CTNF 18/519,549 CTNF 98440 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Objections Claim 2-7 recite “ A system according to claim….” Since these claims are referring back to the independent claim 1, claims 2-7 need to recite “ The system according to claim…” Claims 9-12 need to recite “ The method according to claim…” because they depend from independent claim 8 which recites “ A method comprising…” Claims 14-20 need to recite “ The non-transitory medium according to claim…” because they depend from independent claim 13 which recites “ A non-transitory medium…” Appropriate corrections are required. 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 1-20 are rejected under 35 U.S.C.101 because the claimed invention is directed to an abstract idea without significantly more. Step 1: Claims 8-12 are directed to a process. Claims 1-7 and 13-20 are directed to a machine or an article of manufacture. With respect to claim(s) 1, 8, and 13: 2A Prong 1 : The claim(s) recite(s) an abstract idea. Specifically: […] determine a label and a confidence level associated with the label ; ( Mental process – A person can evaluate data to determine a label and a confidence level for the label via mind or by using a pen and paper – see MPEP § 2106.04(a)(2)(III)) (Claim 8) determining a label and a confidence level associated with the input data […] ( Mental process – A person can mentally determine a label and a confidence level for data via mind or by using a pen and paper – see MPEP § 2106.04(a)(2)(III)) determine/determining a contribution of each of the plurality of features of the input data to the determined label based on the trained classification model and the determined label ; ( Mathematical concepts – Determining a contribution involves mathematical calculations (see specification paragraph [0050]) – see MPEP § 2106.04(a)(2)(I)) determine/determining a set of the plurality of features based on the determined contributions; ( Mental process – A person can mentally determine a set of features based on a contribution – see MPEP § 2106.04(a)(2)(III)) determine/determining a plurality of labeled data instances, each of the plurality of labeled data instances comprising a value for each of the plurality of features and a [fixed] label; ( Mental process – A person can mentally determine a plurality of data instances – see MPEP § 2106.04(a)(2)(III)) for each feature of the set, determine a ratio of a number of the plurality of labeled data instances having a same value as the input data and a fixed label which is the determined label to a number of the plurality of labeled data instances having the same value as the input data and a fixed label which is not the determined label; ( Mathematical concepts – Determining a ratio is a mathematical relationship between two quantities – see MPEP § 2106.04(a)(2)(I)) If claim limitations, under their broadest reasonable interpretation, cover performance of the limitations as a mental process, but for the recitation of generic computer components, then the claim limitations fall within the mathematical or mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea. 2A Prong 2 : The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination. Additional elements: (Claim 1) A system comprising: a memory storing processor-executable program code; and at least one processing unit to execute the processor-executable program code to cause the system to: (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) (Claim 8) A method comprising: (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) (Claim 13) A non-transitory medium storing program code executable by at least one processing unit of a computing system to cause the computing system to : (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) receive/receiving input data comprising a value for each of a plurality of features; (Mere data gathering – Adding insignificant extra-solution activity of mere data gathering to the judicial exception – see § MPEP2106.05(g).) (Claim 8) […] using a trained classification model ; (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) (Claims 1 and 13) input the input data to a trained classification model to […] (Mere data gathering – Adding insignificant extra-solution activity of mere data gathering to the judicial exception – see § MPEP2106.05(g).) present the determined label and the confidence level; (Adding insignificant extra-solution activity to the judicial exception – see § MPEP2106.05(g).) (Claims 1 and 13) present an indication of each feature of the set and its determined ratio based on its determined contribution. (Adding insignificant extra-solution activity to the judicial exception – see § MPEP2106.05(g).) (Claim 8) present an indication of each feature of the set, its determined ratio and its determined contribution . (Adding insignificant extra-solution activity to the judicial exception – see § MPEP2106.05(g).) Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is directed to an abstract idea. 2B : The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: (Claim 1) A system comprising: a memory storing processor-executable program code; and at least one processing unit to execute the processor-executable program code to cause the system to: (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) (Claim 8) A method comprising: (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) (Claim 13) A non-transitory medium storing program code executable by at least one processing unit of a computing system to cause the computing system to : (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) receive/receiving input data comprising a value for each of a plurality of features; (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC)- see MPEP § 2106.05(d)(ll)(i) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).) (Claim 8) […] using a trained classification model ; (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) (Claims 1 and 13) input the input data to a trained classification model to […] (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC)- see MPEP § 2106.05(d)(ll)(i) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).) present the determined label and the confidence level; (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC) - see § MPEP 2106.05(d)(II)) - Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93) (Claims 1 and 13) present an indication of each feature of the set and its determined ratio based on its determined contribution. (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC) - see § MPEP 2106.05(d)(II)) - Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93) (Claim 8) present an indication of each feature of the set, its determined ratio and its determined contribution . (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC) - see § MPEP 2106.05(d)(II)) - Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93) Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible . With respect to claim(s) 2, 9, and 14: 2A Prong 1 : The claim(s) recite(s) an abstract idea. Specifically: determine a set of labeled data ; ( Mental process – A person can mentally determine a set of labeled data – see MPEP § 2106.04(a)(2)(III)) generate/generating a set of labeled training data instances and a set of labeled testing data instances from the set of labeled data ; ( Mental process – A person can generate (think of) a set of labeled data instances for training and testing in the mind or using a pen and paper – see MPEP § 2106.04(a)(2)(III)) 2A Prong 2 : The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination. Additional elements: (Claim 2) the at least one processing unit to execute the processor-executable program code to cause the system to : (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) (Claim 14) the program code executable by at least one processing unit of a computing system to cause the computing system to: (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) train/training the classification model based on the set of labeled training data instances and the set of labeled testing data instances . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) 2B : The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: (Claim 2) the at least one processing unit to execute the processor-executable program code to cause the system to : (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) (Claim 14) the program code executable by at least one processing unit of a computing system to cause the computing system to: (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) train/training the classification model based on the set of labeled training data instances and the set of labeled testing data instances . (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 3, 6, 10, 12, 15, and 18: 2A Prong 2 : The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination. Additional elements: (Claims 3, 6, 15, and 18) wherein presentation of an indication of each feature of the set and its determined ratio based on its determined contribution comprises: (Adding insignificant extra-solution activity to the judicial exception – see § MPEP2106.05(g).) (Claims 10 and 12) wherein presenting an indication of each feature of the set, its determined ratio and its determined contribution comprises : (Adding insignificant extra-solution activity to the judicial exception – see § MPEP2106.05(g).) presentation/presenting of the indication of each feature of the set and its determined ratio in descending order of determined contributions . (Adding insignificant extra-solution activity to the judicial exception – see § MPEP2106.05(g).) 2B : The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: (Claims 3, 6, 15, and 18) wherein presentation of an indication of each feature of the set and its determined ratio based on its determined contribution comprises: (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC) - see § MPEP 2106.05(d)(II)) - Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93) (Claims 10 and 12) wherein presenting an indication of each feature of the set, its determined ratio and its determined contribution comprises : (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC) - see § MPEP 2106.05(d)(II)) - Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93) presentation/presenting of the indication of each feature of the set and its determined ratio in descending order of determined contributions . (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC) - see § MPEP 2106.05(d)(II)) - Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 4, 7, 16, and 19: 2A Prong 2 : The additional elements recited in the claim(s) do not integrate the abstract idea into a practical application, individually or in combination. Additional elements: (Claims 4 and 7) the at least one processing unit to execute the processor-executable program code to cause the system to : (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) (Claims 16 and 19) the program code executable by at least one processing unit of a computing system to cause the computing system to: (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) present the determined contributions . (Adding insignificant extra-solution activity to the judicial exception – see § MPEP2106.05(g).) 2B : The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements: (Claims 4 and 7) the at least one processing unit to execute the processor-executable program code to cause the system to : (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) (Claims 16 and 19) the program code executable by at least one processing unit of a computing system to cause the computing system to: (Mere instructions to apply an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f).) present the determined contributions . (Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC) - see § MPEP 2106.05(d)(II)) - Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93) Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 5, 11, and 17: 2A Prong 1 : The claim(s) recite(s) an abstract idea. Specifically: wherein determination/determining of the set of the plurality of features based on the determined contributions comprises: determination/determining of the features associated with largest contributions . ( Mental process – A person can mentally determine features associated with largest contributions – see MPEP § 2106.04(a)(2)(III)) Additionally, the claim(s) do not recite any new additional elements that would amount to an integration of the abstract idea into a practical application (individually or in combination) or significantly more than the judicial exception. Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . With respect to claim(s) 20: 2A Prong 1 : The claim(s) recite(s) an abstract idea. Specifically: wherein determination of the contribution of each of the plurality of features of the input data to the determined label comprises adding the contribution of one of the features to the contribution of another one of the features . ( Mathematical concepts – Determining a contribution involves mathematical calculations (see specification paragraph [0050]) – see MPEP § 2106.04(a)(2)(I)) Additionally, the claim(s) do not recite any new additional elements that would amount to an integration of the abstract idea into a practical application (individually or in combination) or significantly more than the judicial exception. Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible. Therefore, the claim is not patent eligible . Claim Rejections - 35 USC § 103 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-21-aia AIA Claim s 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over POULIN ("Visual Explanation of Evidence in Additive Classifiers") in view of DAS (US 20220172004 A1) and MOZINA ("Nomograms for Visualization of Naive Bayesian Classifier"), hereafter POULIN, DAS, and MOZINA respectively . Regarding Claim 1: POULIN teaches: receive input data comprising a value for each of a plurality of features ; (POULIN [page 1823, Classifiers as Additive Models] teaches: “A classifier maps an object described by a set of feature values (i.e., receive input data comprising a value for each of a plurality of features ) to one of the possible mutually exclusive class labels.”) input the input data to a trained classification model to determine a label and a confidence level associated with the label ; (POULIN [page 1822, Introduction] teaches: “A classifier assigns a label (i.e., to a trained classification model to determine a label ) to an unlabeled query item (i.e., input the input data ) based on patterns learned from labeled examples.” POULIN [page 1825, Naïve Bayes] teaches: “The naïve Bayes classifier (NB) is a widely used and effective probabilistic classifier. A NB classification is a simple Bayes classifier that assigns each instance x to the class k with the largest posterior probability: […] Capability 0, the decision, can be represented graphically for the NB classifier by using probabilities. Capability 1, decision evidence, is visualized as described in Section 2.” Examiner’s note: Under BRI, determine […] a confidence level associated with the label can be interpreted as POULIN’s posterior probability of the predicted class.) determine a contribution of each of the plurality of features of the input data […] (POULIN [page 1823, Classifiers as Additive Models] teaches: “We can generalize this linear model to an additive model (Hastie, Tibshirani, and Friedman 2001) in which the feature contributions are functions of the feature values. g x = b + ∑ j = 1 m f j x j ( 2 ) Each contribution f j x j (i.e., determine a contribution of each of the plurality of features of the input data ) is the contribution of evidence from feature j with value x j to the score of the discriminant function g x .” POULIN [page 1824, Capability 1 – decision evidence] teaches: “The decision evidence represents the strength of the classification decision and the relative contributions of each feature to the decision.” POULIN [page 1823, Classifiers as Additive Models] teaches: “A classifier maps an object described by a set of feature values to one of the possible mutually exclusive class labels. This assignment can be based on a set of discriminant real-valued functions, one function g k x for each possible class label k (Alpaydin 2004). Given a query instance x (with feature values x 1 … x j … x m ), each discriminant provides a score for assigning a class label. The instance is classified with the label of the highest scoring class given the instance.”) determine a set of the plurality of features based on the determined contributions ; (POULIN [page 1824, Capability 1 – decision evidence] teaches: “When there are many features, it is helpful to show the evidence contributions (i.e., based on the determined contributions ) from only a subset of ‘focus’ features. […] The focus features may be selected (i.e., determine a set of the plurality of features ) interactively or by some specified criteria (such as those that give the most evidence for the classification).”) determine a plurality of labeled data instances, each of the plurality of labeled data instances comprising a value for each of the plurality of features and a fixed label ; (POULIN [page 1825, Applications] teaches: “Each protein instance is associated with a vector of binary features, each corresponding to a text keyword or phrase (encoded as ‘+1’ if the corresponding keyword is associated with the protein and ‘-1’ if it is not). For the subcellular location classifier used in this paper, our training set contains 3904 protein sequences. […] In our example, the keywords associated with the query protein are ‘nitrogen fixation', 'transmembrane', 'plasmid', 'plasma membrane', and 'complete proteome'. The true label for our example protein is ‘inner membrane’ (vs. ‘not inner membrane).” POULIN [page 1825, Capability 4 – Source of evidence] teaches: “To audit the relationship between the decision label and a feature, the training data is sliced by label value and sliced (or sorted) by feature, allowing users to inspect whether classifier parameters are compatible with their expectations, based on training data.” Examiner’s note: Under BRI, determining a plurality of labeled data instances can be interpreted as the used training set containing 3904 protein sequences, each associated with a true label (i.e., fixed label ). The value for each of the plurality of features can be interpreted as the binary value +1 or -1 indicating whether the keyword is associated with the protein.) present the determined label and the confidence level ; (POULIN [page 1823, Classification Explanation Capabilities] teaches: “Decision: Represent a predicted classification graphically (i.e., present the determined label ) (Figure 1 LHS).” POULIN [page 1826, Naïve Bayes] teaches: “Capability 0, the decision, can be represented graphically for the NB classifier by using probabilities (i.e., present […] the confidence level ).”) POULIN is not relied upon for teaching: A system comprising: a memory storing processor-executable program code; and at least one processing unit to execute the processor-executable program code to cause the system to: determine a contribution […] to the determined label based on the trained classification model and the determined label; for each feature of the set, determine a ratio of a number of the plurality of labeled data instances having a same value as the input data and a fixed label which is the determined label to a number of the plurality of labeled data instances having the same value as the input data and a fixed label which is not the determined label; present an indication of each feature of the set and its determined ratio based on its determined contribution. However, DAS teaches: A system comprising: a memory storing processor-executable program code; and at least one processing unit to execute the processor-executable program code to cause the system to : (DAS [0145] teaches: “In the illustrated embodiment, computer system 2000 includes one or more processors 2010 coupled to a system memory 2020 via an input/output (I/O) interface 2030.” DAS [0146] teaches: “In various embodiments, computer system 2000 may be a uniprocessor system including one processor 2010, or a multiprocessor system including several processors 2010 (e.g., two, four, eight, or another suitable number). Processors 2010 may be any suitable processor capable of executing instructions.”) determine a contribution […] to the determined label based on the trained classification model and the determined label ; (DAS [0048] teaches: “In various embodiments, feature attribution may be determined using Shapley values. Feature attribution measurements can be provided at an instance level, for a specific prediction made by a machine learning model and at a global level for the machine learning model as a whole.” DAS [0051] teaches: “For example, SHAP (SHapley Additive exPlanations)”. DAS [0101] teaches: “Feature attribution measurement 340 may support the determination of various feature attribution measurements (e.g., using SHAP) as discussed above with regard to FIG. 1.” Examiner’s note: Paragraph [0029] states: “[0029] Contribution determination component 140 may comprise program code executable to determine a contribution of each feature of input data 120 to the determination of classification 130. Contributions 150 may be determined based on model 110 and classification 130. In some embodiments, contributions 150 may comprise SHapely Additive eXplanations (i.e., SHAP values) for each feature of input data 120 as is known in the art.” Therefore, DAS’ feature attribution measurements using SHAP explicitly teaches determine a contribution […] to the determined label based on the trained classification model and the determined label .) Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of POULIN and DAS before them, to include DAS’ feature attribution technique using SHAP, memory, and processor in POULIN’s decision explanation framework. One would have been motivated to make such a combination in order to provide an explanation for a model prediction of a feature and ensure trust in various domains of application, reliable explanations for the behavior of machine learning models as well as insight into how such machine learning models make decisions, allowing for various users or systems that rely upon such decisions to have confidence in the provided decisions (DAS [0021] and [0048]). POULIN in view of DAS is not relied upon for teaching, but MOZINA teaches: for each feature of the set, determine a ratio of a number of the plurality of labeled data instances having a same value as the input data and a fixed label which is the determined label to a number of the plurality of labeled data instances having the same value as the input data and a fixed label which is not the determined label ; (MOZINA [page 4, section 2.1 Derivation of Naïve Bayesian Nomogram] teaches: “For each attribute value, we compute the individual contributions (i.e. point scores) from the in- stance counts in Table 1. For example, the log odds ratio for the passenger in the first class is 1.25, as the odds for surviving in the first class are 203/122 = 1.67, unconditional odds for surviving are 711/1490 = 0.48, and their log ratio is log(1.67/0.48) = 1.25. Similarly, the log odds ratio for the second-class passengers is log 118 / 167 0.48 = 0.393 ,” Examiner’s note: Under BRI, this limitation can be interpreted as MOZINA’s odds of surviving in the first class 203/122 (i.e., determine a ratio ), where the labels are “YES” for survived and “NO” for not survived, and where 203 is the count of instances with the value “first class” and the determined label “YES” (i.e., a number of the plurality of labeled data instances having a same value as the input data and a fixed label which is the determined label ), and 122 is the count with the value “first class” and a determined label of “NO” (i.e., to a number of the plurality of labeled data instances having the same value as the input data and a fixed label which is not the determined label ). The odds are also calculated for all other values (i.e., for each feature of the set ) in MOZINA’s Table 1, which correspond to feature values. Additionally, MOZINA determines the ratio 118/167 for the second-class values, thus teaching (under BRI) for each feature of the set, determine a ratio .) present an indication of each feature of the set and its determined ratio based on its determined contribution. (MOZINA [page 1, Abstract] teaches: “Nomograms are intuitive and when used for decision support can provide a visual explanation of predicted probabilities.” MOZINA [page 4] teaches: “The individual contribution (point score) of each known attribute value in the nomogram is equal to log OR(ai)”. MOZINA [page 7, section 3.1 Design Principles] teaches: “Attribute axis are aligned to zero-point influence (prior probability), which allows for a straightforward comparison of contributions across different values and attributes.” Additionally, MOZINA [page 5, Fig. 2.] shows a nomogram, where the computed log odds for each value are shown (i.e., present an indication). Therefore, present an indication of each feature of the set and its determined ratio based on its determined contribution can be interpreted as displaying each attribute value together with the point score derived from its ratio.) Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of POULIN, DAS, and MOZINA before them, to include MOZINA’s ratio determination and nomogram in POULIN and DAS’ decision explanation framework. One would have been motivated to make such a combination in order to clearly expose the quantitative information on the effect of attribute values to class probabilities and to use simple graphical objects (points, rulers and lines) that are easier to visualize and comprehend (MOZINA [page 2, section 1 Introduction]). Regarding Claim 2: POULIN in view of DAS and MOZINA teaches the elements of claim 1 as outlined above. DAS further teaches: the at least one processing unit to execute the processor-executable program code to cause the system to : (DAS [0145] teaches: “In the illustrated embodiment, computer system 2000 includes one or more processors 2010 coupled to a system memory 2020 via an input/output (I/O) interface 2030.” DAS [0146] teaches: “In various embodiments, computer system 2000 may be a uniprocessor system including one processor 2010, or a multiprocessor system including several processors 2010 (e.g., two, four, eight, or another suitable number). Processors 2010 may be any suitable processor capable of executing instructions.”) determine a set of labeled data; generate a set of labeled training data instances and a set of labeled testing data instances from the set of labeled data; train the classification model based on the set of labeled training data instances and the set of labeled testing data instances . (DAS [0036] teaches: "Machine learning training stage 130 may take the prepared training data (e.g., which may have passed, been mitigated, or at least understood using pre-training bias measurement 120), and perform various machine learning techniques to train a machine learning model (i.e., train the classification model based on the set of labeled training data instances ). [...] Evaluate test data stage 140 may be where automated or manual techniques (e.g., performed by engineers or data scientists) consider the performance of the machine learning model training stage 130 (i.e., train the classification model based on […] the set of labeled testing data instances ) and the resulting trained model.” DAS [0025] teaches: “The number of true labels of type 0, 1 may be denoted as n 0 , n 1 , respectively, and the number of labels of each class as n a , n d .” Examiner’s note: Under BRI, this limitation can be interpreted as preparing labeled data for training and testing. DAS [0025] and [0036] teaches a labeled training data set because it describes true labels in a training data set. Additionally, under BRI, generate a set of labeled training data instances and a set of labeled testing data instances from the set of labeled data can be interpreted as the determined data from the data set used for training the model during the training stage 130 and the data used for evaluating the model in the evaluate test data stage 140. Furthermore, specification paragraph [0039] states “Training at S215 may comprise execution of training iterations” and “Iterations continue in this manner, with periodic testing of model 340 using the labeled testing data instances”, and thus further supporting training as testing as two separate stages. Moreover, determine a set of labeled data can be interpreted as the determining the training data set that will be used for training and testing DAS’ machine learning model.) Regarding Claim 3: POULIN in view of DAS and MOZINA teaches the elements of claim 1 as outlined above. POULIN further teaches: presentation of the indication of each feature of the set […] in descending order of determined contributions. (POULIN [page 1824, Classification Explanation Capabilities] teaches: “To effectively present the classifier, we display the contribution functions in a meaningful sorted order. Users are often most interested in contributions of features that have the most effect on classification (i.e., in descending order of determined contributions ). By displaying the ranks of evidence, the explanation system displays all the features in the context of the whole classifier.”) MOZINA further teaches: wherein presentation of an indication of each feature of the set and its determined ratio based on its determined contribution comprises: presentation of the indication of each feature of the set and its determined ratio […] (MOZINA [page 1, Abstract] teaches: “Nomograms are intuitive and when used for decision support can provide a visual explanation of predicted probabilities.” MOZINA [page 4] teaches: “The individual contribution (point score) of each known attribute value in the nomogram is equal to log OR(ai)”. MOZINA [page 7, section 3.1 Design Principles] teaches: “Attribute axis are aligned to zero-point influence (prior probability), which allows for a straightforward comparison of contributions across different values and attributes.” Additionally, MOZINA [page 5, Fig. 2.] shows a nomogram, where the computed log odds for each value are shown (i.e., present an indication). Therefore, presentation of the indication of each feature of the set and its determined ratio can be interpreted as displaying each attribute value together with the point score derived from its ratio.) Regarding Claim 4: POULIN in view of DAS and MOZINA teaches the elements of claim 3 as outlined above. POULIN further teaches: present the determined contributions . (POULIN [page 1824, Classification Explanation Capabilities] teaches: “To effectively present the classifier, we display the contribution functions (i.e., present the determined contributions ) in a meaningful sorted order.”) DAS further teaches: the at least one processing unit to execute the processor-executable program code to cause the system to : (DAS [0145] teaches: “In the illustrated embodiment, computer system 2000 includes one or more processors 2010 coupled to a system memory 2020 via an input/output (I/O) interface 2030.” DAS [0146] teaches: “In various embodiments, computer system 2000 may be a uniprocessor system including one processor 2010, or a multiprocessor system including several processors 2010 (e.g., two, four, eight, or another suitable number). Processors 2010 may be any suitable processor capable of executing instructions.”) Regarding Claim 5: POULIN in view of DAS and MOZINA teaches the elements of claim 1 as outlined above. POULIN further teaches: wherein determination of the set of the plurality of features based on the determined contributions comprises: determination of the features associated with largest contributions . (POULIN [page 1824, Capability 1 – decision evidence] teaches: “When there are many features, it is helpful to show the evidence contributions from only a subset of ‘focus’ features. […] The focus features may be selected interactively or by some specified criteria (such as those that give the most evidence for the classification) (i.e., determination of the features associated with the largest contributions ).”) Regarding Claim 6: POULIN in view of DAS and MOZINA teaches the elements of claim 5 as outlined above. POULIN further teaches: presentation of the indication of each feature of the set […] in descending order of determined contributions. (POULIN [page 1824, Classification Explanation Capabilities] teaches: “To effectively present the classifier, we display the contribution functions in a meaningful sorted order. Users are often most interested in contributions of features that have the most effect on classification (i.e., in descending order of determined contributions ). By displaying the ranks of evidence, the explanation system displays all the features in the context of the whole classifier.”) MOZINA further teaches: wherein presentation of an indication of each feature of the set and its determined ratio based on its determined contribution comprises: presentation of the indication of each feature of the set and its determined ratio […] (MOZINA [page 1, Abstract] teaches: “Nomograms are intuitive and when used for decision support can provide a visual explanation of predicted probabilities.” MOZINA [page 4] teaches: “The individual contribution (point score) of each known attribute value in the nomogram is equal to log OR(ai)”. MOZINA [page 7, section 3.1 Design Principles] teaches: “Attribute axis are aligned to zero-point influence (prior probability), which allows for a straightforward comparison of contributions across different values and attributes.” Additionally, MOZINA [page 5, Fig. 2.] shows a nomogram, where the computed log odds for each value are shown (i.e., present an indication). Therefore, presentation of the indication of each feature of the set and its determined ratio can be interpreted as displaying each attribute value together with the point score derived from its ratio.) Regarding Claim 7: POULIN in view of DAS and MOZINA teaches the elements of claim 6 as outlined above. POULIN further teaches: present the determined contributions . (POULIN [page 1824, Classification Explanation Capabilities] teaches: “To effectively present the classifier, we display the contribution functions (i.e., present the determined contributions ) in a meaningful sorted order.”) DAS further teaches: the at least one processing unit to execute the processor-executable program code to cause the system to : (DAS [0145] teaches: “In the illustrated embodiment, computer system 2000 includes one or more processors 2010 coupled to a system memory 2020 via an input/output (I/O) interface 2030.” DAS [0146] teaches: “In various embodiments, computer system 2000 may be a uniprocessor system including one processor 2010, or a multiprocessor system including several processors 2010 (e.g., two, four, eight, or another suitable number). Processors 2010 may be any suitable processor capable of executing instructions.”) Regarding Claim 8: POULIN teaches: A method comprising: receiving input data comprising a value for each of a plurality of features ; (POULIN [page 1823, Classifiers as Additive Models] teaches: “A classifier maps an object described by a set of feature values (i.e., receiving input data comprising a value for each of a plurality of features ) to one of the possible mutually exclusive class labels.”) determining a label and a confidence level associated with the input data using a trained classification model ; (POULIN [page 1822, Introduction] teaches: “A classifier assigns a label (i.e., determining a label […] using a trained classification model ) to an unlabeled query item (i.e., the input data ) based on patterns learned from labeled examples.” POULIN [page 1825, Naïve Bayes] teaches: “The naïve Bayes classifier (NB) is a widely used and effective probabilistic classifier. A NB classification is a simple Bayes classifier that assigns each instance x to the class k with the largest posterior probability: […] Capability 0, the decision, can be represented graphically for the NB classifier by using probabilities. Capability 1, decision evidence, is visualized as described in Section 2.” Examiner’s note: Under BRI, determining a label and a confidence level associated with the input data can be interpreted as POULIN’s posterior probability of the predicted class using a classifier.) determining a contribution of each of the plurality of features of the input data […] (POULIN [page 1823, Classifiers as Additive Models] teaches: “We can generalize this linear model to an additive model (Hastie, Tibshirani, and Friedman 2001) in which the feature contributions are functions of the feature values. g x = b + ∑ j = 1 m f j x j ( 2 ) Each contribution f j x j (i.e., determining a contribution of each of the plurality of features of the input data ) is the contribution of evidence from feature j with value x j to the score of the discriminant function g x .” POULIN [page 1824, Capability 1 – decision evidence] teaches: “The decision evidence represents the strength of the classification decision and the relative contributions of each feature to the decision.” POULIN [page 1823, Classifiers as Additive Models] teaches: “A classifier maps an object described by a set of feature values to one of the possible mutually exclusive class labels. This assignment can be based on a set of discriminant real-valued functions, one function g k x for each possible class label k (Alpaydin 2004). Given a query instance x (with feature values x 1 … x j … x m ), each discriminant provides a score for assigning a class label. The instance is classified with the label of the highest scoring class given the instance.”) determining a set of the plurality of features based on the determined contributions ; (POULIN [page 1824, Capability 1 – decision evidence] teaches: “When there are many features, it is helpful to show the evidence contributions (i.e., based on the determined contributions ) from only a subset of ‘focus’ features. […] The focus features may be selected (i.e., determining a set of the plurality of features ) interactively or by some specified criteria (such as those that give the most evidence for the classification).”) determining a plurality of labeled data instances, each of the plurality of labeled data instances comprising a value for each of the plurality of features and a label ; (POULIN [page 1825, Applications] teaches: “Each protein instance is associated with a vector of binary features, each corresponding to a text keyword or phrase (encoded as ‘+1’ if the corresponding keyword is associated with the protein and ‘-1’ if it is not). For the subcellular location classifier used in this paper, our training set contains 3904 protein sequences. […] In our example, the keywords associated with the query protein are ‘nitrogen fixation', 'transmembrane', 'plasmid', 'plasma membrane', and 'complete proteome'. The true label for our example protein is ‘inner membrane’ (vs. ‘not inner membrane).” POULIN [page 1825, Capability 4 – Source of evidence] teaches: “To audit the relationship between the decision label and a feature, the training data is sliced by label value and sliced (or sorted) by feature, allowing users to inspect whether classifier parameters are compatible with their expectations, based on training data.” Examiner’s note: Under BRI, determining a plurality of labeled data instances can be interpreted as the used training set containing 3904 protein sequences, each associated with a true label (i.e., label ). The value for each of the plurality of features can be interpreted as the binary value +1 or -1 indicating whether the keyword is associated with the protein.) present the determined label and the confidence level ; (POULIN [page 1823, Classification Explanation Capabilities] teaches: “Decision: Represent a predicted classification graphically (i.e., present the determined label ) (Figure 1 LHS).” POULIN [page 1826, Naïve Bayes] teaches: “Capability 0, the decision, can be represented graphically for the NB classifier by using probabilities (i.e., present […] the confidence level ).”) present an indication of each feature of the set, […] and its determined contribution. (POULIN [page 1824, Classification Explanation Capabilities] teaches: “To effectively present the classifier, we display (i.e., present ) the contribution functions in a meaningful sorted order. Users are often most interested in contributions of features that have the most effect on classification (i.e., present an indication of each feature of the set […] and its determined contribution ). By displaying the ranks of evidence, the explanation system displays all the features in the context of the whole classifier.”) POULIN is not relied upon for teaching: determining a contribution […] to the determined label based on the trained classification model and the determined label; for each feature of the set, determine a ratio of a number of the plurality of labeled data instances having a same value as the input data and a label which is the determined label to a number of the plurality of labeled data instances having the same value as the input data and a label which is not the determined label; present an indication of each feature of the set, its determined ratio, […] However, DAS teaches: determining a contribution […] to the determined label based on the trained classification model and the determined label ; (DAS [0048] teaches: “In various embodiments, feature attribution may be determined using Shapley values. Feature attribution measurements can be provided at an instance level, for a specific prediction made by a machine learning model and at a global level for the machine learning model as a whole.” DAS [0051] teaches: “For example, SHAP (SHapley Additive exPlanations)”. DAS [0101] teaches: “Feature attribution measurement 340 may support the determination of various feature attribution measurements (e.g., using SHAP) as discussed above with regard to FIG. 1.” Examiner’s note: Paragraph [0029] states: “[0029] Contribution determination component 140 may comprise program code executable to determine a contribution of each feature of input data 120 to the determination of classification 130. Contributions 150 may be determined based on model 110 and classification 130. In some embodiments, contributions 150 may comprise SHapely Additive eXplanations (i.e., SHAP values) for each feature of input data 120 as is known in the art.” Therefore, DAS’ feature attribution measurements using SHAP explicitly teaches determining a contribution […] to the determined label based on the trained classification model and the determined label .) Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of POULIN and DAS before them, to include DAS’ feature attribution technique using SHAP in POULIN’s decision explanation framework. One would have been motivated to make such a combination in order to provide an explanation for a model prediction of a feature and ensure trust in various domains of application, reliable explanations for the behavior of machine learning models as well as insight into how such machine learning models make decisions, allowing for various users or systems that rely upon such decisions to have confidence in the provided decisions (DAS [0021] and [0048]). POULIN in view of DAS is not relied upon for teaching, but MOZINA teaches: for each feature of the set, determine a ratio of a number of the plurality of labeled data instances having a same value as the input data and a label which is the determined label to a number of the plurality of labeled data instances having the same value as the input data and a label which is not the determined label; (MOZINA [page 4, section 2.1 Derivation of Naïve Bayesian Nomogram] teaches: “For each attribute value, we compute the individual contributions (i.e. point scores) from the in- stance counts in Table 1. For example, the log odds ratio for the passenger in the first class is 1.25, as the odds for surviving in the first class are 203/122 = 1.67, unconditional odds for surviving are 711/1490 = 0.48, and their log ratio is log(1.67/0.48) = 1.25. Similarly, the log odds ratio for the second-class passengers is log 118 / 167 0.48 = 0.393 ,” Examiner’s note: Under BRI, this limitation can be interpreted as MOZINA’s odds of surviving in the first class 203/122 (i.e., determine a ratio ), where the labels are “YES” for survived and “NO” for not survived, and where 203 is the count of instances with the value “first class” and the determined label “YES” (i.e., a number of the plurality of labeled data instances having a same value as the input data and a label which is the determined label ), and 122 is the count with the value “first class” and a determined label of “NO” (i.e., to a number of the plurality of labeled data instances having the same value as the input data and a label which is not the determined label ). The odds are also calculated for all other values (i.e., for each feature of the set ) in MOZINA’s Table 1, which correspond to feature values. Additionally, MOZINA determines the ratio 118/167 for the second-class values, thus teaching (under BRI) for each feature of the set, determine a ratio .) present an indication of each feature of the set, its determined ratio, […]. (MOZINA [page 4, section 2.1 Derivation of Naïve Bayesian Nomogram] teaches: “For each attribute value, we compute the individual contributions (i.e. point scores) from the in- stance counts in Table 1. For example, the log odds ratio for the passenger in the first class is 1.25, as the odds for surviving in the first class are 203/122 = 1.67 (i.e., present an indication of each feature of the set, its determined ratio ).” Examiner’s note: Under BRI, its determined ratio can be interpreted as MOZINA’s odds. The odds are also calculated for all other values (i.e., for each feature of the set ) in MOZINA’s Table 1, which correspond to feature values.) Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of POULIN, DAS, and MOZINA before them, to include MOZINA’s ratio determination and nomogram in POULIN and DAS’ decision explanation framework. One would have been motivated to make such a combination in order to clearly expose the quantitative information on the effect of attribute values to class probabilities and to use simple graphical objects (points, rulers and lines) that are easier to visualize and comprehend (MOZINA [page 2, section 1 Introduction]). Regarding Claim 9: POULIN in view of DAS and MOZINA teaches the elements of claim 8 as outlined above. Additionally, the claim recites similar limitations as corresponding claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale. Regarding Claim 10: POULIN in view of DAS and MOZINA teaches the elements of claim 8 as outlined above. Additionally, the claim recites similar limitations as corresponding claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale. Regarding Claim 11: POULIN in view of DAS and MOZINA teaches the elements of claim 8 as outlined above. Additionally, the claim recites similar limitations as corresponding claim 5 and is rejected for similar reasons as claim 5 using similar teachings and rationale. Regarding Claim 12: POULIN in view of DAS and MOZINA teaches the elements of claim 11 as outlined above. Additionally, the claim recites similar limitations as corresponding claim 10 and is rejected for similar reasons as claim 10 using similar teachings and rationale. Regarding Claim 13: The claim recites similar limitations as corresponding claims 1 and 8 and is rejected for similar reasons as claims 1 and 8 using similar teachings and rationale. POULIN is not relied upon for teaching, but DAS teaches: A non-transitory medium storing program code executable by at least one processing unit of a computing system to cause the computing system to : (DAS [0145] teaches: “In the illustrated embodiment, computer system 2000 includes one or more processors 2010 coupled to a system memory 2020 via an input/output (I/O) interface 2030.” DAS [0146] teaches: “In various embodiments, computer system 2000 may be a uniprocessor system including one processor 2010, or a multiprocessor system including several processors 2010 (e.g., two, four, eight, or another suitable number). Processors 2010 may be any suitable processor capable of executing instructions.” DAS [0154] teaches: “Those skilled in the art will also appreciate that, while various items are illustrated as being stored in memory or on storage while being used, these items or portions of them may be transferred between memory and other storage devices for purposes of memory management and data integrity. Alternatively, in other embodiments some or all of the software components may execute in memory on another device and communicate with the illustrated computer system via inter-computer communication. Some or all of the system components or data structures may also be stored (e.g., as instructions or structured data) on a computer-accessible medium or a portable article to be read by an appropriate drive, various examples of which are described above. In some embodiments, instructions stored on a non-transitory, computer-accessible medium separate from computer system 2000 may be transmitted to computer system 2000 via transmission media or signals such as electrical, electromagnetic, or digital signals, conveyed via a communication medium such as a network and/or a wireless link. Various embodiments may further include receiving, sending or storing instructions and/or data implemented in accordance with the foregoing description upon a computer-accessible medium. Accordingly, the present invention may be practiced with other computer system configurations.”) Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of POULIN and DAS before them, to include DAS’ non-transitory computer-accessible medium storing instructions and processor in POULIN’s decision explanation framework. One would have been motivated to make such a combination in order to implement a method to provide an explanation for a model prediction of a feature and ensure trust in various domains of application, reliable explanations for the behavior of machine learning models as well as insight into how such machine learning models make decisions, allowing for various users or systems that rely upon such decisions to have confidence in the provided decisions (DAS [0021] and [0048]). Regarding Claim 14: POULIN in view of DAS and MOZINA teaches the elements of claim 13 as outlined above. Additionally, the claim recites similar limitations as corresponding claims 2 and 9 and is rejected for similar reasons as claims 2 and 9 using similar teachings and rationale. Regarding Claim 15: POULIN in view of DAS and MOZINA teaches the elements of claim 13 as outlined above. Additionally, the claim recites similar limitations as corresponding claims 3 and 6 and is rejected for similar reasons as claims 3 and 6 using similar teachings and rationale. Regarding Claim 16: POULIN in view of DAS and MOZINA teaches the elements of claim 15 as outlined above. Additionally, the claim recites similar limitations as corresponding claims 4 and 7 and is rejected for similar reasons as claims 4 and 7 using similar teachings and rationale. Regarding Claim 17: POULIN in view of DAS and MOZINA teaches the elements of claim 13 as outlined above. Additionally, the claim recites similar limitations as corresponding claims 5 and 11 and is rejected for similar reasons as claims 5 and 11 using similar teachings and rationale. Regarding Claim 18: POULIN in view of DAS and MOZINA teaches the elements of claim 17 as outlined above. Additionally, the claim recites similar limitations as corresponding claims 3, 6, and 15 and is rejected for similar reasons as claims 3, 6, and 15 using similar teachings and rationale. Regarding Claim 19: POULIN in view of DAS and MOZINA teaches the elements of claim 18 as outlined above. Additionally, the claim recites similar limitations as corresponding claims 4, 7, and 16 and is rejected for similar reasons as claims 4, 7, and 16 using similar teachings and rationale. Regarding Claim 20: POULIN in view of DAS and MOZINA teaches the elements of claim 13 as outlined above. POULIN further teaches: wherein determination of the contribution of each of the plurality of features of the input data to the determined label comprises adding the contribution of one of the features to the contribution of another one of the features . (POULIN [page 1823, Classifiers as Additive Models] teaches: “We can generalize this linear model to an additive model (Hastie, Tibshirani, and Friedman 2001) in which the feature contributions are functions of the feature values. g x = b + ∑ j = 1 m f j x j ( 2 ) Each contribution f j x j is the contribution of evidence from feature j with value x j to the score of the discriminant function g x .” Examiner’s note: Under BRI, adding the contribution of one of the features to the contribution of another one of the features can be interpreted as POULIN’s summation ∑ j = 1 m f j x j , which adds the contribution terms f j x j from j = 1 to m .) Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Alvaro S Laham Bauzo whose telephone number is (571)272-5650. The examiner can normally be reached Mon-Fri 7:30 AM - 11:00 AM | 1:00 PM - 5:30 PM ET. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /A.S.L./ Examiner, Art Unit 2146 /USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146 Application/Control Number: 18/519,549 Page 2 Art Unit: 2146 Application/Control Number: 18/519,549 Page 3 Art Unit: 2146 Application/Control Number: 18/519,549 Page 4 Art Unit: 2146 Application/Control Number: 18/519,549 Page 5 Art Unit: 2146 Application/Control Number: 18/519,549 Page 6 Art Unit: 2146 Application/Control Number: 18/519,549 Page 7 Art Unit: 2146 Application/Control Number: 18/519,549 Page 8 Art Unit: 2146 Application/Control Number: 18/519,549 Page 9 Art Unit: 2146 Application/Control Number: 18/519,549 Page 10 Art Unit: 2146 Application/Control Number: 18/519,549 Page 11 Art Unit: 2146 Application/Control Number: 18/519,549 Page 12 Art Unit: 2146 Application/Control Number: 18/519,549 Page 13 Art Unit: 2146 Application/Control Number: 18/519,549 Page 14 Art Unit: 2146 Application/Control Number: 18/519,549 Page 15 Art Unit: 2146 Application/Control Number: 18/519,549 Page 16 Art Unit: 2146 Application/Control Number: 18/519,549 Page 17 Art Unit: 2146 Application/Control Number: 18/519,549 Page 18 Art Unit: 2146 Application/Control Number: 18/519,549 Page 19 Art Unit: 2146 Application/Control Number: 18/519,549 Page 20 Art Unit: 2146 Application/Control Number: 18/519,549 Page 21 Art Unit: 2146 Application/Control Number: 18/519,549 Page 22 Art Unit: 2146 Application/Control Number: 18/519,549 Page 23 Art Unit: 2146 Application/Control Number: 18/519,549 Page 24 Art Unit: 2146 Application/Control Number: 18/519,549 Page 25 Art Unit: 2146 Application/Control Number: 18/519,549 Page 26 Art Unit: 2146 Application/Control Number: 18/519,549 Page 27 Art Unit: 2146 Application/Control Number: 18/519,549 Page 28 Art Unit: 2146 Application/Control Number: 18/519,549 Page 29 Art Unit: 2146 Application/Control Number: 18/519,549 Page 30 Art Unit: 2146 Application/Control Number: 18/519,549 Page 31 Art Unit: 2146 Application/Control Number: 18/519,549 Page 32 Art Unit: 2146 Application/Control Number: 18/519,549 Page 33 Art Unit: 2146 Application/Control Number: 18/519,549 Page 34 Art Unit: 2146
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