DETAILED ACTION
1. This communication is in response to the amendments filed on April 9, 2026 for Application No. 18/306,103 in which Claims 1-20 are presented for examination.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
3. The amendments filed on April 9, 2026 have been considered. Claims 1-3, 5-6, 8-10, 12-13, and 15-19 have been amended. Thus, Claims 1-20 are pending and presented for examination.
4. Applicant’s arguments filed April 9, 2026 with respect to the 35 U.S.C. 112(b) rejection have been fully considered and are persuasive. Thus, the 35 U.S.C. 112(b) rejection has been withdrawn.
5. Applicant’s arguments filed April 9, 2026 with respect to the 35 U.S.C. 101 rejection have been fully considered but they are not persuasive.
Applicant’s Arguments on Pgs. 10-11 of Arguments/Remarks state:
“The Office Action alleges that the claims are directed to an abstract idea for reciting both mental and mathematical processes. Office Action pp. 4-5. Applicant respectfully disagrees.
As amended, the claims recite particular operations for executing, within a multi-stage model architecture comprising a first machine-learning risk assessment model and a second machine-learning risk assessment model. The particular operations include executing the first machine-learning risk assessment model, and executing the second machine-learning risk assessment model only upon achieving a threshold condition. The claimed approach is analogous to Patent Eligibility Guidance ("PEG") Example 39 reciting multistage operations for training a machine-learning model.
PEG Example 39 recites multistage operations of training a neural network including:
[…]
The PEG analysis notes that the claim is eligible at Prong 1 for not reciting any of the judicial exceptions. Regarding mathematical processes "the claim does not recite any mathematical relationships, formulas, or calculations. While some of the limitations may be based on mathematical concepts, the mathematical concepts are not recited in the claims." Regarding mental processes, "the claim does not recite a mental process because the steps are not practically performed in the human mind." While the claim in PEG Example 39 was directed to training a multi-stage model rather than executing a multi-stage model, the same eligibility analysis applies to the pending claims.
As in Example 39, while some of the limitations may be based on mathematical concepts, no mathematical relationships, formulas, or calculation are recited. Therefore, even if the claim recites operations that are based on mathematical concepts, the claims are not directed to mathematical processes for the same reasons as in Example 39. Similarly, as in Example 39, the human mind is not equipped to execute multi-stage model architecture implemented responses for regenerating first and second risk indicators and for generating explanatory data for the first risk indicator. Therefore, the claims are not directed to mental processes as previously alleged.
The claims therefore do not recite mathematical or mental processes and are not directed to abstract ideas as previously alleged. Additionally, as discussed further below, the claims are directed to improvements in computer technology, particularly towards machine-learning model architecture. The claims are therefore not directed to abstract ideas and instead directed to patent eligible technological improvements.”
Examiner respectfully disagrees. Contrary to Applicant’s arguments, the instant claims are not analogous to PEG Example 39. Although Example 39 recites a similar multistage operation of training a neural network, Example 39 recites technical claim limitations that cannot be feasibly performed by mental process – for example, “applying one or more transformations to each digital facial image including mirroring, rotating, smoothing, or contrast reduction […]” and “creating a first training set comprising the collected set of digital facial images […]”. A human user is not capable of applying transformations to a digital facial image or creating a first training set comprising digital facial images, with only the aid of pen and paper – hence, Example 39 does not recite mental processes/mathematical processes at Step 2A Prong 1. In comparison, the instant claims clearly recite a plurality of “determining” steps (i.e., determining a first risk indicator, determining that the first risk indicator indicates a risk higher than a threshold, determining a second risk indicator) which may feasibly be performed by mental process/manually by a user, as detailed by the subsequent 35 U.S.C. 101 rejection below. Further, the limitation “generating explanatory data for the predictor variables […]” simply refers to a mathematical process comprising the use of an algorithm such as Shapley Additive exPlanations (SHAP) to generate such explanatory data. Thus, at Step 2A Prong 1, the instant claims still recite an abstract idea.
Applicant’s Arguments on Pgs. 13-14 of Arguments/Remarks state:
“The current application describes specific techniques that improve machine-learning model performance. Applicant's detailed description notes the limitations of certain machine- learning architecture when stating "the complex structure and interconnections [of neural networks] can increase the difficulty of explaining relationships between an input variable and the output of the neural network" and that as a result, "explaining the impact of a specific input variable on the prediction results of the neural network might be infeasible or impractical." Applicant's Specification [0003].
Applicant's claims address the limitations of complex model architectures by "provid[ing] improvements to the accuracy of risk assessment while maintaining the explainability of the assessment if needed." Id. [0015]. That is, the described approach can harness multiple models including a first machine-learning risk assessment model and a second machine-learning risk assessment model executed in response to certain identifiers generated by the first machine-learning risk assessment model. "As a result, explanatory data can be provided by the first-stage model and more accurate prediction can be provided by the second-stage risk assessment model to determine the risk assessment results." Id.
Such technical benefits to machine-learning models are recited in the independent claims, as shown in Claim 1, reciting, in part:
[…]
The independent claims therefore reflect specification asserted improvements to machine-learning model implemented systems in a manner similar to that described in Ex Parte Desjardins and M.P.E.P. 2106.05(a)(Ex. xiv) in both instances, computing systems implementing specific tasks and workstreams are improved by controlling parameters of machine-learning model systems. The claims are therefore patent eligible for not being directed to an abstract idea, and in the alternative, for integrating any alleged abstract idea into a practical application. For at least these reasons, withdrawal of the rejections under 35 U.S.C. § 101 is respectfully requested.”
Examiner respectfully disagrees. Although Applicant argues that the instant claims present a technologically improvement, Examiner asserts that this supposed improvement is not reflected by the currently drafted claim language. As stated above, the instant claims still clearly recite mental processes/mathematical processes at Step 2A Prong 1. Furthermore, at Step 2A Prong 2 and Step 2B, the claims recite the limitations “receiving, from a remote computing device, a risk assessment query for a target entity”, “executing, within a multi-stage model architecture comprising a first machine-learning risk assessment model and a second machine-learning risk assessment model, a query response”, “[…] by applying the first/second machine-learning risk assessment model […]”, and “transmitting, to the remote computing device, a response message […]”. The “receiving […]” and “transmitting […]” limitations are considered to be adding insignificant extra-solution activity to the judicial exception at Step 2A Prong 2 & similarly are considered merely “receiving or transmitting data over a network” which is a well-understood, routine, and conventional function when claimed in a merely generic manner at Step 2B. Furthermore, the “executing […]” and “[…] by applying the first/second machine-learning risk assessment model […]” limitations are merely applied at Step 2A Prong 2 and Step 2B – adding the words “apply it” with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea cannot provide an inventive concept. The technical details of the “multi-stage architecture” are not provided, beyond the fact that this multi-stage architecture merely includes a first machine-learning risk assessment model and a second machine-learning risk assessment model without significantly more. There are no further details provided regarding the training/configuration/architecture of these models that would enable one of ordinary skill in the art to understand how they are seemingly pre-configured/equipped to determine a first/second risk indicator without significantly more. These models are considered to be off-the-shelf/black box machine learning models and again, the mere application of black box machine learning models cannot provide an inventive concept. As such, the cited technological improvements are not recognized, nor reflected, by the currently drafted claim language.
Thus, the 35 U.S.C. 101 rejection is maintained.
6. Applicant’s arguments filed April 9, 2026 with respect to the 35 U.S.C. 103 rejection have been fully considered and are persuasive. Thus, the 35 U.S.C. 103 rejection has been withdrawn.
Examiner’s Remarks
7. Examiner notes that the currently drafted claim language states “determining […] a first risk indicator for the target entity […] and responsive to determining that the first risk indicator indicates a risk higher than a threshold value […]” where a first risk indicator is determined, compared against a threshold value, and when the indicator indicates a risk higher than a threshold value, then the subsequent steps “generating explanatory data […]”, “determining a second risk indicator […]”, and “transmitting […] a response message […]” are completed. However, in the case where the first risk indicator indicates a risk lower than the threshold value, the operations comprising the remaining limitations of the claim are not completed – this renders the claim language disjoint/incomplete in these scenarios. Applicant should consider amending the instant claim language such that it is clear what occurs when the first risk indicator is lower than the threshold value (i.e., iterative processing, execution of only the first risk assessment model, query response is transmitted, etc.) to ensure clarity and completeness of the claim language. Additionally, Examiner notes that “explanatory data” is generated after determining that the first risk indicator indicates a risk higher than a threshold value, however, this “explanatory data” is seemingly not used in any subsequent processing/functional steps of the claim – the data is merely “generated” and “transmitted” without any explanation of how it may be used within the multi-stage risk assessment model. Applicant should consider amending the instant claim language to better outline how the explanatory data may be used to evaluate and/or rank predictor variables and subsequently how this applies to the corresponding risk assessment.
Claim Rejections - 35 USC § 101
8. 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.
9. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Step 1: Claim 1 is a method type claim. Therefore, Claims 1-7 are directed to either a process, machine, manufacture, or composition of matter.
2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
determining, responsive to the risk assessment query, a first risk indicator for the target entity by applying the first machine-learning risk assessment model to predictor variables associated with the target entity (mental process – other than reciting “by applying a first machine-learning risk assessment model”, determining a first risk indicator may be performed manually by a user observing/analyzing a set of predictor variables associated with the target entity (such as variables indicating demographic characteristics, variables indicative of prior actions or transactions, variables indicative of one or more behavior traits of an entity, etc. as supported by Applicant’s specification Par. [0030]) and accordingly using judgement/evaluation to determine a “risk indicator” (e.g., credit score per Applicant’s specification Par. [0029]) based on the analysis and consideration of said predictor variables)
responsive to determining that the first risk indicator indicates a risk higher than a threshold value […] (mental process – determining that the first risk indicator indicates a risk higher than a threshold value may be performed manually by a user observing/analyzing the first risk indicator and threshold value and accordingly using judgement/evaluation to determine whether the value of the first risk indicator is higher than/greater than the threshold value)
generating explanatory data for the predictor variables, the explanatory data indicating an effect that a predictor variable has on the first risk indicator (mathematical process – generating explanatory data for the predictor values, where the explanatory data indicates an effect that a predictor variable has on a first risk indicator may be performed by mathematical process, utilizing a mathematical function/algorithm such as SHAP (Shapley Additive exPlanations) which is a commonly used mathematical process for providing explanations of a machine learning model)
determining a second risk indicator for the target entity by applying the second machine-learning risk assessment model to the predictor variables associated with the target entity (mental process – other than reciting “by applying a second machine-learning risk assessment model”, determining a second risk indicator may be performed manually by a user observing/analyzing a set of predictor variables associated with the target entity (such as variables indicating demographic characteristics, variables indicative of prior actions or transactions, variables indicative of one or more behavior traits of an entity, etc. as supported by Applicant’s specification Par. [0030]) and accordingly using judgement/evaluation to determine a “risk indicator” (e.g., credit score per Applicant’s specification Par. [0029]) based on the analysis and consideration of said predictor variables)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
a method that includes one or more processing devices […] (recited at a high-level of generality (i.e., as generic one or more processing devices) such that it amounts to no more than mere instructions to apply the exception using generic computer components)
receiving, from a remote computing device, a risk assessment query for a target entity (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
executing, within a multi-stage model architecture comprising a first machine-learning risk assessment model and a second machine-learning risk assessment model, a query response (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 - see MPEP 2106.05(f) – Examiner’s note: high level recitation of applying machine learning models to previously determined data without significantly more)
[…] by applying a first machine-learning risk assessment model […] (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 - see MPEP 2106.05(f) – Examiner’s note: high level recitation of applying a machine learning model to previously determined data without significantly more)
[…] by applying a second machine-learning risk assessment model […] (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 - see MPEP 2106.05(f) – Examiner’s note: high level recitation of applying a machine learning model to previously determined data without significantly more)
transmitting, to the remote computing device, a response message including the first risk indicator, the explanatory data, and the second risk indicator, for use in controlling access to one or more interactive computing environments by the target entity (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
a method that includes one or more processing devices […] (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
receiving, from a remote computing device, a risk assessment query for a target entity (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
executing, within a multi-stage model architecture comprising a first machine-learning risk assessment model and a second machine-learning risk assessment model, a query response (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 - see MPEP 2106.05(f) – Examiner’s note: high level recitation of applying machine learning models to previously determined data without significantly more. This cannot provide an inventive concept)
[…] by applying a first machine-learning risk assessment model […] (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 - see MPEP 2106.05(f) – Examiner’s note: high level recitation of applying a machine learning model to previously determined data without significantly more. This cannot provide an inventive concept)
[…] by applying a second machine-learning risk assessment model […] (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 - see MPEP 2106.05(f) – Examiner’s note: high level recitation of applying a machine learning model to previously determined data without significantly more. This cannot provide an inventive concept)
transmitting, to the remote computing device, a response message including the first risk indicator, the explanatory data, and the second risk indicator, for use in controlling access to one or more interactive computing environments by the target entity (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
For the reasons above, Claim 1 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 2-7. The additional limitations of the dependent claims are addressed below.
Regarding Claim 2:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 2 depends on.
Step 2A Prong 2 & Step 2B:
wherein the first machine-learning risk assessment model comprises an explainable risk assessment model and the second machine-learning risk assessment model comprises a second-stage risk assessment model that is generated without an explainability constraint (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the first risk assessment model comprises an explainable risk assessment model and the second risk assessment model comprises a second-stage risk assessment model generated without an explainability constraint does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 3:
Step 2A Prong 1:
See the rejection of Claim 2 above, which Claim 3 depends on.
Step 2A Prong 2 & Step 2B:
wherein the first machine-learning risk assessment model comprises a logistic regression model, a linear regression model, monotonic decision trees, or a monotonic neural network (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the first risk assessment model comprises a logistic regression model, a linear regression model, monotonic decision trees, or a monotonic neural network does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 4:
Step 2A Prong 1:
See the rejection of Claim 2 above, which Claim 4 depends on.
Step 2A Prong 2 & Step 2B:
wherein the second-stage risk assessment model comprises a deep neural network, a convolutional neural network, a recurrent neural network, or a recursive neural network (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the second-stage risk assessment model comprises a deep neural network, a convolutional neural network, a recurrent neural network, or a recursive neural network does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 5:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 5 depends on.
generating a second set of explanatory data based on the second risk indicator, the second set of explanatory data indicating whether a favorable action is recommended for the target entity (mathematical process – generating a second set of explanatory data based on the second risk indicator, where the second set of explanatory data indicates whether a favorable action is recommended for the target entity may be performed by mathematical process, utilizing a mathematical function/algorithm such as SHAP (Shapley Additive exPlanations) which is a commonly used mathematical process for providing explanations of a machine learning model)
Step 2A Prong 2 & Step 2B:
[…] including the second set of explanatory data in the response message (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 6:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 6 depends on.
Step 2A Prong 2 & Step 2B:
wherein the explanatory data is generated for a subset of the predictor variables (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the explanatory data is generated for a subset of predictor variables does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 7:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 7 depends on.
wherein the operations further comprise grouping the predictor variables into a plurality of groups […] (mental process – grouping predictor values may be performed manually by a user observing/analyzing the predictor values and accordingly using judgement/evaluation to group predictor values into groups based on their features/characteristics)
[…] wherein generating the explanatory data for the predictor variables comprises generating a same reason code for each group of the plurality of groups (mental process – generating a same reason code for each group may be performed manually by a user observing/analyzing the plurality of groups and accordingly using judgement/evaluation to generate a same reason code for each group of the plurality of groups)
Step 2A Prong 2 & Step 2B:
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Independent Claim 8 recites substantially the same limitations as Claim 1, in the form of a system, including generic computer components. The claim is also directed to performing mental/mathematical processes without significantly more, therefore it is rejected under the same rationale.
For the reasons above, Claim 8 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 9-14. The additional limitations of the dependent claims are addressed below.
Claim 9 recites substantially the same limitations as Claim 2, in the form of a system, including generic computer components. The claim is also directed to performing mental/mathematical processes without significantly more, therefore it is rejected under the same rationale.
Claim 10 recites substantially the same limitations as Claim 3, in the form of a system, including generic computer components. The claim is also directed to performing mental/mathematical processes without significantly more, therefore it is rejected under the same rationale.
Claim 11 recites substantially the same limitations as Claim 4, in the form of a system, including generic computer components. The claim is also directed to performing mental/mathematical processes without significantly more, therefore it is rejected under the same rationale.
Claim 12 recites substantially the same limitations as Claim 5, in the form of a system, including generic computer components. The claim is also directed to performing mental/mathematical processes without significantly more, therefore it is rejected under the same rationale.
Claim 13 recites substantially the same limitations as Claim 6, in the form of a system, including generic computer components. The claim is also directed to performing mental/mathematical processes without significantly more, therefore it is rejected under the same rationale.
Claim 14 recites substantially the same limitations as Claim 7, in the form of a system, including generic computer components. The claim is also directed to performing mental/mathematical processes without significantly more, therefore it is rejected under the same rationale.
Independent Claim 15 recites substantially the same limitations as Claim 1, in the form of a non-transitory computer-readable storage medium, including generic computer components. The claim is also directed to performing mental/mathematical processes without significantly more, therefore it is rejected under the same rationale.
For the reasons above, Claim 15 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 16-20. The additional limitations of the dependent claims are addressed below.
Claim 16 recites substantially the same limitations as Claim 2, in the form of a non-transitory computer-readable storage medium, including generic computer components. The claim is also directed to performing mental/mathematical processes without significantly more, therefore it is rejected under the same rationale.
Claim 17 recites substantially the same limitations as Claims 3 and 4, in the form of a non-transitory computer-readable storage medium, including generic computer components. The claim is also directed to performing mental/mathematical processes without significantly more, therefore it is rejected under the same rationale.
Claim 18 recites substantially the same limitations as Claim 5, in the form of a non-transitory computer-readable storage medium, including generic computer components. The claim is also directed to performing mental/mathematical processes without significantly more, therefore it is rejected under the same rationale.
Claim 19 recites substantially the same limitations as Claim 6, in the form of a non-transitory computer-readable storage medium, including generic computer components. The claim is also directed to performing mental/mathematical processes without significantly more, therefore it is rejected under the same rationale.
Claim 20 recites substantially the same limitations as Claim 7, in the form of a non-transitory computer-readable storage medium, including generic computer components. The claim is also directed to performing mental/mathematical processes without significantly more, therefore it is rejected under the same rationale.
Allowable Subject Matter
10. No prior art rejection is made for Claims 1-20. However, these claims are rejected under 35 U.S.C. 101 – abstract idea.
11. Examiner has disclosed Sardari et al. (US PG-PUB 20230316280) and Song et al. (US PG-PUB 20210342848), which are the closest prior art as compared to the claims of the instant application. Sardari discloses systems and methods for using a plurality of machine learning models for fraud detection, including determining a risk metric associated with a given user. Song discloses systems and methods for multi-staged risk scoring, including receiving a transaction request and iteratively determining a risk score and comparing the risk score to a threshold. However, Sardari and Song seemingly do not explicitly disclose the specific limitations of the Independent Claims, including “responsive to determining that the first risk indicator indicates a risk higher than a threshold value: generating explanatory data for the predictor variables, the explanatory data indicating an effect that a predictor variable has on the first risk indicator; and determining a second risk indicator for the target entity by applying the second machine-learning risk assessment model to the predictor variables associated with the target entity” included in Independent Claim 1 (and Independent Claims 8 and 15 which recite substantially the same limitations), in combination with the remaining limitations of the Independent claims.
Conclusion
12. 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.
13. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Devika S Maharaj whose telephone number is (571)272-0829. The examiner can normally be reached Monday - Thursday 8:30am - 5:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached at (571)270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DEVIKA S MAHARAJ/Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
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