DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 2/26/26 has been entered.
Status of Claims
This action is in reply to RCE filed on 5/11/26, amendment and response filed on 2/26/26 and IDS filed on 5/11/26. Claims 1 and 8 have been amended. Claims 1-13 are pending and examined.
Response to Arguments
101: The Applicant’s amendments and arguments have been fully considered, but are not persuasive.
The Applicant essentially argues that the amended claims do not recite an abstract idea.
p. 7, Prong One, Step 2A,
Applicant argues transforming unstructured borrower information, applying ML model to the information, generating lending recommendation and effectuating presentation with an update component steps are not an abstract idea.
The Examiner disagrees. The claims do not recite transforming unstructured borrower information, they recite converting unstructured borrower information. Converting loan information to a different format is an abstract idea as it is directed to commercial/legal interactions. Applying an ML model, is an additional element that is analyzed in prong two. generating lending recommendation is an abstract idea as it is directed to commercial/legal interactions. Effectuating a presentation and updating the presentation is an additional element that is analyzed in prong two of step 2A.
p. 8-9. Prong Two, Step 2A
Applicant argues that the additional elements are an improvement.
The Examiner disagrees. Application of an ML model to loan information to confidence values do not integrate the abstract idea into a practical application as it is no more than “apply it” because the claim fails to recite the technological details of how applying a trained learning model to loan information computes confidence values, see MPEP 2106.05(f)(1).
The Applicant also argues about a “recommendation engine” being an improvement. The claims do not recite a “recommendation engine”. However, if the Applicant is referring to the “system” as equivalent to the “recommendation engine” then the “system comprising”, “one or more physical processors configured by machine-readable instructions” do not integrate the abstract idea into a practical application as they are no more than “apply it” because they are mere “[u]se of a computer or other machinery in its ordinary capacity for economic or other tasks”, see MPEP 2106.05(f)(2).
Next, the Applicant seems to state that the “structured representation” is not mere use of “ML” for lending. However, as stated above the claims merely provide that the “structured representation” is nothing but loan information and application of an ML model to the loan information amounts to failure to integrate the abstract idea into a practical application as it is no more than “apply it” because the claim fails to recite the technological details of how applying a trained learning model to loan information computes confidence values, see MPEP 2106.05(f)(1).
p. 9-10, Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration into a practical application, the additional elements do no more than provide mere instructions to apply the abstract idea using “a computer or other machinery in its ordinary capacity for economic or other tasks”, see MPEP 2106.05(f)(2), and/or the claim fails to recite the technological details of “how a solution to a problem is accomplished”, see MPEP 2106.05(f)(1). Therefore, the claim elements when considered separately and in an ordered combination, do not add significantly more than implementing the abstract idea of determining a lending negotiation strategy.
As such, an updated rejection is provided that addresses the amended claims.
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 5/11/26. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements were considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. (Step 1) The claims recite a process (claims 1-7) and apparatus (claims 8-13). For the purposes of this analysis, representative claim 8 (from claims 1 and 8) is addressed. (Step 2A, prong 1) Abstract ideas are in bold below, and represent certain methods of organizing human activity as a method of determining a lending negotiation strategy, as are all a form of commercial or legal interactions and managing personal behavior or relationships or interactions between people.
A system comprising, one or more physical processors configured by machine-readable instructions to,
receive borrower input within a conversation interface of an application;
convert the borrower input into a structured, machine-readable representation by parsing and classifying the borrower input to populate fields for borrower information and borrower objective categories, and storing the structured, machine-readable representation in a borrower information database;
identify borrower information from the borrower input;
apply a first trained machine-learning classification model to the structured, machine-readable representation to compute, for respective borrower objectives, likelihood or confidence values that the objectives and generating an incident score representing the likelihood or confidence values;
generate, by a recommendation component, a lending product recommendation tailored to lender criteria generate, by a recommendation component, a lending product recommendation tailored to lender criteria based on the computed incident score, the recommendation comprising a structured negotiation strategy and an explanation indicating which borrower objectives and corresponding likelihood or confidence values contributed to the recommendation; and
effectuate real-time presentation of the lending product recommendation and the structured negotiation strategy to the borrower via a graphical user interface of the application, by a negotiation server transmitting recommendation data to a client computing device for display and interaction, and updating the presentation in response to subsequent borrower input, including recomputing at least one likelihood or confidence value for the borrower objectives.
(Step 2A prong 2) The additional elements are as follows:
“A system comprising”, “one or more physical processors configured by machine-readable instructions”. These additional elements do not integrate the abstract idea into a practical application as they are no more than “apply it” because they are mere “[u]se of a computer or other machinery in its ordinary capacity for economic or other tasks”, see MPEP 2106.05(f)(2).
“[receive …] within a conversation interface of an application”. These additional elements do not integrate the abstract idea into a practical application as they are no more than “apply it” because they are mere “[u]se of a computer or other machinery in its ordinary capacity for economic or other tasks”, see MPEP 2106.05(f)(2).
“[convert … into a structured], machine-readable [representation …], and storing [the structured], machine-readable [representation] in a borrower information database”. Machine-readable information and storing information in a database do not integrate the abstract idea into a practical application as they are no more than “apply it” because they are mere “[u]se of a computer or other machinery in its ordinary capacity for economic or other tasks”, see MPEP 2106.05(f)(2).
“apply a first trained machine-learning classification model [to the structured], machine-readable [representation] to compute, [… likelihood or confidence values]”. The additional elements do not integrate the abstract idea into a practical application as it is no more than “apply it” because the claim fails to recite the technological details of how applying a trained learning model to loan information computes confidence values, see MPEP 2106.05(f)(1).
“a conversation interface of an application”. This is no more than “apply it” as the “conversation interface of an application” is claimed at a high level of generality, receives the information, performs the abstract idea, and outputs the results. This is also general linking as the “conversation interface of an application” does no more than link the use of the abstract idea to a particular technological environment or field of use.
“trained machine-learning [classification model]”. This is no more than “apply it” as the “trained machine-learning [classification model]” is claimed at a high level of generality, receives the information, performs the abstract idea, and outputs the results.
“[generate], by a recommendation component, [a lending product recommendation …]”. The “recommendation component” does not integrate the abstract idea into a practical application as it is no more than “apply it” because they are mere “[u]se of a computer or other machinery in its ordinary capacity for economic or other tasks”, see MPEP 2106.05(f)(2).
“effectuate real-time presentation […] via a graphical user interface of the application, by a negotiation server transmitting [recommendation data] to a client computing device for display and interaction, and updating the presentation in response to [subsequent borrower input], including recomputing [at least one likelihood or confidence value]”. Displaying of loan recommendation, updating the displayed recommendation based on user input of new loan information do not integrate the abstract idea into a practical application as it is no more than “apply it” because they are mere “[u]se of a computer or other machinery in its ordinary capacity for economic or other tasks”, see MPEP 2106.05(f)(2).
(Step 2B) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration into a practical application, the additional elements do no more than provide mere instructions to apply the abstract idea using “a computer or other machinery in its ordinary capacity for economic or other tasks”, see MPEP 2106.05(f)(2), and/or the claim fails to recite the technological details of “how a solution to a problem is accomplished”, see MPEP 2106.05(f)(1). Therefore, the claim elements when considered separately and in an ordered combination, do not add significantly more than implementing the abstract idea of determining a lending negotiation strategy.
Continuing the analysis with dependent claims, claims 2 and 9 recite “wherein the borrower input comprises at least one communication mode including voice, text, and video with voice”, additional details which further narrow the abstract idea and additional elements.
The additional elements are as follows:
“at least one communication mode including voice, text, and video with voice”. These additional elements do not integrate the abstract idea into a practical application as they are no more than “apply it” because they are mere “[u]se of a computer or other machinery in its ordinary capacity for economic or other tasks”, see MPEP 2106.05(f)(2).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration into a practical application, the additional elements do no more than provide mere instructions to apply the abstract idea using “a computer or other machinery in its ordinary capacity for economic or other tasks”, see MPEP 2106.05(f)(2), and/or the claim fails to recite the technological details of “how a solution to a problem is accomplished”, see MPEP 2106.05(f)(1). Therefore, the claim elements when considered separately and in an ordered combination, do not add significantly more than implementing the abstract idea of determining a lending negotiation strategy.
Dependent claims 3-7, 10-13 recite additional details which only further narrow the abstract idea and do not add any additional features, alone or in combination, that would provide a practical application or provide significantly more.
Conclusion
Reference made of record, not relied upon, pertinent to Applicant’s disclosure, include:
US 20190347718 A1 (Ardinger) disclosing loan audit system and method with chained confidence scoring, and
US 11734754 B1 (Chavez) disclosing intelligent loan recommendation agent.
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/BROCK E TURK/Examiner, Art Unit 3692
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
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