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. CLAIM INTERPRETATION The following is a quotation of 35 U.S.C. 112(f): (FP 7.30.03) (f) ELEMENT IN CLAIM FOR A COMBINATION-An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 112, sixth paragraph, is invoked. As explained in MPEP 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as "configured to" or "so that"; and the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. (FP 7.30.05) This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a first determining module , a second determining module, a third determining module and a training model in claim 1 6. Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. (FP 7.30.06) 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself. Applicant is advised to consult the 2019 PEG for more details of the analysis. Step 1 According to the first part of the analysis, in the instant case, claims 1 -10, 11-15, 16-20 are directed to a method, method and device of pruning a NN. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Step 2A, Step 2A, Prong 1 Following the determination of whether or not the claims fall within one of the four categories (Step 1), it must be determined if the claims recite a judicial exception (e.g. mathematical concepts, mental processes, certain methods of organizing human activity) (Step 2A, Prong 1). In this case, the claims are determined to recite a judicial exception as explained below. Regarding Claims 1 , 11 and 16 these claims recite determining a first sub-loss function based on a target task, wherein the first sub-loss function represents a difference between a training sample input to a target convolutional neural network and an output prediction result, the target convolutional neural network is a convolutional neural network used for training, the target convolutional neural network has different network parameters and loss functions in training processes for different training samples, and the training sample is a sample in a training set; obtaining a second sub-loss function based on a channel importance function and a dynamic weight, wherein when a value of the first sub-loss function does not reach a first threshold, a value of the dynamic weight is obtained based on the value of the first sub-loss function, and the value of the dynamic weight is in an inverse correlation with the value of the first sub-loss function; obtaining an objective loss function based on the first sub-loss function and the second sub- loss function; and performing sparse training on the target convolutional neural network by using the objective loss function to obtain a trained target convolutional neural network, wherein the sparse training is pruning a convolution kernel of the target convolutional neural network based on a value of the channel importance function. The claims recite a mental process. As set forth in MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer”. These are recited at a high level and disclosed as a human user performing these functions, simply using a computer as a tool-see spec, [0102-0111], Fig. 2. Thus, the claim recites abstract ideas. Step 2A, Prong 2 Following the determination that the claims recite a judicial exception, it must be determined if the claims recite additional elements that integrate the exception into a practical application of the exception (Step 2A, Prong 2). In this case, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include additional elements that integrate the exception into a practical application of the exception as explained below. In Prong Two, a claim is evaluated as a whole to determine whether the recited judicial exception is integrated into a practical application of that exception. A claim is not “directed to” a judicial exception, and thus is patent eligible, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d). The claims recite an abstract idea and further the claims as a whole does not integrate the recited judicial exception into a practical application of the exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d). Regarding Claims 1 , 11 and 16 these claims This li mitation recites using one or more neural networks as a tool to perform an abstract idea, which is not indicative of integration into a practical application . MPEP 2106.05(f). ) This limitation is understood to be generic computer equipment and 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 2106.0S(f)) Step 2 B Based on the determination in Step 2A of the analysis that the claims are directed to a judicial exception, it must be determined if the claims contain any element or combination of elements sufficient to ensure that the claim amounts to significantly more than the judicial exception (Step 2B). In this case, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reasons given above in the Step 2A, Prong 2 analysis. Furthermore, each additional element identified above as being insignificant extra-solution activity is also well-known, routine, conventional as described below. Claims 1, 11 and 1 6 : The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components and field of use/technological environment which do not amount to significantly more than the abstract idea. The underlying concept merely receives information, analyzes it, and store the results of the analysis – this concept is not meaningfully different than concepts found by the courts to be abstract (see Electric Power Group , collecting information, analyzing it, and displaying certain results of the collection and analysis; see Cybersource , obtaining and comparing intangible data; see Digitech , organizing information through mathematical correlations; see Grams , diagnosing an abnormal condition by performing clinical tests and thinking about the results; see Cyberfone , using categories to organize store and transmit information; see Smartgene , comparing new and stored information and using rules to identify options). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. For example, claim 1 recites the additional elements of “determining…”,”obtaining…”,”obtaining..”, “performing” … These elements are recited at a high level of generality and are well-understood, routine, and conventional activities in the computer art. Generic computers performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea. Looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims do not amount to significantly more than the abstract idea itself. Step 2A /2B Prong 2 Dependent Claims Regarding to claim 2 -4, 6, 8, 10, 17 -19 Claim 2 -4, 6, 8, 10, 17 -19 merely recite other additional elements that define loss function which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible. Regarding to claim 5 , 20 Claim 5 , 20 merely recite other additional elements that define similarities which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible. Regarding to claim 7, 9, 13, 14 Claim 7, 9, 13, 14 merely recite other additional elements that define value of weight which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Regarding to claim 12 Claim 12 merely recite other additional elements that define pruning the NN based on input data which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible. Regarding to claim 1 5 Claim 1 5 merely recite other additional elements that define input data which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible. 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. Claim s 1- 9, 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (Liu) 2021/0264278 in view of Chen et al. (Chen) US 2021/0182077 In regard to claim 1 , Liu A convolutional neural network pruning processing method, comprising: ([0023]-[0037] CNN pruning) determining a first sub-loss function based on a target task, wherein the first sub-loss function represents a difference between a training sample input to a target convolutional neural network and an output prediction result, the target convolutional neural network is a convolutional neural network used for training, the target convolutional neural network has different network parameters and loss functions in training processes for different training samples, and the training sample is a sample in a training set; ([0026]-[0044] [0052]-[0054] [0079]- [008 9 ] [0102]-[0104] training and pruning the NN with the training data set, determine a network loss based on a particular task and training dataset (current mini-batch) , the loss function represent the error amount corresponding to the predicted output of the CNN to a ground truth based on the input, the CNN has different parameters and loss functions for different dataset for different task s ) obtaining a second sub-loss function based on a channel importance function and a dynamic weight, ([0026]-[004 4 ] [0052]-[0054][0079]-[008 9 ] determine the second loss based on channel importance and weights to the channel ) wherein when a value of the first sub-loss function does not reach a first threshold, a value of the dynamic weight is obtained based on the value of the first sub-loss function, ([0026]-[004 6 ] [0052]- [0065] [0079]-[008 9 ] when the minimum amount of network is not reached or until the minimum amount of network loss is achieved, determine network weight value based on the network loss) and the value of the dynamic weight is in an inverse correlation with the value of the first sub-loss function; ([0026]-[0046] [0052]- [0065] [0079]-[0089] [0094]-[0096] Fig. 3C, line 5-6, the network weight has inverse relationship with the network loss (part of the Ltot) since the more the loss , the less is the weight) obtaining an objective loss function based on the first sub-loss function and the second sub- loss function; ([0026]-[0044] [0052]-[0054][0079]-[0083] [0113]-[0114] the total loss is the addition of first term network loss and the second term channel scaling factor loss) and performing sparse training on the target convolutional neural network by using the objective loss function to obtain a trained target convolutional neural network, ([0026]-[0044] [0052]-[0054][0079]-[0083] [0113]-[0114] sparse training the CNN by using the loss function to obtain a trained CNN, pruning the CNN) wherein the sparse training is pruning the target convolutional neural network based on a value of the channel importance function . [0026]-[0044] [0052]-[0054][0079]-[008 9 ] [0113]-[0114] sparse training the CNN based on importance value of the channel) But Liu fail to explicitly disclose “ wherein the sparse training is pruning a convolution kernel of the target convolutional neural networ k.” Chen disclose wherein the sparse training is pruning a convolution kernel of the target convolutional neural network based on a value of the channel importance function . ( [0094]-[0098] [0588]-[059 7 ] etc. prune the kernel of the CNN) It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Chen ‘s neural network pruning into Liu ’s invention as they are related to the same field endeavor of neural network pruning . The motivation to combine these arts, as proposed above, at least because Chen ‘s convolution kernel pruning would help to provide kernel prunin g functionality to Liu ’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that provid ing kernel pruning functionality would help to improve accuracy of NN prunin g. In regard to claim 2 , Liu and Chen disclose The method according to claim 1, Liu disclose wherein the obtaining an objective loss function based on the first sub-loss function and the second sub-loss function comprises: adding the first sub-loss function and the second sub-loss function to obtain the objective loss function. ([0026]-[0044] [0052]-[0054][0079]-[0083] [0113]-[0114] the total loss is the addition of first term network loss and the second term channel scaling factor loss) In regard to claim 3 , Liu and Chen disclose The method according to claim 1, further comprising: before the obtaining an objective loss function based on the first sub-loss function and the second sub-loss function, ( Fig. 3C [0026]-[0044] [0079]-[0083] [0094]-[0097] [01 08 ]-[0114] the total loss is the addition of first term network loss and the second term channel scaling factor loss , before the total loss is obtained as Fig. 3C ) the first channel importance parameter is a value that is obtained by inputting the first training sample and that is of the channel importance function, the second channel importance parameter is a value that is obtained by inputting the second training sample and that is of the channel importance function, and the first training sample and the second training sample are two different samples in the training set; (Fig. 3C [0026]-[0044] [0079]-[0083] [0094]-[0097] [0108]-[0114] the channel importance value is a tunable hyper-parameter to indicate the channel importance and can be inputted based on the various training data set (mini-batch ) and wherein the obtaining an objective loss function based on the first sub-loss function and the second sub-loss function comprises: ([0026]-[0044] [0079]-[0083] [0094]-[0097] [0108]-[0114] the total loss is the addition of first term network loss and the second term channel scaling factor loss ) obtaining the objective loss function based on the first sub-loss function, the second sub-loss function, and the third sub-loss function ([0026]-[0044] [0079]-[0083] [0094]-[0097] [0108]-[0114] the total loss is the addition of first term network loss and the second term channel scaling factor loss which is a loss summation form channel scaling parameters ) But Liu and Chen fail to explicitly disclose “ obtaining a third sub-loss function based on a first similarity and a second similarity, wherein the first similarity represents a similarity between a feature map extracted from a first training sample and a feature map extracted from a second training sample, the second similarity represents a similarity between a first channel importance parameter and a second channel importance parameter, ” obtaining a third sub-loss function based on a first similarity and a second similarity, wherein the first similarity represents a similarity between a feature map extracted from a first training sample and a feature map extracted from a second training sample, the second similarity represents a similarity between a first channel importance parameter and a second channel importance parameter, ([2049]-[2050][2165]-[21 92 ] [2333]-[2336] [3211]-[3227] the loss function is based on the similarities , including similarity between feature maps of the training set (minimize the difference) and similarity of the weights of the NN data samples) It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Chen ‘s neural network pruning into Liu ’s invention as they are related to the same field endeavor of neural network pruning . The motivation to combine these arts, as proposed above, at least because Chen ‘s neural network pruning based on similarities between the feature map and weights would help to provide more loss functionality to Liu ’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that provid ing loss functionality based on similarities between the feature map and weights would help to improve accuracy of NN pruning. In regard to claim 4 , Liu and Chen disclose The method according to claim 3, But Liu fail to explicitly disclose “ wherein the obtaining a third sub-loss function based on a first similarity and a second similarity comprises: using a distance function between the first similarity and the second similarity as the third sub-loss function. ” Chen disclose herein the obtaining a third sub-loss function based on a first similarity and a second similarity comprises: using a distance function between the first similarity and the second similarity as the third sub-loss function ([2049]-[2050][2165]-[2192][2333]-[2336] [3211]-[3227] the loss function is based on the similarities, including similarity between feature maps of the training set (minimize the difference) and similarity of the weights of the NN data samples) It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Chen ‘s neural network pruning into Liu ’s invention as they are related to the same field endeavor of neural network pruning . The motivation to combine these arts, as proposed above, at least because Chen ‘s neural network pruning based on similarities between the feature map and weights would help to provide more loss functionality to Liu ’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that provid ing loss functionality based on similarities between the feature map and weights would help to improve accuracy of NN pruning. In regard to claim 5 , Liu and Chen disclose The method according to claim 3, But Liu fail to explicitly disclose “ wherein the first similarity represents a cosine similarity between the feature map extracted from the first training sample and the feature map extracted from the second training sample; and the second similarity represents a cosine similarity between the first channel importance parameter and the second channel importance parameter. ” Chen disclose wherein the first similarity represents a cosine similarity between the feature map extracted from the first training sample and the feature map extracted from the second training sample; and the second similarity represents a cosine similarity between the first channel importance parameter and the second channel importance parameter. ( [0838][0984] [2049]-[2050][2165]-[2192][2333]-[2336] [3211]-[3227] [03250] t similarity between feature maps of the training set (minimize the difference) and similarity of the weights of the NN data samples and it can be trigonometric function which include cosine function ) It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Chen ‘s neural network pruning into Liu ’s invention as they are related to the same field endeavor of neural network pruning . The motivation to combine these arts, as proposed above, at least because Chen ‘s neural network pruning based on similarities between the feature map and weights would help to provide more loss functionality to Liu ’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that provid ing loss functionality based on similarities between the feature map and weights would help to improve accuracy of NN pruning. In regard to claim 6 , Liu and Chen disclose The method according to claim 3, Liu disclose wherein the obtaining the objective loss function based on the first sub-loss function, the second sub-loss function, and the third sub-loss function comprises: adding the first sub-loss function, the second sub-loss function, and the first product result to obtain the objective loss function . ([0026]-[0044] [0079]-[0083] [0094]-[0097] [0108]-[0114] the total loss is the addition of first term network loss and the second term channel scaling factor loss which is a loss summation form channel scaling parameters) But Liu fail to explicitly disclose “ a first product result , wherein the first product result is a result obtained by multiplying the third sub-loss function by a first preset coefficient. ” Chen disclose a first product result , wherein the first product result is a result obtained by multiplying the third sub-loss function by a first preset coefficient . ([2049]-[2050][2165]-[2192][2333]-[2336] [3211]-[3227] [03250] a product by multiplying the loss function by a or b) It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Chen ‘s neural network pruning into Liu ’s invention as they are related to the same field endeavor of neural network pruning . The motivation to combine these arts, as proposed above, at least because Chen ‘s neural network pruning based on similarities between the feature map and weights would help to provide more loss functionality to Liu ’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that provid ing loss functionality based on similarities between the feature map and weights would help to improve accuracy of NN pruning. In regard to claim 7 , Liu and Chen disclose The method according to claim 1, Liu disclose wherein that a value of the dynamic weight is obtained based on the value of the first sub-loss function comprises: obtaining the value of the dynamic weight by multiplying a first ratio by a second preset coefficient, wherein the first ratio is a ratio of a first difference to the first threshold, and the first difference is a difference between the first threshold and the value of the first sub-loss function. ([0026]-[0046] [0052]- [0065] [0079]-[0089][0094]-[0096] Fig. 3C, line 3 -6, the network weight is obtained by multiplying a learning rate which is a ratio of the difference between the mini. loss and the value of the first loss/ the mini. loss until the neural network converge) In regard to claim 8 , Liu and Chen disclose The method according to claim 1, Liu disclose wherein the obtaining a second sub-loss function based on a channel importance function and a dynamic weight comprises: multiplying the channel importance function by the dynamic weight to obtain the second sub- loss function. ([0083]-[0089] [0110]-[0114] “ X can represent a tunable hyper-parameter that measures relative importance and weights to the channel scaling parameters ” multiple the channel scaling parameter with the tunable hyper-parameter that measures relative importance to obtain the second loss function) In regard to claim 9 , Liu and Chen disclose The method according to claim 1, wherein when the value of the first sub-loss function reaches the first threshold, the value of the dynamic weight is 0. ([0026]-[0046] [0052]-[0065] [007 6 ]-[0089][0094]-[0096] [0110]-[0114] when the network reach mini. loss or converge, the channel scaling parameter v alu e is reach to zero) In regard to claim 11 , Liu disclose A data processing method, ([0023]-[0037] CNN pruning) comprising: obtaining input data related to a target task; pruning a trained target convolutional neural network based on the input data to obtain a pruned sub-network, wherein the trained target convolutional neural network is obtained through training by using an objective loss function, the objective loss function is obtained based on a first sub-loss function and a second sub-loss function, ([0026]-[0044] [0052]-[0054][0079]-[0089] [0102]-[0104] training and pruning the NN with the training data set, determine a network loss based on a particular task and training dataset (current mini-batch), the loss function represent the error amount corresponding to the predicted output of the CNN to a ground truth based on the input, the CNN has different parameters and loss functions for different dataset for different tasks and determine the second loss based on channel importance and weights to the channel) the first sub-loss function is determined based on the target task, ([0026]-[0044] [0052]-[0054][0079]-[0089] [0102]-[0104] determine a network loss based on a particular task and training dataset (current mini-batch), the second sub-loss function is determined based on a channel importance function and a dynamic weight, ([0026]-[0044] [0052]-[0054][0079]-[0089] determine the second loss based on channel importance and weights to the channel) the first sub-loss function represents a difference between a training sample input to the target convolutional neural network and an output prediction result, when a value of the first sub-loss function does not reach a first threshold, ([0026]-[0044] [0052]-[0054][0079]-[0089] [0094]-[0096] [0102]-[0104] training and pruning the NN with the training data set, determine a network loss based on a particular task and training dataset (current mini-batch), the loss function represent the error amount corresponding to the predicted output of the CNN to a ground truth based on the input , (([0026]-[0046] before the neural network converge to a threshold ) a value of the dynamic weight is obtained based on the value of the first sub-loss function, the value of the dynamic weight is in an inverse correlation with the value of the first sub-loss function, ([0026]-[0046] [0052]- [0065] [0079]-[0089][0094]-[0096] Fig. 3C, line 5-6, the network weight has inverse relationship with the network loss (part of the Ltot) since the more the loss, the less is the weight) the objective loss function is used to perform sparsening on the target convolutional neural network during training of the target convolutional neural network, ([0026]-[0044] [0052]-[0054][0079]-[0083] [0113]-[0114] sparse training the CNN by using the loss function to obtain a trained CNN, pruning the CNN) and the sparsening is pruning the target convolutional neural network based on a value of the channel importance function; [0026]-[0044] [0052]-[0054][0079]-[0089] [0113]-[0114] sparse training the CNN based on importance value of the channel) and processing the input data by using the sub-network to obtain output data. ([0026]-[0044] [0052]-[0054][0079]-[0089] [0102]-[0104] training and pruning the NN with the training data set, generate the predicted output of the CNN based on the input ) But Liu fail to explicitly disclose “ wherein the sparse training is pruning a convolution kernel of the target convolutional neural networ k.” Chen disclose wherein the sparse training is pruning a convolution kernel of the target convolutional neural network based on a value of the channel importance function . ([0094]-[0098] [0588]-[0597] etc. prune the kernel of the CNN) It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Chen ‘s neural network pruning into Liu ’s invention as they are related to the same field endeavor of neural network pruning . The motivation to combine these arts, as proposed above, at least because Chen ‘s convolution kernel pruning would help to provide kernel pruning functionality to Liu ’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that provid ing kernel pruning functionality would help to improve accuracy of NN pruning. In regard to claim 12 , Liu and Chen disclose The method according to claim 11, Liu disclose wherein the pruning a trained target convolutional neural network based on the input data to obtain a pruned sub-network comprises: pruning the trained target convolutional neural network based on the input data and a pruning rate to obtain the pruned sub-network, ([0025]-[0026] [0041]-[0042] [0101]-[0103] pruning the NN based on input data and a pruning ratio to obtain the pruned network) wherein the pruning rate is a proportion of the pruned convolution kernel to all convolution kernels in the target convolutional neural network. ( [0025]-[0026] [0041]-[0042] [0101]-[0103] the pruning ratio is a portion of the channel/channel of the NN before pruning ) In regard to claims 1 3-14 , claims 1 3-14 are method claims corresponding to the method claims 7-8 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 7-8 . In regard to claim 15 , Liu and Chen disclose The method according to claim 11, Liu disclsoe wherein the input data comprises: image data, audio data, and text data. ([0142] image, text, etc.) In regard to claims 16-20 , claims 16-20 are device claims corresponding to the method claims 1-5 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-5 . Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (Liu) 2021/0264278 and Chen et al. (Chen) US 2021/0182077 as applied to claim 1, further in view of Azarian et al. (Azarian) US 2021/0110268 In regard to claim 10 , Liu and Chen disclose The method according to claim 1, But Liu and Chen fail to explicitly disclose “ wherein the determining a first sub-loss function based on a target task comprises: when the target task is a classification task, determining a cross-entropy loss function as the first sub-loss function. ” Azarian disclose wherein the determining a first sub-loss function based on a target task comprises: when the target task is a classification task, determining a cross-entropy loss function as the first sub-loss function . ([0024] [0028] [0045] [0053]-[0060] obtain a cross-entropy loss function as the first loss function when performing a classification) It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Azarian ‘s neural network pruning into Chen and Liu ’s invention as they are related to the same field endeavor of neural network pruning . The motivation to combine these arts, as proposed above, at least because Azarian ‘s cross-entropy loss would help to provide more loss functionality to Chen and Liu ’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that provid ing cross-entropy loss functionality would help to improve accuracy of NN pruning. Conclusion The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure. PATENT PUB. # PUB. DATE INVENTOR(S) TITLE US 20200372340 A1 2020-11-26 Lee et al. NEURAL NETWORK PARAMETER OPTIMIZATION METHOD AND NEURAL NETWORK COMPUTING METHOD AND APPARATUS SUITABLE FOR HARDWARE IMPLEMENTATION Lee et al. disclose The present invention relates to a neural network parameter optimization method and a neural network computation method and apparatus suitable for hardware implementation. The neural network parameter optimization method suitable for hardware implementation according to the present invention may include transforming an existing parameter of a neural network into a signed parameter and a magnitude parameter having a single value for each channel and generating an optimized parameter by prun in g the transformed magnitude parameter … see abstract. 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