the meta-information database, information describing at least one block of the second plurality of blocks to reduce computational overhead of the subsequent neural architecture search.
Liu teaches storing results of the neural architecture search into a meta-information database, wherein the meta-information database includes information describing each block in the plurality of blocks of the initial seed network; (“Since the block search space is too large, we cannot measure the latencies of all possible blocks. To solve this problem, we use the sum of latencies from all paths in each block to estimate the latency of this block. This approximation strategy works well for most devices. We enumerate all possible input image resolutions and the number of channels, then separately measure the latency of each possible path, and store them in a lookup table.”, pgs. 4-5) (Latency of the blocks, which corresponds to information describing each block, is stored in a lookup table, the lookup table corresponding to a database, teaching the limitation)
Therefore, it would have been considered obvious to one of ordinary skill in the art, prior to the filing date of the current application, to combine the block-wise network architecture of Li, as modified by Ci, with the storing of block information as depicted in Liu. One would be motivated to combine the teachings, prior to the filing date of the current application, as it allow for the storage of data to be retrieved to use at a later time, like how is described in Liu. (“We stack the super blocks described in Section III-C-1 to construct the supernet by using the backbone network shown in Fig. 2. We adopt the lookup table to produce the latency of the i-th block proposal at the lth layer, which is represented as LATENCY(blockl i ), and use the corresponding sampling probability P(i;l) to estimate the latency of the whole supernet a”, pg. 5, right column, bottom paragraph)
However, the combination does not explicitly teach in response to a subsequent neural architecture search involving a second initial seed network divided into a second plurality of blocks, retrieving, from the meta-information database, information describing at least one block of the second plurality of blocks to reduce computational overhead of the subsequent neural architecture search.
Lee teaches in response to a subsequent neural architecture search involving a second initial seed network divided into a second plurality of blocks, retrieving, from the meta-information database, information describing at least one block of the second plurality of blocks to reduce computational overhead of the subsequent neural architecture search. (“Our goal is to output a high-performing neural architecture for a given dataset rapidly by learning the prior knowledge obtained from the rich database consisting of datasets and their corresponding neural architecture… We want the generator to generate even novel architectures, which are not contained in the source database, at meta-test. Thus, the generator learns the continuous cross-modal latent space Z of datasets and neural architectures from the source database. For each task τ, the generator encodes dataset D as a vector z through the set encoder qφ(z|D) parameterized by φ and then decodes a new graph ˜G from z which are sampled from the prior p(z) by using the graph decoder pθ(G|z) parameterized by θ. Then, our goal is that ˜ G generated from D to be the true G which is pair of D.”, pgs. 3 and 4; pg. 4, under “3.1.1 LEARNING TO GENERATE GRAPHS FROM DATASETS”)
Therefore, it would have been considered obvious to one of ordinary skill in the art, prior to the filing date of the current application, to combine the block-wise network architecture of Li, as modified by Ci and Liu, with the NAS dataset to retrieve NAS data of Lee. One would be motivated to combine the teachings, prior to the filing date of the current application, as this allows for the development of a high-performing neural architecture for datasets based on prior information from a database. (“Our goal is to output a high-performing neural architecture for a given dataset rapidly by learning the prior knowledge obtained from the rich database consisting of datasets and their corresponding neural architecture… We want the generator to generate even novel architectures, which are not contained in the source database, at meta-test. Thus, the generator learns the continuous cross-modal latent space Z of datasets and neural architectures from the source database. For each task τ, the generator encodes dataset D as a vector z through the set encoder qφ(z|D) parameterized by φ and then decodes a new graph ˜G from z which are sampled from the prior p(z) by using the graph decoder pθ(G|z) parameterized by θ. Then, our goal is that ˜ G generated from D to be the true G which is pair of D.”, pgs. 3 and 4; pg. 4, under “3.1.1 LEARNING TO GENERATE GRAPHS FROM DATASETS”)
Regarding claim 4, Li, as modified by Ci, Liu and Lee teaches dividing an initial seed network into a second plurality of blocks, wherein the second initial seed network includes a second plurality of a candidate neural architectures; (“After that, all of our searched architectures are retrained from scratch on the original training set without supervision from the teacher network and tested on the original validation set.”, pg. 6, left column, under “Choice of dataset and teacher model” (Li)) (The retrained architectures correspond to a second plurality of blocks and candidate architectures.)
Regarding claim 5, Li, as modified by Ci, Liu and Lee teaches the computing system of claim 4, wherein the step of retrieving, from the database, information describing at least one block of the second plurality of blocks is performed before defining, for each block in the second plurality of blocks, a second plurality of sample-based search spaces, wherein each sample-based search space in the second plurality of sample-based search spaces includes a second plurality of candidate block configurations, (“We apply our meta-trained model on four unseen datasets, comparing with transferable NAS (NS GANetV2 (Lu et al., 2020)) under the same search space of MobileNetV3, where it contains more than 1019 architectures. Each CNN architecture consists of five sequential blocks and the targets of searching are the number of layers, the number of channels, kernel size, and input resolutions”, pg. 6, under “4.2.1 EXPERIMENT SETUP”; See also Figure 1 on pg. 2 (Lee) (Using the meta-learning model of Lee, the second plurality of search spaces corresponds to the search space of the source datasets.) and the second plurality of candidate block configurations are determined by determining candidate block configurations that minimize the block-wise knowledge distillation loss. (“All the three strategy is performed block by block by minimizing the MSE loss between feature maps of student supernet and the teacher.” pg. 7, right column, bottom paragraph (Li))
Regarding claim 12 and 19, Li, as modified by Liu, and Lee, teaches the system and method of claims 6 and 14 respectively, but does not explicitly teach determining the first set of block configurations are Pareto optimal block configurations.
Ci teaches determining the first set of block configurations are Pareto optimal block configurations (“During the iterative process, instead of keeping a single architecture as the intermediate result, we combine all architectures on the Pareto front found by a supernet trained with One-Shot [2] method to obtain an optimized search space, which will be inherited to the next round of search.”, pg. 2, left column, bottom paragraph)
Therefore, it would have been considered obvious to one of ordinary skill in the art, prior to the filing date of the current application, to combine the block-wise network architecture of Li, as modified by Liu and Lee, with the Pareto optimization of Ci. One would be motivated to combine the two teachings, prior to the filing date of the current application, as Patero optimal architectures help obtain optimal search spaces, as disclosed in Ci (“After Pareto front retrieval, we take the union of operations from all P Pareto-optimal architectures to get the optimized search space Aˆ s. Mathematically, we denote e p l = {opl n |g l n = 1, n ∈ Kl} as the selected operations of l-th layer for the p-th Pareto-optimal architecture ap, and denote Eˆ s l as the optimized search space subset of the layer l in Aˆ s”, pg. 5, right column, under “Aggregation.”)
Claims 3 is rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Ci, in further view of Liu, in further view of Lee, and in further view of Jiemin Fang et al. (Herein referred to as Fang.) (Densely Connected Search Space for More Flexible Neural Architecture Search) (As cited in the IDS)
Regarding claim 3, Li, and modified by Ci, Liu, and Lee, teach the computing system of claim 1, wherein the information describing each block includes at least one of a position in the seed network, and a size of each block. (“Let d denote the depth of the i-th block and C denote the number of the candidate operations in each layer. Then the size of the search space of the i-th block is C di , ∀i ∈ [1, N]; the size of the search space A is QN i=0 C di .”, pg. 3, right column, second to last paragraph (Li))
However, the combination does not explicitly teach the information describing each block includes at least one of an input resolution, an output resolution
Fang teaches the information describing each block includes at least one of an input resolution, an output resolution (“As shown in Fig. 2, the input tensors from these routing blocks differ in terms of width and spatial resolution. Each input tensor is transformed to a same size by the corresponding branch of shape-alignment layers in Bi .”, pg. 5, left column, first paragraph) (As shown in Figure 2, and described on pages 4 and 5, the blocks have input and output resolutions.)
Therefore it would have been considered obvious to one of ordinary skill in the art, prior to the filing date of the current application, to combine the system of Li, as modified by Ci, Liu, and Lee, with the resolution of blocks as disclosed in Fang. One would be motivated to combine the teachings, prior to the filing date of the current application, as spatial resolution helps provide better connections between routing blocks, as disclosed in Fang. (“We define the connection between the routing block Bi and its subsequent routing block Bj (j > i) as Cij. The spatial resolutions of Bi and Bj are Hi × Wi and Hj ×Wj respectively (normally Hi = Wi and Hj = Wj). We set some constraints on the connections to avoid the stride of the spatial down-sampling exceeding 2. Specifically, Cij only exists when j − i ≤ M and Hi Hj ≤ 2.”, pg. 4, right column, first paragraph)
Claims 6, 10, 11, 13, 14, 17, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Li (Evolving Search Space for Neural Architecture Search) in view of Liu et al. (Herein referred to as Liu) (Block Proposal Neural Architecture Search) and in further view of Lee et al. (Herein referred to as Lee) (RAPID NEURAL ARCHITECTURE SEARCH BY LEARNING TO GENERATE GRAPHS FROM DATASETS)
Regarding claim 6, Li teaches a computing system, comprising: one or more processors; and one or more non-transitory computer-readable media that stores instructions that, when executed by the one or more processors, cause the computing system to implement a neural architecture search (While not explicitly disclosed in Li, one would implicitly need these components to run the NAS with knowledge distillation of Li) by performing the steps of: determining a network search space that includes a plurality of a candidate neural architectures based on an initial seed network (“We consider a network architecture has several blocks, conceptualized as analogous to the ventral visual blocks… As Fig. 1 shows, we find that different blocks of an existing architecture have different knowledge in extracting different patterns of an image”, Figure 1; pg. 2, left column, third paragraph) (Figure 1 shows candidate neural architectures) defining, for each block in the plurality of blocks, a plurality of sample-based search spaces, wherein each sample-based search space includes a plurality of candidate block configurations, (“we propose to modularize the large search space of NAS into blocks to ensure that the potential candidate architectures are fully trained; this reduces the representation shift caused by the shared parameters and leads to the correct rating of the candidates.”, pg. 1, Abstract) (The search spaces are divided into blocks.) determining a plurality of sub-super-net search spaces for each block configuration in a first set of block configurations of the plurality of candidate block configurations (“To improve the accuracy of the evaluation, we divide the supernet into blocks of smaller sub-space.”, pg. 3, right column, under “Block-wise NAS.”) training, using a set of input training data, each of the plurality of sub-super-net search spaces to generate a plurality of trained candidate models (“Illustration of our DNA. The teacher’s previous feature map is used as input for both teacher and student block. Each cell of the supernet is trained independently to mimic the behavior of the corresponding teacher block by minimizing the l2-distance between their output feature maps. The dotted lines indicate randomly sampled paths in a cell… As shown in Figure 2, in each training step, the teacher’s previous feature map is first fed to several cells (as suggested by the solid line), and one of the candidate operations of each layer in the cell is randomly chosen to form a path (as suggested by the dotted line).”, pg. 4, Figure 2; pg. 5, left column, second paragraph) and determining an optimized neural architecture by determining a first trained candidate model of the plurality of trained candidate models wherein the first trained candidate model minimizes a knowledge distillation loss of the plurality of trained candidate models. (“We tested two progressive block-wise distillation strategy and compare their effectiveness with ours by experiments. All the three strategy is performed block by block by minimizing the MSE loss between feature maps of student supernet and the teacher.”, pg. 7, right column, under “4.4. Ablation Study”, See also Tables 4 and 5 on pg. 8) (A student model is selected with minimal distillation loss and high accuracy.)
However, Li does not teach storing results of the neural architecture search into a meta-information database, wherein the meta-information database includes information describing each block in the plurality of blocks of the initial seed network; nor in response to a subsequent neural architecture search involving a second initial seed network divided into a second plurality of blocks, retrieving, from the meta-information database, information describing at least one block of the second plurality of blocks to reduce computational overhead of the subsequent neural architecture search.
Liu teaches storing results of the neural architecture search into a meta-information database, wherein the meta-information database includes information describing each block in the plurality of blocks of the initial seed network; (“Since the block search space is too large, we cannot measure the latencies of all possible blocks. To solve this problem, we use the sum of latencies from all paths in each block to estimate the latency of this block. This approximation strategy works well for most devices. We enumerate all possible input image resolutions and the number of channels, then separately measure the latency of each possible path, and store them in a lookup table.”, pgs. 4-5) (Latency of the blocks, which corresponds to information describing each block, is stored in a lookup table, the lookup table corresponding to a database, teaching the limitation)
Therefore, it would have been considered obvious to one of ordinary skill in the art, prior to the filing date of the current application, to combine the block-wise network architecture of Li, with the storing of block information as depicted in Liu. One would be motivated to combine the teachings, prior to the filing date of the current application, as it allow for the storage of data to be retrieved to use at a later time, like how is described in Liu. (“We stack the super blocks described in Section III-C-1 to construct the supernet by using the backbone network shown in Fig. 2. We adopt the lookup table to produce the latency of the i-th block proposal at the lth layer, which is represented as LATENCY(blockl i ), and use the corresponding sampling probability P(i;l) to estimate the latency of the whole supernet a”, pg. 5, right column, bottom paragraph)
However, the combination does not explicitly teach in response to a subsequent neural architecture search involving a second initial seed network divided into a second plurality of blocks, retrieving, from the meta-information database, information describing at least one block of the second plurality of blocks to reduce computational overhead of the subsequent neural architecture search.
Lee teaches in response to a subsequent neural architecture search involving a second initial seed network divided into a second plurality of blocks, retrieving, from the meta-information database, information describing at least one block of the second plurality of blocks to reduce computational overhead of the subsequent neural architecture search. (“Our goal is to output a high-performing neural architecture for a given dataset rapidly by learning the prior knowledge obtained from the rich database consisting of datasets and their corresponding neural architecture… We want the generator to generate even novel architectures, which are not contained in the source database, at meta-test. Thus, the generator learns the continuous cross-modal latent space Z of datasets and neural architectures from the source database. For each task τ, the generator encodes dataset D as a vector z through the set encoder qφ(z|D) parameterized by φ and then decodes a new graph ˜G from z which are sampled from the prior p(z) by using the graph decoder pθ(G|z) parameterized by θ. Then, our goal is that ˜ G generated from D to be the true G which is pair of D.”, pgs. 3 and 4; pg. 4, under “3.1.1 LEARNING TO GENERATE GRAPHS FROM DATASETS”)
Therefore, it would have been considered obvious to one of ordinary skill in the art, prior to the filing date of the current application, to combine the block-wise network architecture of Li, as modified by Liu, with the NAS dataset to retrieve NAS data of Lee. One would be motivated to combine the teachings, prior to the filing date of the current application, as this allows for the development of a high-performing neural architecture for datasets based on prior information from a database. (“Our goal is to output a high-performing neural architecture for a given dataset rapidly by learning the prior knowledge obtained from the rich database consisting of datasets and their corresponding neural architecture… We want the generator to generate even novel architectures, which are not contained in the source database, at meta-test. Thus, the generator learns the continuous cross-modal latent space Z of datasets and neural architectures from the source database. For each task τ, the generator encodes dataset D as a vector z through the set encoder qφ(z|D) parameterized by φ and then decodes a new graph ˜G from z which are sampled from the prior p(z) by using the graph decoder pθ(G|z) parameterized by θ. Then, our goal is that ˜ G generated from D to be the true G which is pair of D.”, pgs. 3 and 4; pg. 4, under “3.1.1 LEARNING TO GENERATE GRAPHS FROM DATASETS”)
Regarding claim 14, Li teaches a method, comprising: determining a network search space that includes a plurality of a candidate neural architectures based on an initial seed network, (“We consider a network architecture has several blocks, conceptualized as analogous to the ventral visual blocks… As Fig. 1 shows, we find that different blocks of an existing architecture have different knowledge in extracting different patterns of an image”, Figure 1; pg. 2, left column, third paragraph) (Figure 1 shows candidate neural architectures) defining, for each block in the plurality of blocks, a plurality of sample-based search spaces, wherein each sample-based search space includes a plurality of candidate block configurations, (“we propose to modularize the large search space of NAS into blocks to ensure that the potential candidate architectures are fully trained; this reduces the representation shift caused by the shared parameters and leads to the correct rating of the candidates.”, pg. 1, Abstract) (The search spaces are divided into blocks.) determining a plurality of sub-super-net search spaces for each block configuration in a first set of block configurations in the plurality of candidate block configurations, (“To improve the accuracy of the evaluation, we divide the supernet into blocks of smaller sub-space.”, pg. 3, right column, under “Block-wise NAS.”) training, using a set of input training data, each of the plurality of sub-super-net search spaces to generate a plurality of trained candidate models, (“Illustration of our DNA. The teacher’s previous feature map is used as input for both teacher and student block. Each cell of the supernet is trained independently to mimic the behavior of the corresponding teacher block by minimizing the l2-distance between their output feature maps. The dotted lines indicate randomly sampled paths in a cell… As shown in Figure 2, in each training step, the teacher’s previous feature map is first fed to several cells (as suggested by the solid line), and one of the candidate operations of each layer in the cell is randomly chosen to form a path (as suggested by the dotted line).”, pg. 4, Figure 2; pg. 5, left column, second paragraph) and determining an optimized neural architecture by determining a first trained candidate model of the plurality of trained candidate models, wherein the first trained candidate model minimizes a knowledge distillation loss of the plurality of trained candidate models. (“We tested two progressive block-wise distillation strategy and compare their effectiveness with ours by experiments. All the three strategy is performed block by block by minimizing the MSE loss between feature maps of student supernet and the teacher.”, pg. 7, right column, under “4.4. Ablation Study”, See also Tables 4 and 5 on pg. 8) (A student model is selected with minimal distillation loss and high accuracy.)
However, Li does not explicitly teach storing results of the neural architecture search into a meta-information database, wherein the meta-information database includes information describing each block in the plurality of blocks of the initial seed network; and in response to a subsequent neural architecture search involving a second initial seed network divided into a second plurality of blocks, retrieving, from the meta-information database, information describing at least one block of the second plurality of blocks to reduce computational overhead of the subsequent neural architecture search.
Liu teaches storing results of the neural architecture search into a meta-information database, wherein the meta-information database includes information describing each block in the plurality of blocks of the initial seed network; (“Since the block search space is too large, we cannot measure the latencies of all possible blocks. To solve this problem, we use the sum of latencies from all paths in each block to estimate the latency of this block. This approximation strategy works well for most devices. We enumerate all possible input image resolutions and the number of channels, then separately measure the latency of each possible path, and store them in a lookup table.”, pgs. 4-5) (Latency of the blocks, which corresponds to information describing each block, is stored in a lookup table, the lookup table corresponding to a database, teaching the limitation)
Therefore, it would have been considered obvious to one of ordinary skill in the art, prior to the filing date of the current application, to combine the block-wise network architecture of Li, with the storing of block information as depicted in Liu. One would be motivated to combine the teachings, prior to the filing date of the current application, as it allow for the storage of data to be retrieved to use at a later time, like how is described in Liu. (“We stack the super blocks described in Section III-C-1 to construct the supernet by using the backbone network shown in Fig. 2. We adopt the lookup table to produce the latency of the i-th block proposal at the lth layer, which is represented as LATENCY(blockl i ), and use the corresponding sampling probability P(i;l) to estimate the latency of the whole supernet a”, pg. 5, right column, bottom paragraph)
However, the combination does not explicitly teach in response to a subsequent neural architecture search involving a second initial seed network divided into a second plurality of blocks, retrieving, from the meta-information database, information describing at least one block of the second plurality of blocks to reduce computational overhead of the subsequent neural architecture search.
Lee teaches in response to a subsequent neural architecture search involving a second initial seed network divided into a second plurality of blocks, retrieving, from the meta-information database, information describing at least one block of the second plurality of blocks to reduce computational overhead of the subsequent neural architecture search. (“Our goal is to output a high-performing neural architecture for a given dataset rapidly by learning the prior knowledge obtained from the rich database consisting of datasets and their corresponding neural architecture… We want the generator to generate even novel architectures, which are not contained in the source database, at meta-test. Thus, the generator learns the continuous cross-modal latent space Z of datasets and neural architectures from the source database. For each task τ, the generator encodes dataset D as a vector z through the set encoder qφ(z|D) parameterized by φ and then decodes a new graph ˜G from z which are sampled from the prior p(z) by using the graph decoder pθ(G|z) parameterized by θ. Then, our goal is that ˜ G generated from D to be the true G which is pair of D.”, pgs. 3 and 4; pg. 4, under “3.1.1 LEARNING TO GENERATE GRAPHS FROM DATASETS”)
Therefore, it would have been considered obvious to one of ordinary skill in the art, prior to the filing date of the current application, to combine the block-wise network architecture of Li, as modified by Liu, with the NAS dataset to retrieve NAS data of Lee. One would be motivated to combine the teachings, prior to the filing date of the current application, as this allows for the development of a high-performing neural architecture for datasets based on prior information from a database. (“Our goal is to output a high-performing neural architecture for a given dataset rapidly by learning the prior knowledge obtained from the rich database consisting of datasets and their corresponding neural architecture… We want the generator to generate even novel architectures, which are not contained in the source database, at meta-test. Thus, the generator learns the continuous cross-modal latent space Z of datasets and neural architectures from the source database. For each task τ, the generator encodes dataset D as a vector z through the set encoder qφ(z|D) parameterized by φ and then decodes a new graph ˜G from z which are sampled from the prior p(z) by using the graph decoder pθ(G|z) parameterized by θ. Then, our goal is that ˜ G generated from D to be the true G which is pair of D.”, pgs. 3 and 4; pg. 4, under “3.1.1 LEARNING TO GENERATE GRAPHS FROM DATASETS”)
Regarding claims 10 and 17, Li, as modified by Liu and Lee, teaches the computing system and method of claims 6 and 14 respectively, as well as dividing an initial seed network into a second plurality of blocks, wherein the second initial seed network includes a second plurality of a candidate neural architectures; (“After that, all of our searched architectures are retrained from scratch on the original training set without supervision from the teacher network and tested on the original validation set.”, pg. 6, left column, under “Choice of dataset and teacher model” (Li)) (The retrained architectures correspond to a second plurality of blocks and candidate architectures.)
Regarding claims 11 and 18, Li, as modified by Liu and Lee, teaches the computing system and method of claims 10 and 17 respectively, as well as the step of retrieving, from the database, information describing at least one block of the second plurality of blocks is performed before defining, for each block in the second plurality of blocks, a second plurality of sample-based search spaces, wherein each sample-based search space in the second plurality of sample-based search spaces includes a second plurality of candidate block configurations, (“we propose to modularize the large search space of NAS into blocks to ensure that the potential candidate architectures are fully trained; this reduces the representation shift caused by the shared parameters and leads to the correct rating of the candidates.”, pg. 1, Abstract (Li)) (With the retrieval of data of Li, it would be easy to configure the retrieval of information describing at least block to happen before it is used to define a second plurality of block and search spaces.) and the second plurality of candidate block configurations are determined by determining candidate block configurations that minimize the block-wise knowledge distillation loss. (“All the three strategy is performed block by block by minimizing the MSE loss between feature maps of student supernet and the teacher.” pg. 7, right column, bottom paragraph (Li))
Regarding claim 13 and 20, Li, as modified by Liu and Lee teaches the computing system and method of claims 6 and 14 respectively, as well as training, using a set of input training data, each of the plurality of sub-super-net search spaces to generate the plurality of trained candidate models. (“We evaluated our method on ImageNet [11], a large-scale classification dataset that has been used to evaluate various NAS methods. During the architecture search, we randomly select 50 images from each class of the original training set to form a 50k-image validation set for the rating step of the NAS and use the remainder as the supernet training set. After that, all of our searched architectures are retrained from scratch on the original training set without supervision from the teacher network and tested on the original validation set.”, pg. 6, left column, under “4.1 Setups”)
Claims 8, 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Liu, in further view of Lee, and in further view of Fang.
Regarding claims 8 and 16, Li, as modified by Liu and Lee, teaches the computing system and method of claims 6 and 14 respectively, as well as the information describing each block includes at least a position in the seed network, and a size of each block. (“Let d denote the depth of the i-th block and C denote the number of the candidate operations in each layer. Then the size of the search space of the i-th block is C di , ∀i ∈ [1, N]; the size of the search space A is QN i=0 C di .”, pg. 3, right column, second to last paragraph)
However, the combination does not explicitly teach the information describing each block includes at least one of an input resolution, an output resolution
Fang teaches the information describing each block includes at least one of an input resolution, an output resolution (“As shown in Fig. 2, the input tensors from these routing blocks differ in terms of width and spatial resolution. Each input tensor is transformed to a same size by the corresponding branch of shape-alignment layers in Bi .”, pg. 5, left column, first paragraph) (As shown in Figure 2, and described on pages 4 and 5, the blocks have input and output resolutions.)
Therefore it would have been considered obvious to one of ordinary skill in the art, prior to the filing date of the current application, to combine the system of Li, as modified by Liu and Lee, with the resolution of blocks as disclosed in Fang. One would be motivated to combine the two teachings, prior to the filing date of the current application, as spatial resolution helps provide better connections between routing blocks, as disclosed in Fang. (“We define the connection between the routing block Bi and its subsequent routing block Bj (j > i) as Cij. The spatial resolutions of Bi and Bj are Hi × Wi and Hj ×Wj respectively (normally Hi = Wi and Hj = Wj). We set some constraints on the connections to avoid the stride of the spatial down-sampling exceeding 2. Specifically, Cij only exists when j − i ≤ M and Hi Hj ≤ 2.”, pg. 4, right column, first paragraph)
Regarding claim 9, Li, as modified by Liu, Lee, and Fang, teaches the computing system of claim 8, wherein the information describing each block includes each of the input resolution, the output resolution, the position in the seed network, and the size of each block. (“Let d denote the depth of the i-th block and C denote the number of the candidate operations in each layer. Then the size of the search space of the i-th block is C di , ∀i ∈ [1, N]; the size of the search space A is QN i=0 C di .”, pg. 3, right column, second to last paragraph (Li)) (Li teaches the position and size for each block) (“As shown in Fig. 2, the input tensors from these routing blocks differ in terms of width and spatial resolution. Each input tensor is transformed to a same size by the corresponding branch of shape-alignment layers in Bi .”, pg. 5, left column, first paragraph (Feng)) (Feng teaches the resolution for each block)
Response to Arguments
Applicant's arguments filed on June 3rd, 2026 have been fully considered but they are not persuasive. The applicant argues in substance:
Argument 1: The claim is not directed to an abstract idea, as the claims limitation cannot be reasonably performed in the human mind.
The Examiner respectfully disagrees. According to MPEP 2106.04(a)(2)(III)(C.), a claim that requires a computer may still recite a mental process if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept is performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. The “dividing”, “defining”, and “determining” steps of claims 1, 6, and 14 are all interpreted to be mental processes performed within a computer environment.
Argument 2: The claims’ additional element successfully integrate any alleged abstract idea into a practical application as it provides an improvement to the technology of neural architecture search.
The examiner respectfully disagrees. As explained in this action, the additional elements do not integrate the abstract idea into a practical application as the claim recite components and steps that do not amount to significantly more. Even if the limitations recite improvements, they do not recite improvements to a specific field of technology, but rather improvements to the abstract ideas. Furthermore, the new limitations do not integrate the abstract idea into a practical application, nor do the limitations amount to significantly more.
Argument 3: The claims recite additional elements that amount to significantly more than the judicial exception.
The examiner respectfully disagrees. As stated in this action, the additional elements do not amount to significantly more, as the elements, view individually or as a whole, merely recites an improvement of an abstract idea. Furthermore the newly added limitations of the “storing” and “retrieving” step are considered to be well-understood, routine, and conventional as explained in this action, with the recited improvement being “to reduce computational overhead” being interpreted as “intended use” giving the limitation negligible patentable weight. For these reasons, the 101 rejections are maintained.
Argument 4: Li does not teach “defining, for each block in the plurality of blocks, a plurality of sample-based search spaces, wherein each sample-based search space includes a plurality of candidate block configurations, and the plurality of candidate block configurations are determined by determining candidate block configurations”
The examiner respectfully disagrees. As described in Li, they propose to “modularize the large search space into blocks” which, under the broadest reasonable interpretation, corresponds to defining, for a plurality of blocks, a plurality of sample-based search spaces. Furthermore, under Figure 1, one can see that the different blocks can contain different architecture consisting of different shapes and paths to ultimately determine candidate architectures, which corresponds to “a plurality of candidate block configurations, and the plurality of candidate block configurations are determined by determining candidate block configurations”, thereby fully teaching the limitation.
Argument 5: The current references do not teach the newly added limitations of claims 1, 6, and 14.
Applicant’s arguments with respect to claim(s) 1, 6, and 14 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
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/T.E.I./ Patent Examiner, Art Unit 2122
/BRIAN M SMITH/ Primary Examiner, Art Unit 2122
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