CTNF 18/349,982 CTNF 87929 DETAILED ACTION Remarks This office action is issued in response to communication filed on 07/11/2023. Claims 1-16 are pending in this Office Action. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. 2. Claims 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1 and 9: Step 1: Statutory Category ?: Yes. claim 1 recites a system (i.e., a “machine”) and claim 9 recites a method (i.e., a “process”) which are statutory categories. Claim 1: Step 2A-Prong 1: Judicial Exception Recited ?: Yes. The claim 1 recites one or limitations that can be performed in human mind using observation, evaluation, judgment and opinion including using a pen and paper: “dividing a search space of the neural network into a plurality of search blocks, wherein each of the search blocks comprises a plurality of candidate blocks”; “sequentially selecting one of the candidate blocks from each of the search blocks as selected candidate blocks, combining the selected candidate blocks into a plurality of neural networks to be evaluated” and “selecting one neural network to be evaluated with the highest network potential from the neural networks to be evaluated to determine the selected candidate blocks corresponding to the neural network to be evaluated with the highest network potential” The claim 1 also recites one or limitations that are mathematical calculations and falls within the mathematical concepts grouping of abstract idea: “ calculating network potential of the neural networks to be evaluated according to scores of the selected candidate blocks” Step 2A-Prong 2 : Integrated into a practical application? No. Claim 1 recites additional elements of “a memory configured to store a neural network; and a processor coupled to the memory ” and “ guiding and scoring the candidate blocks through a latent pattern generator; scoring the candidate blocks in each of the search blocks through a zero-cost accuracy proxy ”. The memory and processor are recited at the very high level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. The use of pattern generator and zero-cost accuracy proxy to guiding and scoring amount to mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea . See MPEP 2106.05(f)) Step 2B: Recites additional elements that amount to significantly more than the judicial exception? No. Claim 1 does not include additional elements that are sufficient to amount to significantly more than judicial exception. As indicates above, the additional element of “ memory and processor’ and The use of pattern generator and zero-cost accuracy proxy” are at best equivalent of adding the words “apply it” to the judicial exception. Even when considered in combination, the additional elements do not provide an inventive concept, claim 1 therefore is ineligible. Claim 2 recites additional element of “ wherein the latent pattern generator comprises a pre-trained teacher neural network model and a Gaussian normal distributed random model, and the processor is further configured to guide and score the candidate blocks through the pre-trained teacher neural network model or the Gaussian normal distributed random model ” which is amounts to mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea and at best equivalent of adding the words “apply it” to the judicial exception. Even when considered in combination, the additional element does not provide an inventive concept, claim 2 therefore is ineligible. Claim 3 recites additional element of “ wherein the processor guides and scores the candidate blocks through the pre-trained teacher neural network model or the Gaussian normal distributed random model ” which amounts to mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea and at best equivalent of adding the words “apply it” to the judicial exception. Even when considered in combination, the additional element does not provide an inventive concept, claim 3 therefore is ineligible. Claim 4 recites additional element of “ wherein the processor is further configured to modify score distribution of the candidate blocks through a distribution tuner ” which amounts to mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea and at best equivalent of adding the words “apply it” to the judicial exception. Even when considered in combination, the additional element does not provide an inventive concept, claim 4 therefore is ineligible. Claim 5 recites additional element of “ wherein the distribution tuner comprises a score conversion ranking sub-module and a score normalization sub-module ” which amounts to mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea and at best equivalent of adding the words “apply it” to the judicial exception. Even when considered in combination, the additional element does not provide an inventive concept, claim 5 therefore is ineligible. Claim 6 recites additional element of “ wherein when the processor scores the candidate blocks in each of the search blocks through the zero-cost accuracy proxy, the processor converts scores of the candidate blocks in each of the search blocks into candidate block rankings corresponding to each of the search blocks through the score conversion ranking sub-module and modifies the score distribution of the candidate blocks according to the candidate block rankings ” which is a mathematical calculations that falls within the mathematical concepts grouping of abstract idea. Claim 6 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 6 is not patent eligible. Claim 7 recites additional element of “ wherein when the processor scores the candidate blocks in each of the search blocks through the zero-cost accuracy proxy, the processor normalizes scores of the candidate blocks in each of the search blocks through the score normalization sub-module and modifies the score distribution of the candidate blocks according to the normalized scores of the candidate blocks in each of the search blocks ” which is a mathematical calculations that falls within the mathematical concepts grouping of abstract idea. Claim 7 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 7 is not patent eligible. Claim 8 recites additional element of “w herein when the processor scores the candidate blocks in each of the search blocks through the zero-cost accuracy proxy, the processor converts scores of the candidate blocks in each of the search blocks into candidate block rankings corresponding to each of the search blocks through the score conversion ranking sub-module, then normalizes the candidate block rankings in each of the search blocks through the score normalization sub-module, and modifies the score distribution of the candidate blocks according to the normalized scores of the candidate blocks rankings in each of the search blocks ” which is a mathematical calculations that falls within the mathematical concepts grouping of abstract idea. Claim 8 does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 8 is not patent eligible. Claim 9: Step 2A-Prong 1: Judicial Exception Recited ?: Yes. The claim 9 recites one or limitations that can be performed in human mind using observation, evaluation, judgment and opinion including using a pen and paper: “dividing a search space of the neural network into a plurality of search blocks, wherein each of the search blocks comprises a plurality of candidate blocks”; “sequentially selecting one of the candidate blocks from each of the search blocks as selected candidate blocks, combining the selected candidate blocks into a plurality of neural networks to be evaluated” and “selecting one neural network to be evaluated with the highest network potential from the neural networks to be evaluated to determine the selected candidate blocks corresponding to the neural network to be evaluated with the highest network potential” The claim 1 also recites one or limitations that are mathematical calculations and falls within the mathematical concepts grouping of abstract idea: “ calculating network potential of the neural networks to be evaluated according to scores of the selected candidate blocks” Step 2A-Prong 2 : Integrated into a practical application? No. Claim 1 recites additional elements of “ guiding and scoring the candidate blocks through a latent pattern generator; scoring the candidate blocks in each of the search blocks through a zero-cost accuracy proxy ”. The use of pattern generator and zero-cost accuracy proxy to guiding and scoring amount to mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea . See MPEP 2106.05(f)) Step 2B: Recites additional elements that amount to significantly more than the judicial exception? No. Claim 9 does not include additional elements that are sufficient to amount to significantly more than judicial exception. As indicates above, the use of pattern generator and zero-cost accuracy proxy” are at best equivalent of adding the words “apply it” to the judicial exception. Even when considered in combination, the additional elements do not provide an inventive concept, claim 9 therefore is ineligible. Claims 10-16 recites similar elements of claims 2-8 and being rejected for the same rationale as indicates in the above rejection of claims 2-8. Allowable Subject Matter 12-151-07 AIA 07-97 12-51-07 Claim s 6-8 and 14-16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Although these claims are allowable over prior art , all other rejections and/or objections (if any) such as 101/112/claim objection must be overcome before the claims are allowed. Claim Rejections - 35 USC § 103 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-21-aia AIA Claim s 1-5 and 9-13 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al., “Block-wisely supervised Neural Architecture Search with Knowledge Distillation”, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, March 6, 2020, hereinafter “Li” ( cited on IDS dated 7/11/2023) , further in view of Kim et al.(US Patent Application Publication 2023/0153577 A1, hereinafter “Kim”) and further in view of Keserwani et al.(US Patent Application Publication 2024/0160892 A1, hereinafter “Keserwani”) As to claim 1 , Li teaches a hardware-aware zero-cost neural network architecture search system, comprising: a memory configured to store a neural network; and a processor coupled to the memory to perform the following: dividing a search space of the neural network into a plurality of search blocks, wherein each of the search blocks comprises a plurality of candidate blocks ; (Li Page 3, section 3.1 teaches to improve the accuracy of the evaluation we divide the supernet into blocks of smaller sub-space) guiding and scoring the candidate blocks through a latent pattern generator; scoring the candidate blocks in each of the search blocks through a zero-cost accuracy proxy; (Li page 5, section 3.4 teaches by evaluating all cells in a block of the supernet, we can get the evaluation loss of all possible paths in one block. We can easily sort this list) Li fails to expressly teach the guiding and scoring the candidate blocks t hrough a latent pattern generator . However, Kim teaches a latent pattern generator .(Kim par [0071] teaches a pre-defined teacher model) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Li and Kim to achieve the claimed invention. One would have been motivated to make such combination to improve the scalability of architecture searching.(Kim par [0049]) Li and Kim further teach sequentially selecting one of the candidate blocks from each of the search blocks as selected candidate blocks, combining the selected candidate blocks into a plurality of neural networks to be evaluated, and calculating network potential of the neural networks to be evaluated according to scores of the selected candidate blocks ( Li page 5, section 3.4 teaches by evaluating all cells in a block of the supernet, we can get the evaluation loss of all possible paths in one block. We can easily sort this list. After this, we can select the top-1 partial model from every block to assemble a best student) ; and selecting one neural network to be evaluated with the highest network potential from the neural networks to be evaluated to determine the selected candidate blocks corresponding to the neural network to be evaluated with the highest network potential .( Li page 5, section 3.4 teaches after performing evaluation and sorting , the partial model rankings of each stage are used to find the best model under a certain constraint) Li and Kim fail to expressly teach scoring the candidate blocks in each of the search blocks through a zero-cost accuracy proxy. However, Keserwani teaches scoring the candidate blocks in each of the search blocks through a zero-cost accuracy proxy. (Keserwani par [004] teaches NAS processor generates a neural network and the zero-cost proxies computes the score without detailed training). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Li. Kim and Keserwani to achieve the claimed invention. One would have been motivated to make such combination to provide the benefit of lesser training time and lesser neural networks are trained (Keserwani par [004]) As to claim 2 , Li, Kim and Keserwani teach the hardware-aware zero-cost neural network architecture search system according to claim 1 but fail to teach wherein the latent pattern generator comprises a pre-trained teacher neural network model (Kim par [0071] teaches predefined teacher model) and a Gaussian normal distributed random model (Kim par [0058] teaches Gaussian process samples the sequence from the bounded region) , and the processor is further configured to guide and score the candidate blocks through the pre-trained teacher neural network model or the Gaussian normal distributed random model (Kim par [0069] teaches a KD-guided score is obtained using the obtained accuracy, number of parameters, flops, and latency of each teacher/student model. The entire process repeats for a number of different input images 408 to obtain the KD-guided score) As to claim 3 , Li, Kim and Keserwani teach the hardware-aware zero-cost neural network architecture search system according to claim 2, wherein the processor guides and scores the candidate blocks through the pre-trained teacher neural network model or the Gaussian normal distributed random model . (Kim par [0069] teaches a KD-guided score is obtained using the obtained accuracy, number of parameters, flops, and latency of each teacher/student model. The entire process repeats for a number of different input images 408 to obtain the KD-guided score) As to claim 4 , Li, Kim and Keserwani teach the hardware-aware zero-cost neural network architecture search system according to claim 1, wherein the processor is further configured to modify score distribution of the candidate blocks through a distribution tuner . (Li page 5, section 3.4 teaches we can easily sort this list with about 10^4 element in a few seconds) As to claim 5 , Li, Kim and Keserwani teach the hardware-aware zero-cost neural network architecture search system according to claim 4, wherein the distribution tuner comprises a score conversion ranking sub-module (Li page 5, section 3.4 teaches we can easily sort this list with about 10^4 element in a few seconds) and a score normalization sub-module . (Li page 3, left column teaches softwax layer) Claims 9-13 merely recite a method performed by the system of claims 1-5. Accordingly , Li, Kim and Keserwani teach every limitation of claims 9-13 as indicates in the above rejection of claims 1-5 . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to HIEN DUONG whose telephone number is (571)270-7335. The examiner can normally be reached Monday-Friday 8:00AM-5:00PM. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /HIEN L DUONG/Primary Examiner, Art Unit 2147 Application/Control Number: 18/349,982 Page 2 Art Unit: 2147 Application/Control Number: 18/349,982 Page 3 Art Unit: 2147 Application/Control Number: 18/349,982 Page 4 Art Unit: 2147 Application/Control Number: 18/349,982 Page 5 Art Unit: 2147 Application/Control Number: 18/349,982 Page 6 Art Unit: 2147 Application/Control Number: 18/349,982 Page 7 Art Unit: 2147 Application/Control Number: 18/349,982 Page 8 Art Unit: 2147 Application/Control Number: 18/349,982 Page 9 Art Unit: 2147 Application/Control Number: 18/349,982 Page 10 Art Unit: 2147 Application/Control Number: 18/349,982 Page 11 Art Unit: 2147 Application/Control Number: 18/349,982 Page 12 Art Unit: 2147 Application/Control Number: 18/349,982 Page 13 Art Unit: 2147 Application/Control Number: 18/349,982 Page 14 Art Unit: 2147
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