ONLINE FAIRNESS MONITORING IN DYNAMIC ENVIRONMENT

§101 §103

DETAILED ACTION Examiner Remarks In light of Applicant’s Remarks and Amendments submitted on 04/24/2026, Examiner has withdrawn the 112(b) rejection for claims 17-18 and the 101 rejection for claims 1-14 and 23. Response to Arguments Applicant believes that the proposed amendments submitted in the Remarks of 04/24/2026 overcome the prior art. See pgs., 9-10 of Applicant’s Remarks submitted on 04/24/2026. Respectfully, Examiner disagrees. The prior art of Chaloulos in view of Wang and in view of Mishraky teaches the amended claim limitations. See the Current Office Action for the detailed teachings. 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 . 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 04/24/2026 has been entered. Specification The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required: “computer readable storage media” is not recited in the Specification as filed on 08/26/2022. Appropriate correction is required. Claim Objections Claim 23 is objected to because of the following informalities: Claim 23 recites “a first iteration operating over the time” however claim 1 recites “for operation over time.” Claim 1 does not recite “a time” but rather “over time.” Appropriate correction is required. 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 15-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claims incorporate transitory forms of signal transmission i.e., signal per se. Amended claim 15 recites “one or more computer readable storage media.” Applicant’s Specification as filed on 08/26/2022 does not explicitly disavow transitory signals for “computer readable storage media.” Only paragraph [0056] explicitly disavows transitory signals per se for a “computer readable storage medium.” Appropriate correction is required. Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. Claims 1-2, 5, 7-9, 12, 14-16 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Chaloulos et al., US 2020/0320428 Al (“Chaloulos’) in view of Wang, Lequn, et al. "Fairness of exposure in stochastic bandits." International Conference on Machine Learning. PMLR, 2021(“Wang”) and in view of Mishraky et al., US 2021/0209499 Al(“Mishraky”). Regarding claim 1, Chaloulos teaches a method of online fairness monitoring in a machine learning model, executable by a processor(Chaloulos, paras. 0076, see also fig. 8, “As shown in the figure, computer system/server 800 is shown in the form of a general-purpose computing device. The components of computer system/server 800 may include, but are not limited to, one or more processors or processing units 802….”), comprising: receiving, during deployment of the trained machine learning model, a subsequent dataset having a plurality of entries with one or more attributes and data corresponding to decisions produced by the trained machine learning model, the data including [reward probabilities] associated with the plurality of attributes (Chaloulos, paras. 0062-0073, see also figs. 6 and 7, “There are, however, multiple other attributes such as income, wealth, and others that are expected to have a significant effect on the assignment of a credit score[the data including associated with the plurality of attributes]…[t]he performance metric of the model[during deployment of the trained machine learning model] is evaluated, 604, by computing the F-score on the test dataset. To illustrate the example, an F-score of 0.8 is assumed for this iteration. Next, the bias on the protected metric (e.g., gender) is measured. In this example, this is done by randomly sampling a set of 100 women and a matching set of 100 men that match the selected women as closely as possible on all attributes except the gender[a subsequent dataset having a plurality of entries with one or more attributes and data corresponding to decisions produced by the trained machine learning model,]….”);1 selecting a first entry [having a maximum reward based on the reward probabilities] associated with a first attribute of the plurality of attributes(Chaloulos, paras. 0062-0069, see also fig. 6, “Next, the bias on the protected metric (e.g., gender) is measured. In this example, this is done by randomly sampling a set of 100 women and a matching set of 100 men that match the selected women as closely as possible on all attributes except the gender, 606… for the later used reinforcement learning algorithm a reward function is computed, 612, which is a combination of performance and bias.”);2 predicting, using an artificial intelligence agent, implementing the trained machine learning model, a decision associated with the selected first entry(Chaloulos, paras. 0070-0071, “[T]he RL engine 616 therefore finds an optimal combination of parameters and/or hyper-parameters that decreases the bias under the specified threshold while maintaining a high performance which results in a fair credit scoring across gender[predicting, using an artificial intelligence agent, implementing the trained machine learning model, a decision associated with the selected first entry].”); updating, based on the predicted decision, a reward probability distribution associated with the first attribute (Chaloulos, paras. 0062-0073, see also figs. 6 and 7, “Next, the bias on the protected metric (e.g., gender) is measured. In this example, this is done by randomly sampling a set of 100 women and a matching set of 100 men[probability distribution associated with the first attribute]… [a] fair evaluation (608) would require each pair to have a similar credit score…[i]n this example, the fairness metric is calculated as the average relative difference between all pairs[updating, based on the predicted decision, a reward]”); and determining that a threshold bias has developed in the trained machine learning model , after deployment by comparing differences between reward probability distributions associated with the plurality of attributes to a bias tolerance threshold, wherein bias toward the first attribute is determined when a difference to at least one other attribute exceeds the bias tolerance threshold. (Chaloulos, paras. 0062-0073, see also figs. 6 and 7, “An SML model for predicting a credit score based on a set of attributes (protected ones and other attributes) is set up… [t]he performance metric of the model is evaluated, 604, by computing the F-score on the test dataset. To illustrate the example, an F-score of 0.8 is assumed for this iteration. Next, the bias on the protected metric (e.g., gender) is measured[reward]. In this example, this is done by randomly sampling a set of 100 women and a matching set of 100 men[probability distributions associated with the plurality of attributes] … [a] fair evaluation (608) would require each pair to have a similar credit score[wherein bias toward the first attribute is determined when a difference to at least one other attribute]… [a] termination engine 614 uses the computed reward to decide if any iteration is to be initiated. It terminates the process if… the reward is not improving anymore. It may also be specified that the fairness metric must be lower than 0.1 % to terminate[to a bias tolerance threshold; exceeds the bias tolerance threshold]. In case of the discussed example, it is also assumed that the termination engine initiates a further iteration[and determining that a threshold bias has developed in the trained machine learning model, after deployment by comparing differences]….”).3 Chaloulos does not teach: reward probabilities; having a maximum reward based on the reward probabilities. However, Wang teaches: reward probabilities(Wang, pg., 5, “[E]ach arm a at round t comes with a context vector x t ,   a ∈ R d . A stochastic linear bandits instance v = ( P x : x ∈ R d ) is a collection of reward distributions for each context vector[reward probabilities].”); having a maximum reward based on the reward probabilities (Wang, pg., 4, see also algorithm 1, “At each round t, the algorithm constructs a confidence region C R t which contains the true parameter μ* with high probability. Then the algorithm optimistically selects a parameter μ t ∈ R K within the confidence region C R t that maximizes the estimated expected reward subject to the constraint that we construct a fair policy as if the selected parameter is the true parameter[having a maximum reward based on the reward probabilities]”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Chaloulos with the teachings of Wang the motivation to do so would be to modify the learning problem associated with standard linear contextual bandit reinforcement learning to include fairness towards during the learning process(Wang, pgs. 1-2, “To overcome these problems of the conventional bandit objective, we propose a new formulation of the bandit problem that implements the principle of Merit-based Fairness of Exposure… [i]t incorporates the additional fairness requirement that each item/arm receives a share of exposure that is proportional to its merit.”). Chaloulos in view of Wang does not teach: debiasing, by a preprocessing module, an initial dataset for training a machine learning model, the initial dataset having a plurality of entries with a plurality of attributes and corresponding values; training the machine learning model using distributions of the values for each of the plurality of attributes in the debiased initial dataset, the trained machine learning model at an initial state being deemed fair; deploying the trained machine learning model for operation over time. However, Mishraky teaches: debiasing, by a preprocessing module, an initial dataset for training a machine learning model, the initial dataset having a plurality of entries with a plurality of attributes and corresponding values(Mishraky, paras., [0036-0052], see also figs., 4, 5, and 6, “[A]t step 402, the process 302 may input the model's dataset from the database 310… at step 404, the domain expert may create a set of consistency pairs based on the input dataset. In one or more embodiments, the domain expert may be equipped with a notion of fairness, and of the potential attributes that must be protected from discrimination. These attributes may be explicit (i.e., contained in the dataset as variable d and may include protected attributes such as e.g., gender or age)[ the initial dataset having a plurality of entries with a plurality of attributes and corresponding values]… the expert creates a set of consistency pairs… [e]ach of these pairs may consist of the features from two examples of the original dataset that obey two requirements. First, the pair may represents two examples that are different with respect to the protected attribute as determined by the expert. Second, based on the remainder of the information, the expert may be able to judge that the two samples warrant a similar response by the model… [a]t step 408, the process 302 may output the dataset with the consistency pairs created during steps 404-406[debiasing, by a preprocessing module, an initial dataset for training a machine learning model].”); training the machine learning model using distributions of the values for each of the plurality of attributes in the debiased initial dataset, the trained machine learning model at an initial state being deemed fair(Mishraky, paras., [0036-0052], see also figs., 4, 5, and 6, “At step 404, the domain expert may create a set of consistency pairs based on the input dataset…the domain expert may be equipped with a notion of fairness, and of the potential attributes that must be protected from discrimination. These attributes may be explicit (i.e., contained in the dataset as variable d and may include protected attributes such as e.g., gender or age)[ using distributions of the values for each of the plurality of attributes in the debiased initial dataset]… the process 304 at step 502 may apply the dataset with the consistency pairs to the model 314. At step 504, the process 304 may determine the PRC score based on the model's output. At step 506, the fairness of the model may be evaluated based on the PRC score. [training the machine learning model; the trained machine learning model at an initial state being deemed fair].”); deploying the trained machine learning model for operation over time(Mishraky, para., [0018], see also fig., 1, “First service 122 or second service 142 may implement an information service, which may maintain data run through the model being evaluated and or regulated. The information service may be any network 110 accessible service that maintains the data run through the model being evaluated and or regulated[deploying the trained machine learning model for operation over time].”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Chaloulos in view of Wang with the teachings of Mishraky the motivation to do so would be to mitigate discrimination in machine learning models in a less complex manner and without scarifying accuracy of the model(Mishraky, paras. [0007-0014], “While explicit discrimination is easy to detect and remove, features that are correlated to discriminating attributes are much harder to detect. Features that are correlated to discriminating attributes may be referred to as bias by proxy features… [a] good classifier will be accurate, but also consistent in the scoring of the pairs (i.e., the loan request should be denied or accepted for both subjects). As a result, in accordance with the disclosed principles, domain experts may take part in the process and mitigate discrimination without the need to understand how the model works and or what its input features are.”). Regarding claim 2, Chaloulos in view of Wang and Mishraky teaches the method of claim 1, wherein determining the bias toward the first attribute further comprises: selecting a sample associated with the first entry(Chaloulos, paras. 0062-0069, see also fig. 6, “Next, the bias on the protected metric (e.g., gender) is measured. In this example, this is done by randomly sampling a set of 100 women and a matching set of 100 men that match the selected women as closely as possible on all attributes except the gender, 606[selecting a sample associated with the first entry]….”); and wherein: updating the reward probability distribution(Wang, pgs. 4-5, see also line 7 of algorithm 2, “Finally, the algorithm observes the feedback and updates the posterior distribution of the true parameter.”) of the first attribute is further based on observed rewards associated with the selected sample(Chaloulos, paras. 0069-0073, see also figs. 6 and 7, “Chaloulos, paras. 0062-0069, see also fig. 6, “To illustrate the example, a fairness metric value of 10% (average relative difference) is assumed… a reward function is computed, 612, which is a combination of performance and bias…[t]he bias metric is in this example defined as ( -1) if fairness metric>0.1%, 0 if fairness metric<0.l %. In the case of the example, therefore a reward of R=0.8-1=-0.2 (since 10%>0.1%) is fed to the RL network. A termination engine 614 uses the computed reward to decide if any iteration is to be initiated. It terminates the process if…the reward is not improving anymore”); and a change to the reward probability of the first attribute is, based on comparing the updated reward probability distribution to the received reward probability distribution of the first attribute (Chaloulos, paras. 0062-0069, see also fig. 6, “To illustrate the example, an F-score of 0.8 is assumed for this iteration. Next, the bias on the protected metric (e.g., gender) is measured[of the first attribute]. In this example, this is done by randomly sampling a set of 100 women and a matching set of 100 men that match the selected women as closely as possible on all attributes except the gender, 606…a fairness metric value of 10% (average relative difference) is assumed… a reward function is computed, 612, which is a combination of performance and bias…[t]he bias metric is in this example defined as ( -1) if fairness metric>0.1%, 0 if fairness metric<0.l %. In the case of the example, therefore a reward of R=0.8-1=-0.2 (since 10%>0.1%) is fed to the RL network. A termination engine 614 uses the computed reward[to the received reward probability distribution] to decide if any iteration is to be initiated. It terminates the process if…the reward is not improving anymore (no convergence)[ a change to the reward probability based on comparing the updated reward probability distribution to the received reward probability distribution].”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Wang with the above teachings of Chaloulos for the same rationale stated at Claim 1. Regarding claim 5, Chaloulos in view of Wang and Mishraky teaches the method of claim 1, further comprising: determining, for each iteration of selecting and predicting, whether the bias has developed in the trained machine learning model (Chaloulos, paras. 0057-0061, see also fig. 5, “To this end, the RL engine navigates the space of possible machine-learning models…and model parameters as well as hyper-parameters by calculating a reward which is based both, on performance and fairness of the predictions by the supervised machine-learning model. The reinforcement learning engine 516 will iteratively optimize this reward function value by exploring several different configurations of the supervised machine-learning model, and the termination engine 514 determines that a suitable predictor has been found based on the convergence of the reward function[determining, for each iteration of selecting and predicting, whether the bias has developed in the trained machine learning model].”). Regarding claim 7, Chaloulos in view of Wang and Mishraky teaches the method of claim 1, further comprising: remediating the bias within the trained machine learning model(Chaloulos, paras. 0057-0061, see also fig. 5, “The reinforcement learning engine 516 will iteratively optimize this reward function value by exploring several different configurations of the supervised machine-learning model, and the termination engine 514 determines that a suitable predictor has beenfound based on the convergence of the reward function. The termination engine 514 may also have other explicit criteria that must be fulfilled before termination occurs, e.g., constraints on the achieved fairness. If the reward function outcomes have converged and the supervised machine learning model meets the pre-specified performance and fairness requirements, the optimized supervised machine learning model (SML) is presented as a result, 518[remediating the bias within the trained machine learning model].”). Regarding claim 23, Chaloulos in view of Wang and Mishraky teaches the method of claim 1, wherein: the selecting and the predicting is performed during a first iteration operating over the time(Chaloulos, paras. [0057-0062], see also figs., 5 and 6, “FIG. 5 shows a block diagram 500 of a set of interacting components which fall into three phases/three categories… [t]he evaluation portion may be symbolized by the blocks 504, 506, 508, 510, 512. After the supervised machine-learning model has been trained, it is evaluated with respect to both, prediction performance and prediction fairness. This is achieved by letting the trained supervised machine-learning model perform a set of predictions on a validation dataset that has not been used in the training process of the SML model. The quantities are then calculated from the resulting predictions: a performance metric and a fairness metric. This specification of these metrics depends on a problem at hand. An example of a performance metric is F-score. An example of a fairness metric is the average relative difference between data samples that only differ on protected attributes. This may be done by splitting a test dataset of pre-selected critical/protected features, 506, and by evaluating a fairness measurement on each of the subsets, 508… [b]oth metrics-i.e., performance and fairness-are then used as input into a reward function, 512[the selecting and the predicting is performed during a first iteration operating over the time].”). Regarding claim 8, Chaloulos teaches a computer readable storage medium configured to store computer program code; and one or more computer processors configured to access said computer program code and operate as instructed by said computer program code(Chaloulos, paras. 0074-0080, “As shown in the figure, computer system/server 800 is shown in the form of a general-purpose computing device. The components of computer system/server 800 may include, but are not limited to, one or more processors or processing units 802, a system memory 804…[t]he system memory 804 may include computer system readable media[a computer readable storage medium configured to store computer program code; and one or more computer processors configured to access said computer program code and operate as instructed by said computer program code]”) and for all other claim limitations they are rejected on the same basis as independent claim 1 since they are analogous claims.4 Referring to dependent claims 9, 12, and 14, they are rejected on the same basis as dependent claims 2, 5, and 7 since they are analogous claims. Regarding claim 15, Chaloulos teaches a computer program product comprising one or more computer readable storage medium having stored thereon a computer program for online fairness monitoring, the computer program, when executed by a processor(Chaloulos, paras. 0072-0080, “[A] repetition unit 708 is adapted for triggering the selector unit and the controller iteratively for an improvement of a fairness value of the supervised machine-learning model[a computer program for online fairness monitoring]…[a]s shown in the figure, computer system/server 800 is shown in the form of a general-purpose computing device. The components of computer system/server 800 may include, but are not limited to, one or more processors or processing units 802, a system memory 804…[t]he system memory 804 may include computer system readable media[one or more computer readable storage medium having stored thereon a computer program for online fairness monitoring, the computer program, when executed by a processor]”) and for all other claim limitations they are rejected on the same basis as independent claim 1 since they are analogous claims. Referring to dependent claim 16 it is rejected on the same basis as dependent claims 2 since they are analogous claims. Claims 3-4, 10-11, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Chaloulos et al., US 2020/0320428 Al (“Chaloulos’) in view of Wang, Lequn, et al. "Fairness of exposure in stochastic bandits." International Conference on Machine Learning. PMLR, 2021(“Wang”) and in view of Mishraky et al., US 2021/0209499 Al(“Mishraky”) and further in view of D'Amour, Alexander, et al. "Fairness is not static: deeper understanding of long term fairness via simulation studies." Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 2020(“D'Amour”) Regarding claim 3, Chaloulos in view of Wang and Mishraky teaches the method of claim 1, but do not teach: wherein the entries in the dataset correspond to one or more borrowers and the reward probabilities corresponds to a credit score associated with each of the one or more borrowers. However, D'Amour teaches: wherein the plurality of entries in the dataset correspond to one or more borrowers and the reward probabilities correspond to credit scores associated with the one or more borrowers(D'Amour, pgs. 3-5, “In particular, we consider the lending scenario… where an agent representing a bank makes decisions about whether to approve or reject applications for loans from a stream of individuals[wherein the plurality of entries in the dataset correspond to one or more borrowers]… [i]n this environment, each loan applicant has an observable group membership variable A and a discrete credit score C ∈ 1 ,   … , C m a x … [i]f the applicant defaults, the agent’s profit decreases by r - and the applicant’s C value is decreased by c - . If the applicant pays back, the agent’s profit is increased by r + and the applicant’s C value is increased by c + . In this simulation, probability of repaying is a deterministic function of credit score π(C); when an applicant’s score increases or decreases, so, too, does their probability of repaying[and the reward probabilities correspond to credit scores associated with the one or more borrowers].”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Chaloulos in view of Wang and Mishraky with the teachings of D'Amour the motivation to do so would be to simulate the long-term behavior characteristics of deployed machine learning models with respect to fairness in decision making (D'Amour, pg. 1, “As machine learning becomes increasingly incorporated within high impact decision ecosystems, there is a growing need to understand the long-term behaviors of deployed ML-based decision systems and their potential consequences. Most approaches to understanding or improving the fairness of these systems have focused on static settings without considering long-term dynamics… long term dynamics are hard to assess, particularly because they do not align with the traditional supervised ML research framework that uses fixed data sets. To address this structural difficulty in the field, we advocate for the use of simulation as a key tool in studying the fairness of algorithms.”). Regarding claim 4, Chaloulos in view of Wang, Mishraky and D'Amour teaches the method of claim 3, wherein determining the bias toward the first attribute further comprises: determining a distribution of credit scores associated with one or more groups of the borrowers having a common attribute(Chaloulos, paras. 0062-0069, see also fig. 6, “An SML model for predicting a credit score based on a set of attributes (protected ones and other attributes) is set up… [n]ext, the bias on the protected metric (e.g., gender) is measured[of credit scores associated with one or more groups of the borrowers having a common attribute]…[a] fair evaluation (608) would require each pair to have a similar credit score, any differences are summarized, 610 into the fairness metric. In this example, the fairness metric is calculated as the average relative difference between all pairs. In any other method of measuring bias is acceptable as well. To illustrate the example, a fairness metric value of 10% (average relative difference) is assumed[determining a distribution].”); updating the distribution of credit scores based on a prediction of the trained machine learning model(Chaloulos, paras. 0062-0070, see also fig. 6, “By going through multiple iterations, the RL engine 616 therefore finds an optimal combination of parameters and/or hyper-parameters that decreases the bias under the specified threshold while maintaining a high performance which results in a fair credit scoring across gender[updating the distribution of credit scores based on a prediction of the trained machine learning model].”) and a repayment probability associated with each group of the borrowers(D'Amour, pgs. 3-5, see also fig. 4, “In this simulation, probability of repaying is a deterministic function of credit score π(C); when an applicant’s score increases or decreases, so, too, does their probability of repaying[and a repayment probability associated with each group of the borrowers].”); detecting whether the updated distribution of credit scores crosses a pre-defined distribution tolerance threshold(Chaloulos, paras. 0062-0069, see also fig. 6, “A termination engine 614 uses the computed reward to decide if any iteration is to be initiated…[i]t may also be specified that the fairness metric must be lower than 0.1 % to terminate[detecting whether the updated distribution of credit scores crosses a pre-defined distribution tolerance threshold]”); and updating the credit score of the borrowers based on the repayment probability(D'Amour, pgs. 3-5, see also fig. 4, “In this environment, each loan applicant has an observable group membership variable A and a discrete credit score C ∈ 1 ,   … , C m a x … [i]f the applicant pays back, the agent’s profit is increased by r + and the applicant’s C value is increased by c + . In this simulation, probability of repaying is a deterministic function of credit score π(C)[ and updating the credit score of the borrowers based on the repayment probability]….”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Chaloulos in view of Wang and Mishraky with the above teachings of D'Amour for the same rationale stated at Claim 3. Referring to dependent claims 10-11 they are rejected on the same basis as dependent claims 3-4 since they are analogous claims. Referring to dependent claims 17-18 they are rejected on the same basis as dependent claims 3-4 since they are analogous claims. Claims 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Chaloulos et al., US 2020/0320428 Al (“Chaloulos’) in view of Wang, Lequn, et al. "Fairness of exposure in stochastic bandits." International Conference on Machine Learning. PMLR, 2021(“Wang”) and in view of Mishraky et al., US 2021/0209499 Al(“Mishraky”) and further in view of Ramchandani et al. US 2023/0060452 Al (“Ramchandani”) Regarding claim 6, Chaloulos in view of Wang and Mishraky teaches the method of claim 1, but do not teach further comprising notifying a user of bias within the machine learning model. However, Ramchandani teaches: further comprising notifying a user of the bias within the trained machine learning model(Ramchandani, para. 0061, see also fig. 4, “The model analysis system 402 executes the model 410 for each subgroup of a plurality of subgroups. In some examples, the model analysis system 402 may include tools (e.g., accessible via one or more graphical user interfaces (GUis)) that are provided to users (e.g., such as users of the issuer system 108, merchants, and/or the like) to screen for certain parameters (e.g., sensitive attributes) and detecting a level of bias by a subgroup[notifying a user of the bias within the trained machine learning model].”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Chaloulos in view of Wang and Mishraky with the teachings of Ramchandani the motivation to do so would be to remove discrimination and unethical practices in models used for classification and propensity(Ramchandani, paras. 0003-0004, “Models, such as classification and propensity models, are often used for targeted offers and personalization of products/services. Such models may have unintended consequences such as discrimination in who receives offers, overwhelming customers with offers, sending unethical offers to vulnerable demographic groups (e.g., alcohol promotion in teenage groups), and/or the like.”). Referring to dependent claim 13 it is rejected on the same basis as dependent claim 6 since they are analogous claims. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Bhide et al. US 2020/0184350 Al (details a post-processing method to handle bias in a given deployed model by composing a bias detector to perturb the data and modify a given customer’s model) Lohia et al. US 2021/0158076 Al(details identifying data points associated with inaccurate class designation based on confidence values and thresholds and training a second model in which the identified data points are excluded) Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM C STANDKE whose telephone number is (571)270-1806. The examiner can normally be reached Gen. M-F 9-9PM EST. 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 J Huntley can be reached at (303) 297-4307. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Adam C Standke/ Primary Examiner Art Unit 2129 1 Examiner Remarks: the claim limitations that are not in bold and contained within square parenthesis i.e., [ ] are claim limitations not taught by Chaloulos 2 Examiner Remarks: the claim limitations that are not in bold and contained within square parenthesis i.e., [ ] are claim limitations not taught by Chaloulos 3 Examiner Remarks: According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all. 4 Examiner Remarks: 112(f) was not applied since the first step of the analysis as determined by Examiner was not satisfied since “code” connotes structure. See Dyfan, LLC v. Target Corp., 28 F.4th 1360, 1368-69 (Fed., Cir. 2022); Zeroclick, LLC v. Apple Inc., 891 F.3d 1003, 1008-1009 (Fed. Cir. 2018).