DETAILED ACTION
This Office Action is in response to the amendments filed on 02/02/2026.
Claims 1, 10, and 17 are currently amended.
Claims 1-20 are currently pending in this application and have been examined.
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 .
Response to Arguments
In reference to Applicant’s arguments on page(s) 8-11 regarding rejections made under 35 U.S.C. 101:
Claims 1-20 stand rejected under 35 U.S.C. § 101 for allegedly being directed to an abstract idea without significantly more. Office Action, at 6.
Regarding independent claims 1, 10, and 17, the Office maintains that the claims recite mental processes and/or mathematical concepts, and that the "additional elements" of the claims do not integrate the alleged abstract idea(s) into a practical application. More specifically, the Office asserts that the following three "additional elements" of the claims do not integrate the alleged abstract idea(s) into a practical application: (1) generating a network comprising a plurality of layers, wherein each of the plurality of layers comprises cells with different structures each of which is represented by a directed acyclic graph (DAG) that comprises nodes and edges directed from one node to another node; (2) wherein the local mutation model comprises a mutation window for removing redundant edges from each selected cell; and (3) integrating the search space into a neural architecture search (NAS) system to generate a network architecture that optimizes a performance of the target network. Applicant respectfully disagrees. These three limitations, especially as further clarified, integrate any alleged abstract idea into a practical application.
As explained in the previous response filed on May 29, 2025, the claims are inextricably tied to an improved technique for automating search space design. Applicant's as-filed Specification explains limitations of existing NAS systems. For example, Applicant's as-filed Specification explains "[s]ome NAS systems deploy an open search space with minimal manual constraints . . . However, directly searching for network architectures within such a huge search space is time consuming ... [and] utilizing such a huge search space increases the risk that a suboptimal network architecture may be selected by the system."
That is, Applicant's specification describes a framework that overcomes the limitations of existing NAS systems by using three models-a local mutator to remove redundant structures, a reference DAG to guide mutations and avoid suboptimal solutions, and a differentiable scoring function for efficient performance evaluation.
Applicant's claims reflect this framework described in Applicant's specification. More specifically, Applicant's claims recite "generating a network comprising a plurality of layers, wherein each of the plurality of layers comprises cells with different structures each of which is represented by a directed acyclic graph (DAG) that comprises nodes and edges directed from one node to another node," and then "selecting a predetermined number of cells from each of the plurality of layers using a local mutation model, wherein the local mutation model comprises a mutation window for removing redundant edges from each selected cell . . ." As explained in the Specification, this local mutation model removes redundant cell structures from the full population of cell structures from which the search space is designed. By removing redundant cell structures, the efficiency of the NAS system may prove. Applicant's claim, especially as clarified, require that the local mutation model comprises a mutation window for removing redundant edges from each selected cell until a similarity between the DAG associated with each selected cell and a reference DAG satisfies a predetermined threshold." As explained in the Specification, utilizing a reference network architecture during mutation speeds up the evolutionary process and avoids falling into sub-optimal solutions. Applicant's claims recite "evaluating performance of the plurality of cells using a differentiable fitness scoring function by computing a classification loss of each cell over a training dataset, wherein the performance of multiple cells of the plurality of cells is evaluated concurrently." As explained in the Specification, evaluating the performance of the plurality of cells using a differentiable fitness scoring function (which can evaluate the performance of multiple cells of the plurality of cells concurrently) allows for the efficient evaluation of the performance of mutated cells. Finally, Applicant's claims recite "generating a network architecture for the target network that optimizes a performance of the target network by integrating the search space into a neural architecture search (NAS) system." As explained in the Specification, the performance of recent NAS systems improves significantly with the learned search space as opposed to manually designed search spaces. Thus, the claimed network architecture generated from the learned search space can achieve improved accuracy (e.g., can achieve 77.78% top-1 accuracy on ImageNet under the mobile setting (MAdds <SOOM)) as compared to network architectures generated by existing NAS systems. In particular, the claimed network architecture can improve the accuracy of the claimed pattern recognition task performed by inputting the video into the target network having the network architecture.
Consequently, the claims recite a practical application and are not directed to a judicial exception.
For at least the foregoing reasons, Applicant submits that the claims pass muster under 35 U.S.C. § 101. Accordingly, Applicant respectfully requests reconsideration and withdrawal of the rejection under 35 U.S.C. § 101.
Examiner’s response:
Applicant’s arguments have been fully considered but are found to be not persuasive.
Applicant argues that the additional elements integrate the judicial exceptions into a practical application. Examiner disagrees. The practical application of the instant application being a more efficient and accurate NAS system inherently relies on the removal of redundant edges in the NAS. The removal of redundant edges is, in essence, the removal of data to be processed, that is that the redundant edges no longer need to be processed/traced within the NAS. The practical application in this instance is that the NAS system becomes more efficient simply because there is less data (less edges) to process. Any information processing system will work faster and be more efficient when presented with less data to process. As such, since the practical application relies on the removal of a predetermined number of edges, a similarity comparison between each cell and a reference graph, and a differentiable fitness scoring function, the practical application therefore relies on abstract ideas and mathematical concepts.
In light of the amendments made on the independent claims, the rejections made under 35 U.S.C. 101 are maintained and updated 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 they are directed to an abstract idea without significantly more.
Step 1 analysis:
Independent Claim 1 recites, in part, a method of automatically and efficiently generating search spaces for a target network, therefore falling into the statutory category of process. Independent Claim 10 recites, in part, a system comprising a processor and a memory, therefore falling into the statutory category of product. Independent Claim 17 recites, in part, a non-transitory computer-readable storage medium, storing computer-readable instructions that upon execution on a computing device cause the computing device to perform operations, therefore falling into the statutory category of product.
Regarding Claim 1:
Step 2A: Prong 1 analysis:
Claim 1 recites in part:
“selecting a predetermined number of cells from each of the plurality of layers”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses selecting a number of cells from a number of layers.
“generating a plurality of cells based on the predetermined number of cells selected by the local mutation model”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses creating a subset of cells based on the output of a mutation model.
“evaluating performance of the plurality of cells using a differentiable fitness scoring function by computing a classification loss of each cell over a training dataset, wherein the performance of multiple cells of the plurality of cells is evaluated concurrently”. As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical calculation.
“minimizing a cross-entropy loss by updating fitness scores for the plurality of cells using gradient back-propagation during training the network”. As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical calculation.
“generating a search space for each layer of the target network based on a predetermined top number of cells with largest fitness scores”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses creating a subset of cells based on calculated fitness scores for a plurality of cells.
“performing a pattern recognition task associated with a video”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses identifying a pattern.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“generating a network comprising a plurality of layers, wherein each of the plurality of layers comprises cells with different structures each of which is represented by a directed acyclic graph (DAG) that comprises nodes and edges directed from one node to another node”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (graphs) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
“wherein the local mutation model comprises a mutation window for removing redundant edges from each selected cell”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (mutation models) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
“generating a network architecture for the target network that optimizes a performance of the target network by integrating the search space into a neural architecture search (NAS) system”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (search spaces) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
“by inputting the video into the target network having the network architecture”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (neural network) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “generating a network comprising a plurality of layers, wherein each of the plurality of layers comprises cells with different structures each of which is represented by a directed acyclic graph (DAG) that comprises nodes and edges directed from one node to another node”, “wherein the local mutation model comprises a mutation window for removing redundant edges from each selected cell”, and “integrating the search space into a neural architecture search (NAS) system to generate a network architecture that optimizes a performance of the target network” is/are directed to particular field(s) of use (graphs, mutation models, and search spaces) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
As discussed above, the additional element of “by inputting the video into the target network having the network architecture” is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception
Regarding Claim 2:
Step 2A: Prong 1 analysis:
Claim 1 recites in part:
“iterating operations of generating a plurality of cells using the local mutation model, evaluating performance of the plurality of cells using the differentiable fitness scoring function and selecting the subset of cells based on the evaluation results until the network converges”. As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical calculation.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 3:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“applying a reference DAG to a first half iteration of an entire process, wherein the reference DAG is a verified well performing cell structure.”. This additional element is directed to a particular field of use (graph models).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element of “applying a reference DAG to a first half iteration of an entire process, wherein the reference DAG is a verified well performing cell structure.” is directed to a particular field of use (graph models) (MPEP 2106.05(h)) and therefore does not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 4:
Step 2A: Prong 1 analysis:Claim 4 recites in part:
“determining a hamming distance between a generated DAG and the reference DAG after each mutation, wherein the hamming distance is indicative of similarities between DAGs”. As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical calculation.
“repeating a mutation process until the hamming distance between a generated DAG and the reference DAG is below a threshold”. As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical calculation.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 5:
Claim 4 recites in part:
“setting the mutation window at a randomly selected node”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses randomly selecting a node to set a mutation window at.
“selecting a predecessor node i and a successor node j within the window”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses selecting a node previous to the node at which the mutation window is set, and a node after.
“replacing an edge Gij between the predecessor node i and the successor node j with an operation selected from a set of predetermined basic operators”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses selecting an operator from a predetermined set of operators.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 6:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the predetermined set of basic operators comprise 1x1 convolution of C channels, 3x3 convolution of C channels, depth-wise convolution, identity mapping, and zero operation”. This additional element is directed to a particular field of use (neural network operators).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element of “wherein the predetermined set of basic operators comprise 1x1 convolution of C channels, 3x3 convolution of C channels, depth-wise convolution, identity mapping, and zero operation” is directed to a particular field of use (neural network operators) (MPEP 2106.05(h)) and therefore does not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 7:
Step 2A: Prong 1 analysis:
Claim 7 recites in part:
“determining a fitness core for each selected cell via gradient optimization”. As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical calculation.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 8:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein an output of each selected cell is weighted by its corresponding fitness score”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element of “wherein an output of each selected cell is weighted by its corresponding fitness score” is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 9:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the target network is a network for image classification or a network for object detection”. This additional element is directed to a particular field of use (classification networks).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element of “wherein the target network is a network for image classification or a network for object detection” is directed to a particular field of use (classification networks) (MPEP 2106.05(h)) and therefore does not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 10:
Due to claim language similar to that of Claim 1, Claim 10 is rejected for the same reasons as presented above in the rejection of Claim 1, with the exception of two limitations covered below.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“at least one processor”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
“at least one memory communicatively coupled to the at least one processor to configure the system at least to perform operations”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional elements of “at least one processor” and “at least one memory communicatively coupled to the at least one processor to configure the system at least to perform operations” are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 11:
Due to claim language similar to that of Claim 2, Claim 11 is rejected for the same reasons as presented above in the rejection of Claim 2.
Regarding Claim 12:
Due to claim language similar to that of Claim 3, Claim 12 is rejected for the same reasons as presented above in the rejection of Claim 3.
Regarding Claim 13:
Due to claim language similar to that of Claim 4, Claim 13 is rejected for the same reasons as presented above in the rejection of Claim 4.
Regarding Claim 14:
Due to claim language similar to that of Claim 5, Claim 14 is rejected for the same reasons as presented above in the rejection of Claim 5.
Regarding Claim 15:
Due to claim language similar to that of Claim 7, Claim 15 is rejected for the same reasons as presented above in the rejection of Claim 7.
Regarding Claim 16:
Due to claim language similar to that of Claim 8, Claim 16 is rejected for the same reasons as presented above in the rejection of Claim 8.
Regarding Claim 16:
Due to claim language similar to that of Claim 8, Claim 16 is rejected for the same reasons as presented above in the rejection of Claim 8.
Regarding Claim 17:
Due to claim language similar to that of claims 1 and 10, Claim 17 is rejected for the same reasons as presented above in the rejection of claims 1 and 10.
Regarding Claim 18:
Due to claim language similar to that of claims 2 and 3, Claim 18 is rejected for the same reasons as presented above in the rejection of claims 2 and 3.
Regarding Claim 19:
Due to claim language similar to that of claims 5 and 14, Claim 19 is rejected for the same reasons as presented above in the rejection of claims 5 and 14.
Regarding Claim 20:
Due to claim language similar to that of claims 7 and 8, Claim 20 is rejected for the same reasons as presented above in the rejection of claims 7 and 8.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20220004879 A1 – A method for receiving training data for training a neural network (NN) to perform a machine learning (ML) task and for determining, using the training data, an optimized NN architecture for performing the ML task
US 20190370659 A1 – Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing neural network architectures
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to COREY M SACKALOSKY whose telephone number is (703)756-1590. The examiner can normally be reached M-F 7:30am-3:30pm EST.
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/COREY M SACKALOSKY/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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