DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is responsive to pending claims 1-20 filed 12/9/2025 .
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The 35 U.S.C. 101 subject matter eligibility analysis first asks whether the claim is directed to one of the four statutory categories (Step 1). It next asks whether the claim is directed to an abstract idea (Step 2A), via Prong 1, whether an abstract idea (e.g., mathematical concept, mental process, certain methods of organizing human activity) is recited, and Prong 2, whether it is integrated into a practical application. It finally asks whether the claim as a whole includes additional elements that amount to significantly more than the judicial exception (Step 2B). See MPEP 2106.
STEP 1: The claims falls within one of the four statutory categories:
As the claims recite methods, non-transitory computer media, and hardware systems, the claims are statutory.
STEP 2A PRONG 1: The claims recite a judicial exception:
The claims recite a technique of evaluating a bias metric in a classification model by perturbing an original input and comparing the confidence results of the original input and the perturbed input. Hence, it is directed to a mathematical process of determining bias via perturbation and a mental process of evaluating the bias. The additional elements are underlined below. In particular:
For claim 1: a computer-implemented method, comprising operations for:
for each original record of a plurality of original records of a data set that are processed by a classification model (This constitutes iterative performance of the mathematical process below):
perturbing the original record to generate a perturbed record (Perturbation is a mathematical adjustment made to an original record);
for the original record, obtaining an original confidence value for each class of a plurality of classes for an outcome, wherein a first class has a first confidence value, and wherein a second class has a second confidence value (confidence values as data input for the mathematical model below are obtained);
for the perturbed record, obtaining a perturbed confidence value for each class of the plurality of classes for the outcome, wherein the first class has a third confidence value, and wherein the second class has a fourth confidence value (likewise, confidence values as data input are obtained for the perturbed classes);
determining a first confidence change comprising an absolute difference of the first confidence value and the third confidence value (this constitutes applying mathematical operations to the input data);
determining a second confidence change comprising an absolute difference of the second confidence value and the third confidence value (likewise, this constitutes applying mathematical operations to the input data);
determining a final confidence value by adding the first confidence change and the second confidence change and based on a direction of distance travelled (This constitutes applying mathematical operations to generate a final confidence value); and
determining whether the original record is biased based on the final confidence value (determining bias based on a metric is a mental process)
determining whether the classification model is biased based on how many original records are determined to be biased (considering bias based on an aggregate of iterative determinations is a mental process);
in response to determining that the classification model is biased, correcting the classification model (making alterations to a classification technique is a mental process); and
in response to determining that the classification model is not biased, deploying the classification model (making an overall determination that the model is acceptable, and using the model for additional functions, is a mental process).
For claim 2: the computer-implemented method of claim 1, further comprising operations for:
in response to determining that bias is increasing, treating the confidence change as a positive value; and
in response to determining that the bias is decreasing, treating the confidence change as a negative value (Considering confidence changes as negative or positive based on increase or decrease is a mental process).
For claim 3: wherein the original record is determined to be biased when the final confidence value is equal to or below a record confidence threshold (comparison to a threshold value is a mathematical process).
For claim 4: wherein the classification model is determined to be biased when a number of the original records exceeds a model bias threshold (comparison to a threshold value in an iterative process is a mathematical process).
For claim 5: in response to determining whether the original record is biased, labelling the original record as biased (labeling or associating tags with an input is a mental process).
For claim 6: wherein correcting the classification model further comprises operations for:
re-training the classification model using each original record labelled as biased (Using past instances of bias to modify a classifier technique, e.g., such as via experience, is a mental process).
For claim 7: wherein the classification model is one of a binary classification model and a multi-class classification model (classification into two or many categories does not render the method incapable of being performed in the mind).
The remaining limitations are rejected for similar reasons.
STEP 2A PRONG 2: The claims do not integrate the exception into a practical application:
As shown above, the additional elements comprise use of a computer to implement the mathematical concept. However, this constitutes mere instructions to implement an abstract idea on a computer and hence does not constitute an integration into a practical application.
Claim 8 contains the additional limitations directed to a computer-program product for execution on a processor to perform said steps. Likewise, claim 15 recite processors in communication with computer-readable devices for performing said steps.
However, this amounts to mere instructions to implement an abstract idea on a computer and hence does not meaningfully limit the practice of the abstract idea and hence does not constitute an integration into a practical application (2a-2). Furthermore, the use of computers to implement routines is well-understood, routine and conventional (WURC) and hence does not constitute significantly more (2b).
STEP 2B: The claim as a whole do not include additional elements that amount to significantly more than the abstract idea:
Claim 8 contains the additional limitations directed to a computer-program product for execution on a processor to perform said steps. Likewise, claim 15 recite processors in communication with computer-readable devices for performing said steps.
As shown above, the additional elements comprise use of a computer to implement the mathematical concept. However, the use of general purpose computers to implement evaluation algorithms is well-understood, routine and conventional (WURC) in the field of classification model evaluation and hence does not constitute significantly more.
Response to Arguments
Applicant’s arguments have been fully considered. In the remarks, the following arguments were made:
I. Regarding the 101 rejections of the claims: the determining step, the correcting step and the deploying step cannot be practically performed in the mind (p.12).
Examiner respectfully disagrees. The determining step is merely the evaluation of how many records are biased, which can be performed by the mind. A classification model is any model to classify data, including those used mentally or heuristically. Hence, making adjustments to a classification model may be performed mentally. Lastly, deploying a classification model may be performed in the mind, i.e., making a decision to use the classification model or heuristic, etc.
II. Regarding the 103 rejections of the claims, the amendments overcome the art of record.
Examiner agrees and the rejections are withdrawn.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Miroshnikov.268 (US 20210383268 A1) discloses a bias mitigation technique using a sum of distances, see eq. 18-27.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIANG LI whose telephone number is (303)297-4263. The examiner can normally be reached Mon-Fri 9-12p, 3-11p MT (11-2p, 5-1a ET).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Jennifer Welch can be reached on (571)272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LIANG LI/
Primary examiner AU 2143