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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 06/18/2026 has been entered.
Response to Amendment
The Amendment filed 06/05/2026 has been entered. Claims 1-20 remain pending in this application.
Allowable Subject Matter
Claims 5-7 and 15-17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 of this title, 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.
Claim 1-2, 8-12 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Miroshnikov et al. (US 20210383268 A1) Miroshnikov in view of Zhang et al. (US 11586849 B2 hereinafter Zhang) and Bhide et al. (US 20200184350 A1 hereinafter Bhide)
As to independent claim 1, Miroshnikov teaches a computer-implemented method comprising:
receiving, by one or more processors, an aggregate bias correction function for a machine learning model;
[function for fairness and bias mitigation ¶5 "constructing a post-processed score function that at least partially neutralizes one or more groups of input variables contributing to the bias or by constructing a fair score approximation of the model and then projecting the distributions of the trained score based on a joint probability of the predictors and the protected attributes"]
generating, by the one or more processors and using the aggregate bias correction function, an individualized threshold comprising a modified classification threshold [classifiers include population dependent thresholds (modified by population) ¶60 " constructing a post-processed score that yields classifiers with population dependent thresholds"]
(ii) configured to adjust for bias [input predictor specific transformations (adjustments) ¶24 "Partial neutralization refers to replacing certain predictors with a scaled value for the predictor, as defined by a transformation. In another approach to mitigation"] wherein the individualized threshold is based at least in part on (a) a plurality of contextual attributes of the input data object and [predictors (contextual attributes) ¶23-24 credit history etc. ¶81] (b) a plurality of protected attributes of the input data object; [gender, race, age (protected) ¶35]
generating, by the one or more processors and using the machine learning model, a predictive output for the input data object based at least in part on the plurality of contextual attributes; [output (extend credit or not) based on model and input with attributes ¶81-83 "input vector 102 is transmitted to the AI engine 110, which processes the input vector 102 via an ML model 150 stored in the memory 120 to generate an output vector 104"]
Miroshnikov does not specifically teach (i) tailored to an individual attribute corresponding to an input data object for the machine learning model and generating, by the one or more processors, a bias adjusted output for the input data object based at least in part on a comparison between the individualized threshold and the predictive output.
wherein the bias adjusted output comprises a reduced bias relative to the predictive output without using a randomized component for the machine learning model; and [reduces bias using post processing (non-random) and neutralizes predictors ¶9 "mitigating bias in the model comprises constructing a post-processed score function. In an embodiment, the post-processed score function neutralizes at least one group of input variables that contribute to the bias."]
providing, by the one or more processors, data indicative of the bias adjusted output. [output (extend credit or not) based on model and input with attributes ¶81-83 "input vector 102 is transmitted to the AI engine 110, which processes the input vector 102 via an ML model 150 stored in the memory 120 to generate an output vector 104"]
However, Zhang teaches (i) tailored to an individual attribute corresponding to an input data object for the machine learning model and Zhang: [thresholds for each attribute (tailored) Col. 14 ln. 36-50 " threshold settings can regard one or more classification of the data, such as age-based classifications. For instance, wherein the threshold setting is 0.6 for people younger than 26 years old and 0.7 for people older than 26;"]
generating, by the one or more processors, a bias adjusted output for the input data object based at least in part on a comparison between the individualized threshold and the predictive output, [employs model which compares probability to threshold (repayment exceeds age threshold Col. 14 ln. 36-50 ) Col. 13-14 ln. 56-19 "policy makers can employ the system 100 to mitigate bias in the exemplary machine learning model and/or implement one or more fairness policies"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the bias mitigation disclosed by Miroshnikov by incorporating the (i) tailored to an individual attribute corresponding to an input data object for the machine learning model and generating, by the one or more processors, a bias adjusted output for the input data object based at least in part on a comparison between the individualized threshold and the predictive output disclosed by Zhang because both techniques address the same field of machine learning and by incorporating Zhang into Miroshnikov assist in making algorithms fairer and better optimize models [Zhang Col. 1 ln. 22-47]
Miroshnikov and Zhang do not specifically teach (ii) configured to adjust for bias specific to the input data object.
However, Bhide teaches (ii) configured to adjust for bias specific to the input data object, [sample specific (object specific) bias and mitigation via label change (adjustment) ¶8-9 " take a subset of samples and change their predicted labels appropriately to meet a group fairness requirement. An interesting observation about post-processing is that any sample can be altered to achieve group fairness requirements because the metrics are expectations"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the input data disclosed by Miroshnikov and Zhang by incorporating the (ii) configured to adjust for bias specific to the input data object disclosed by Bhide because all techniques address the same field of machine learning and by incorporating Bhide into Miroshnikov and Zhang addresses individual fairness to better mitigate bias for trustworthy models [Bhide ¶1-2]
As to dependent claim 2, the rejection of claim 1 is incorporated, Miroshnikov, Zhang and Bhide further teach a contextual bias correction function configured to output an individualized contextual threshold for the input data object based at least in part on the plurality of contextual attributes; and [Zhang thresholds set for contextual attribute Col. 14 ln. 36-50 " threshold settings can regard one or more classification of the data, such as age-based classifications. For instance, wherein the threshold setting is 0.6 for people younger than 26 years old and 0.7 for people older than 26;"]
a protected bias correction function configured to output an individualized protection threshold for the input data object based at least in part on the plurality of protected attributes. [Zhang threshold based on age (protected attribute) Col. 14-15 ln .62-6 "classification thresholds are set to the same for the age groups younger and older than 26"], [Gueret protected attributes (females, southerners) ¶17-19]
As to dependent claim 8, the rejection of claim 1 is incorporated, Miroshnikov, Zhang and Bhide further teach wherein the predictive output comprises a classification probability corresponding to one or more classifications, and wherein the bias adjusted output comprises a predicted classification from the one or more classifications. [Miroshnikov classifier and probability of output vector ¶29]
As to dependent claim 9, the rejection of claim 8 is incorporated, Miroshnikov, Zhang and Bhide further teach wherein modified classification threshold corresponds to the one or more classifications, and wherein the predicted classification is based at least in part on a comparison between the modified classification threshold and the classification probability [Zhang employs model which compares probability to threshold (repayment exceeds age threshold Col. 14 ln. 36-50 ) Col. 13-14 ln. 56-19 "policy makers can employ the system 100 to mitigate bias in the exemplary machine learning model and/or implement one or more fairness policies"]
As to dependent claim 10, the rejection of claim 1 is incorporated, Miroshnikov, Zhang and Bhide further teach wherein the individualized threshold is a real number between zero and one. [Zhang 0.5 Col. 13 ln. 56-67]
As to independent claim 11, Miroshnikov teaches a system comprising: [hardware ¶62] one or more processors; [processor ¶62] and one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising [instructions and memory ¶76]
receiving, by one or more processors, an aggregate bias correction function for a machine learning model; [function for fairness and bias mitigation ¶5 "constructing a post-processed score function that at least partially neutralizes one or more groups of input variables contributing to the bias or by constructing a fair score approximation of the model and then projecting the distributions of the trained score based on a joint probability of the predictors and the protected attributes"]
generating, by the one or more processors and using the aggregate bias correction function, an individualized threshold comprising a modified classification threshold [classifiers include population dependent thresholds (modified by population) ¶60 " constructing a post-processed score that yields classifiers with population dependent thresholds"]
(ii) configured to adjust for bias [input predictor specific transformations (adjustments) ¶24 "Partial neutralization refers to replacing certain predictors with a scaled value for the predictor, as defined by a transformation. In another approach to mitigation"] wherein the individualized threshold is based at least in part on (a) a plurality of contextual attributes of the input data object and [predictors (contextual attributes) ¶23-24 credit history etc. ¶81] (b) a plurality of protected attributes of the input data object; [gender, race, age (protected) ¶35]
generating, by the one or more processors and using the machine learning model, a predictive output for the input data object based at least in part on the plurality of contextual attributes; [output (extend credit or not) based on model and input with attributes ¶81-83 "input vector 102 is transmitted to the AI engine 110, which processes the input vector 102 via an ML model 150 stored in the memory 120 to generate an output vector 104"]
Miroshnikov does not specifically teach (i) tailored to an individual attribute corresponding to an input data object for the machine learning model and generating, by the one or more processors, a bias adjusted output for the input data object based at least in part on a comparison between the individualized threshold and the predictive output.
wherein the bias adjusted output comprises a reduced bias relative to the predictive output without using a randomized component for the machine learning model; and [reduces bias using post processing (non-random) and neutralizes predictors ¶9 "mitigating bias in the model comprises constructing a post-processed score function. In an embodiment, the post-processed score function neutralizes at least one group of input variables that contribute to the bias."]
providing, by the one or more processors, data indicative of the bias adjusted output. [output (extend credit or not) based on model and input with attributes ¶81-83 "input vector 102 is transmitted to the AI engine 110, which processes the input vector 102 via an ML model 150 stored in the memory 120 to generate an output vector 104"]
However, Zhang teaches (i) tailored to an individual attribute corresponding to an input data object for the machine learning model and Zhang: [thresholds for each attribute (tailored) Col. 14 ln. 36-50 " threshold settings can regard one or more classification of the data, such as age-based classifications. For instance, wherein the threshold setting is 0.6 for people younger than 26 years old and 0.7 for people older than 26;"]
generating, by the one or more processors, a bias adjusted output for the input data object based at least in part on a comparison between the individualized threshold and the predictive output, [employs model which compares probability to threshold (repayment exceeds age threshold Col. 14 ln. 36-50 ) Col. 13-14 ln. 56-19 "policy makers can employ the system 100 to mitigate bias in the exemplary machine learning model and/or implement one or more fairness policies"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the bias mitigation disclosed by Miroshnikov by incorporating the (i) tailored to an individual attribute corresponding to an input data object for the machine learning model and generating, by the one or more processors, a bias adjusted output for the input data object based at least in part on a comparison between the individualized threshold and the predictive output disclosed by Zhang because both techniques address the same field of machine learning and by incorporating Zhang into Miroshnikov assist in making algorithms fairer and better optimize models [Zhang Col. 1 ln. 22-47]
Miroshnikov and Zhang do not specifically teach (ii) configured to adjust for bias specific to the input data object.
However, Bhide teaches (ii) configured to adjust for bias specific to the input data object, [sample specific (object specific) bias and mitigation via label change (adjustment) ¶8-9 " take a subset of samples and change their predicted labels appropriately to meet a group fairness requirement. An interesting observation about post-processing is that any sample can be altered to achieve group fairness requirements because the metrics are expectations"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the input data disclosed by Miroshnikov and Zhang by incorporating the (ii) configured to adjust for bias specific to the input data object disclosed by Bhide because all techniques address the same field of machine learning and by incorporating Bhide into Miroshnikov and Zhang addresses individual fairness to better mitigate bias for trustworthy models [Bhide ¶1-2]
As to dependent claim 12, the rejection of claim 1 is incorporated, Miroshnikov, Zhang and Bhide further teach a contextual bias correction function configured to output an individualized contextual threshold for the input data object based at least in part on the plurality of contextual attributes; and [Zhang thresholds set for contextual attribute Col. 14 ln. 36-50 " threshold settings can regard one or more classification of the data, such as age-based classifications. For instance, wherein the threshold setting is 0.6 for people younger than 26 years old and 0.7 for people older than 26;"]
a protected bias correction function configured to output an individualized protection threshold for the input data object based at least in part on the plurality of protected attributes. [Zhang threshold based on age (protected attribute) Col. 14-15 ln .62-6 "classification thresholds are set to the same for the age groups younger and older than 26"], [Gueret protected attributes (females, southerners) ¶17-19]
As to independent claim 18, Miroshnikov teaches one or more non-transitory computer-readable media storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: [processor, instructions and memory ¶62, ¶76]
receiving, by one or more processors, an aggregate bias correction function for a machine learning model; [function for fairness and bias mitigation ¶5 "constructing a post-processed score function that at least partially neutralizes one or more groups of input variables contributing to the bias or by constructing a fair score approximation of the model and then projecting the distributions of the trained score based on a joint probability of the predictors and the protected attributes"]
generating, by the one or more processors and using the aggregate bias correction function, an individualized threshold comprising a modified classification threshold [classifiers include population dependent thresholds (modified by population) ¶60 " constructing a post-processed score that yields classifiers with population dependent thresholds"]
(ii) configured to adjust for bias [input predictor specific transformations (adjustments) ¶24 "Partial neutralization refers to replacing certain predictors with a scaled value for the predictor, as defined by a transformation. In another approach to mitigation"] wherein the individualized threshold is based at least in part on (a) a plurality of contextual attributes of the input data object and [predictors (contextual attributes) ¶23-24 credit history etc. ¶81] (b) a plurality of protected attributes of the input data object; [gender, race, age (protected) ¶35]
generating, by the one or more processors and using the machine learning model, a predictive output for the input data object based at least in part on the plurality of contextual attributes; [output (extend credit or not) based on model and input with attributes ¶81-83 "input vector 102 is transmitted to the AI engine 110, which processes the input vector 102 via an ML model 150 stored in the memory 120 to generate an output vector 104"]
Miroshnikov does not specifically teach (i) tailored to an individual attribute corresponding to an input data object for the machine learning model and generating, by the one or more processors, a bias adjusted output for the input data object based at least in part on a comparison between the individualized threshold and the predictive output.
wherein the bias adjusted output comprises a reduced bias relative to the predictive output without using a randomized component for the machine learning model; and [reduces bias using post processing (non-random) and neutralizes predictors ¶9 "mitigating bias in the model comprises constructing a post-processed score function. In an embodiment, the post-processed score function neutralizes at least one group of input variables that contribute to the bias."]
providing, by the one or more processors, data indicative of the bias adjusted output. [output (extend credit or not) based on model and input with attributes ¶81-83 "input vector 102 is transmitted to the AI engine 110, which processes the input vector 102 via an ML model 150 stored in the memory 120 to generate an output vector 104"]
However, Zhang teaches (i) tailored to an individual attribute corresponding to an input data object for the machine learning model and Zhang: [thresholds for each attribute (tailored) Col. 14 ln. 36-50 " threshold settings can regard one or more classification of the data, such as age-based classifications. For instance, wherein the threshold setting is 0.6 for people younger than 26 years old and 0.7 for people older than 26;"]
generating, by the one or more processors, a bias adjusted output for the input data object based at least in part on a comparison between the individualized threshold and the predictive output, [employs model which compares probability to threshold (repayment exceeds age threshold Col. 14 ln. 36-50 ) Col. 13-14 ln. 56-19 "policy makers can employ the system 100 to mitigate bias in the exemplary machine learning model and/or implement one or more fairness policies"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the bias mitigation disclosed by Miroshnikov by incorporating the (i) tailored to an individual attribute corresponding to an input data object for the machine learning model and generating, by the one or more processors, a bias adjusted output for the input data object based at least in part on a comparison between the individualized threshold and the predictive output disclosed by Zhang because both techniques address the same field of machine learning and by incorporating Zhang into Miroshnikov assist in making algorithms fairer and better optimize models [Zhang Col. 1 ln. 22-47]
Miroshnikov and Zhang do not specifically teach (ii) configured to adjust for bias specific to the input data object.
However, Bhide teaches (ii) configured to adjust for bias specific to the input data object, [sample specific (object specific) bias and mitigation via label change (adjustment) ¶8-9 " take a subset of samples and change their predicted labels appropriately to meet a group fairness requirement. An interesting observation about post-processing is that any sample can be altered to achieve group fairness requirements because the metrics are expectations"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the input data disclosed by Miroshnikov and Zhang by incorporating the (ii) configured to adjust for bias specific to the input data object disclosed by Bhide because all techniques address the same field of machine learning and by incorporating Bhide into Miroshnikov and Zhang addresses individual fairness to better mitigate bias for trustworthy models [Bhide ¶1-2].
As to dependent claim 19, the rejection of claim 18 is incorporated, Miroshnikov, Zhang and Bhide further teach wherein the predictive output comprises a classification probability corresponding to one or more classifications, and wherein the bias adjusted output comprises a predicted classification from the one or more classifications. [Miroshnikov classifier and probability of output vector ¶29]
As to dependent claim 20, the rejection of claim 19 is incorporated, Miroshnikov, Zhang and Bhide further teach wherein modified classification threshold corresponds to the one or more classifications, and wherein the predicted classification is based at least in part on a comparison between the modified classification threshold and the classification probability [Zhang employs model which compares probability to threshold (repayment exceeds age threshold Col. 14 ln. 36-50) Col. 13-14 ln. 56-19 "policy makers can employ the system 100 to mitigate bias in the exemplary machine learning model and/or implement one or more fairness policies"]
Claims 3-4 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Miroshnikov in view of Zhang and Bhide as applied to the rejection of claim 2 and 12 above, and further in view of Murugesan et al. (US 20220114225 A1 hereinafter Murugesan)
As to dependent claim 3, the combination of Miroshnikov, Zhang and Bhide teach all the limitations of claim 2 that is incorporated.
Miroshnikov, Zhang and Bhide do not specifically teach wherein the input data object is associated with a contextual tensor comprising the plurality of contextual attributes.
However, Murugesan teaches wherein the input data object is associated with a contextual tensor comprising the plurality of contextual attributes. [contextual tensor with user/product features ¶33 "a user context tensor X.sub.z.sup.xj that is formed from three feature vectors: user feature vectors of the i-th user and j-th user, and a product feature vector z"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the input data disclosed by Miroshnikov, Zhang and Bhide by incorporating the wherein the input data object is associated with a contextual tensor comprising the plurality of contextual attributes disclosed by Murugesan because all techniques address the same field of machine learning and by incorporating Murugesan into Miroshnikov, Zhang and Bhide better adapts models to user preferences and marketing strategies [Murugesan ¶33]
As to dependent claim 4, the rejection of claim 3 is incorporated, Miroshnikov, Zhang, Bhide and Murugesan further teach wherein generating the individualized threshold for the input data object comprises: generating, by the processors, the individualized contextual threshold for the input data object by applying the contextual bias correction function to the contextual tensor; and [Murugesan tensor with influence ¶13], [Miroshnikov bias correction transformations ¶23-24]
generating, by the processors, the individualized threshold for the input data object based at least in part on the individualized contextual threshold for the input data object. [Zhang threshold settings for repayment and age input Col. 14 ln. 36-50, Col. 13-14 ln. 56-19]
As to dependent claim 13, the combination of Miroshnikov, Zhang and Bhide teach all the limitations of claim 12 that is incorporated.
Miroshnikov, Zhang and Bhide do not specifically teach wherein the input data object is associated with a contextual tensor comprising the plurality of contextual attributes.
However, Murugesan teaches wherein the input data object is associated with a contextual tensor comprising the plurality of contextual attributes. [contextual tensor with user/product features ¶33 "a user context tensor X.sub.z.sup.xj that is formed from three feature vectors: user feature vectors of the i-th user and j-th user, and a product feature vector z"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the input data disclosed by Miroshnikov, Zhang and Bhide by incorporating the wherein the input data object is associated with a contextual tensor comprising the plurality of contextual attributes disclosed by Murugesan because all techniques address the same field of machine learning and by incorporating Murugesan into Miroshnikov, Zhang and Bhide better adapts models to user preferences and marketing strategies [Murugesan ¶33]
As to dependent claim 14, the rejection of claim 13 is incorporated, Miroshnikov, Zhang, Bhide and Murugesan further teach wherein generating the individualized threshold for the input data object comprises: generating the individualized contextual threshold for the input data object by applying the contextual bias correction function to the contextual tensor; and [Murugesan tensor with influence ¶13], [Miroshnikov bias correction transformations ¶23-24]
generating, by the processors, the individualized threshold for the input data object based at least in part on the individualized contextual threshold for the input data object. [Zhang threshold settings for repayment and age input Col. 14 ln. 36-50, Col. 13-14 ln. 56-19]
Response to Arguments
Applicant's arguments filed 06/05/2026. In the remark, applicant argues that:
(1) The cited references fail to teach or suggest at least "generating, ... using the aggregate bias correction function, an individualized threshold comprising a modified classification threshold ... (ii) configured to adjust for bias specific to the input data object," as recited by the claims. Applicant's representative understood the Examiner to agree to this distinction during the interview.
As to point (1), Applicant’s arguments with respect to claim 1 have been considered but are moot in view of a new ground of rejection as set forth above of Miroshnikov in view of Zhang and Bhide.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
Ward et al. (US 20240046349 A1) teaches correcting for fairness using sensitive attributes and comparing datasets (see ¶36)
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Beau Spratt whose telephone number is 571 272 9919. The examiner can normally be reached 8:30am to 5:00pm (PST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch can be reached at 571 272 7212. The fax phone number for the organization where this application or proceeding is assigned is 571 483 7388.
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/BEAU D SPRATT/ Primary Examiner, Art Unit 2143
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