Notice of Pre-AIA or AIA Status 1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 2. Claims 1- 18 are pending in this office action. This action is responsive to Applicant’s application filed 05/ 08/2023 . Information Disclosure Statement 3. The references listed in the IDS filed 05/08/2023, 05/19/2023, and 12/11/2023 has been considered. A copy of the signed or initialed IDS is hereby attached. Claim Objections 4 . Claims 2-3, 8-9, and 14-15 are objected to because of the following informalities: The claim limitation “ by performing selection such that a difference between a total number of training data classified in the first group … ” , include the clause “ such that ” which make the claim scope is not limited by claim language (see MPEP 2173.05 (d)). Appropriate correction is required. MPEP 2173.05 (d) “such as,” “such that,” Clauses Description of examples or preferences is properly set forth in the specification rather than the claims. If stated in the claims, examples and preferences may lead to confusion over the intended scope of a claim. In those instances where it is not clear whether the claimed narrower range is a limitation, a rejection under 35 U.S.C. 112 , second paragraph should be made. The examiner should analyze whether the metes and bounds of the claim are clearly set forth. Examples of claim language which have been held to be indefinite because the intended scope of the claim was unclear are: (A) "R is halogen, for example, chlorine"; (B) "material such as rock wool or asbestos" Ex parte Hall , 83 USPQ 38 (Bd. App. 1949); (C) "lighter hydrocarbons, such, for example, as the vapors or gas produced" Ex parte Hasche , 86 USPQ 481 (Bd. App. 1949); and (D) "normal operating conditions such as while in the container of a proportioner" Ex parte Steigerwald , 131 USPQ 74 (Bd. App. 1961). >The above examples of claim language which have been held to be indefinite are fact specific and should not be applied as per se rules. See MPEP § 2173.02 for guidance regarding when it is appropriate to make a rejection under 35 U.S.C. 112 , second paragraph. Claim Rejections - 35 USC § 102 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (e) the invention was described in (1) an application for patent, published under section 122(b), by another filed in the United States before the invention by the applicant for patent or (2) a patent granted on an application for patent by another filed in the United States before the invention by the applicant for patent, except that an international application filed under the treaty defined in section 351(a) shall have the effects for purposes of this subsection of an application filed in the United States only if the international application designated the United States and was published under Article 21(2) of such treaty in the English language. 5 . Claims 1- 18 are rejected under 35 U.S.C. 102(a1)(a2) as being anticipated by Wang et al. (US Patent Publication No. 20 24/0177071 A1, hereinafter “ Wang ”) . As to Claim 1, Wang teaches the claimed limitations: “ A non-transitory recording medium storing a program that causes a computer to execute a training data generation process comprising ” as a computer program product comprising at least one non-transitory computer-readable medium including program instructions that, when executed by at least one processor, cause the at least one processor to: receive a dataset of data instances (paragraph 0028). “ classifying, based on a feature value, each of a first plurality of training data having a first attribute and each of a second plurality of training data having a second attribute that are contained in a plurality of training data ” as i nterpreting classification models is attracting increasingly more attention in recent years and numerous solutions have been proposed. M odel interpretations can be categorized into model-specific interpretations and model-agnostic interpretations. Model-specific interpretations consider classification models as white-boxes, where people have access to all internal details, most interpretations for deep learning models visualize and investigate the internal neurons' activation to disclose how data were transformed internally. Model-agnostic interpretations regard predictive models as black-boxes, where only the models' input and output are available. These approaches often employ an interpretable surrogate model to mimic or probe the behavior of the interpreted models locally or globally. For example, Local Interpretable Model-Agnostic Explanation uses a linear model as a surrogate to simulate the local behavior of the more complicated classifier to be interpreted. Deep Visual Interpretation and Diagnosis for Image Classifiers via Knowledge Distillation trains an interpretable model using the knowledge distilled from the original classifier for interpretation. RuleMatrix converts classification models as a set of standardized IF-THEN-ELSE rules using only the models' input-output behaviors. A common goal for both groups of interpretation solutions is to answer the question. There are also solutions to statistically quantify features' importance (paragraph s 0003- 0004). To see which oracle features contribute most to the disagreement at a given point, model comparison system may train two XGBoost trees, one on instances in Group Z1 and Group Y1, and another on instances in Group Y2 and Group Z2 in the paper entitled Learning-from-disagreement: A model comparison and visual analytics framework submitted to IEEE Transactions on Visualization and Computer Graphics, the entire contents of which are incorporated by reference, and rank feature impact magnitude or importance based on their SHAP values . A unified approach to interpreting model predictions, the entire contents of each of which are incorporated by reference. Because XGBoost trees need all of the features to be numerical, all categorical features are converted into numerical features using some feature encoding mechanism (paragraphs 0173, 0279). “ based on a comparison of a number of training data classified in a first group from among the first plurality of training data against a number of training data classified in a second group from among the first plurality of training data, selecting a third plurality of training data from training data classified in a third group from among the second plurality of training data and training data classified in a fourth group from among the second plurality of training data, the third group corresponding to the first group, the fourth group corresponding to the second group ” as s ystems, methods, and computer program products may compare machine learning models by identifying data instances with disagreed predictions and learning from the disagreement. Based on a model interpretation technique, differences between the compared machine learning models may be interpreted. Multiple metrics to prioritize meta-features from different perspectives (abstract). Two classifiers can be compared from various perspectives using different numerical metrics (e.g., accuracy, precision ) which may help to select models with an overall better performance. Multiple model-agnostic visualization and comparison solutions have been proposed based on these metrics because generating these metrics does not need to open the “black-box” of different classifiers (paragraphs 0003-0005). Many visual analytics works have tried to go beyond these aggregated metrics for more comprehensive model comparisons. For example, Manifold® compares two models by disclosing the agreed and disagreed predictions. The comparison is model-agnostic, and for user-selected instances . These existing comparison works mostly rely on humans' visual comprehension to identify models' behavior differences (paragraph s 0006 , 0012 ). G enerating the plurality of groups of samples of the plurality of samples includes: determining a first group of samples of the plurality of samples for which a first prediction of the plurality of first predictions matches a label of the plurality of labels and a second prediction of the plurality of second predictions matches the label of the plurality of labels; determining, with the at least one processor, a second group of samples of the plurality of samples for which the second prediction of the plurality of second predictions matches the label of the plurality of labels and the first prediction of the plurality of first predictions does not match the label of the plurality of labels; determining a third group of samples of the plurality of samples for which the first prediction of the plurality of first predictions matches the label of the plurality of labels and the second prediction of the plurality of second predictions does not match the label of the plurality of labels; determining, with the at least one processor, a fourth group of samples of the plurality of samples for which the second prediction of the plurality of second predictions does not match the label of the plurality of labels and the first prediction of the plurality of first predictions does not match the label of the plurality of labels; determining, with the at least one processor, a fifth group of samples of the plurality of samples for which the first prediction of the plurality of first predictions does not match the label of the plurality of labels and the second prediction of the plurality of second predictions matches the label of the plurality of labels; and determining, with the at least one processor, a sixth group of samples of the plurality of samples for which the second prediction of the plurality of second predictions does not match the label of the plurality of labels and the first prediction of the plurality of first predictions matches the label of the plurality of labels (paragraphs 0036-0037, 0040). “ converting each of the third plurality of training data into a fourth plurality of training data having the first attribute ” as t here are multiple existing solutions to the individual problems of model interpretation and model comparison. For interpretation, Local Interpretable Model-Agnostic Explanation (LIME) and SHapley Additive exPlanations (SHAP) are two well-known examples, which attribute a classifier's prediction output back to individual input features. For comparison, Manifold® uses likelihood scores from a pair of classifiers to reflect a level of agreement/disagreement between the classifiers. However, existing solutions fail to address and solve each of these problems simultaneously by comparatively interpreting multiple classifiers. For Example, considering the following scenario in a spam filtering (e.g., remove) application, where machine learning (ML) practitioners need to choose (e.g., select) between two spam classifiers model A and model B, the ML practitioners, using LIME, may find the number of URLs (n_url) in an email is an important feature to model A. Similarly, n_url may also be an important feature to model B based on LIME's interpretations. Comparing A and B with different numerical metrics (e.g., accuracy), each model may show similar overall performance with small differences. If a new email with a large n_url value is received and the predictions from model A and model B are very different, which prediction should be trusted? select models in this scenario, as n_url is an important feature to each of the models A and B (paragraphs 0139, 0279). As to Claim 2, Wang teaches the claimed limitations: “ wherein the selecting of the third plurality of training data includes selecting for the third group and for the fourth group a respective number of training data corresponding to an augmentation item number in a case in which the number of training data is to be augmented in each of the first group and the second group, by performing selection such that a difference between a total number of training data classified in the first group and the second group and a total number of training data classified in the third group and the fourth group from the second plurality of training data is a difference lying within a first threshold while maintaining a proportion of a number of training data classified in the first group to a number of training data classified in the second group from among the first plurality of training data ” as (paragraphs 0006, 0009, 0012, 0141, 0170-0171, 0205-0209, 0226, 0229). As to Claim 3 , Wang teaches the claimed limitations: “ wherein the selecting of the third plurality of training data includes: selecting training data classified in the third group such that a difference between a rate of positive prediction of training data classified in the first group and a rate of positive prediction of training data classified in the third group is a difference lying within a second threshold; and selecting training data classified in the fourth group such that a difference between a rate of positive prediction of training data classified in the second group and a rate of positive prediction of training data classified in the fourth group is a difference lying within a third threshold ” as (paragraphs 0005-0009). As to Claim 4 , Wang teaches the claimed limitations: “ wherein the augmentation item number is: an augmentation item number of the first group in a case in which a number of training data classified in the third group is greater than the augmentation item number of the first group, and is the number of training data classified in the third group in a case in which the number of training data classified in the third group is not greater than the augmentation item number of the first group, and an augmentation item number of the second group in a case in which a number of training data classified in the fourth group is greater than the augmentation item number of the second group, and is the number of training data classified in the fourth group in a case in which the number of training data classified in the fourth group is not greater than the augmentation item number of the second group ” as (paragraphs 0009, 0141, 0190-0192, 0208-0209, 0216). As to Claim 5 , Wang teaches the claimed limitations: “ wherein in a case in which the number of training data classified in the third group is greater than the augmentation item number of the first group, training data of an amount of the augmentation item number of the first group is selected from the training data classified in the third group in sequence from a highest similarity to the training data classified in the first group; and in a case in which the number of training data classified in the fourth group is greater than the augmentation item number of the second group, training data of an amount of the augmentation item number of the second group is selected from the training data classified in the fourth group in sequence from a highest similarity to the training data classified in the second group ” as (paragraphs 0005, 0139, 0203, 0212, 0233 , 0242, 0260-0261). As to Claim 6 , Wang teaches the claimed limitations: “ the training data generation process further comprising: removing from the fourth plurality of training data any training data not classifiable in the first group from among the fourth plurality of training data resulting from converting the third plurality of training data selected from the third group; and removing from the fourth plurality of training data any training data not classifiable in the second group from among the fourth plurality of training data resulting from converting the third plurality of training data selected from the fourth group ” as (paragraphs 0003, 0009, 0139, 0142, 0171, 0185, 0205, 0208, 0214, 0229, 0263, 0265, 0280). As to claims 7-12 are rejected under 35 U.S.C 103(a), the limitations therein have substantially the same scope as claims 1- 6 . In addition, Wang teaches a system for comparing machine learning models, the system including: at least one processor programmed or configured to: receive a dataset of data instances, wherein each data instance includes a feature value for each feature of a plurality of features (paragraph 0012 ). Therefore, these claims are rejected for at least the same reasons as claims 1- 6 . As to claims 12-18 are rejected under 35 U.S.C 103(a), the limitations therein have substantially the same scope as claims 1- 6 . In addition, Wang teaches a computer-implemented method, including: receiving, with at least one processor, a dataset of data instances, wherein each data instance comprises a feature value for each feature of a plurality of features; generating, with the at least one processor, outputs of a first machine learning model and outputs of a second machine learning model based on the dataset of data instances (paragraph 0020 ). Therefore, these claims are rejected for at least the same reasons as claims 1- 6 . Examiner’s Note Examiner has cited particular columns/paragraph and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention. This will assist in expediting compact prosecution. MPEP 714.02 recites: “Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06. An amendment which does not comply with the provisions of 37 CFR 1.121(b), (c), (d), and (h) may be held not fully responsive. See MPEP § 714.” Amendments not pointing to specific support in the disclosure may be deemed as not complying with provisions of 37 C.F.R. 1.131(b), (c), (d), and (h) and therefore held not fully responsive. Generic statements such as “Applicants believe no new matter has been introduced” may be deemed insufficient. Contact Information Any inquiry concerning this communication or earlier communications from the examiner should be directed to James Hwa whose telephone number is 571-270-1285, email address is james.hwa@uspto.gov . The examiner can normally be reached on 9:00 am – 5:30 pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ajay Bhatia can be reached on 571-272-3906. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only, for more information about the PAIR system, see http://pair-direct.uspto.gov . Should you have questions on access to the PAIR system contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. 12/19 /2025 /SHYUE JIUNN HWA/ Primary Examiner, Art Unit 2156