DETAILED ACTION 1. The present application 18/471,014, filed on 09/20/2023, is being examined under the first inventor to file provisions of the AIA. Clams 1- 6 are pending in this application. Drawings 2. The drawings received on 09 / 20 /202 3 are accepted by the Examiner. Priority 3. Acknowledgment is made of applicant's claim for priority to foreign application No. IN20232101420 filed on 03 /0 2 /202 3. Information Disclosure Statement 4. The information disclosure statement (IDS) submitted on 09/20/2023 is being considered by the examiner. Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b ) CONCLUSION.— The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. 5. Claims 2, 4 and 6 are rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 2, 4 and 6 recite an equation CV/Length/4. It is unclear what the symbol “/” means in the equation. Allowable Subject Matter The closest prior art Nia et al. (US 2022/0198277 A1) discloses local machine learning (ML) explainability (MLX) techniques that e xplain any ML classifier or regressor for tabular datasets. An interpretable surrogate ML model is used to explain the behavior of a complex black-box ML model on a generated local neighborhood of local sample samples. For evaluating the black-box model and training the surrogate explainer model, a generator ML model of a Generative Adversarial Network (GAN) is trained and used to generate a realistic local sample neighborhood according to the characteristics of the dataset that the black-box model was trained on. The generator ML model generat es explanations for specific predictions from the black-box mode l. Nia et al. fails to anticipate or render obvious all the recited features including generating a plurality of perturbed datapoints around the local instance by varying values for each continuous features, which is constrained by a Coefficient of Variation (CV) score, obtained from distribution of a percentage of sample data selected from a test dataset and generating a plurality of perturbed datapoints around the local instance by varying values for each categorical features generated by randomly selecting a value from a categorical column that covers more than a predefined percentage of the percentage of the sample data and other features in the instant claims 1, 3 and 5. Another closest prior art Nourian et al. (US 2021/0049503 A1) discloses a machine learning model, the machine learning model trained, during a training phase, to learn patterns to correctly classify input data associated with risk analysis. One or more features of the machine learning model may be analyzed, the one or more features being defined based on one or more constraints associated with one or more values and relationships and whether said one or more values and relationships satisfy at least one of the one or more constraints. In response to further analyzing the one or more features and the training data, providing at least a global and a local explanation about a given instance, and generating a report summarizing the global and local explanations, the global explanation providing general information about one or more functionalities of the machine learning model and a visualization that summarizes the machine learning model's global behavior with respect to the one or more features that are influential in generating one or more identifiable outcomes, and the local explanation providing an understanding of how possible changes to the instance's feature values adjust or shift an expected result or projected outcome beyond a first threshold. Nourian et al. fails to anticipate or render obvious all the recited features including generating a plurality of perturbed datapoints around the local instance by varying values for each continuous features, which is constrained by a Coefficient of Variation (CV) score, obtained from distribution of a percentage of sample data selected from a test dataset and generating a plurality of perturbed datapoints around the local instance by varying values for each categorical features generated by randomly selecting a value from a categorical column that covers more than a predefined percentage of the percentage of the sample data and other features in the instant claims 1, 3 and 5. The features in independent claims 1, 3 and 5 are novel and non-obvious over closest prior art. The dependent claims 2, 4 and 6 are being definite, enabled by the specification, and further limiting to the independent claims, are also allowable. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT YUK TING CHOI whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-1637 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Monday-Friday 9am-6pm . 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, FILLIN "SPE Name?" \* MERGEFORMAT AMY NG can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT 5712701698 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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