CTNF 18/289,173 CTNF 99377 DETAILED ACTION This action is responsive to the Application/amendment filed on 11/01/2023 . Claims 1-20 are pending in the case. Claims 1, 4, and 15 are independent claims. 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. 07-06 AIA 15-10-15 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. Domestic Benefit Domestic Benefit for dated for 05/10/2021 is acknowledged. Information Disclosure Statement The information disclosure statement (IDS) submitted on 05/20/2025 , 01/16/2025 , 02/23/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Objections 07-29-01 AIA Claim 11 is objected to because of the following informalities: the claim ends with 2 periods (i.e. …group are generated by one or more scaling factor s.. ) . Appropriate correction is required. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claims 12 and 13 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 12 recites the limitation based on the a respective portion of a quantize . There is insufficient antecedent basis for this limitation in the claim. The claim will be interpreted as based on a respective portion of a quantize. Claim 13 recites the limitation outputting the a new machine learning . There is insufficient antecedent basis for this limitation in the claim. The claim will be interpreted as outputting a new machine learning. 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. Claims 15 and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 15 does/do not fall within at least one of the four categories of patent eligible subject matter as the claimed computer storage medium covers both transitory and non-transitory as the claims and specification are silent on any further definition or details concerning the computer storage medium. Further, claims dependent from 15 (claims 19 and 20) are rejected as claims inherit the deficiencies of the parent claim. Claims 1-20 are rejected under 35 U.S.C. 101 as the claims are directed toward judicial exceptions without significantly more. Regarding Claim 1: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites selecting one or more groups of layers from the plurality of layers, each group of layers comprising one or more layers adjacent to each other in the sequence which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement and choosing from multiple options. See 2106.04.(a)(2).III.C. The claim recites generating a new machine learning model that corresponds to the neural network wherein generating the new machine learning model comprises: for each group of layers, selecting a respective decision tree that replaces the group of layers which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement and choosing a tree to replace a group of layers. See 2106.04.(a)(2).III.C. The claim recites wherein the respective decision tree…generates as output a quantized version of outputs which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))). Subject Matter Eligibility Analysis Step 2A Prong 2: receiving data representing a neural network comprising a plurality of layers arranged in a sequence recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)) wherein the respective decision tree receives as input a quantized version of inputs to a respective first layer in the group recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)) …of a respective last layer in the group specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)) wherein a tree depth of the respective decision tree is based at least in part on a number of layers of the group specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)) Subject Matter Eligibility Analysis Step 2B: Further, additional element (a) and (b) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 ). Additional elements (c) and (d) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)). The additional element(s) (a) (b) (c) and (d) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding Claim 2: The rejection of claim 1 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim does not contain elements that would warrant a Step 2A Prong 1 analysis. Subject Matter Eligibility Analysis Step 2A Prong 2: training based on training data for the neural network the new machine learning model by training at least a portion of the layers in the neural network that were not replaced by respective decision trees recites the is Insignificant Extra-Solution Activity of training a neural network based on training data(see MPEP §2106.05(g)) Subject Matter Eligibility Analysis Step 2B: Additional element (a) recites a well understood and conventional practice of training layers of neural network layers with training data as quoted from A Review on Conventional Machine Learning vs Deep Learning (Page 347, Col. 2, Paragraph 3, “Conventional machine learning algorithms are based on learning of system by training set to develop a trained model as shown in fig 2”) The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding Claim 3: The rejection of claim 2 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim does not contain elements that would warrant a Step 2A Prong 1 analysis. Subject Matter Eligibility Analysis Step 2A Prong 2: training the layers of the neural network that succeed the one or more groups of layers of the neural network according to the sequence recites training at a high level as merely a step and is Insignificant Extra-Solution Activity (see MPEP §2106.05(g)) Subject Matter Eligibility Analysis Step 2B: Additional element (a) recites a well understood and conventional practice of training a neural network as quoted from Text, Speech, and Dialogue (Page 37, Paragraph 2,“After pre-training, the network has to be trained further using some conventional training method like backpropagation”) as the BRI of according to the sequence is understood to be denoting the layers to be trained and not training the layers in a specific sequence. The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding Claim 4: The rejection of claim 1 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites selecting a respective initial layer which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement and choosing from multiple options. See 2106.04.(a)(2).III.C. The claim recites generating a respective plurality of candidate groups that each have the respective initial layer as the first layer in the candidate group which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement and choosing multiple sets of an ordered arrangement of a set of items where the first item is fixed. See 2106.04.(a)(2).III.C. The claim recites for each of the respective plurality of candidate groups, determining a respective performance measure for the candidate group that measures a performance which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass performing an evaluation based on an observation and using judgement to evaluate performance. See 2106.04.(a)(2).III.C. The claim recites selecting, as the group, one of the candidate groups based on respective performance measures for the respective plurality of candidate groups which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement and choosing from multiple options based on a evaluation of observed judgements. See 2106.04.(a)(2).III.C. Subject Matter Eligibility Analysis Step 2A Prong 2: …in the neural network recited as merely linking the use of a judicial exception to a particular technological environment or field of use(see MPEP 2106.05(h))) of a corresponding new machine learning model that has the layers in the candidate group replaced by a respective decision tree recited as merely linking the use of a judicial exception to a particular technological environment or field of use(see MPEP 2106.05(h))) Subject Matter Eligibility Analysis Step 2B: Additional elements (a) and (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely links the use of a judicial exception to a particular technological environment or field of use(see MPEP 2106.05(h)). The additional element(s) (a) and (b) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding Claim 5: The rejection of claim 4 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites selecting the respective initial layer which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement and choosing from multiple options. See 2106.04.(a)(2).III.C. Subject Matter Eligibility Analysis Step 2A Prong 2: … by a random process or based on the sequence of the neural network recited as merely linking the use of a judicial exception to a particular technological environment or field of use(see MPEP 2106.05(h))). Subject Matter Eligibility Analysis Step 2B: Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely links the use of a judicial exception to a particular technological environment or field of use(see MPEP 2106.05(h)). The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding Claim 6: The rejection of claim 4 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites determining a maximum performance measure among the respective performance measures which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))). The claim recites selecting, as the group, a candidate group associated with the maximum performance measure from the respective plurality of candidate groups which is an abstract idea (Mathematical Relationships (see MPEP 2106.04(a)(2)(I)(A)))). Subject Matter Eligibility Analysis Step 2A Prong 2: The claim does not contain elements that would warrant a Step 2A Prong 2 analysis. Subject Matter Eligibility Analysis Step 2B: The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible. Regarding Claim 7: The rejection of claim 1 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites wherein the quantized version of the inputs to a respective first layer in the group and the quantized version of the outputs of a respective last layer in the group are generated using binary or ternary quantization which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))). Subject Matter Eligibility Analysis Step 2A Prong 2: The claim does not contain elements that would warrant a Step 2A Prong 2 analysis. Subject Matter Eligibility Analysis Step 2B: The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible. Regarding Claim 8: The rejection of claim 1 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim does not contain elements that would warrant a Step 2A Prong 1 analysis. Subject Matter Eligibility Analysis Step 2A Prong 2: wherein the respective decision tree layer replacing the group of layers comprises a GradientBoost decision tree or AdaBoost decision tree recited as merely linking the use of a judicial exception to a particular technological environment or field of use(see MPEP 2106.05(h))) Subject Matter Eligibility Analysis Step 2B: Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely links the use of a judicial exception to a particular technological environment or field of use(see MPEP 2106.05(h)). The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding Claim 9: The rejection of claim 1 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites quantizing at least a portion of weights which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))). Subject Matter Eligibility Analysis Step 2A Prong 2: for each layer in the neural network not in the one or more groups of layers…associated with the layer recited as merely linking the use of a judicial exception to a particular technological environment or field of use(see MPEP 2106.05(h))) Subject Matter Eligibility Analysis Step 2B: Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely links the use of a judicial exception to a particular technological environment or field of use(see MPEP 2106.05(h)). The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding Claim 10: The rejection of claim 1 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim does not contain elements that would warrant a Step 2A Prong 1 analysis. Subject Matter Eligibility Analysis Step 2A Prong 2: wherein the tree depth of the respective decision tree equals the number of layers in the group recited as merely linking the use of a judicial exception to a particular technological environment or field of use(see MPEP 2106.05(h))) Subject Matter Eligibility Analysis Step 2B: Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely links the use of a judicial exception to a particular technological environment or field of use(see MPEP 2106.05(h)). The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception Regarding Claim 11: The rejection of claim 1 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites wherein the quantized version of the inputs to a respective first layer in the group or the quantized version of the outputs of a respective last layer in the group are generated by one or more scaling factors which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))). Subject Matter Eligibility Analysis Step 2A Prong 2: The claim does not contain elements that would warrant a Step 2A Prong 2 analysis. Subject Matter Eligibility Analysis Step 2B: The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible. Regarding Claim 12: The rejection of claim 1 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim does not contain elements that would warrant a Step 2A Prong 1 analysis. Subject Matter Eligibility Analysis Step 2A Prong 2: wherein the neural network represented by the received data is an initially trained neural network by a training data set merely specifies a data gathering step that is limited to a particular data source or a particular type of data, i.e. a field of use (see MPEP 2106.05(h)) wherein each decision tree that replaces a respective group of layers has been trained based on a respective portion of a quantized version of the training data set merely linking the use of a judicial exception to a particular technological environment or field of use(see MPEP 2106.05(h))) wherein each training sample of the respective portion of the quantized version of the training data set comprises: (i) a quantized version of layer inputs to the first layer of the group, and (ii) a quantized version of layer outputs from the last layer of the group merely specifies a data gathering step that is limited to a particular data source or a particular type of data, i.e. a field of use (see MPEP 2106.05(h)) Subject Matter Eligibility Analysis Step 2B: Additional elements (a) and (c) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a data gathering step that is limited to a particular data source or a particular type of data, i.e. a field of use (see MPEP 2106.05(h)). Additional elements (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely links the use of a judicial exception to a particular technological environment or field of use(see MPEP 2106.05(h)). The additional element(s) (a) (b) and (c) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception Regarding Claim 13: The rejection of claim 1 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim does not contain elements that would warrant a Step 2A Prong 1 analysis. Subject Matter Eligibility Analysis Step 2A Prong 2: a system configured to implement the new machine learning model wherein the system comprises one or more computing units for implementing the decision trees through one or more functions selected from add, select or switch functions (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))) Subject Matter Eligibility Analysis Step 2B: Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f). The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception Regarding Claim 14: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites selecting one or more groups of layers from the plurality of layers, each group of layers comprising one or more layers adjacent to each other in the sequence which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement and choosing from multiple options. See 2106.04.(a)(2).III.C. The claim recites generating a new machine learning model that corresponds to the neural network, wherein generating the new machine learning model comprises: for each group of layers, selecting a respective decision tree that replaces the group of layers, which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement and choosing a tree to replace a group of layers. See 2106.04.(a)(2).III.C. The claim recites wherein the respective decision tree…generates as output a quantized version of outputs which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))). Subject Matter Eligibility Analysis Step 2A Prong 2: receiving data representing a neural network comprising a plurality of layers arranged in a sequence recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)) wherein the respective decision tree receives as input a quantized version of inputs to a respective first layer in the group recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)) …of a respective last layer in the group specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)) wherein a tree depth of the respective decision tree is based at least in part on a number of layers of the group specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)) one or more computers and one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))) Subject Matter Eligibility Analysis Step 2B: Further, additional element (a) and (b) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 ). Additional elements (c) and (d) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)). Additional elements (e) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f). The additional element(s) (a) (b) (c) (d) and (e) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding Claim 15: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites selecting one or more groups of layers from the plurality of layers, each group of layers comprising one or more layers adjacent to each other in the sequence which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement and choosing from multiple options. See 2106.04.(a)(2).III.C. The claim recites generating a new machine learning model that corresponds to the neural network, wherein generating the new machine learning model comprises: for each group of layers, selecting a respective decision tree that replaces the group of layers, which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement and choosing a tree to replace a group of layers. See 2106.04.(a)(2).III.C. The claim recites wherein the respective decision tree…generates as output a quantized version of outputs which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))). Subject Matter Eligibility Analysis Step 2A Prong 2: receiving data representing a neural network comprising a plurality of layers arranged in a sequence recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)) wherein the respective decision tree receives as input a quantized version of inputs to a respective first layer in the group recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)) …of a respective last layer in the group specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)) wherein a tree depth of the respective decision tree is based at least in part on a number of layers of the group specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)) One or more computer storage media storing instructions that, when executed by one or more computers, cause the one or more computers to perform operations (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))) Subject Matter Eligibility Analysis Step 2B: Further, additional element (a) and (b) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 ). Additional elements (c) and (d) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)). Additional elements (e) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f). The additional element(s) (a) (b) (c) (d) and (e) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding claim 16: The rejection of claim 14 is incorporated in claim 16. Claim 16 is rejected under the same rationale as set forth in the rejection of claim 2. Regarding claim 17: The rejection of claim 16 is incorporated in claim 17. Claim 17 is rejected under the same rationale as set forth in the rejection of claim 3. Regarding claim 18: The rejection of claim 14 is incorporated in claim 18. Claim 18 is rejected under the same rationale as set forth in the rejection of claim 4. Regarding claim 19: The rejection of claim 15 is incorporated in claim 19. Claim 19 is rejected under the same rationale as set forth in the rejection of claim 2. Regarding claim 20: The rejection of claim 19 is incorporated in claim 20. Claim 20 is rejected under the same rationale as set forth in the rejection of claim 3. Claim Rejections - 35 USC § 102 Claim(s) 1, 7, 8, 10 and 13-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated over Abdelsalam et al.(“POLYBiNN: Binary Inference Engine for Neural Networks using Decision Trees” henceforth known as Abdelsalam) Regarding claim 1: Abdelsalam discloses receiving data representing a neural network comprising a plurality of layers arranged in a sequence (Abdelsalam, Page 99, Figure 4 of PolyBinn where the convolutional layers with fully connected layers corresponds to receiving data representing a neural network comprising a plurality layers arranged in a sequence ) Abdelsalam discloses selecting one or more groups of layers from the plurality of layers, each group of layers comprising one or more layers adjacent to each other in the sequence (Abdelsalam, Page 103, Col. 2, Paragraph 4-5, “CNV is made up of a CNN in which a trained POLY BiNN replaces the FC layers” where selecting to replace FC layers in CNN’s that are a sequential stack arranged at the end of the network correspond to selecting a group of layers adjacent to each other ) Abdelsalam discloses generating a new machine learning model that corresponds to the neural network, wherein generating the new machine learning model (Abdelsalam, Page 99, Figure 4, where the POLYBiNN architecture generated corresponds to generating a new machine learning model ) comprises for each group of layers, selecting a respective decision tree that replaces the group of layers (Abdelsalam, Page 96, Col. 1, Paragraph 3, “A method to integrate POLYBiNN into existing DNNs and CNNs by replacing their fully connected layers”) wherein the respective decision tree receives as input a quantized version of inputs to a respective first layer in the group (Abdelsalam, Page 103, Col. 1, Paragraph 2, “The input data was normalized to [0, 1] and binarized using a fixed threshold of 0.5”) and generates as output a quantized version of outputs of a respective last layer in the group (Abdelsalam, Page 99, Fig. 4, where one-hot vector being the output of the last layer in the group corresponds to quantized version of outputs of a last layer in the group ) , wherein a tree depth of the respective decision tree is based at least in part on a number of layers of the group (Abdelsalam, Page 100, Col. 2, Paragraph 3, “As the number of splits increases, the included features for each branch increase, which leads to conditions that are more specific of the training dataset” where more splits correspond to deeper trees and deeper networks requiring more splits corresponds tree depth in part on a number of fully connected layers of the group ) Regarding claim 7: The rejection of claim 1 with prior art Abdelsalam is incorporated and further: Abdelsalam discloses wherein the quantized version of the inputs to a respective first layer in the group and the quantized version of the outputs of a respective last layer in the group are generated using binary or ternary quantization (Abdelsalam, Page 103, Col. 1, Paragraph 2, “The input data was normalized to [0, 1] and binarized using a fixed threshold of 0.5” where each feature being converted to a binary value of 0 or 1 based on the threshold corresponds to input being generated using binary quantization and Fig. 4, where the one-hot vector being the output of the last layer and the values of the hot-vector being 0 or 1 corresponds to binary quantization as the outputs are all either 0 or 1 ) Regarding claim 8: The rejection of claim 1 with prior art Abdelsalam is incorporated and further: Abdelsalam discloses wherein the respective decision tree layer replacing the group of layers comprises a GradientBoost decision tree or AdaBoost decision tree (Abdelsalam, Page 100, Col. 1, Paragraph 3, “We train POLYBiNN using AdaBoost…an ensemble learning algorithm that creates complex classifiers by combining many weak classifiers…We choose DTs as weak binary classifiers” where PolyBiNN using AdaBoost to train and create complex classifiers by combining weak classifiers and PolyBiNN using decision trees as weak binary classifiers corresponds to PolyBiNN comprising AdaBoosted decision trees ) Regarding claim 10: The rejection of claim 1 with prior art Abdelsalam is incorporated and further: Abdelsalam discloses wherein the tree depth of the respective decision tree equals the number of layers in the group (Abdelsalam, Page 103, Paragraph 4-5, “CNV is made up of a CNN in which a trained POLY BiNN replaces the FC layers” and Abdelsalam, Page 104, footnote 3, “CNV, 10 Trees and 400 Splits” where the replacement of the 3 fully connected layers of FINN to create the CNV architecture corresponds to having a tree depth of decision tree equal to the number of layers in a group. Additionally, examiner notes that the BRI of claim would be met for any number of fully connected layers being replaced with any given decision tree as a tree with 9 depth replacing 3 layers meets the BRI of the claim insofar as the tree has a depth of 3 by the nature of having a depth 9 and, likewise, the converse is true: a tree with 3 depth replacing 9 layers meets the BRI of the language as the tree has a depth that would equal the number of layers being replaced as the group of layers has 3 layers in the group by the nature of having 9 layers in the group ) Regarding claim 13: The rejection of claim 1 with prior art Abdelsalam is incorporated and further: Abdelsalam discloses outputting a new machine learning model to a system configured to implement the new machine learning model wherein the system comprises one or more computing units for implementing the decision trees through one or more functions selected from add, select or switch functions (Abdelsalam, Page 96, Abstract, “POLYBiNN is composed of a stack of decision trees, which are binary classifiers in nature, and it utilizes AND-OR gates” where AND-OR gates correspond a select function and the decision trees utilizing AND-OR gates corresponds to one or more computing units for implementing the decision trees through one or more functions selected from add, select or switch functions ) Regarding claim 14: Abdelsalam discloses one or more computers and one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations (Abdelsalam, Page 1, Abstract, “when implemented in a ZYNQ-7000 ZC706 FPGA, the system achieves a throughput of up to 100 million image classifications per second with 90 ns latency and 97.26% accuracy”) Abdelsalam discloses receiving data representing a neural network comprising a plurality of layers arranged in a sequence (Abdelsalam, Page 99, Figure 4 of PolyBinn where the convolutional layers with fully connected layers corresponds to receiving data representing a neural network comprising a plurality layers arranged in a sequence ) Abdelsalam discloses selecting one or more groups of layers from the plurality of layers, each group of layers comprising one or more layers adjacent to each other in the sequence (Abdelsalam, Page 103, Col. 2, Paragraph 4-5, “CNV is made up of a CNN in which a trained POLY BiNN replaces the FC layers” where selecting to replace FC layers in CNN’s that are a sequential stack arranged at the end of the network correspond to selecting a group of layers adjacent to each other ) Abdelsalam discloses generating a new machine learning model that corresponds to the neural network, wherein generating the new machine learning model (Abdelsalam, Page 99, Figure 4, where the POLYBiNN architecture generated corresponds to generating a new machine learning model ) comprises for each group of layers, selecting a respective decision tree that replaces the group of layers (Abdelsalam, Page 96, Col. 1, Paragraph 3, “A method to integrate POLYBiNN into existing DNNs and CNNs by replacing their fully connected layers”) , wherein the respective decision tree receives as input a quantized version of inputs to a respective first layer in the group (Abdelsalam, Page 103, Col. 1, Paragraph 2, “The input data was normalized to [0, 1] and binarized using a fixed threshold of 0.5”) and generates as output a quantized version of outputs of a respective last layer in the group (Abdelsalam, Page 99, Fig. 4, where one-hot vector being the output of the last layer in the group corresponds to quantized version of outputs of a last layer in the group ) , wherein a tree depth of the respective decision tree is based at least in part on a number of layers of the group (Abdelsalam, Page 100, Col. 2, Paragraph 3, “As the number of splits increases, the included features for each branch increase, which leads to conditions that are more specific of the training dataset” where more splits correspond to deeper trees and deeper networks requiring more splits corresponds tree depth in part on a number of fully connected layers of the group ) Regarding claim 15: Abdelsalam discloses One or more computer storage media storing instructions that, when executed by one or more computers, cause the one or more computers to perform operations (Abdelsalam, Page 1, Abstract, “when implemented in a ZYNQ-7000 ZC706 FPGA, the system achieves a throughput of up to 100 million image classifications per second with 90 ns latency and 97.26% accuracy”) Abdelsalam discloses receiving data representing a neural network comprising a plurality of layers arranged in a sequence (Abdelsalam, Page 99, Figure 4 of PolyBinn where the convolutional layers with fully connected layers corresponds to receiving data representing a neural network comprising a plurality layers arranged in a sequence ) Abdelsalam discloses selecting one or more groups of layers from the plurality of layers, each group of layers comprising one or more layers adjacent to each other in the sequence (Abdelsalam, Page 103, Col. 2, Paragraph 4-5, “CNV is made up of a CNN in which a trained POLY BiNN replaces the FC layers” where selecting to replace FC layers in CNN’s that are a sequential stack arranged at the end of the network correspond to selecting a group of layers adjacent to each other ) Abdelsalam discloses generating a new machine learning model that corresponds to the neural network, wherein generating the new machine learning model (Abdelsalam, Page 99, Figure 4, where the POLYBiNN architecture generated corresponds to generating a new machine learning model ) comprises for each group of layers, selecting a respective decision tree that replaces the group of layers (Abdelsalam, Page 96, Col. 1, Paragraph 3, “A method to integrate POLYBiNN into existing DNNs and CNNs by replacing their fully connected layers”) , wherein the respective decision tree receives as input a quantized version of inputs to a respective first layer in the group and generates as output a quantized version of outputs of a respective last layer in the group (Abdelsalam, Page 99, Fig. 4, where one-hot vector being the output of the last layer in the group corresponds to quantized version of outputs of a last layer in the group ) , wherein a tree depth of the respective decision tree is based at least in part on a number of layers of the group (Abdelsalam, Page 100, Col. 2, Paragraph 3, “As the number of splits increases, the included features for each branch increase, which leads to conditions that are more specific of the training dataset” where more splits correspond to deeper trees and deeper networks requiring more splits corresponds tree depth in part on a number of fully connected layers of the group ) 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claim (s) 2-6, 11 and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abdelsalam et al.(“POLYBiNN: Binary Inference Engine for Neural Networks using Decision Trees” henceforth known as Abdelsalam) in view of Chidambaram et al.(“POET-BIN: POWER EFFICIENT TINY BINARY NEURONS” henceforth known as Chidambaram) Regarding claim 2: The rejection of claim 1 with prior art Abdelsalam is incorporated and further: Abdelsalam does not disclose, however Chidambaram does disclose training, based on training data for the neural network, the new machine learning model by training at least a portion of the layers in the neural network that were not replaced by respective decision trees (Chidambaram, Page 6, Col. 1, Paragraph 4, “We developed the workflow shown in Fig. 5 to train the RINC modules starting from a vanilla CNN network. Firstly, we use a pretrained full precision CNN as our base architecture (Vanilla network)” where the convolution and feature extraction layers before the fully connected layer are trained correspond to training at least a portion of layers in the neural network that were not replaced by decision trees ) References Abdelsalam and Chidambaram are analogous art because they are from the same field of endeavor of implementing neural networks with decision tree architecture. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Abdelsalam and Chidambaram before him or her, to modify training of Abdelsalam to include the training of layers not replaced as described in Chidambaram to have a full precision base to compare accuracy loss after quantization. The suggestion/motivation for doing so would have been “We report four sets of accuracies for each dataset in Table 2. Firstly, we report the accuracy of the vanilla network (A1), followed by the accuracy with binary sigmoid activation after the last convolutional layer to obtain the binary features(A2).” (Chidambaram Page 7, Col. 1, Paragraph 4) Regarding claim 3: The rejection of claim 2 with prior art Abdelsalam-Chidambaram is incorporated and further: Abdelsalam does not disclose, however Chidambaram does disclose training the layers of the neural network that succeed the one or more groups of layers of the neural network according to the sequence (Chidambaram, Page 6, Col. 2, Paragraph 1, “Finally, the output layer is retrained with the RINC outputs” where retraining the output layer that comes after the replaced group of layers corresponds training a layers that succeed the group of layers according to the sequence ) References Abdelsalam and Chidambaram are analogous art because they are from the same field of endeavor of implementing neural networks with decision tree architecture. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Abdelsalam and Chidambaram before him or her, to modify training of Abdelsalam to include the training of layer(s) after the replaced layers as described in Chidambaram to adapt to the quantized representation output by the replaced layers. The suggestion/motivation for doing so would have been “The output layer is separately retrained with RINC-L outputs to adapt the weights of the output layer).” (Chidambaram Page 6, Col. 1, Paragraph 1) Regarding claim 4: The rejection of claim 1 with prior art Abdelsalam-Chidambaram is incorporated and further: Abdelsalam does not disclose selecting a respective initial layer in the neural network , generating a respective plurality of candidate groups that each have the respective initial layer as the first layer in the candidate group and for each of the respective plurality of candidate groups, determining a respective performance measure for the candidate group that measures a performance of a corresponding new machine learning model that has the layers in the candidate group replaced by a respective decision tree Chidambaram discloses selecting a respective initial layer in the neural network (Chidambaram, Page 6, Col. 2, Paragraph 1, “we replace all the hidden layers and the intermediate layer in the classifier using our RINC architecture which is the student architecture in our work” where selecting all the hidden layers corresponds to selective a respective initial layer ) Chidambaram discloses generating a respective plurality of candidate groups that each have the respective initial layer as the first layer in the candidate group (Chidambaram, Page 5, Col. 1, Paragraph 3, “We use a back to front approach where we start replacing the binary neurons in the network with our RINC-L architecture from the last layers and progressively move towards the initial layers.” where replacing all neurons in each layer with RINC modules and training with hyperparameters corresponds to generating a candidate group and doing the process back to front to the initial layers corresponds to generating a plurality of candidate groups that each have the initial layer as the first layer in the candidate group ) Chidambaram discloses for each of the respective plurality of candidate groups, determining a respective performance measure for the candidate group that measures a performance of a corresponding new machine learning model that has the layers in the candidate group replaced by a respective decision tree( Chidambaram, Page 7, Col. 1, Paragraph 4, “Finally, we replace the classifier portion of the teacher network with the RINC classifiers and quantize final layer whose accuracy is reported as (A4). This helps isolate and study the effect of each modification.” where replacing the classifier portion with RINC modules and determining accuracy of the RINC module corresponds to determining a respective performance measure for the candidate group measures a performance of a corresponding new machine learning model that has the layers in the candidate group replaced by a respective decision tree ) and selecting, as the group, one of the candidate groups based on respective performance measures for the respective plurality of candidate groups (Chidambaram, Page 7, Col. 1, Paragraph 4, “We report the best accuracy achieved over different sets of hyper-parameters such as number of DTs and LUT size for RINC modules” where determining accuracy of the RINC module with the adjustment of hyper-parameters of DT’s and LUT size for the RINC models corresponds to selecting different groups based on respective performance measures ) References Abdelsalam and Chidambaram are analogous art because they are from the same field of endeavor of implementing neural networks with decision tree architecture. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Abdelsalam and Chidambaram before him or her, to modify training of Abdelsalam to include the incremental selection of groupings and measurement of performance as described in Chidambaram to measure and compare to select the best performing groupings/settings balancing resource usage. The suggestion/motivation for doing so would have been “the hyper parameter P must be chosen carefully to balance the trade-off between accuracy and resources.” (Chidambaram Page 5, Col. 2, Paragraph 1) Regarding claim 5: The rejection of claim 4 with prior art Abdelsalam-Chidambaram is incorporated and further: Chidambaram discloses selecting the respective initial layer by a random process or based on the sequence of the neural network (Chidambaram, Page 6, Col. 1, Figure 5, where the sequence of hidden layers of classifier being chosen corresponds to selecting an initial layer based on the sequence of the neural network ) Regarding claim 6: The rejection of claim 4 with prior art Abdelsalam-Chidambaram is incorporated and further: Chidambaram discloses determining a maximum performance measure among the respective performance measures and selecting, as the group, a candidate group associated with the maximum performance measure from the respective plurality of candidate groups (Chidambaram, Page 7, Col. 1, Paragraph 4, “We report the best accuracy achieved over different sets of hyper-parameters such as number of DTs and LUT size for RINC modules” where choosing a final a final set of hyper-parameters that has the highest accuracy corresponds to determining a maximum performance measure among the respective performance measures and selecting, as the group, a candidate group associated with the maximum performance measure from the respective plurality of candidate groups ) Regarding claim 11: The rejection of claim 1 with prior art Abdelsalam-Chidambaram is incorporated and further: Chidambaram discloses wherein the quantized version of the inputs to a respective first layer in the group or the quantized version of the outputs of a respective last layer in the group are generated by one or more scaling factors (Chidambaram, Page 4, Col. 1, Paragraph 2, “The output of each classifier is multiplied with its respective weight and added. Finally, this weighted sum is thresholded and the binary output is obtained. The architecture is detailed in the MAT unit shown in Fig. 2.”) Regarding claim 16: The rejection of claim 14 is incorporated in claim 16. Claim 16 is rejected under the same rationale as set forth in the rejection of claim 2. Regarding claim 17: The rejection of claim 16 is incorporated in claim 17. Claim 17 is rejected under the same rationale as set forth in the rejection of claim 3. Regarding claim 18: The rejection of claim 14 is incorporated in claim 18. Claim 18 is rejected under the same rationale as set forth in the rejection of claim 4. Regarding claim 19: The rejection of claim 15 is incorporated in claim 19. Claim 19 is rejected under the same rationale as set forth in the rejection of claim 2. Regarding claim 20: The rejection of claim 19 is incorporated in claim 20. Claim 20 is rejected under the same rationale as set forth in the rejection of claim 3 . 07-21-aia AIA Claim (s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abdelsalam et al.(“POLYBiNN: Binary Inference Engine for Neural Networks using Decision Trees” henceforth known as Abdelsalam) in view of Young et al.(“Transform Quantization for CNN Compression” henceforth known as Young) Regarding claim 9: The rejection of claim 1 with prior art Abdelsalam is incorporated and further: Abdelsalam does not disclose however Young discloses wherein each layer comprises a respective set of weights, the method further comprising: for each layer in the neural network not in the one or more groups of layers, quantizing at least a portion of weights associated with the layer (Young, Page 1, Abstract, “In this paper, we compress convolutional neural network (CNN) weights post-training via transform quantization.”) References Chidambaram and Hada are analogous art because they are from the same field of endeavor of using machine learning and neural networks with decision-tree methods. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Chidambaram and Hada before him or her, to modify the training data of the decision tree of Chidambaram to include similar training data as described in Hada to achieve a tree that replicates the replaced neural network layers. The suggestion/motivation for doing so would have been “Our goal is to achieve a tree that both mimics well the deep net and is as simple as possible. We achieve this by training the tree on the same training set as the net (using the latter’s features but the ground-truth labels)” (Hada Page 6, paragraph 1) 07-21-aia AIA Claim (s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abdelsalam et al.(“POLYBiNN: Binary Inference Engine for Neural Networks using Decision Trees” henceforth known as Abdelsalam) in view of Chidambaram et al.(“POET-BIN: POWER EFFICIENT TINY BINARY NEURONS” henceforth known as Chidambaram) and further in view of Hada et al.(“Sparse Oblique Decision Trees: A Tool to Understand and Manipulate Neural Net Features” henceforth known as Hada) Regarding claim 12: The rejection of claim 1 with prior art Abdelsalam is incorporated and further: Abdelsalam does not disclose wherein the neural network represented by the received data is an initially trained neural network by a training data set, wherein each decision tree that replaces a respective group of layers has been trained based on a respective portion of a quantized version of the training data set, wherein each training sample of the respective portion of the quantized version of the training data set comprises: (i) a quantized version of layer inputs to the first layer of the group, and (ii) a quantized version of layer outputs from the last layer of the group Chidambaram discloses wherein the neural network represented by the received data is an initially trained neural network by a training data set, (Chidambaram, Page 6, Col. 1, Paragraph 4, “Firstly, we use a pretrained full precision CNN as our base architecture(Vanilla network)”) wherein each decision tree that replaces a respective group of layers has been trained based on a respective portion of a quantized version of the training data set, (Chidambaram, Page 6, Col. 2, Paragraph 1, “Further, an intermediate layer and a binary sigmoid activation are added after the last hidden layer. This forms the teacher network. Then, we replace all the hidden layers and the intermediate layer in the classifier using our RINC architecture which is the student architecture in our work” where replacing hidden layers with a student architecture (decision tree RINC modules) that is trained on a binarized representation of the training data from the teacher network corresponds to each decision tree that replaces layers is trained based on a respective portion of a quantized version of the training data set ) References Abdelsalam and Chidambaram are analogous art because they are from the same field of endeavor of implementing neural networks with decision tree architecture. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Abdelsalam and Chidambaram before him or her, to modify the training data Abdelsalam to include the quantization of training data as described in Chidambaram to achieve lower memory consumption and regularization. The suggestion/motivation for doing so would have been “These networks use 32-bit floating point representations that require expensive MAC operations and memory read operations. Methods such as quantization of weights and activations address these challenges…Surprisingly, quantization may not be detrimental to the accuracy of the network as it can provide a form of regularization similar to dropout…which helps to better generalize on the testset” (Chidambaram Page 1, Col. 1, Paragraph 2) Abdelsalam-Chidambaram does not disclose wherein each training sample of the respective portion of the quantized version of the training data set comprises: (i) a quantized version of layer inputs to the first layer of the group, and (ii) a quantized version of layer outputs from the last layer of the group Hada discloses wherein each training sample of the respective portion of the quantized version of the training data set comprises: (i) a quantized version of layer inputs to the first layer of the group, and (ii) a quantized version of layer outputs from the last layer of the group (Hada, Page 5, Paragraph 3, “Assume we have a dataset (usually the one used to train the net) {(x n ,y n )} … of input instances and their labels. Then: 1. Train a sparse oblique tree y = T(z) with TAO on the training set {(F(x n ),y n )})” where each training sample for the decision tree consisting of the feature vector and label corresponds to a training sample that is input into the decision tree/first layer and decision tree prediction T(z) corresponds to the output ) References Abdelsalam-Chidambaram and Hada are analogous art because they are from the same field of endeavor of using machine learning and neural networks with decision-tree methods. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Abdelsalam-Chidambaram and Hada before him or her, to modify the training data of the decision tree of Abdelsalam-Chidambaram to include similar training data as described in Hada to achieve a tree that replicates the replaced neural network layers. The suggestion/motivation for doing so would have been “Our goal is to achieve a tree that both mimics well the deep net and is as simple as possible. We achieve this by training the tree on the same training set as the net (using the latter’s features but the ground-truth labels)” (Hada Page 6, paragraph 1). Relevant Art: Examiner found the following arts related, however were not used in the art: Application CN112085157A as model focuses on replacing neural network layers/neurons with decision trees. NPL “Training Decision Trees as Replacement for Convolution Layer” as the paper discusses replacing layers with decision trees. NPL “The Tree Ensemble Layer: Differentiability meets Conditional Computation” as the paper discusses replacing dense layers with a tree layer NPL “POLYBiNN: A Scalable and Efficient Combinatorial Inference Engine for Neural Networks on FPGA” as paper discusses replacing their fully connected layers with decision trees. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES JEFFREY JONES JR whose telephone number is (703)756-1414. The examiner can normally be reached Monday - Friday 8:00 - 5:00 EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached at 571-272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /C.J.J./Examiner, Art Unit 2122 /KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122 Application/Control Number: 18/289,173 Page 2 Art Unit: 2122 Application/Control Number: 18/289,173 Page 3 Art Unit: 2122 Application/Control Number: 18/289,173 Page 4 Art Unit: 2122 Application/Control Number: 18/289,173 Page 5 Art Unit: 2122 Application/Control Number: 18/289,173 Page 6 Art Unit: 2122 Application/Control Number: 18/289,173 Page 7 Art Unit: 2122 Application/Control Number: 18/289,173 Page 8 Art Unit: 2122 Application/Control Number: 18/289,173 Page 9 Art Unit: 2122 Application/Control Number: 18/289,173 Page 10 Art Unit: 2122 Application/Control Number: 18/289,173 Page 11 Art Unit: 2122 Application/Control Number: 18/289,173 Page 12 Art Unit: 2122 Application/Control Number: 18/289,173 Page 13 Art Unit: 2122 Application/Control Number: 18/289,173 Page 14 Art Unit: 2122 Application/Control Number: 18/289,173 Page 15 Art Unit: 2122 Application/Control Number: 18/289,173 Page 16 Art Unit: 2122 Application/Control Number: 18/289,173 Page 17 Art Unit: 2122 Application/Control Number: 18/289,173 Page 18 Art Unit: 2122 Application/Control Number: 18/289,173 Page 19 Art Unit: 2122 Application/Control Number: 18/289,173 Page 20 Art Unit: 2122 Application/Control Number: 18/289,173 Page 21 Art Unit: 2122 Application/Control Number: 18/289,173 Page 22 Art Unit: 2122 Application/Control Number: 18/289,173 Page 23 Art Unit: 2122 Application/Control Number: 18/289,173 Page 24 Art Unit: 2122 Application/Control Number: 18/289,173 Page 25 Art Unit: 2122 Application/Control Number: 18/289,173 Page 26 Art Unit: 2122 Application/Control Number: 18/289,173 Page 27 Art Unit: 2122 Application/Control Number: 18/289,173 Page 28 Art Unit: 2122 Application/Control Number: 18/289,173 Page 29 Art Unit: 2122 Application/Control Number: 18/289,173 Page 30 Art Unit: 2122 Application/Control Number: 18/289,173 Page 31 Art Unit: 2122 Application/Control Number: 18/289,173 Page 32 Art Unit: 2122 Application/Control Number: 18/289,173 Page 33 Art Unit: 2122 Application/Control Number: 18/289,173 Page 34 Art Unit: 2122 Application/Control Number: 18/289,173 Page 35 Art Unit: 2122 Application/Control Number: 18/289,173 Page 36 Art Unit: 2122 Application/Control Number: 18/289,173 Page 37 Art Unit: 2122 Application/Control Number: 18/289,173 Page 38 Art Unit: 2122 Application/Control Number: 18/289,173 Page 39 Art Unit: 2122
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