CTNF 17/985,457 CTNF 94458 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. Response to Arguments Regarding the 35 USC 101 rejections, Applicant's arguments filed 02/17/2026 have been fully considered but they are not persuasive. Applicant argues: The claimed invention is directed to an improvement to computer-implemented data-processing technology. Examiner response: Examiner respectfully disagrees. Regarding the limitation “performing an iterative process that includes increasing generalization of the plurality of feature values in the domain of the generalization group and re-evaluating the accuracy of the predictive model to obtain a re-evaluated accuracy, wherein the iterative process continues while the re-evaluated accuracy satisfies the threshold performance value”, a human can reasonably increase the range of features values and observe the accuracy of the ML model to re-evaluate an accuracy. Evaluating an accuracy is directed to a mental process (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III. Furthermore, MPEP 2106.05(a)(II) states “[h]owever, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.” Therefore, re-evaluating of a ML model based on expanding the range of feature values cannot be an improvement. Arguments are not persuasive. Regarding the 35 USC 103 rejections of claims 1, 13, and 18, Applicant’s arguments with respect to claim(s) 1, 13, and 18 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. 07-38-01 AIA Applicant’s arguments, see pages 20-22 of remarks , filed 02/17/2026 , with respect to claim 11 have been fully considered and are persuasive. The 35 USC 103 rejection of claim 11 has been withdrawn. 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 1-8 and 11-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 According to the first part of the analysis, in the instant case, claims 1-8 and 11-12 are directed to a method, claims 13-17 are directed to a computer readable media defined to exclude signals/carrier waves in paragraph [0052] of the specification, and claims 18-20 are directed to a system comprising at least a processor. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Claim 1 recites: Step 2A, Prong 1 “ generating feature explainability data associated with a feature, wherein the feature is in a set of features of a sample represented by input data for a predictive model, wherein the feature explainability data is representative of an explainability value of the feature, wherein the explainability value is based at least in part on a significance of the feature for an output of the predictive model for the sample ” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can generate feature explainability data by determining a score that indicates the importance of the feature. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). “extracting feature value data from the input data, wherein the feature value data is representative of a feature value of the feature for the sample” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can determine the value of a feature based on an observation. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). “constructing a generalization group comprising the feature of the sample, wherein the constructing of the generalization group comprises detecting that the feature value of the feature and the explainability value of the feature satisfy a predetermined condition” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can group feature based on their feature and explainability values. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). “generating generalized domain data indicative of a generalized domain, wherein the generalized domain comprises a generalized feature value, wherein the generalized feature value corresponds to a plurality of feature values in a domain of the generalization group, wherein the plurality of feature values comprises the feature value of the feature for the sample, whereby the generalized feature is a generalization of the feature of the sample” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can generate domain data by normalizing feature values into a range for a feature sample. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). “comparing an accuracy of the predictive model to a threshold performance value, wherein the accuracy is based on outputs of the predictive model using the generalized feature value” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can compare the accuracy of a model to a threshold. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). “mapping, responsive to the accuracy being above the threshold performance value, the plurality of feature values in the domain of the generalization group to the generalized feature value” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can map feature values to a group based on the accuracy exceeding a threshold. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). “ performing an iterative process that includes increasing generalization of the plurality of feature values in the domain of the generalization group and re-evaluating the accuracy of the predictive model to obtain a re-evaluated accuracy, wherein the iterative process continues while the re-evaluated accuracy satisfies the threshold performance value ” (This step is a recitation of a mental process that is practical to perform in the human mind. A can increase the range for feature values and re-evaluate the accuracy of the model to determine the accuracy threshold is satisfied. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 & 2B The claim does not recite any additional elements. Claim 2 recites: Step 2A, Prong 1 “ wherein the predetermined condition comprises at least one of a first threshold minimum difference between feature values and a second threshold minimum difference between explainability values ” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can group features based on the feature value and the explainability value meeting a minimum threshold. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 & 2B The claim does not recite any additional elements. Claim 3 recites: Step 2A, Prong 1 “ wherein the predetermined condition comprises at least one of a first range of feature values and a second range of explainability values ” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can group features based on the feature value and the explainability value falling within a particular range of values. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 & 2B The claim does not recite any additional elements. Claim 4 recites: Step 2A, Prong 1 “wherein the predetermined condition comprises at least one of a first variance of feature values and a second variance of explainability values” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can group features based on the variance of the feature value and the explainability value. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 & 2B The claim does not recite any additional elements. Claim 5 recites: Step 2A, Prong 1 “wherein the constructing of the generalization group comprises identifying a continuous range of numerical feature values having explainability values that satisfy the predetermined condition” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can group features based on the feature value falling withing a particular range and the explainability value meeting a threshold. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 & 2B The claim does not recite any additional elements. Claim 6 recites: Step 2A, Prong 1 “computing the generalized feature value to be a median value of the continuous range of numerical feature values” (This step is directed to a mathematical concept. See MPEP § 2106.04(a)(2), subsection I.) Step 2A, Prong 2 & 2B The claim does not recite any additional elements. Claim 7 recites: Step 2A, Prong 1 “wherein the constructing of the generalization group comprises identifying a plurality of categorical feature values having explainability values that satisfy the predetermined condition” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can group features based on the feature value belonging to a same category. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 & 2B The claim does not recite any additional elements. Claim 8 recites: Step 2A, Prong 1 “selecting one of the plurality of categorical feature values as the generalized feature value in the generalized domain” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can select a categorical feature value. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 & 2B The claim does not recite any additional elements. Claim 11 recites: Step 2A, Prong 1 “including at least one additional feature value in the plurality of feature values in the domain of the generalization group resulting in an expanded range of the plurality of feature values” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can incorporate an additional feature value to expand a range. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). “mapping, responsive to the expanded range of the plurality of feature values satisfying the threshold performance value, the expanded range of the plurality of feature values to the generalized feature value” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can map feature values to a expanded range of values based on the accuracy exceeding a threshold. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). “determining the accuracy of the predictive model based on the outputs of the predictive model while the predictive model is receiving the generalized feature value in place of the plurality of feature values in the expanded range of the plurality of feature values” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can map feature values to a group based on the accuracy exceeding a threshold. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 & 2B The claim does not recite any additional elements. Claim 12 recites: Step 2A, Prong 1 This claim recites at least the abstract idea identified above in claim 1. Step 2A, Prong 2 “ retraining the predictive model using generalized input data, wherein the generalized input data includes the generalized feature value ” (linking judicial exception to a field of use. See MPEP 2106.05(h).) This judicial exception is not integrated into a practical application. Step 2B “ retraining the predictive model using generalized input data, wherein the generalized input data includes the generalized feature value ” (linking judicial exception to a field of use. See MPEP 2106.05(h).) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 13 recites: Step 2A, Prong 1 See rejection of claim 1. Same rationale applies. Step 2A, Prong 2 & 2B The claim recites additional elements (“A computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by a processor to cause the processor to perform operations”). (Mere instructions to apply the exception using a generic computer component. See 2106.05(f).) This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 14 recites: Step 2A, Prong 1 This claim recites at least the abstract idea identified above in claim 13. Step 2A, Prong 2 “ wherein the stored program instructions are stored in a computer readable storage device in a data processing system ” (insignificant extra-solution activity) “ wherein the stored program instructions are transferred over a network from a remote data processing system ” (insignificant extra-solution activity) This judicial exception is not integrated into a practical application. Step 2B “ wherein the stored program instructions are stored in a computer readable storage device in a data processing system ” (This step appears to be directed to storing and retrieving data from memory, which is well-understood, routine, and conventional. iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; See MPEP 2106.05 (d) (II).) “ wherein the stored program instructions are transferred over a network from a remote data processing system ” (This step appears to be directed to transmitting or receiving information, which is well-understood, routine, and conventional. 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); See MPEP 2106.05 (d) (II).) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 15 recites: Step 2A, Prong 1 This claim recites at least the abstract idea identified above in claim 13. Step 2A, Prong 2 “ wherein the stored program instructions are stored in a computer readable storage device in a server data processing system, and wherein the stored program instructions are downloaded in response to a request over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system ” (insignificant extra-solution activity) “ program instructions to meter use of the program instructions associated with the request ” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) “ program instructions to generate an invoice based on the metered use ” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) This judicial exception is not integrated into a practical application. Step 2B “ wherein the stored program instructions are stored in a computer readable storage device in a server data processing system, and wherein the stored program instructions are downloaded in response to a request over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system ” (This step appears to be directed to transmitting or receiving information, which is well-understood, routine, and conventional. 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); See MPEP 2106.05 (d) (II).) “ program instructions to meter use of the program instructions associated with the request ” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) “ program instructions to generate an invoice based on the metered use ” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 16 recites: See rejection of claim 5. Same rationale applies. Claim 17 recites: See rejection of claim 7. Same rationale applies. Claim 18 recites: Step 2A, Prong 1 See rejection of claim 1. Same rationale applies. Step 2A, Prong 2 & 2B The claim recites additional elements (“A computer system comprising a processor and one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by the processor to cause the processor to perform operations”). (Mere instructions to apply the exception using a generic computer component. See 2106.05(f).) This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 19 recites: See rejection of claim 5. Same rationale applies. Claim 20 recites: See rejection of claim 7. Same rationale applies. Claim Rejections - 35 USC § 103 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. 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, 4-5, 7-8, 12-13, 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Khasanova et al. (US-20230334343-A1) in view of Lafond et al. (US-20220027764-A1) and Sikka et al. (US-20230075932-A1) . Regarding Claim 1 , Khasanova (US 20230334343 A1) teaches a computer-implemented method comprising: generating feature explainability data associated with a feature, wherein the feature is in a set of features of a sample represented by input data for a predictive model ( para [0040], [0045], [0145] ), wherein the feature explainability data is representative of an explainability value of the feature, wherein the explainability value is based at least in part on a significance of the feature for an output of the predictive model for the sample ( para [0091] “Super-features 131-133 may be ranked (i.e. sorted) by importance score to establish a relative ordering of influence of super-features 131-133 on the inferential operation of ML model 160.” Importance score (i.e., feature explainability data. ); extracting feature value data from the input data, wherein the feature value data is representative of a feature value of the feature for the sample ( para [0109] “Step 204 generates the target feature vector that contains values for features F1-F7.” ); constructing a generalization group comprising the feature of the sample ( para [0025] “The features are grouped into predefined super-features that each contain a disjoint (i.e. nonintersecting, mutually exclusive) subset of features.” ), wherein the constructing of the generalization group comprises detecting that the feature value of the feature ( para [0025] “The features are grouped into predefined super-features that each contain a disjoint (i.e. nonintersecting, mutually exclusive) subset of features.” Features are grouped based on a predefined condition. ) and the explainability value of the feature satisfy a predetermined condition ( para [0092] “Within memory of computer 100, a local explanation may be a data structure that is based on or contains a ranking of super-features 131-133 by importance score and/or exclude a threshold count of least influential super-features or super-features whose importance score falls below a threshold.” Importance score (explainability data) meets a threshold for super feature group. ); and generating generalized domain data indicative of a generalized domain, wherein the generalized domain comprises a generalized feature value, wherein the generalized feature value corresponds to a plurality of feature values in a domain of the generalization group, wherein the plurality of feature values comprises the feature value of the feature for the sample, whereby the generalized feature is a generalization of the feature value of the sample ( para [0098] “Feature values may be normalized in various ways for feature encoding and/or distance calculation. For example, a difference/distance between adjacent months in a year may be less than a distance between adjacent days in a week. A Mahalanobis distance is based on feature values that are normalized to standard deviations of the respective features. For example if most prices are in range of $1-$100, then a Mahalanobis distance between $30 and $90 may be less than the distance between $90 and $110.” Generalized (i.e., normalized). ). comparing an accuracy of the predictive model to a threshold performance value ( para [0147] “In effect, the output of the objective function indicates the accuracy of the machine learning model based on the particular state of the model artifact in the iteration. By applying an optimization algorithm based on the objective function, the theta values of the model artifact are adjusted. An example of an optimization algorithm is gradient descent. The iterations may be repeated until a desired accuracy is achieved or some other criteria is met.” ), wherein the accuracy is based on outputs of the predictive model using the generalized feature value ( para [0098] “Feature values may be normalized in various ways for feature encoding and/or distance calculation. ). Khasanova does not explicitly disclose mapping, responsive to the accuracy being above the threshold performance value, the plurality of feature values in the domain of the generalization group to the generalized feature value; and performing an iterative process that includes increasing generalization of the plurality of feature values in the domain of the generalization group and re-evaluating the accuracy of the predictive model to obtain a re-evaluated accuracy, wherein the iterative process continues while the re-evaluated accuracy satisfies the threshold performance value. However, Lafond (US 20220027764 A1) teaches mapping, responsive to the accuracy being above the threshold performance value ( para [0263] “The query strategy procedure obtains an aggregated prediction and an associated aggregated accuracy metric by summing the respective predictions weighted by their respective accuracy metrics, and selects the feature vector for labelling only if the aggregated accuracy metric is above (or below) a predetermined threshold.” ), the plurality of feature values in the domain of the generalization group to the generalized feature value ( para [0229] “In one or more embodiments, the instance selection procedure obtains, for each feature of a set of features relevant to the prediction task, a respective feature type, and a respective feature value range. The set of features including feature types and feature value ranges may be for example input by an operator or may be obtained from the database 230. In one or more alternative embodiments, the set of features including feature types and feature value ranges may be obtained using unsupervised learning methods.” Feature mapped to range (i.e., generalized feature value). ). Khasanova and Lafond are analogous because they are directed towards the same field of endeavor of machine learning. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the machine learning model of Khasanova with the training sample selection of Lafond. Doing so would allow for incrementally improving the sample selection process based on new knowledge gained by learning after each successive example and building a balanced and representative training dataset to improve a model's prediction (Lafond para [0018] ). However, Sikka (US 20230075932 A1) teaches mapping, responsive to the accuracy being above the threshold performance value ( para [0035] “Thus, input quantization engine 122 finds the lowest quantization resolution that can be applied to individual feature values 210 and/or a given set of feature values 210 to produce a “correct” output (i.e., an output that is the same as or within a threshold of the output generated by machine learning model 208 from the unquantized feature values 210 and/or a target value for the unquantized feature values 210).” ), the plurality of feature values in the domain of the generalization group to the generalized feature value ( para [0111] “Feature values in the test dataset for the machine learning model could also be mapped to different quantization levels for weights and biases in the neural network.” ); and performing an iterative process that includes increasing generalization of the plurality of feature values in the domain of the generalization group and re-evaluating the accuracy of the predictive model to obtain a re-evaluated accuracy ( para [0110] “When a quantized version of the machine learning model associated with a given quantization resolution results in a correct prediction for a range, combination, or set of feature values, the quantization resolution is gradually lowered to find the lowest quantization resolution of the machine learning model that produces the correct prediction for that range, combination, or set of feature values.” Lowering quantization resolution (i.e., increasing generalization). Para [0040] “During this column-based quantization, input quantization engine 122 initially converts full-precision feature values 210 for each feature in training dataset 202 (and/or another dataset with feature values 210 and/or target values 212) into a set of quantized feature values 220 at a low quantization resolution (i.e., a low number of quantization levels).” Low number of quantization levels (i.e., increased generalization). ) , wherein the iterative process continues while the re-evaluated accuracy satisfies the threshold performance value ( para [0049] “These best performance metrics 226 could be used as one or more benchmarks for additional quantization of machine learning model 208, so that machine learning model 208 is further quantized to lower quantization resolutions until a given performance metric and/or the overall performance metric falls below a threshold from the corresponding benchmark.” Para [0110] “More specifically, the test accuracy, inference speed, or another measure of performance for the quantized machine learning model is evaluated at different quantization resolutions for various ranges, combinations, or sets of feature values in a test dataset (which can be quantized or unquantized” ) . Khasanova and Sikka are analogous because they are directed towards the same field of endeavor of machine learning. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the machine learning model of Lafond with the feature quantization of Sikka. Doing so would allow for adapting the quantization of the machine learning model to different numbers and/or types of features, target values to be predicted from the features, target hardware platforms, and/or latency requirements (Sikka para [0010] ). Regarding Claim 4 , Khasanova, Lafond, and Sikka teach the computer-implemented method of claim 1. Khasanova further teaches wherein the predetermined condition comprises at least one of a first variance of feature values ( para “[0098] Feature values may be normalized in various ways for feature encoding and/or distance calculation. For example, a difference/distance between adjacent months in a year may be less than a distance between adjacent days in a week. A Mahalanobis distance is based on feature values that are normalized to standard deviations of the respective features. For example if most prices are in range of $1-$100, then a Mahalanobis distance between $30 and $90 may be less than the distance between $90 and $110.” The deviation (i.e., variance). ) and a second variance of explainability values ( para [0092] The importance score (i.e., explainability) cannot vary beyond the specified threshold (e.g., less than .4). ). Regarding Claim 5 , Khasanova, Lafond, and Sikka teach the computer-implemented method of claim 1. Khasanova further teaches wherein the constructing of the generalization group comprises identifying a continuous range of numerical feature values ( para [0098] “A Mahalanobis distance is based on feature values that are normalized to standard deviations of the respective features. For example if most prices are in range of $1-$100, then a Mahalanobis distance between $30 and $90 may be less than the distance between $90 and $110.” ) having explainability values that satisfy the predetermined condition ( para [0092] ). Regarding Claim 7 , Khasanova, Lafond, and Sikka teach the computer-implemented method of claim 1. Khasanova further teaches wherein the constructing of the generalization group comprises identifying a plurality of categorical feature values ( para [0041] ) having explainability values that satisfy the predetermined condition ( para [0092] ). Regarding Claim 8 , Khasanova, Lafond, and Sikka teach the computer-implemented method of claim 7. Khasanova further teaches further comprising: selecting one of the plurality of categorical feature values as the generalized feature value in the generalized domain ( para [0098] “Feature values may be normalized in various ways for feature encoding and/or distance calculation. For example, a difference/distance between adjacent months in a year may be less than a distance between adjacent days in a week.” The feature value belongs to a category which is normalized (i.e., generalized). ). Regarding Claim 12 , Khasanova, Lafond, and Sikka teach the computer-implemented method of claim 1. Khasanova further teaches further comprising: retraining the predictive model using generalized input data, wherein the generalized input data includes the generalized feature value ( para [0114] “Step 206 regenerates and retrains surrogate model 145 for each distinct tuple to explain T5.” ). Regarding Claim 13 , Claim 13 is the program product corresponding to the method of claim 1. Claim 13 is substantially similar to claim 1 and is rejected on the same grounds. Regarding Claim 16 , Claim 16 is the program product corresponding to the method of claim 5. Claim 16 is substantially similar to claim 5 and is rejected on the same grounds. Regarding Claim 17 , Claim 17 is the program product corresponding to the method of claim 7. Claim 17 is substantially similar to claim 7 and is rejected on the same grounds. Regarding Claim 18 , Claim 18 is the system corresponding to the method of claim 1. Claim 18 is substantially similar to claim 1 and is rejected on the same grounds. Regarding Claim 19 , Claim 19 is the system corresponding to the method of claim 5. Claim 19 is substantially similar to claim 5 and is rejected on the same grounds. Regarding Claim 20 , Claim 20 is the system corresponding to the method of claim 7. Claim 20 is substantially similar to claim 7 and is rejected on the same grounds . 07-21-aia AIA Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Khasanova et al. (US-20230334343-A1) in view of Lafond et al. (US-20220027764-A1), Sikka et al. (US-20230075932-A1), and Nagase et al. (US-20220391718-A1) . Regarding Claim 2 , Khasanova, Lafond, and Sikka teach the computer-implemented method of claim 1, wherein the predetermined condition comprises at least a second threshold minimum difference between explainability values ( para [0092] ). Khasanova and Lafond do not explicitly disclose wherein the predetermined condition comprises at least one of a first threshold minimum difference between feature values However, Nagase (US 20220391718 A1) teaches wherein the predetermined condition comprises at least one of a first threshold minimum difference between feature values ( para [0144] “Of course, the selection processing unit 104 may consider only one of the equivalent feature set or the inclusion feature set as the similarity, or may create another index. For example, a method of obtaining a vector distance between the feature amounts (distance between discrimination possibility vectors) and regarding the vector distance equal to or less than a certain threshold value as the similar feature amount can be considered.” ). Khasanova, Lafond, Sikka, and Nagase are analogous because they are directed towards the same field of endeavor of machine learning. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the machine learning model of Khasanova with the feature selection of Nagase. Doing so would allow for improving the accuracy of the classification for the machine learning model (Nagase para [0221] ) . 07-21-aia AIA Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Khasanova et al. (US-20230334343-A1) in view of Lafond et al. (US-20220027764-A1), Sikka et al. (US-20230075932-A1), and Wei et al. (US-20220036187-A1) . Regarding Claim 3 , Khasanova, Lafond, and Sikka teach the computer-implemented method of claim 1. Khasanova further teaches wherein the predetermined condition comprises a second range of explainability values ( para [0092] “For example a local explanation may be limited to a top two most influential super-features or a variable count of super-features having an importance score of at least 0.4. Explanation generation is discussed later herein.” ). Khasanova, Lafond, and Sikka do not explicitly disclose wherein the predetermined condition comprises at least one of a first range of feature values However, Wei (US 20220036187 A1) teaches wherein the predetermined condition comprises at least one of a first range of feature values ( para [0129] “The method further includes: selecting target features from the real category feature vectors, and evenly selecting feature values within a preset range for the target features, and maintaining feature values of non-target features of the real category feature vectors unchanged, to obtain a plurality of associated real category feature vectors.” ) Khasanova, Lafond, Sikka, and Wei are analogous because they are directed towards the same field of endeavor of machine learning. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the machine learning model of Khasanova and Lafond with the feature selection of Wei. Doing so would allow for generating a synthetic training set to update and train the model in order to improve the accuracy of the classifier (Wei para [0063] ) . 07-21-aia AIA Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Khasanova et al. (US-20230334343-A1) in view of Lafond et al. (US-20220027764-A1), Sikka et al. (US-20230075932-A1), and Ishii et al. (US-20210174702-A1) . Regarding Claim 6 , Khasanova, Lafond, and Sikka teach the computer-implemented method of claim 5. Khasanova, Lafond, and Sikka do not explicitly disclose further comprising: computing the generalized feature value to be a median value of the continuous range of numerical feature values. However, Ishii (US 20210174702 A1) teaches further comprising: computing the generalized feature value to be a median value of the continuous range of numerical feature values ( para [0158] “As the prescribed values, it is possible to use, for example, a value such as the median value in the range of the feature quantity that was not acquired, or the mode value, the mean value, the minimum value, or the like of the feature quantities in the training data.” ). Khasanova, Lafond, Sikka, and Ishii are analogous because they are directed towards the same field of endeavor of machine learning. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the machine learning model of Khasanova with the training data of Ishii. Doing so would allow for substituting missing feature values in training data with a median value as the replacement value for the missing feature (Ishii para [0158] ) . 07-21-aia AIA Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Khasanova et al. (US-20230334343-A1) in view of Lafond et al. (US-20220027764-A1), Sikka et al. (US-20230075932-A1), and Kim et al. (US-20230419962-A1) . Regarding Claim 14 , Khasanova, Lafond, and Sikka teach the computer program product of claim 13. Khasanova, Lafond, and Sikka do not explicitly disclose wherein the stored program instructions are stored in a computer readable storage device in a data processing system, and wherein the stored program instructions are transferred over a network from a remote data processing system. However, Kim (US 20230419962 A1) teaches wherein the stored program instructions are stored in a computer readable storage device in a data processing system, and wherein the stored program instructions are transferred over a network from a remote data processing system ( para [0053] “Here, the electronic device 101 can transmit requests including inputs (such as captured audio data) to the server 106 for processing of the inputs using the machine learning models, and the results can be sent back to the electronic device 101. In addition, in some embodiments, the electronic device 101 can be replaced by the server 106, which receives audio inputs from a client device and transmits instructions back to the client device to execute functions associated with instructions included in utterances.” ) Khasanova, Lafond, Sikka and Kim are analogous because they are directed towards the same field of endeavor of machine learning. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the machine learning model of Khasanova, Lafond, and Sikka with the method of transmitting instructions of Kim. Doing so would allow for providing the services of the machine learning model to a client device (Kim para [0053] ) . 07-21-aia AIA Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Khasanova et al. (US-20230334343-A1) in view of Lafond et al. (US-20220027764-A1), Sikka et al. (US-20230075932-A1), and Yu et al. (US-20210297322-A1) . Regarding Claim 15 , Khasanova, Lafond, and Sikka teach the computer program product of claim 13. Khasanova, Lafond, and Sikka do not explicitly disclose wherein the stored program instructions are stored in a computer readable storage device in a server data processing system, and wherein the stored program instructions are downloaded in response to a request over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system, further comprising: program instructions to meter use of the program instructions associated with the request; and program instructions to generate an invoice based on the metered use. However, Yu (US 20210297322 A1) teaches wherein the stored program instructions are stored in a computer readable storage device in a server data processing system, and wherein the stored program instructions are downloaded in response to a request over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system ( para [0113] “Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network.” ), further comprising: program instructions to meter use of the program instructions associated with the request ( para [0071] “In one example, management layer 80 may provide the functions described below. Resource provisioning 81 provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources.” ); and program instructions to generate an invoice based on the metered use ( para [0071] “Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources.” ). Khasanova, Lafond, Sikka, and Yu are analogous because they are directed towards the same field of endeavor of machine learning. It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the machine learning model of Khasanova with the cloud services of Yu. Doing so would allow for doing so would allow for dynamically providing resources to a client device at a cost (Yu para [0071] ). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to HENRY K NGUYEN whose telephone number is (571)272-0217. The examiner can normally be reached Mon - Fri 7:00am-4:30pm. 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, Li B Zhen can be reached at 5712723768. 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. /HENRY NGUYEN/Examiner, Art Unit 2121 Application/Control Number: 17/985,457 Page 2 Art Unit: 2121 Application/Control Number: 17/985,457 Page 3 Art Unit: 2121 Application/Control Number: 17/985,457 Page 4 Art Unit: 2121 Application/Control Number: 17/985,457 Page 5 Art Unit: 2121 Application/Control Number: 17/985,457 Page 6 Art Unit: 2121 Application/Control Number: 17/985,457 Page 7 Art Unit: 2121 Application/Control Number: 17/985,457 Page 8 Art Unit: 2121 Application/Control Number: 17/985,457 Page 9 Art Unit: 2121 Application/Control Number: 17/985,457 Page 10 Art Unit: 2121 Application/Control Number: 17/985,457 Page 11 Art Unit: 2121 Application/Control Number: 17/985,457 Page 12 Art Unit: 2121 Application/Control Number: 17/985,457 Page 13 Art Unit: 2121 Application/Control Number: 17/985,457 Page 14 Art Unit: 2121 Application/Control Number: 17/985,457 Page 15 Art Unit: 2121 Application/Control Number: 17/985,457 Page 16 Art Unit: 2121 Application/Control Number: 17/985,457 Page 17 Art Unit: 2121 Application/Control Number: 17/985,457 Page 18 Art Unit: 2121 Application/Control Number: 17/985,457 Page 19 Art Unit: 2121 Application/Control Number: 17/985,457 Page 20 Art Unit: 2121 Application/Control Number: 17/985,457 Page 21 Art Unit: 2121 Application/Control Number: 17/985,457 Page 22 Art Unit: 2121 Application/Control Number: 17/985,457 Page 23 Art Unit: 2121 Application/Control Number: 17/985,457 Page 24 Art Unit: 2121 Application/Control Number: 17/985,457 Page 25 Art Unit: 2121 Application/Control Number: 17/985,457 Page 26 Art Unit: 2121 Application/Control Number: 17/985,457 Page 27 Art Unit: 2121 Application/Control Number: 17/985,457 Page 28 Art Unit: 2121 Application/Control Number: 17/985,457 Page 29 Art Unit: 2121 Application/Control Number: 17/985,457 Page 30 Art Unit: 2121 Application/Control Number: 17/985,457 Page 31 Art Unit: 2121
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