Notice of Pre-AIA or AIA Status 1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. DETAILED ACTION 2. This action is in response to the original filing on 09/01/2023. Claims 1-15 are pending and have been considered below. Information Disclosure Statement 3. The information disclosure statement (IDS(s)) submitted on 09/04/2023 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Rejections - 35 USC § 101 4. 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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the abstract idea without significantly more. S tep 1 , the claims are directed to a process , machine, and manufacture. S tep 2A Prong 1, Claims 1, 6, and 11 recite, in part determining, a set of criteria based redundant filters from a set of filters, in a corresponding layer, based on each of the plurality of predefined redundancy detection criteria (Mental processes, evaluations and judgments) . identifying, a set of redundant filters from the set of criteria based redundant filters based on: a first intersection score among the set of criteria based redundant filters for the plurality of predefined redundancy detection criteria, wherein the first intersection score is proportional to a frequency of occurrence of a criteria based redundant filter for the plurality of predefined redundancy detection criteria (Mathematical concepts, mathematical calculations) . a normalized minimum or a normalized maximum value determined based on the at least one type of feature extracted for each of the plurality of predefined redundancy detection criteria (Mathematical concepts, mathematical calculations) . wherein a number of the set of redundant filters is equal to the predefined number of filters to be removed (Mental processes, evaluations and judgments) . Step 2A Prong 2 , this judicial exception is not integrated into a practical application. The additional elements: a processor; and a memory communicably coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution by the processor, cause the processor to, a computing device (mere instructions to apply the exception using a generic computer component). receiving, by a computing device, a predefined number of filters to be removed in each of a plurality of layers of the NNM; for each of the plurality of layers of the NNM: receiving, by the computing device, a plurality of predefined redundancy detection criteria, wherein each of the plurality of predefined redundancy detection criteria corresponds to at least one type of feature extracted by the NNM; (mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity). compressing, by the computing device, the NNM based on the redundant set of filters for each of the layer (mere instructions to apply the exception using a generic computer component) . Step 2B , the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, either alone or in combination. The additional elements: a processor; and a memory communicably coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution by the processor, cause the processor to, a computing device (mere instructions to apply the exception using a generic computer component). receiving, by a computing device, a predefined number of filters to be removed in each of a plurality of layers of the NNM; for each of the plurality of layers of the NNM: receiving, by the computing device, a plurality of predefined redundancy detection criteria, wherein each of the plurality of predefined redundancy detection criteria corresponds to at least one type of feature extracted by the NNM; (mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity). compressing, by the computing device, the NNM based on the redundant set of filters for each of the layer (mere instructions to apply the exception using a generic computer component) . Claims 2-5, 7-10, and 12-15 provide further limitations to the abstract idea ( Mathematical concepts and/or Mental processes ) as rejected in claims 1, 6, 11, however, they do not disclose any additional elements that would amount to a practical application or significantly more than an abstract idea ( data gathering / insignificant extra-solution activity and/or generic computer component ). Claim Rejections – 35 USC § 103 5. 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 of this title, 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 . 6. Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Shim (U.S. Patent Application Pub. No. US 20230368010 A1) in view of Yao et al. (U.S. Patent Application Pub. No. US 20190294929 A1) and further in view of Zhang et al. (U.S. Patent Application Pub. No. US 20200184131 A1). Claim 1: Shim teaches a method of compressing a neural network model (NNM) (i.e. The electronic apparatus 100 may compress an original model by removing at least one filter among a plurality of filters based on the normalized importance and the compression ratio (S140); para. [0051]) , the method comprising: receiving, by a computing device, a predefined number of filters to be removed in each of a plurality of layers of the NNM (i.e. When normalization is completed, the electronic apparatus 100 may sort all filters in order of an importance irrespective of layers. In addition, the electronic apparatus 100 may remove a filter with a low importance by a predetermined number based on a compression ratio configured by the user. As described above, when the electronic apparatus 100 normalizes an importance of each filter, a filter with an importance less than an importance of a corresponding filter is disregarded. The electronic apparatus 100 may select at least one filter of a low importance normalized based on a compression ratio; para. [0050, 0051]), the system receives the model plus a user-configured compression ratio and from that received compression ratio, it determines a predetermined number of filters to remove ; for each of the plurality of layers of the NNM: receiving, by the computing device, a plurality of predefined redundancy detection criteria (i.e. an electronic apparatus 100 may receive an original model including a plurality of layers each including a plurality of filters and a metric for determining a compression ratio to be applied to the original model and an importance of each of the plurality of filters. The metric may include an L2 norm, a geometric median, a nuclear norm, and/or an L1 norm; para. [0038, 0041, 0044]), importance is determined using a metric and give multiple examples of metrics , wherein each of the plurality of predefined redundancy detection criteria (i.e. The electronic apparatus 100 may adjust an importance of each filter according to a redundancy of the importance of each filter. For example, the electronic apparatus 100 may increase the size of an importance with a low redundancy and decrease the size of an importance with a high redundancy. When a filter having an importance with a low redundancy is remove; para. [0053-0058]) ; determining, by the computing device, a set of criteria based redundant filters from a set of filters, in a corresponding layer, based on each of the plurality of predefined redundancy detection criteria (i.e. the electronic apparatus 100 may calculate an importance of each of a plurality of filters using a metric. The electronic apparatus 100 may recalculate an importance of each of the plurality of filters based on a redundancy of the calculated importance … Since the electronic apparatus 100 removes a filter with a low recalculated importance, a filter with a high redundancy may be more likely to be removed; para. [0046, 0053, 0054]) ; identifying, by the computing device, a set of redundant filters from the set of criteria based redundant filters (i.e. the electronic apparatus 100 may calculate an importance of each of a plurality of filters using a metric. The electronic apparatus 100 may recalculate an importance of each of the plurality of filters based on a redundancy of the calculated importance … Since the electronic apparatus 100 removes a filter with a low recalculated importance, a filter with a high redundancy may be more likely to be removed; para. [0046, 0053, 0054]) based on: a first intersection score among the set of criteria based redundant filters for the plurality of predefined redundancy detection criteria, wherein the first intersection score is based redundant filter for the plurality of predefined redundancy detection criteria (i.e. According to the first policy, among removal candidates of each layer, a filter having the same index as a removal candidate of another layer may be determined as a removal target in the corresponding layer. For example, from the first removal candidate, a filter having the same index as the second removal candidate may be determined as a removal target in the first layer. The first policy may also be referred to as ‘intersection’; para. [0074, 0075, 0088]) ; and/or a normalized or a normalized value determined based on the at least one type of feature extracted by the NNM for each of the plurality of predefined redundancy detection criteria (i.e. The electronic apparatus 100 may normalize an importance of each of a plurality of filters layer by layer by normalizing an importance of each filter based on an importance of the remaining filters except for a filter with an importance less than the importance of each filter among a plurality of filters included in each layer of the original model; para. [0047-0050]) ; wherein a number of the set of redundant filters is equal to the predefined number of filters to be removed (i.e. The electronic apparatus 100 may select at least one filter of a low importance normalized based on a compression ratio. The number of filters selected may be determined according to the compression ratio; para. [0050, 0051]) ; and compressing, by the computing device, the NNM based on the redundant set of filters for each of the layer (i.e. The electronic apparatus 100 may compress an original model by removing at least one filter among a plurality of filters based on the normalized importance and the compression ratio; para. [0051]) . Shim does not explicitly teach at least one type of feature extracted by the NNM; wherein the first intersection score is proportional to a frequency of occurrence of a criteria; and/or a normalized minimum or a normalized maximum value determined based on the at least one type of feature extracted. However, Yao teaches receiving, by a computing device (i.e. computing device; para. [0050]) , a predefined number of filters to be removed in each of a plurality of layers of the NNM (i.e. The convolutional neural network is then pruned at 83 using the output from the scaling neural subnetwork. Filters are removed from the convolutional layer based on elements of a scale vector output by the respective scaling neural subnetwork. In one embodiment, a threshold method is employed. When the scale value corresponding to a particular filer is below a predefined threshold, the particular filter is removed from the respective convolutional layer; para. [0033-0036]), the predefined threshold as controlling how many filters end up removed ; for each of the plurality of layers of the NNM: receiving, by the computing device, a plurality of predefined redundancy detection criteria (i.e. the scaling subnetwork performs feature extraction on W, where n features are extracted for each filter, respectively and the feature vector for the ith filter can be denoted as X i =[x i1 , x i2 , . . . , x im ]. For example, the feature extraction in the scaling subnetwork can be performed by evaluating size of the filters in the respective convolutional layer. Additionally or alternatively, the feature extraction in the scaling subnetwork can be performed by evaluating similarity amongst filters in the respective convolutional layer. It is envisioned that other types of feature extractions are contemplated by this disclosure; para. [0023, 0029, 0034]), predefined measures used to detect which filters are redundant or less important , wherein each of the plurality of predefined redundancy detection criteria corresponds to at least one type of feature extracted by the NNM (i.e. a number of features/descriptors are extracted for each filter; para. [0010, 0023, 0029]) ; Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Shim to include the feature of Yao. One would have been motivated to make this modification because it improves identification of redundant or less important filters for removal while preserving model performance. However, Zhang teaches a normalized minimum or a normalized maximum value determined based on the at least one type of feature extracted by the NNM (i.e. Since the magnitude difference between the features after sparse representation of the aero-engine test data is large, the data sample is normalized by a maximum value method to avoid the model error caused by the magnitude difference. In the present invention, the features of the aero-engine transition state acceleration process test data after sparse representation are normalized into the interval [1,2] according to the following formula Xk=(Xk-Xmin)/(Xmax-Xmin) +1; para. [0028]). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Shim and Yao to include the feature of Zhang . One would have been motivated to make this modification because it provides a method to avoid the model error caused by the magnitude difference. 7. Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Shim in view of Yao, Zhang, and further in view of Mohamed et al. (U.S. Patent Application Pub. No. US 20150371360 A1). Claim 4: Shim, Yao, and Zhang teach the method of claim 1. Shim further teaches wherein the normalized or the normalized value is determined for each of the plurality of predefined redundancy detection criteria (i.e. The electronic apparatus 100 may normalize an importance of each of a plurality of filters layer by layer by normalizing an importance of each filter based on an importance of the remaining filters except for a filter with an importance less than the importance of each filter among a plurality of filters included in each layer of the original model; para. [0047-0050]) . Shim does not explicitly teach wherein the normalized minimum or the normalized maximum value is determined based on a sum of an output of the at least one type of feature. However, Yao further teaches wherein the normalized minimum or the normalized maximum value is determined based on a sum of an output of the at least one type of feature extracted by the NNM for each of the plurality of predefined redundancy detection criteria (i.e. a number of features/descriptors are extracted for each filter; para. [0010, 0023, 0029]) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Shim to include the feature of Yao. One would have been motivated to make this modification because it improves identification of redundant or less important filters for removal while preserving model performance. However, Zhang further teaches the normalized minimum or the normalized maximum value (i.e. Since the magnitude difference between the features after sparse representation of the aero-engine test data is large, the data sample is normalized by a maximum value method to avoid the model error caused by the magnitude difference. In the present invention, the features of the aero-engine transition state acceleration process test data after sparse representation are normalized into the interval [1,2] according to the following formula Xk=(Xk-Xmin)/(Xmax-Xmin) +1; para. [0028]). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Shim and Yao to include the feature of Zhang . One would have been motivated to make this modification because it provides a method to avoid the model error caused by the magnitude difference. However, Mohamed teaches wherein the normalized minimum or the normalized maximum value is determined based on a sum of an output of the at least one type of feature (i.e. The values in each zone 3545 a-z in each feature image may be summed. The sums may be normalized between zero and one by dividing by the maximum possible sum in a zone. For example, the value of each feature is the sum of the GDT values in the zone, normalized by the maximum possible value; para. [0194]) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Shim, Yao, and Zhang to include the feature of Mohamed. One would have been motivated to make this modification because it improves consistency and comparability of the values used to decide which filters are redundant or low-importance. 8. Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Shim in view of Yao, Zhang, and further in view of Swaminathan et al. (U.S. Patent Pub. No. US 11809992 B1). Claim 5: Shim, Yao, and Zhang teach the method of claim 1. Shim further teaches wherein the compression of the NNM based on the set of redundant filters for each of the layer comprises: removing, by the computing device, the set of redundant filters from the set of filters (i.e. The electronic apparatus 100 may compress an original model by removing at least one filter among a plurality of filters based on the normalized importance and the compression ratio (S140); para. [0051]) . Shim does not explicitly teach fine tuning the NNM based on knowledge distillation based technique. However, Swaminathan teaches fine tuning, by the computing device, the NNM based on knowledge distillation based technique (i.e. As indicated at 550, the compressed version of the trained neural network may be trained with a tuning data set for the trained neural network, in some embodiments. For example, the same training technique used to train the uncompressed data set may be specified for the neural network and applied again using the same data set as the tuning data set in order to fine tune the neural network to adjust for the changes made during compression. In other embodiments, pseudo-rehearsal and/or distillation-based techniques may be performed to fine tune a compressed version of the trained neural network; col. 10, lines 35-50) . Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Shim, Yao, and Zhang to include the feature of Swaminathan. One would have been motivated to make this modification because network compression may be performed to reduce the size of a trained network. Such compression may be applied to minimize a change in the accuracy of results provided by the neural network 9. Claims 6, 9-11, 14, and 15 are similar in scope to Claims 1, 4, 5 and are rejected under a similar rationale. Allowable Subject Matter Claims 2, 3, 7, 8, 12, and 13 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 and if the 35 USC §101 for being directed to an abstract idea is successfully addressed. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Chen et al. (Pub. No. US 20190122113 A1), a direct way of reducing the redundancy of a CNN is pruning kernels of convolutional layers and neurons in the FC layers. Although model redundancy generally increases the generality of a CNN model, a selective reduction in the CNN's redundancy can properly reduce its redundancy, a balance between a model's predictive power, inference speed, memory usage, storage space and power consumption can be achieved. It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck , 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson , 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)). Any inquiry concerning this communication or earlier communications from the examiner should be directed to TAN TRAN whose telephone number is (303)297-4266. 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For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /TAN H TRAN/ Primary Examiner, Art Unit 2141