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 .
DETAILED ACTION
1. This office action is in response to an amendment received on 5/26/26 for patent application 18/293,755.
2. Claims 1, 4, 13, 18 are amended.
3. Claims 1-7, 9-20 are pending.
Response to Arguments
Applicant argues#1
Step 2A - Prong 1
Just as the district court oversimplified the claims in McRO, interpreting the features recited in claims 1, 13, and 18 as allegedly being directed to an abstract idea because they are "recited at a high level of generality and are being used in their ordinary capacity...," is a gross oversimplification because this analysis (1) fails to meaningfully analyze or account for the specificity of the claim language and (2) fails to analyze the claim as a whole.
At best, the Office recites some claim language, verbatim, followed by a conclusion that is unsupported by any reasoning or meaningful analysis. Specifically, the Office's statement that claims are allegedly directed to an abstract idea because "risk strategy decision model explainable artificial intelligence (XAI) architecture [are] recited at a high level of generality is precisely the type of oversimplification against which the Federal Circuit in McRO warns.
Applicant submits that the use of "an explainable artificial intelligence (XAI) architecture to group one or more variables into one or more fraud reasons as recited in claims 1, 13, and 18, (1) streamlines, and simplifies existing risk management processes, and (2) decrypts an unexplainable model score into several explainable reason groups in plain language for risk decision management. Consequently, instances of failed transactions resulting from false determinations of fraudulent activity may be significantly reduced, particularly as the XAI architecture can be implemented across thousands, or even hundreds of thousands, of transactions in near real time. Further, newly amended claims 1, 13, and 18 recite, in significant detail, the steps that are implemented to generate a risk score, including, e.g, "weighting at least a subset of the fraud reasons to generate weights corresponding to the subset of the fraud reasons, summing the weights, generating, using a decision tree that is based on the subset, a decision tree result, and combining a feature-attribution value with the decision tree result among other features.
Furthermore, the use of "explainable artificial intelligence (XAI)" as recited in the claims constitutes a specific means of improving relevant technology, and as such, is not directed to an abstract idea. See McRO, 837 F.3d at 1314 ("A patent may issue 'for the means or method of producing a certain result, or effect, and not for the result or effect produced' [CAFC looks] to whether the claims focus on a specific means or method that improves the relevant technology ").
For at least these reasons, the claims, as recited, are not directed to an abstract idea.
Examiner Response
Examiner respectfully disagrees.
The claims are reciting the identified abstract idea. The recited abstract elements are shown, below, see step 2a prong 1 analysis of the section 101 rejection.
The section 101 rejection did consider the combination of elements when determining under step 2a prong 2 that there was no integration into a practical application (see page 17 of the Non-Final rejection mailed on 2/24/26; “Accrdingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea”.
Therefore, the claims were evaluated as a whole (the way that the additional elements interact with the identified abstract idea) under step 2a prong 2.
The limitations from claims 1,13, 18 (weighting at least a subset of the fraud reasons to generate weights corresponding to the subset of the fraud reasons, summing the weights, generating, using a decision tree that is based on the subset, a decision tree result, and combining a feature-attribution value with the decision tree result, the feature-attribution value being: specific to the subset, and
generating a final risk score based on the combining of the
feature-attribution value and the decision tree result) are part of the identified abstract idea (steps for determining whether to approve/decline a transaction based on potential fraud).
Examiner disagrees that the use of "explainable artificial intelligence (XAI)" as recited in the claims constitutes a specific means of improving relevant technology.
Spec paras 32-33, 80 are reproduced below:
[0032] As discussed above, the fraud detector 116 may include the machine learning model 120 for determining whether a transaction is potentially fraudulent. However, machine learning models are generally unable to provide an output indicative of why the transaction may be flagged as potentially fraudulent. As a result, in some embodiments, the fraud detector 116 may not be able to be leveraged to explain to the user why a transaction is potentially fraudulent. Instead, the transaction authenticator 118 including an explainable artificial intelligence (XAI) architecture 122 can be leveraged in combination with the fraud detector 116 to determine why the transaction was potentially fraudulent, and ultimately, whether to approve or decline the transaction. The XAI architecture 122 is shown and described in additional detail in accordance with FIG. 7 below.
[0033] In some embodiments, the transaction authenticator 118 can receive a risk score from the fraud detector 116. In some embodiments, the transaction authenticator 118 can use the risk score as an input into the XAI architecture 122. The transaction authenticator 118 can use the XAI architecture 122 to determine reasons that the transaction has been flagged by the fraud detector 116 as being potentially fraudulent. For example, in some embodiments, the transaction authenticator 118 and the XAI architecture 122 can use a Shapley (SHAP) algorithm to generate one or more XAI reasons. In some embodiments, the SHAP algorithm provides an indication of the importance of each variable that contributes to the risk score in fraud detector 116. The one or more XAI reasons can be based on a mapping of the variables from the fraud detector 116 to a defined set of XAI reasons stored in a memory of the transaction processing server 106.
[0080] Prior risk solutions leverage simple decision trees based on several reasons and suffer from some instability such as high bias/variance which means they cannot fully maximize the benefits and the models have some errors. The combined SHAP weighted decision tree 270 improves the performance by encompassing all the contributing reasons and improving the average performance by reducing the mixed reason and inaccurate reason bias through combining all the top contributing reasons selected via SHAP. Adding SHAP weights also reduces the variance introduced by lower weight reasons. High variance comes with high complexity of the decision tree. Adding SHAP weights excludes some error introduced by lower weight reasons as higher SHAP value reasons have the higher differentiation powers.
These spec paras disclose that the XAI architecture and decisions tree along with the use of the SHAP weights and feature value are used in their ordinary capacity to implement the steps of the identified abstract idea.
Examiner submits that SHAP values are commonly used with decision trees to make predictions about a decision.
Applicant argued the claims present a technical improvement. Examiner does not find this argument persuasive. Applicant’s claims do not improve technology; the underlying technology remains unaffected by the claims. Applicant is addressing a business problem (steps for determining whether to approve/decline a transaction based on potential fraud) with a business solution. Applicant is merely using existing technology (for its intended purpose) to implement the business solution. Any improvements lie in the abstract idea itself, not in underlying technology
The rejection is maintained.
Applicant argues#2
Step 2A - Prong 2
Even assuming arguendo that the claims are directed to an abstract idea (which they are not), the subject matter of the claims does in fact impose meaningful limits on any SO called abstract idea to which the claims may be directed. In particular, claims 1, 13, and 18 are directed, at least in part, to the specific steps of "determining, by the processor of the transaction processing entity, a reason for approving or declining the transaction attempt based on one or more variables contributing to the risk score using an explainable artificial intelligence architecture to group one or more variables contributing to the risk score into one or more fraud reasons, the reason for approving or declining the attempt being based on the one or more fraud reasons, the determining being based on: weighting at least a subset of the fraud reasons to generate weights corresponding to the subset of the fraud reasons, summing the weights, generating, using a decision tree that is based on the subset, a decision tree result, and combining a feature-attribution value with the decision tree result, the feature-attribution value being: specific to the subset, and generated using the explainable artificial intelligence architecture, and generating a final risk score based on the combining of the feature-attribution value and the decision tree result," in the realm of risk management processes for the purpose of fraud detection.
For at least these reasons, the claims include additional limitations that integrate the abstract idea (to which these claims are allegedly directed) into a practical application, namely, one that decrypts unexplainable model scores into plain language and thereby significantly reduces instances of false determinations of fraudulent activity based on failed transactions.
Examiner Response
Examiner respectfully disagrees.
The limitations (determining a reason for approving or declining the transaction attempt based on one or more variables contributing to the risk score to group one or more variables contributing to the risk score into one or more fraud reasons, the reason for approving or declining the attempt being based on the one or more fraud reasons, the determining being based on: weighting at least a subset of the fraud reasons to generate weights corresponding to the subset of the fraud reasons, summing the weights, generating, using a decision tree that is based on the subset, a decision tree result, and combining a feature-attribution value with the decision tree result, the feature-attribution value being: specific to the subset, and generating a final risk score based on the combining of the feature-attribution value and the decision tree result)
is part of the identified abstract idea.
The additional elements (the processor of the transaction processing entity, and the explainable artificial intelligence architecture) are recited at a high level of generality, all operating in their ordinary capacity and are being used as a tool to implement the steps of the identified abstract idea, see MPEP 2106.05(f).
Also see the Response to Applicant argues#1 above.
The rejection is maintained.
Applicant argues#3
Regarding step 2B, Applicant respectfully refutes the Office's assertion that the claims do not recite any additional elements beyond the alleged judicial exception and submits that the Office's reasoning is legally insufficient in view of the Berkheimer Memorandum. Specifically, Applicant respectfully submits that the Office has not only failed to demonstrate that the features recited in the claims are abstract ideas but has also failed to demonstrate that these features are well-understood, routine, conventional activity in the manner required by the Berkheimer Memorandum. In fact, the absence of prior art that allegedly discloses the combination of features recited in the claims, is instructive of and strongly supports the conclusion that the claimed combination of elements are unconventional or non-routine, and as such, are not widely prevalent or in common use. Accordingly, the claims recite more than the alleged judicial exception, and, as such, recite patent eligible subject matter under § 101.
For at least the aforementioned reasons, Applicant submits that claims 1-7 and 9-20 are patent eligible as recited. As such, Applicant requests the Office to withdraw all pending rejections under § 101.
Examiner Response
Applicant misapprehends when a Berkheimer analysis is required under current examination policy. Simply put, Examiner is not required under current Examination policy to evaluate under Step 2B, whether additional elements constitute "well- understood, routine, and conventional activities," ["WURC activities"] unless an additional element(s) were found to be insignificant extra-solution activity in Step 2A, Prong 2. MPEP § 2106.05(d)(I). Here, the condition precedent was not met and the Non-Final Office Action determined the additional elements were no more than mere instructions to apply the abstract idea exception using a computer. MPEP § 2106.05(f). Thus, Examiner was not required to determine a Berkheimer analysis. MPEP § 2106.05(d)(I). (See Section 101 rejection below).
The rejection is maintained.
Claim Rejections- 35 U.S.C § 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.
1. Claims 1-7, 9-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 13, 18 are directed to a method, system and computer readable medium which are statutory categories of invention. (Step 1: YES).
Representative Claim 1 recites the limitations of:
A computer-implemented method, comprising:
receiving, by a processor of a transaction processing entity, a transaction attempt;
receiving, by the processor of the transaction processing entity, a risk score from a risk strategy decision model, wherein the risk score is determined from a machine learning model;
in response to receiving the risk score:
determining, by the processor of the transaction processing entity, whether the risk score exceeds a threshold indicating the transaction attempt is potentially fraudulent;
in response to determining the risk score exceeds the threshold:
determining, by the processor of the transaction processing entity, whether to approve or decline the transaction attempt; and
determining, by the processor of the transaction processing entity, a reason for approving or declining the transaction attempt based on one or more variables contributing to the risk score using an explainable artificial intelligence architecture to group one or more variables contributing to the risk score, into one or more fraud reasons, the reason for approving or declining the attempt being based on the one or more fraud reasons, the determining based on:
weighting at least a subset of the fraud reasons to generate weights corresponding to the subset of fraud reasons;
summing the weights;
generating using a decision tree that is based on the subset, a decision tree result and
combining a feature attribute value with the decision tree result, the feature attribute value being:
specific to the subset, and
generating using the explainable artificial intelligence architecture;
generating a final risk score based on the combining of the feature-attribution value and the decision tree result;
outputting, by the processor of the transaction processing entity, an indication to approve or decline the transaction attempt in response to the determining whether to approve or decline the transaction attempt; and
outputting, by the processor of the transaction processing entity, the reason for approving or declining the transaction attempt.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity.
The claim recites elements that are in bold above, which covers performance of the limitation as a commercial interaction, steps for determining whether to approve/decline a transaction based on potential fraud (e.g., receiving, by a transaction processing entity, a transaction attempt; receiving, by the transaction processing entity, a risk score, wherein the risk score is determined; in response to receiving the risk score: determining, by the transaction processing entity, whether the risk score exceeds a threshold indicating the transaction attempt is potentially fraudulent; in response to determining the risk score exceeds the threshold:
determining, the transaction processing entity, whether to approve or decline the transaction attempt; and determining, by the transaction processing entity, a reason for approving or declining the transaction attempt based on one or more variables contributing to the risk score to group one or more variables contributing to the risk score, into one or more fraud reasons, the reason for approving or declining the attempt being based on the one or more fraud reasons, the determining based on: weighting at least a subset of the fraud reasons to generate weights corresponding to the subset of fraud reasons;
summing the weights; generating using a decision tree that is based on the subset, a decision tree result and combining a feature attribute value with the decision tree result, the feature attribute value being: specific to the subset;
generating a final risk score based on the combining of the feature-attribution value and the decision tree result; outputting, by the transaction processing entity, an indication to approve or decline the transaction attempt in response to the determining whether to approve or decline the transaction attempt; and outputting, the transaction processing entity, the reason for approving or declining the transaction attempt)
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a Commercial Interaction, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
Claims 13, 18 are abstract for similar reasons.
(Step 2A-Prong 1: YES. The claims are abstract).
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05.f), (2) Adding insignificant extra solution activity to the judicial exception (MPEP 2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h).
Claims 1, 13,18 includes the following additional elements:
-A processor of a transaction processing entity
-A risk strategy decision model
-A machine learning model
- A non-transitory computer readable medium
- A transaction authenticator
-An explainable artificial intelligence (XAI) architecture
The additional elements (processor, risk strategy decision model, machine learning model, non-transitory computer readable medium, transaction authenticator and the
explainable artificial intelligence (XAI) architecture, are recited at a high level of generality and are being used in their ordinary capacity and are being used as a tool for implementing the steps of the identified abstract idea, see MPEP 2106.05(f), where applying a computer or using a computer as a tool to perform the abstract idea is not indicative of a practical application.
Spec paras (32-33,80) disclose that the XAI architecture and decisions tree along with the use of the SHAP weights and feature value are used in their ordinary capacity to implement the steps of the identified abstract idea.
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Therefore claims 1, 13, 18 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and
as an ordered combination, they do not add significantly more (also known as an "inventive concept") to the exception. As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements recited in the claim beyond the judicial exception
Mere instructions to implement an abstract idea, on or with the use of generic computer components, or even without any computer components, cannot provide an inventive concept - rendering the claim patent ineligible. Thus claims 1, 13, 18 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-7, 9-12, 14-17, 19-20 further define the abstract idea that is present in their respective independent claims 1, 13, 18 and thus correspond to Certain Methods of Organizing Human Activity and hence are abstract for the reasons presented above.
Claim 3 further defines the identified abstract idea as recited in claim 1. The additional element of the decision tree is recited a high level of generality, operating in its ordinary capacity, and are being used as a tool to implement the steps of the identified abstract idea, see MPEP 2106.05(f).
Claim 4 further defines the identified abstract idea as recited in claim 1. The additional element of Shapley (SHAP) algorithm is recited a high level of generality, operating in its ordinary capacity, and are being used as a tool to implement the steps of the identified abstract idea.
Claim 7 further defines the identified abstract idea as recited in claim 1. The additional element of explainable artificial intelligence architecture is recited a high level of generality, operating in its ordinary capacity, and are being used as a tool to implement the steps of the identified abstract idea.
Claim 20 further defines the identified abstract idea as recited in claim 18.
The additional element of an underlying fraud risk model is recited a high level of generality, operating in its ordinary capacity, and are being used as a tool to implement the steps of the identified abstract idea.
Therefore, the dependent claims 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, the dependent claims (2-7, 9-12, 14-17, 19-20) are directed to an abstract idea. Thus, the claims 1-7, 9-20 are not patent-eligible.
Conclusion
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD Z SHAIKH whose telephone number is (571)270-3444. The examiner can normally be reached M-T, 9-600; Fri, 8-11, 3-5.
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/MOHAMMAD Z SHAIKH/Primary Examiner, Art Unit 3694 8/3/2026
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