MACHINE LEARNING BASED SYSTEMS AND METHODS FOR CREDIT RISK MANAGEMENT

Application No. 18/399,766

• Filed: Dec 29, 2023
• Examiner: MUSTAFA, MOHAMMED H
• Art Unit: 3693
• Tech Center: 3600 — Transportation & Electronic Commerce
• Assignee: Highradius Corporation
• OA Round: 3 (Non-Final)

Office Action

DETAILED ACTION

Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .

Status of Claims
This action is in reply to the communications filed on 08/19/2026 and 09/08/2026.
Claims 1, 4-6, 9, 10, 13-15, and 18-20 has been amended and are hereby entered.
Claims 3 and 12 have been canceled.
Claims 1-2, 4-11, and 13-20 are currently pending and have been examined.
This action is made Non-Final.

Examiner Request
The Applicant is requested to indicate where in the specification there is support for U.S.C. §112(a) paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.

Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09/08/2026 has been entered.
Claim Rejections - 35 USC § 101
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 1-2, 4-11, and 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of processing credit decisions and recommended credit values and limits; without significantly more.
Claim 1 is directed to a method, which is one of the statutory categories of invention; Claim 10 is directed to a system, which is one of the statutory categories of invention; and Claim 19 is directed to a non-transitory computer-readable storage medium, which is one of the statutory categories of invention. (Step 1: YES).
Claim 1 is directed to a machine-learning based (ML-based) computing method for managing one or more credit risks of one or more first users, the ML-based computing method comprising: receiving, by one or more hardware processors, one or more inputs from one or more electronic devices associated with one or more second users, wherein the one or more inputs comprise information related to at least one of: one or more entities associated with the one or more first users; retrieving, by the one or more hardware processors, one or more data associated with the one or more first users from one or more databases, based on the one or more inputs received from the one or more electronic devices associated with the one or more second users, wherein the one or more data comprise at least one of: one or more credit agency data, one or more accounts receivables data, one or more financial metrics, and one or more entity data, associated with the one or more first users; preprocessing, by the one or more hardware processors, the one or more data to normalize and standardize the one or more data by at least one of: removing one or more noises, one or more outliers, one or more missing values, filtering the one or more entities with an active status, consolidating multiple credit changes on a single day, and converting currencies in the one or more data to a single currency; training, by the one or more hardware processors, one or more machine learning models, by: obtaining, by the one or more hardware processors, one or more labelled datasets from the one or more databases, wherein the one or more labelled datasets comprise preprocessed historical data corresponding to the one or more data; selecting, by the one or more hardware processors, a reduced subset of features from one or more features associated with the preprocessed historical data for training the one or more machine learning models by executing at least one of: a forward feature selection process, a backward feature selection process, an exhaustive feature selection process, a recursive feature elimination process, a random forest importance process and a boosted feature extractor process; segmenting, by the one or more hardware processors, the one or more labelled datasets into at least one of: one or more training datasets and one or more validation datasets; and training, by the one or more hardware processors, the one or more machine learning models to correlate the reduced subset of features and one or more historical credit decisions, and wherein the one or more machine learning models comprise at least one of: a random forest model, an extreme gradient boosting (XGBoost) classifier model, a K-means clustering model, a light gradient-boosting machine (LightGBM) classifier model; determining, by the one or more hardware processors, the one or more credit risks of the one or more entities associated with the one or more first users based on the preprocessed one or more data, by the one or more trained machine learning models; generating, by the one or more hardware processors, one or more credit decisions for the one or more entities associated with the one or more first users based on the determined one or more credit risks of the one or more entities associated with the one or more first users, by the one or more trained machine learning models, wherein the one or more credit decisions comprise at least one of: one or more first credit decisions, one or more second credit decisions, one or more third credit decisions; generating, by the one or more hardware processors, one or more confidence scores for each credit decision of the one or more credit decisions to classify the one or more credit decisions, based on a correlation between the preprocessed one or more data and the one or more credit decisions and based on the one or more trained machine learning models, wherein the classification of the one or more credit decisions comprises at least one of: one or more first credit decisions, one or more second credit decisions, one or more third credit decisions; retrieving, by the one or more hardware processors, one or more current credit limits; determining, by the one or more hardware processors, at least one of: one or more recommended credit values, one or more recommended first credit limits, one or more recommended second credit limits, and one or more recommended third credit limits, based on the classification of at least one of: the one or more first credit decisions, the one or more second credit decisions, and the one or more third credit decisions, wherein at least one of: the one or more recommended first credit limits and the one or more recommended second credit limits are determined upon generation of at least one of: the one or more first credit decisions and the one or more second credit decisions, based on one or more key statistical parameters, wherein the one or more key statistical parameters comprise at least one of: the one or more current credit limits, median, and standard deviation, and wherein the one or more current credit limits are associated with one or more reference points when the median and the standard deviation provide one or more insights into a central tendency and variability of the preprocessed one or more data; providing, by the one or more hardware processors, one or more automated approvals for the one or more credit decisions, based on one or more pre-configured rules and parameters; and providing, by the one or more hardware processors, an output of at least one of: the one or more credit decisions, the one or more recommended credit values, the one or more recommended first credit limits, and the one or more recommended second credit limits, to the one or more second users on one or more user interfaces associated with the one or more electronic devices. These series of steps describe the abstract idea of processing credit decisions and recommended credit values and limits (with the exception of the italicized and bolded terms above), which is mitigating risk of potential losses arising from credit risks by determining and managing the credit risks of entities associated with users based on preprocessed data; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also processing and analyzing credit risks of users to assist lenders and financial entities in making credit decisions, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Furthermore, the series of steps describe the abstract idea of determining a recommended credit limit in a credit upgrade decision using statistical parameters to measure an entity's credit limit relative to a median credit limit of all entities across accounts at a specific point in time; where, the median and standard deviation provide insights into a central tendency and variability of the data. Therefore, corresponding to a mathematical calculation, relationship, and/or equation. Hence, a mathematical calculation, relationship, and/or equation is a Mathematical Concept. The system limitations, e.g., one or more hardware processors, one or more electronic devices, one or more databases, one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, one or more trained machine learning models, and one or more user interfaces, do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 1 recites an abstract idea (Step 2A-Prong 1: YES).
This judicial exception is not integrated into a practical application because the additional elements of one or more hardware processors, one or more electronic devices, one or more databases, one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, one or more trained machine learning models, and one or more user interfaces, are no more than simply applying the abstract idea using generic computer elements. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. Merely invoking one or more hardware processors, one or more electronic devices, one or more databases, one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, one or more trained machine learning models, and one or more user interfaces is similar to invoking software and software components. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Thus, claim 1 does not integrate the abstract idea into a practical application (Step 2A-Prong 2: NO).
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of one or more hardware processors, one or more electronic devices, one or more databases, one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, one or more trained machine learning models, and one or more user interfaces, are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment (Step 2B: NO). Thus, claim 1 is not patent eligible.
Dependent claims 2 and 4-9 are directed to a method that recites a series steps that describe the abstract idea of processing credit decisions and recommended credit values and limits. These series of steps describe the abstract idea of processing credit decisions and recommended credit values and limits, which is mitigating risk of potential losses arising from credit risks by determining and managing the credit risks of entities associated with users based on preprocessed data; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also processing and analyzing credit risks of users to assist lenders and financial entities in making credit decisions, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Additionally, dependent claims 4-5 recite a series of steps that describe the abstract idea of determining a recommended credit limit in a credit upgrade decision using statistical parameters to measure an entity's credit limit relative to a median credit limit of all entities across accounts at a specific point in time; where, the median and standard deviation provide insights into a central tendency and variability of the data. Therefore, corresponding to a mathematical calculation, relationship, and/or equation. Hence, a mathematical calculation, relationship, and/or equation is a Mathematical Concept. The system limitations, e.g., one or more hardware processors, one or more electronic devices, one or more databases, one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, one or more trained machine learning models, and one or more user interfaces, do not necessarily restrict the claim from reciting an abstract idea. Thus, claims 2 and 4-9 recite an abstract idea. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. Specifically, the additional elements, one or more hardware processors, one or more electronic devices, one or more databases, one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, one or more trained machine learning models, and one or more user interfaces, are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. Merely invoking one or more hardware processors, one or more electronic devices, one or more databases, one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, one or more trained machine learning models, and one or more user interfaces is similar to invoking software and software components. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea, and hence do not integrate the abstract idea into a practical application. Furthermore, the additional elements: one or more hardware processors, one or more electronic devices, one or more databases, one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, one or more trained machine learning models, and one or more user interfaces do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment.
Claim 10 is directed to a machine learning based (ML-based) computing system for managing one or more credit risks of one or more first users, the ML-based computing system comprising: one or more hardware processors; a memory coupled to the one or more hardware processors, wherein the memory comprises a plurality of subsystems in form of programmable instructions executable by the one or more hardware processors, and wherein the plurality of subsystems comprises: an input receiving subsystem configured to receive one or more inputs from one or more electronic devices associated with one or more second users, wherein the one or more inputs comprise information related to at least one of: one or more entities associated with the one or more first users; a data retrieval subsystem configured to retrieve one or more data associated with the one or more first users from one or more databases, based on the one or more inputs received from the one or more electronic devices associated with the one or more second users, wherein the one or more data comprise at least one of: one or more credit agency data, one or more accounts receivables data, one or more financial metrics, and one or more entity data, associated with the one or more first users; a data preprocessing subsystem configured to preprocess the one or more data to normalize and standardize the one or more data by at least one of: removing one or more noises, one or more outliers, one or more missing values, filtering the one or more entities with an active status, consolidating multiple credit changes on a single day, and converting currencies in the one or more data to a single currency; a training subsystem configured to train one or more machine learning models, wherein in training the one or more machine learning models, the training subsystem is configured to: obtain one or more labelled datasets from the one or more databases, wherein the one or more labelled datasets comprise preprocessed historical data corresponding to the one or more data; select a reduced subset of features from one or more features associated with the preprocessed historical data for training the one or more machine learning models by executing at least one of: a forward feature selection process, a backward feature selection process, an exhaustive feature selection process, a recursive feature elimination process, a random forest importance process and a boosted feature extractor process; segment the one or more labelled datasets into at least one of: one or more training datasets and one or more validation datasets; and train the one or more machine learning models to correlate the reduced subset of features and one or more historical credit decision, wherein the one or more machine learning models comprise at least one of: a random forest model, an extreme gradient boosting (XGBoost) classifier model, a K-means clustering model, a light gradient-boosting machine (LightGBM) classifier model; a credit risk determining subsystem configured to determine the one or more credit risks of the one or more entities associated with the one or more first users based on the one or more preprocessed data, by the one or more trained machine learning models; a credit decision generation subsystem configured to generate one or more credit decisions for the one or more entities associated with the one or more first users based on the determined one or more credit risks of the one or more entities associated with the one or more first users, by the one or more trained machine learning models, wherein the one or more credit decisions comprise at least one of: one or more first credit decisions, one or more second credit decisions, one or more third credit decisions; a confidence score generation subsystem configured to generate one or more confidence scores for each credit decision of the one or more credit decisions to classify the one or more credit decisions, based on a correlation between the one or more preprocessed data and the one or more credit decisions and based on the one or more trained machine learning models, wherein the classification of the one or more credit decisions comprises at least one of: one or more first credit decisions, one or more second credit decisions, one or more third credit decisions; a credit limit determining subsystem configured to retrieve one or more current credit limits and determine at least one of: one or more recommended credit values, one or more recommended first credit limits, one or more recommended second credit limits, and one or more recommended third credit limits, based on the classification of at least one of: the one or more first credit decisions, the one or more second credit decisions, and the one or more third credit decisions, wherein at least one of: the one or more recommended first credit limits and the one or more recommended second credit limits are determined upon generation of at least one of: the one or more first credit decisions and the one or more second credit decisions, based on one or more key statistical parameters, wherein the one or more key statistical parameters comprise at least one of: the one or more current credit limits, median, and standard deviation, and wherein the one or more current credit limits are associated with one or more reference points when the median and the standard deviation provide one or more insights into a central tendency and variability of the one or more preprocessed data; an auto-approval subsystem configured to provide one or more automated approvals for the one or more credit decisions, based on one or more pre-configured rules and parameters; and an output subsystem configured to provide an output of at least one of: the one or more credit decisions, the one or more recommended credit values, the one or more recommended first credit limits, and the one or more recommended second credit limits, to one or more second users on one or more user interfaces associated with the one or more electronic devices. These series of steps describe the abstract idea of processing credit decisions and recommended credit values and limits (with the exception of the italicized and bolded terms above), which is mitigating risk of potential losses arising from credit risks by determining and managing the credit risks of entities associated with users based on preprocessed data; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also processing and analyzing credit risks of users to assist lenders and financial entities in making credit decisions, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Furthermore, the series of steps describe the abstract idea of determining a recommended credit limit in a credit upgrade decision using statistical parameters to measure an entity's credit limit relative to a median credit limit of all entities across accounts at a specific point in time; where, the median and standard deviation provide insights into a central tendency and variability of the data. Therefore, corresponding to a mathematical calculation, relationship, and/or equation. Hence, a mathematical calculation, relationship, and/or equation is a Mathematical Concept. The system limitations, e.g., a machine learning based (ML-based) computing system, one or more hardware processors, memory, plurality of subsystems, programmable instructions, input receiving subsystem, one or more electronic devices, data retrieval subsystem, one or more databases, data preprocessing subsystem, training subsystem, credit risk determining subsystem, one or more machine learning models, credit decision generation subsystem, trained one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, confidence score generation subsystem, credit limit determining subsystem, auto-approval subsystem, output subsystem, and one or more user interfaces, do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 10 recites an abstract idea (Step 2A-Prong 1: YES).
This judicial exception is not integrated into a practical application because the additional elements of a machine learning based (ML-based) computing system, one or more hardware processors, memory, plurality of subsystems, programmable instructions, input receiving subsystem, one or more electronic devices, data retrieval subsystem, one or more databases, data preprocessing subsystem, training subsystem, credit risk determining subsystem, one or more machine learning models, credit decision generation subsystem, trained one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, confidence score generation subsystem, credit limit determining subsystem, auto-approval subsystem, output subsystem, and one or more user interfaces, are no more than simply applying the abstract idea using generic computer elements. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. Merely invoking a machine learning based (ML-based) computing system, one or more hardware processors, memory, plurality of subsystems, programmable instructions, input receiving subsystem, one or more electronic devices, data retrieval subsystem, one or more databases, data preprocessing subsystem, training subsystem, credit risk determining subsystem, one or more machine learning models, credit decision generation subsystem, trained one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, confidence score generation subsystem, credit limit determining subsystem, auto-approval subsystem, output subsystem, and one or more user interfaces, is similar to invoking software and software components. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Thus, claim 10 does not integrate the abstract idea into a practical application (Step 2A-Prong 2: NO).
Claim 10 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of a machine learning based (ML-based) computing system, one or more hardware processors, memory, plurality of subsystems, programmable instructions, input receiving subsystem, one or more electronic devices, data retrieval subsystem, one or more databases, data preprocessing subsystem, training subsystem, credit risk determining subsystem, one or more machine learning models, credit decision generation subsystem, trained one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, confidence score generation subsystem, credit limit determining subsystem, auto-approval subsystem, output subsystem, and one or more user interfaces, are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment (Step 2B: NO). Thus, claim 10 is not patent eligible.
Dependent claims 11 and 13-18 are directed to a system that performs a series steps that describe the abstract idea of processing credit decisions and recommended credit values and limits. These series of steps describe the abstract idea of processing credit decisions and recommended credit values and limits, which is mitigating risk of potential losses arising from credit risks by determining and managing the credit risks of entities associated with users based on preprocessed data; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also processing and analyzing credit risks of users to assist lenders and financial entities in making credit decisions, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Additionally, dependent claims 13-14 recite a series of steps that describe the abstract idea of determining a recommended credit limit in a credit upgrade decision using statistical parameters to measure an entity's credit limit relative to a median credit limit of all entities across accounts at a specific point in time; where, the median and standard deviation provide insights into a central tendency and variability of the data. Therefore, corresponding to a mathematical calculation, relationship, and/or equation. Hence, a mathematical calculation, relationship, and/or equation is a Mathematical Concept. The system limitations, e.g., a machine learning based (ML-based) computing system, one or more hardware processors, memory, plurality of subsystems, programmable instructions, input receiving subsystem, one or more electronic devices, data retrieval subsystem, one or more databases, data preprocessing subsystem, training subsystem, credit risk determining subsystem, one or more machine learning models, credit decision generation subsystem, trained one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, confidence score generation subsystem, credit limit determining subsystem, auto-approval subsystem, output subsystem, and one or more user interfaces, do not necessarily restrict the claim from reciting an abstract idea. Thus, claims 11 and 13-18 recite an abstract idea. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. Specifically, the additional elements, a machine learning based (ML-based) computing system, one or more hardware processors, memory, plurality of subsystems, programmable instructions, input receiving subsystem, one or more electronic devices, data retrieval subsystem, one or more databases, data preprocessing subsystem, training subsystem, credit risk determining subsystem, one or more machine learning models, credit decision generation subsystem, trained one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, confidence score generation subsystem, credit limit determining subsystem, auto-approval subsystem, output subsystem, and one or more user interfaces, are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. Merely invoking a machine learning based (ML-based) computing system, one or more hardware processors, memory, plurality of subsystems, programmable instructions, input receiving subsystem, one or more electronic devices, data retrieval subsystem, one or more databases, data preprocessing subsystem, training subsystem, credit risk determining subsystem, one or more machine learning models, credit decision generation subsystem, trained one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, confidence score generation subsystem, credit limit determining subsystem, auto-approval subsystem, output subsystem, and one or more user interfaces, is similar to invoking software and software components. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea, and hence do not integrate the abstract idea into a practical application. Furthermore, the additional elements: a machine learning based (ML-based) computing system, one or more hardware processors, memory, plurality of subsystems, programmable instructions, input receiving subsystem, one or more electronic devices, data retrieval subsystem, one or more databases, data preprocessing subsystem, training subsystem, credit risk determining subsystem, one or more machine learning models, credit decision generation subsystem, trained one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, confidence score generation subsystem, credit limit determining subsystem, auto-approval subsystem, output subsystem, and one or more user interfaces, do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment.

Claim 19 is directed to a non-transitory computer-readable storage medium having instructions stored therein that when executed by one or more hardware processors, cause the one or more hardware processors to execute operations of: receiving one or more inputs from one or more electronic devices associated with one or more second users, wherein the one or more inputs comprise information related to at least one of: one or more entities associated with the one or more first users; retrieving one or more data associated with the one or more first users from one or more databases, based on the one or more inputs received from the one or more electronic devices associated with the one or more second users, wherein the one or more data comprise at least one of: one or more credit agency data, one or more accounts receivables data, one or more financial metrics, and one or more entity data, associated with the one or more first users; preprocessing the one or more data to normalize and standardize the one or more data by at least one of: removing one or more noises, one or more outliers, one or more missing values, filtering the one or more entities with an active status, consolidating multiple credit changes on a single day, and converting currencies in the one or more data to a single currency; training one or more machine learning models, by: obtaining one or more labelled datasets from the one or more databases, wherein the one or more labelled datasets comprise preprocessed historical data corresponding to the one or more data; selecting a reduced subset of features from one or more features associated with the preprocessed historical data for training the one or more machine learning models by executing at least one of: a forward feature selection process, a backward feature selection process, an exhaustive feature selection process, a recursive feature elimination process, a random forest importance process and a boosted feature extractor process; segmenting the one or more labelled datasets into at least one of: one or more training datasets and one or more validation datasets; and training the one or more machine learning models to correlate the reduced subset of features and one or more historical credit decisions, wherein the one or more machine learning models comprise at least one of: a random forest model, an extreme gradient boosting (XGBoost) classifier model, a K-means clustering model, a light gradient-boosting machine (LightGBM) classifier model; determining the one or more credit risks of the one or more entities associated with the one or more first users based on the one or more preprocessed data, by the one or more trained machine learning models; generating one or more credit decisions for the one or more entities associated with the one or more first users based on the determined one or more credit risks of the one or more entities associated with the one or more first users, by the one or more trained machine learning models, wherein the one or more credit decisions comprise at least one of: one or more first credit decisions, one or more second credit decisions, one or more third credit decisions; generating one or more confidence scores for each credit decision of the one or more credit decisions to classify the one or more credit decisions, based on a correlation between the one or more data and the one or more credit decisions and the one or more trained machine learning models, wherein the classification of the one or more credit decisions comprises at least one of: one or more first credit decisions, one or more second credit decisions, one or more third credit decisions; retrieving one or more current credit limits; determining at least one of: one or more recommended credit values, one or more recommended first credit limits, one or more recommended second credit limits, and one or more recommended third credit limits, based on the classification of at least one of: the one or more first credit decisions, the one or more second credit decisions, and the one or more third credit decisions, wherein at least one of: the one or more recommended first credit limits and the one or more recommended second credit limits are determined upon generation of at least one of: the one or more first credit decisions and the one or more second credit decisions, based on one or more key statistical parameters, wherein the one or more key statistical parameters comprise at least one of: the one or more current credit limits, median, and standard deviation, and wherein the one or more current credit limits are associated with one or more reference points when the median and the standard deviation provide one or more insights into a central tendency and variability of the preprocessed one or more data; providing one or more automated approvals for the one or more credit decisions with at least one of: the one or more recommended credit values, the one or more recommended first credit limits, and the one or more recommended second credit limits, based on one or more preconfigured rules and parameters; and providing an output of at least one of: the one or more credit decisions, the one or more recommended credit values, the one or more recommended first credit limits, and the one or more recommended second credit limits, to one or more second users on one or more user interfaces associated with the one or more electronic devices. These series of steps describe the abstract idea of processing credit decisions and recommended credit values and limits (with the exception of the italicized and bolded terms above), which is mitigating risk of potential losses arising from credit risks by determining and managing the credit risks of entities associated with users based on preprocessed data; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also processing and analyzing credit risks of users to assist lenders and financial entities in making credit decisions, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Furthermore, the series of steps describe the abstract idea of determining a recommended credit limit in a credit upgrade decision using statistical parameters to measure an entity's credit limit relative to a median credit limit of all entities across accounts at a specific point in time; where, the median and standard deviation provide insights into a central tendency and variability of the data. Therefore, corresponding to a mathematical calculation, relationship, and/or equation. Hence, a mathematical calculation, relationship, and/or equation is a Mathematical Concept. The system limitations, e.g., a non-transitory computer-readable storage medium, one or more hardware processors, one or more electronic devices, one or more databases, one or more machine learning models, trained one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, and one or more user interfaces, do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 19 recites an abstract idea (Step 2A-Prong 1: YES).
This judicial exception is not integrated into a practical application because the additional elements of a non-transitory computer-readable storage medium, one or more hardware processors, one or more electronic devices, one or more databases, one or more machine learning models, trained one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, and one or more user interfaces, are no more than simply applying the abstract idea using generic computer elements. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. Merely invoking a non-transitory computer-readable storage medium, one or more hardware processors, one or more electronic devices, one or more databases, one or more machine learning models, trained one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, and one or more user interfaces, is similar to invoking software and software components. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Thus, claim 19 does not integrate the abstract idea into a practical application (Step 2A-Prong 2: NO).
Claim 19 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of a non-transitory computer-readable storage medium, one or more hardware processors, one or more electronic devices, one or more databases, one or more machine learning models, trained one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, and one or more user interfaces, are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment (Step 2B: NO). Thus, claim 19 is not patent eligible.
Dependent claim 20 is directed to a non-transitory computer-readable storage medium that performs a series steps that describe the abstract idea of processing credit decisions and recommended credit values and limits. These series of steps describe the abstract idea of processing credit decisions and recommended credit values and limits, which is mitigating risk of potential losses arising from credit risks by determining and managing the credit risks of entities associated with users based on preprocessed data; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also processing and analyzing credit risks of users to assist lenders and financial entities in making credit decisions, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. The system limitations, e.g., a non-transitory computer-readable storage medium, one or more hardware processors, one or more electronic devices, one or more databases, one or more machine learning models, trained one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, and one or more user interfaces, do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 20 recites an abstract idea. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. Merely invoking a non-transitory computer-readable storage medium, one or more hardware processors, one or more electronic devices, one or more databases, one or more machine learning models, trained one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, and one or more user interfaces, is similar to invoking software and software components. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea, and hence do not integrate the abstract idea into a practical application. Furthermore, the additional elements: a non-transitory computer-readable storage medium, one or more hardware processors, one or more electronic devices, one or more databases, one or more machine learning models, trained one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, and one or more user interfaces, do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment.

Dependent claims 2, 4-9, 11, 13-18, and 20 have further defined the abstract idea that is present in their respective independent claims: Claim 1, 10, and 19, and thus correspond to Certain Methods of Organizing Human Activity, and hence are abstract in nature for the reason presented above. The dependent claims 2, 4-9, 11, 13-18, and 20 do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, dependent claims 2, 4-9, 11, 13-18, and 20 are directed to an abstract idea, without significantly more.
Thus, claims 1-2, 4-11, and 13-20 are not patent-eligible.

Prior Art Rejection
Examiner respectfully notes that with respect to the 35 US.C. 103 rejection of claims 1-2, 10-11, 18, and 19, the rejection is withdrawn in view of Applicant’s arguments/remarks made in an amendment filed on 08/19/2026. Specifically, the closest prior art the examiner has been able to locate are Relova (U.S. Patent Application Publication No. US 2025/0029178 A1 hereinafter “Relova”), in view of Speirs (U.S. Patent Application Publication No. US 2023/0274349 A1; hereinafter “Speirs”), and further in view of Zimmerman (U.S. Patent Application Publication No. US 2025/0200654 A1; hereinafter “Zimmerman”). While Relova, Speirs, and Zimmerman are similar to the instant application in many respects, there are clear patentable distinctions. Unlike the prior art, the present invention teaches a method, system, and non-transitory computer-readable storage medium for managing one or more credit risks of one or more first users, the ML-based computing method comprising: receiving, by one or more hardware processors, one or more inputs from one or more electronic devices associated with one or more second users, wherein the one or more inputs comprise information related to at least one of: one or more entities associated with the one or more first users; retrieving, by the one or more hardware processors, one or more data associated with the one or more first users from one or more databases, based on the one or more inputs received from the one or more electronic devices associated with the one or more second users, wherein the one or more data comprise at least one of: one or more credit agency data, one or more accounts receivables data, one or more financial metrics, and one or more entity data, associated with the one or more first users; preprocessing, by the one or more hardware processors, the one or more data to normalize and standardize the one or more data by at least one of: removing one or more noises, one or more outliers, one or more missing values, filtering the one or more entities with an active status, consolidating multiple credit changes on a single day, and converting currencies in the one or more data to a single currency; training, by the one or more hardware processors, one or more machine learning models, by: obtaining, by the one or more hardware processors, one or more labelled datasets from the one or more databases, wherein the one or more labelled datasets comprise preprocessed historical data corresponding to the one or more data; selecting, by the one or more hardware processors, a reduced subset of features from one or more features associated with the preprocessed historical data for training the one or more machine learning models by executing at least one of: a forward feature selection process, a backward feature selection process, an exhaustive feature selection process, a recursive feature elimination process, a random forest importance process and a boosted feature extractor process; segmenting, by the one or more hardware processors, the one or more labelled datasets into at least one of: one or more training datasets and one or more validation datasets; and training, by the one or more hardware processors, the one or more machine learning models to correlate the reduced subset of features and one or more historical credit decisions, and wherein the one or more machine learning models comprise at least one of: a random forest model, an extreme gradient boosting (XGBoost) classifier model, a K-means clustering model, a light gradient-boosting machine (LightGBM) classifier model; determining, by the one or more hardware processors, the one or more credit risks of the one or more entities associated with the one or more first users based on the preprocessed one or more data, by the one or more trained machine learning models; generating, by the one or more hardware processors, one or more credit decisions for the one or more entities associated with the one or more first users based on the determined one or more credit risks of the one or more entities associated with the one or more first users, by the one or more trained machine learning models, wherein the one or more credit decisions comprise at least one of: one or more first credit decisions, one or more second credit decisions, one or more third credit decisions; generating, by the one or more hardware processors, one or more confidence scores for each credit decision of the one or more credit decisions to classify the one or more credit decisions, based on a correlation between the preprocessed one or more data and the one or more credit decisions and based on the one or more trained machine learning models, wherein the classification of the one or more credit decisions comprises at least one of: one or more first credit decisions, one or more second credit decisions, one or more third credit decisions; retrieving, by the one or more hardware processors, one or more current credit limits; determining, by the one or more hardware processors, at least one of: one or more recommended credit values, one or more recommended first credit limits, one or more recommended second credit limits, and one or more recommended third credit limits, based on the classification of at least one of: the one or more first credit decisions, the one or more second credit decisions, and the one or more third credit decisions, wherein at least one of: the one or more recommended first credit limits and the one or more recommended second credit limits are determined upon generation of at least one of: the one or more first credit decisions and the one or more second credit decisions, based on one or more key statistical parameters, wherein the one or more key statistical parameters comprise at least one of: the one or more current credit limits, median, and standard deviation, and wherein the one or more current credit limits are associated with one or more reference points when the median and the standard deviation provide one or more insights into a central tendency and variability of the preprocessed one or more data; providing, by the one or more hardware processors, one or more automated approvals for the one or more credit decisions, based on one or more pre-configured rules and parameters; and providing, by the one or more hardware processors, an output of at least one of: the one or more credit decisions, the one or more recommended credit values, the one or more recommended first credit limits, and the one or more recommended second credit limits, to the one or more second users on one or more user interfaces associated with the one or more electronic devices.
Independently the claims are obvious; however, the claims as a whole are not obvious because the examiner would have to improperly use the claims as a road map to combine the individual obvious claims together. The limitations of the present invention below teaches the following elements that eludes the prior art search. Specifically, the claim limitations that resulted in no combination of the prior arts to be found to render the claims obvious without applying improper hindsight are: “receiving, by one or more hardware processors, one or more inputs from one or more electronic devices associated with one or more second users, wherein the one or more inputs comprise information related to at least one of: one or more entities associated with the one or more first users; retrieving, by the one or more hardware processors, one or more data associated with the one or more first users from one or more databases, based on the one or more inputs received from the one or more electronic devices associated with the one or more second users, wherein the one or more data comprise at least one of: one or more credit agency data, one or more accounts receivables data, one or more financial metrics, and one or more entity data, associated with the one or more first users; preprocessing, by the one or more hardware processors, the one or more data to normalize and standardize the one or more data by at least one of: removing one or more noises, one or more outliers, one or more missing values, filtering the one or more entities with an active status, consolidating multiple credit changes on a single day, and converting currencies in the one or more data to a single currency; training, by the one or more hardware processors, one or more machine learning models, by: obtaining, by the one or more hardware processors, one or more labelled datasets from the one or more databases, wherein the one or more labelled datasets comprise preprocessed historical data corresponding to the one or more data; selecting, by the one or more hardware processors, a reduced subset of features from one or more features associated with the preprocessed historical data for training the one or more machine learning models by executing at least one of: a forward feature selection process, a backward feature selection process, an exhaustive feature selection process, a recursive feature elimination process, a random forest importance process and a boosted feature extractor process; segmenting, by the one or more hardware processors, the one or more labelled datasets into at least one of: one or more training datasets and one or more validation datasets; and training, by the one or more hardware processors, the one or more machine learning models to correlate the reduced subset of features and one or more historical credit decisions, and wherein the one or more machine learning models comprise at least one of: a random forest model, an extreme gradient boosting (XGBoost) classifier model, a K-means clustering model, a light gradient-boosting machine (LightGBM) classifier model; determining, by the one or more hardware processors, the one or more credit risks of the one or more entities associated with the one or more first users based on the preprocessed one or more data, by the one or more trained machine learning models; generating, by the one or more hardware processors, one or more credit decisions for the one or more entities associated with the one or more first users based on the determined one or more credit risks of the one or more entities associated with the one or more first users, by the one or more trained machine learning models, wherein the one or more credit decisions comprise at least one of: one or more first credit decisions, one or more second credit decisions, one or more third credit decisions; generating, by the one or more hardware processors, one or more confidence scores for each credit decision of the one or more credit decisions to classify the one or more credit decisions, based on a correlation between the preprocessed one or more data and the one or more credit decisions and based on the one or more trained machine learning models, wherein the classification of the one or more credit decisions comprises at least one of: one or more first credit decisions, one or more second credit decisions, one or more third credit decisions; retrieving, by the one or more hardware processors, one or more current credit limits; determining, by the one or more hardware processors, at least one of: one or more recommended credit values, one or more recommended first credit limits, one or more recommended second credit limits, and one or more recommended third credit limits, based on the classification of at least one of: the one or more first credit decisions, the one or more second credit decisions, and the one or more third credit decisions, wherein at least one of: the one or more recommended first credit limits and the one or more recommended second credit limits are determined upon generation of at least one of: the one or more first credit decisions and the one or more second credit decisions, based on one or more key statistical parameters, wherein the one or more key statistical parameters comprise at least one of: the one or more current credit limits, median, and standard deviation, and wherein the one or more current credit limits are associated with one or more reference points when the median and the standard deviation provide one or more insights into a central tendency and variability of the preprocessed one or more data; providing, by the one or more hardware processors, one or more automated approvals for the one or more credit decisions, based on one or more pre-configured rules and parameters; and providing, by the one or more hardware processors, an output of at least one of: the one or more credit decisions, the one or more recommended credit values, the one or more recommended first credit limits, and the one or more recommended second credit limits, to the one or more second users on one or more user interfaces associated with the one or more electronic devices.”
Hence, no combination of the prior arts were found to render the claims obvious without applying improper hindsight; thus, the claims are novel and non-obvious.

Response to Arguments
With respect to the 35 U.S.C. 103 rejection of claims 1-2, 10-11, 18, and 19, the rejection is withdrawn in view of Applicant’s arguments/remarks made in an amendment filed on 08/19/2026.
Applicant's arguments filed on 08/19/2026 have been fully considered, but are not persuasive due to the following reasons:
With respect to the rejection of claims 1-20 under 35 U.S.C. 101, Applicant arguments are moot in view of the grounds of rejections presented above in this office action. The arguments are addressed to the extent they apply to the amended claims.
Applicant argues that “the claims therefore do not merely recite a mental process or a method of organizing human activity. Instead, they recite a specific computer-implemented processing architecture for transforming and processing electronic data through defined computational operations ”
Examiner respectfully disagrees.
Under Step 2A: Prong 1, as previously discussed, Examiner respectfully notes that claims 1, 10, and 19, as amended, are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of processing credit decisions and recommended credit values and limits; without significantly more. The series of steps recited in claims 1, 10, and 19, as amended, describe the abstract idea of processing credit decisions and recommended credit values and limits, which is mitigating risk of potential losses arising from credit risks by determining and managing the credit risks of entities associated with users based on preprocessed data; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also processing and analyzing credit risks of users to assist lenders and financial entities in making credit decisions, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Furthermore, the system limitations (claim 1), e.g., one or more hardware processors, one or more electronic devices, one or more databases, one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, one or more trained machine learning models, and one or more user interfaces do not necessarily restrict the claim from reciting an abstract idea.
Moreover, Examiner respectfully notes that the claims are first analyzed in the absence of technology to determine if it recites an abstract idea. The additional limitations of technology are then considered to determine if it restricts the claim from reciting an abstract idea. In this case, it is determined that the additional limitations of technology do not necessarily restrict claims 1, 10, and 19, as amended, from reciting an abstract idea. Furthermore, Examiner respectfully notes that the recited features in the limitations: “receiving, by one or more hardware processors, one or more inputs from one or more electronic devices associated with one or more second users, wherein the one or more inputs comprise information related to at least one of: one or more entities associated with the one or more first users; retrieving, by the one or more hardware processors, one or more data associated with the one or more first users from one or more databases, based on the one or more inputs received from the one or more electronic devices associated with the one or more second users, wherein the one or more data comprise at least one of: one or more credit agency data, one or more accounts receivables data, one or more financial metrics, and one or more entity data, associated with the one or more first users; preprocessing, by the one or more hardware processors, the one or more data to normalize and standardize the one or more data by at least one of: removing one or more noises, one or more outliers, one or more missing values, filtering the one or more entities with an active status, consolidating multiple credit changes on a single day, and converting currencies in the one or more data to a single currency; training, by the one or more hardware processors, one or more machine learning models, by: obtaining, by the one or more hardware processors, one or more labelled datasets from the one or more databases, wherein the one or more labelled datasets comprise preprocessed historical data corresponding to the one or more data; selecting, by the one or more hardware processors, a reduced subset of features from one or more features associated with the preprocessed historical data for training the one or more machine learning models by executing at least one of: a forward feature selection process, a backward feature selection process, an exhaustive feature selection process, a recursive feature elimination process, a random forest importance process and a boosted feature extractor process; segmenting, by the one or more hardware processors, the one or more labelled datasets into at least one of: one or more training datasets and one or more validation datasets; and training, by the one or more hardware processors, the one or more machine learning models to correlate the reduced subset of features and one or more historical credit decisions, and wherein the one or more machine learning models comprise at least one of: a random forest model, an extreme gradient boosting (XGBoost) classifier model, a K-means clustering model, a light gradient-boosting machine (LightGBM) classifier model; determining, by the one or more hardware processors, the one or more credit risks of the one or more entities associated with the one or more first users based on the preprocessed one or more data, by the one or more trained machine learning models; generating, by the one or more hardware processors, one or more credit decisions for the one or more entities associated with the one or more first users based on the determined one or more credit risks of the one or more entities associated with the one or more first users, by the one or more trained machine learning models, wherein the one or more credit decisions comprise at least one of: one or more first credit decisions, one or more second credit decisions, one or more third credit decisions; generating, by the one or more hardware processors, one or more confidence scores for each credit decision of the one or more credit decisions to classify the one or more credit decisions, based on a correlation between the preprocessed one or more data and the one or more credit decisions and based on the one or more trained machine learning models, wherein the classification of the one or more credit decisions comprises at least one of: one or more first credit decisions, one or more second credit decisions, one or more third credit decisions; retrieving, by the one or more hardware processors, one or more current credit limits; determining, by the one or more hardware processors, at least one of: one or more recommended credit values, one or more recommended first credit limits, one or more recommended second credit limits, and one or more recommended third credit limits, based on the classification of at least one of: the one or more first credit decisions, the one or more second credit decisions, and the one or more third credit decisions, wherein at least one of: the one or more recommended first credit limits and the one or more recommended second credit limits are determined upon generation of at least one of: the one or more first credit decisions and the one or more second credit decisions, based on one or more key statistical parameters, wherein the one or more key statistical parameters comprise at least one of: the one or more current credit limits, median, and standard deviation, and wherein the one or more current credit limits are associated with one or more reference points when the median and the standard deviation provide one or more insights into a central tendency and variability of the preprocessed one or more data; providing, by the one or more hardware processors, one or more automated approvals for the one or more credit decisions, based on one or more pre-configured rules and parameters; and providing, by the one or more hardware processors, an output of at least one of: the one or more credit decisions, the one or more recommended credit values, the one or more recommended first credit limits, and the one or more recommended second credit limits, to the one or more second users on one or more user interfaces associated with the one or more electronic devices.” are simply making use of a computer and the computer limitations do not necessarily restrict the claim from reciting an abstract idea as discussed above under Step 2A-Prong 1 of the 35 U.S.C. 101 rejection.
Hence, Examiner has also considered each and every arguments under Step 2A-Prong 1 and concludes that these arguments are not persuasive. For example, under Step 2A-Prong 1, Examiner considers each and every limitation to determine if the claim recites an abstract idea. In this case, it is determined that the claim recites an abstract idea and the additional limitations of a computer device does not necessarily restrict the claim from reciting an abstract idea. The recited steps, as amended, are abstract in nature as there are no technical/technology improvements as a result of these steps. Thus, the claim recites an abstract idea. Whether the claim integrates the abstract idea into a practical application by providing technical/technology improvements are considered under Step 2A-Prong 2.

Applicant argues that “The claimed system provides a technical improvement to computing systems and machine learning pipeline operations by programmatically transforming raw, heterogeneous enterprise databases into standardized, low-dimensional execution structures. ….. the claims do not merely recite an abstract business practice, but rather define a specific technical architecture that directly improves the operational processing speed, scalability, and memory efficiency of machine learning systems ….The claimed combination defines a specific computer-implemented machine- learning processing pipeline that transforms electronic datasets and performs defined computational operations before producing the claimed outputs. The claims therefore integrate any alleged judicial exception into a practical application.”

Examiner respectfully disagrees.
Under Step 2A: Prong II, as previously discussed, Examiner respectfully notes that there is no improved technology in simply receiving, inputting, retrieving, training (processing), obtaining, selecting, segmenting, correlating, determining, preprocessing, generating, classifying, providing, approving, outputting, and presenting (displaying) data (i.e., user input data, entity information, user data, credit agency data, account receivables data, financial metrics, entity data, labelled datasets, validation datasets, historical credit decisions data, credit risks data, credit limits data, credit decision approval data, recommended credit values, and etc.). The disclosed invention simply cannot be equated to improvement to technological practices or computers. There is no technical improvement at all. Instead, Applicant recites in claim 1: “receiving, by one or more hardware processors, one or more inputs from one or more electronic devices associated with one or more second users, wherein the one or more inputs comprise information related to at least one of: one or more entities associated with the one or more first users; retrieving, by the one or more hardware processors, one or more data associated with the one or more first users from one or more databases, based on the one or more inputs received from the one or more electronic devices associated with the one or more second users, wherein the one or more data comprise at least one of: one or more credit agency data, one or more accounts receivables data, one or more financial metrics, and one or more entity data, associated with the one or more first users; preprocessing, by the one or more hardware processors, the one or more data to normalize and standardize the one or more data by at least one of: removing one or more noises, one or more outliers, one or more missing values, filtering the one or more entities with an active status, consolidating multiple credit changes on a single day, and converting currencies in the one or more data to a single currency; training, by the one or more hardware processors, one or more machine learning models, by: obtaining, by the one or more hardware processors, one or more labelled datasets from the one or more databases, wherein the one or more labelled datasets comprise preprocessed historical data corresponding to the one or more data; selecting, by the one or more hardware processors, a reduced subset of features from one or more features associated with the preprocessed historical data for training the one or more machine learning models by executing at least one of: a forward feature selection process, a backward feature selection process, an exhaustive feature selection process, a recursive feature elimination process, a random forest importance process and a boosted feature extractor process; segmenting, by the one or more hardware processors, the one or more labelled datasets into at least one of: one or more training datasets and one or more validation datasets; and training, by the one or more hardware processors, the one or more machine learning models to correlate the reduced subset of features and one or more historical credit decisions, and wherein the one or more machine learning models comprise at least one of: a random forest model, an extreme gradient boosting (XGBoost) classifier model, a K-means clustering model, a light gradient-boosting machine (LightGBM) classifier model; determining, by the one or more hardware processors, the one or more credit risks of the one or more entities associated with the one or more first users based on the preprocessed one or more data, by the one or more trained machine learning models; generating, by the one or more hardware processors, one or more credit decisions for the one or more entities associated with the one or more first users based on the determined one or more credit risks of the one or more entities associated with the one or more first users, by the one or more trained machine learning models, wherein the one or more credit decisions comprise at least one of: one or more first credit decisions, one or more second credit decisions, one or more third credit decisions; generating, by the one or more hardware processors, one or more confidence scores for each credit decision of the one or more credit decisions to classify the one or more credit decisions, based on a correlation between the preprocessed one or more data and the one or more credit decisions and based on the one or more trained machine learning models, wherein the classification of the one or more credit decisions comprises at least one of: one or more first credit decisions, one or more second credit decisions, one or more third credit decisions; retrieving, by the one or more hardware processors, one or more current credit limits; determining, by the one or more hardware processors, at least one of: one or more recommended credit values, one or more recommended first credit limits, one or more recommended second credit limits, and one or more recommended third credit limits, based on the classification of at least one of: the one or more first credit decisions, the one or more second credit decisions, and the one or more third credit decisions, wherein at least one of: the one or more recommended first credit limits and the one or more recommended second credit limits are determined upon generation of at least one of: the one or more first credit decisions and the one or more second credit decisions, based on one or more key statistical parameters, wherein the one or more key statistical parameters comprise at least one of: the one or more current credit limits, median, and standard deviation, and wherein the one or more current credit limits are associated with one or more reference points when the median and the standard deviation provide one or more insights into a central tendency and variability of the preprocessed one or more data; providing, by the one or more hardware processors, one or more automated approvals for the one or more credit decisions, based on one or more pre-configured rules and parameters; and providing, by the one or more hardware processors, an output of at least one of: the one or more credit decisions, the one or more recommended credit values, the one or more recommended first credit limits, and the one or more recommended second credit limits, to the one or more second users on one or more user interfaces associated with the one or more electronic devices” The recited features in the limitations do not result in computer functionality or technical improvement. Examiner respectfully notes that Applicant is simply using a computer to input, process, and output data. The recited features in the limitations does not disclose a technical solution to technical problem, but simply a business solution. Specifically, the recited steps, as amended, are merely managing/processing data (MPEP 2106.05(d)(II)) and does not result in computer functionality or technical improvement. Thus, Applicant has simply provided a business method practice of processing data (user input data, entity information, user data, credit agency data, account receivables data, financial metrics, entity data, labelled datasets, validation datasets, historical credit decisions data, credit risks data, credit limits data, credit decision approval data, recommended credit values, and etc.), and no technical solution or improvement has been disclosed.
Moreover, there is no technology/technical improvement as a result of implementing the abstract idea. The recited features in the claims, as amended, simply amount to the abstract idea of processing credit decisions and recommended credit values and limits; and there is no computer functionality improvement or technology improvement. The claim does not provide a technical solution to a technical problem. If there is an improvement, it is to the abstract idea and not to technology. Additionally, Examiner notes that it is important to keep in mind that an improvement in the judicial exception itself (e.g., recited fundamental economic principle or practice and/or commercial interaction) is not an improvement in technology (See, MPEP 2106.05(a)(II)).
Additionally, Claim 1, as amended, recites steps at a high level of generality. In addition, all uses of the recited judicial exceptions require such data gathering and outputting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and output. See MPEP 2106.05. The claim simply makes use of a computer as a tool to apply the abstract idea without transforming the abstract idea into a patent eligible subject matter. Furthermore, these steps, as amended, are recited as being performed by one or more hardware processors, one or more electronic devices, one or more databases, one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, one or more trained machine learning models, and one or more user interfaces. The additional elements: one or more hardware processors, one or more electronic devices, one or more databases, one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, one or more trained machine learning models, and one or more user interfaces are recited at a high level of generality, and are used as a tool to perform the generic computer function of receiving, processing, and outputting data. See MPEP 2106.05(f). Amended claim 1 recites one or more hardware processors, one or more electronic devices, one or more databases, one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, one or more trained machine learning models, and one or more user interfaces, which are simply used to perform an abstract idea, as discussed above in Step 2A, Prong I of the 35 U.S.C. 101 rejection, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Specifically, the recitation of “one or more hardware processors, one or more electronic devices, one or more databases, one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, one or more trained machine learning models, and one or more user interfaces” in the limitations merely indicates a field of use or technological environment in which the judicial exception is performed. The claims, as amended, merely confines the use of the abstract idea to a particular technological environment; and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception. Hence, claim 1, 10, and 19, as amended, do not integrate the abstract idea into a practical application. Thus, these arguments are not persuasive.

Applicant argues that “even assuming, arguendo, that the claims recite a judicial exception, the amended claims recite significantly more than such exception… patent eligibility does not require specialized hardware. Rather, the relevant inquiry is whether the claims, considered as an ordered combination, recite significantly more than the alleged judicial exception. …. The amended claims do not rely upon generic processors merely as tools for automating a credit-management practice. Instead, they define a specific machine-learning processing pipeline in which electronic datasets undergo defined transformations before a reduced feature representation is generated and supplied to specified machine-learning models….. the inventive concept resides not in any individual limitation viewed in isolation, but in the ordered combination of the claimed data transformations, feature-space reduction, machine-learning processing, confidence-score generation, and statistical determination of recommended credit values and credit limits.”

Examiner respectfully disagrees.
Under Step 2B, as previously discussed, Examiner respectfully notes that all of Applicant's arguments have been reviewed, and the inventive concept cannot be furnished by a judicial exception. The improvements argued are to the abstract idea and not to technology. The technical limitations are simply utilized as a tool to implement the abstract idea without adding significantly more. Thus, the claim is directed to an abstract idea, and hence these arguments are not persuasive. The presence of a computer does not make the claimed solution necessarily rooted in computer technology. Furthermore, Examiner notes that the courts have determined that processing data is well-understood, routine, and conventional functions of a computer when they are claimed in a merely generic manner (see MPEP 2106.05(d)(II)). Thus, the recited combination of steps in claims 1, 10, and 19 operate in a well-understood, routine, conventional and generic way. As noted above, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of “one or more hardware processors, one or more electronic devices, one or more databases, one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, one or more trained machine learning models, and one or more user interfaces are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment. Applying the 2024 Guidance Update on Patent Subject Matter Eligibility here, and as explained with respect to Step 2A, Prong 2, the additional elements: one or more hardware processors, one or more electronic devices, one or more databases, one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, one or more trained machine learning models, and one or more user interfaces, are at best mere instructions to “apply” the abstract idea, which cannot provide an inventive concept. See MPEP 2106.05(f).
Furthermore, as previously discussed, the additional elements: one or more hardware processors, one or more electronic devices, one or more databases, one or more machine learning models, random forest model, extreme gradient boosting (XGBoost) classifier model, K-means clustering model, light gradient-boosting machine (LightGBM) classifier model, one or more trained machine learning models, and one or more user interfaces, were found to be insignificant extra-solution activity in Step 2A, Prong II, because they were determined to be insignificant limitations as necessary for data gathering, processing, and outputting.. As discussed in Step 2A, Prong II above, the claims’ limitations are recited at a high level of generality. These elements simply amount to receiving and outputting data and are well-understood, routine, conventional activity. See MPEP 2106.05(d)(II). As discussed in Step 2A, Prong II above, the recitation of a computer/processor to perform recited limitations, as amended, amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Hence, Examiner respectfully declines Applicant’s request to withdraw the 35 U.S.C. 101 rejection of claims 1-2, 4-11, and 13-20.

Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is the following:
Vinay (U.S. Patent Pub. No. US-2018/0182029-A1) “Systems and methods for custom ranking objectives for machine learning models applicable to fraud and credit risk assessments”
Dalinina (U.S. Patent Pub. No. US-2020/0357060-A1) “Rules/model-based data processing system for intelligent default risk prediction”
Hubard (U.S. Patent Pub. No. US- 2022/0122171-A1) “Client server system for financial scoring with cash transactions”
Anasta (U.S. Patent Pub. No. US-2022/0383406-A1) “Account risk detection and account limitation generation using machine learning”
Bradford (U.S. Patent Pub. No. US-2023/0084370-A1) “Dynamically updating account access based on employment data”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED H MUSTAFA whose telephone number is [phone]. The examiner can normally be reached M-F 8:00 - 5:00.
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, Michael W Anderson can be reached on 571-270-0508. 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.

/MOHAMMED H MUSTAFA/Examiner, Art Unit 3693

/ELIZABETH H ROSEN/Primary Examiner, Art Unit 3693

Prosecution Timeline

• Dec 29, 2023: Application Filed
• Jan 05, 2026: Non-Final Rejection mailed — §101
• Apr 06, 2026: Response Filed
• Jun 16, 2026: Final Rejection mailed — §101
• Aug 19, 2026: Response after Non-Final Action
• Sep 08, 2026: Request for Continued Examination
• Sep 12, 2026: Response after Non-Final Action
• Sep 24, 2026: Non-Final Rejection mailed — §101 (current)