DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, 18/392,951, was filed on Dec. 21, 2023, and does not claim foreign priority or domestic benefit to any other application.
The effective filing date is after the AIA date of March 16, 2013, and so the application is being examined under the “first inventor to file” provisions of the AIA .
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.
Status of the Application
This Final Office Action is in response to Applicant’s communication of 06/11/2026.
Claims 1, 3, 5-8, 11, 12, 14, 16, 19, 20, and 23-27 are pending, of which claims 1, 12, and 20 are independent.
In the most recent response, claims 1, 3, 5, 12, 14, 20, and 23-26 are currently amended, claims 21 and 22 are currently cancelled, and claims 2, 4, 9, 10, 13, 15, 17, and 18 were previously cancelled.
All pending claims have been examined on the merits.
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, 3, 5-8, 11, 12, 14, 16, 19, 20, and 23-27 are rejected under 35 U.S.C. §101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to an abstract idea, without “significantly more”.
In regards to Step 1 of the Alice/Mayo analysis, independent claim 1 is a method claim, claim 12 is an apparatus claim, and claim 20 is an article of manufacture claim or product by process claim (“non-transitory computer readable medium”).
For the sake of compact prosecution, we continue with the Alice/Mayo “abstract idea” analysis.
The abstract idea elements recited in independent claim 12 are shown in italic font. The “additional elements” and “extra solution steps” are shown in underlined font:
12. A computing system comprising:
one or more memories; and
processing circuitry in communication with the one or more memories, the processing circuitry configured to:
generate a unique global user identifier for a user and associating the unique global user identifier with local user identifiers used at multiple data repositories;
periodically obtain data associated with a current state of a current loan on a secured property of the user, wherein to periodically obtain the data, the processing circuitry is configured to automatically retrieve the data from the multiple data repositories using the unique global user identifier on a fixed schedule;
in response to obtaining the data and in the absence of a request from the user for a refinanced loan, automatically determine, using a machine learning model, predicted information associated with a refinanced loan on the secured property, the predicted information including:
a predicted refinance rate for the secured property and an associated first confidence score indicating a first level of certainty of the machine learning model that the predicted refinance rate is accurate,
a predicted refinanced loan amount for the secured property at the predicted refinance rate and an associated second confidence score indicating a second level of certainty of the machine learning model that the predicted refinanced loan amount is accurate, and
a predicted risk value of the refinanced loan for the predicted refinanced loan amount at the predicted refinance rate and an associated third confidence score indicating a third level of certainty of the machine learning model that the predicted risk value is accurate;
determine whether to present an offer for the refinanced loan on the secured property for the predicted refinanced loan amount at the predicted refinance rate to the user based on whether the first confidence score, the second confidence score, and the third confidence score satisfy corresponding thresholds and a determination of an advantage of the refinanced loan over the current loan on the secured property;
based on determining to present the offer for the refinanced loan to the user, generate and send a message including an indication of the offer for the refinanced loan for the predicted refinanced loan amount at the predicted refinance rate to a user device of the user;
receive a user response to the offer for the refinanced loan, wherein the user response indicates an acceptance or rejection of the offer;
label data of the offer as accepted or rejected to produce labeled data based on the user response to the offer; and
retrain parameters of the machine learning model based on the labeled data of the offer.
More specifically, claims 1, 3, 5-8, 11, 12, 14, 16, 19, 20, and 23-27 recite an abstract idea: “Certain Methods of Organizing Human Activity", specifically “Commercial or Legal Interactions (Including Agreements in the form of Contracts; Legal Obligations; Advertising, Marketing, or Sales Activities or Behaviors; Business Relations)”, as discussed in MPEP §2106(a)(2) Parts (I) and (II), and in the 2019 Revised Patent Subject Matter Eligibility Guidance.
The “Commercial or Legal Interactions” elements include:
“generate a unique global user identifier for a user and associating the unique global user identifier with local user identifiers used at multiple data repositories”
“automatically determine, using a machine learning model, predicted information associated with a refinanced loan on the secured property, the predicted information including:”
“a predicted refinance rate for the secured property and an associated first confidence score indicating a first level of certainty of the machine learning model that the predicted refinance rate is accurate”
“a predicted refinanced loan amount for the secured property at the predicted refinance rate and an associated second confidence score indicating a second level of certainty of the machine learning model that the predicted refinanced loan amount is accurate”
“a predicted risk value of the refinanced loan for the predicted refinanced loan amount at the predicted refinance rate and an associated third confidence score indicating a third level of certainty of the machine learning model that the predicted risk value is accurate”
“determine whether to present an offer for the refinanced loan on the secured property for the predicted refinanced loan amount at the predicted refinance rate to the user based on whether the first confidence score, the second confidence score, and the third confidence score satisfy corresponding thresholds and a determination of an advantage of the refinanced loan over the current loan on the secured property”.
“label data of the offer as accepted or rejected to produce labeled data based on the user response to the offer”.
Moreover, claims 1, 3, 5-8, 11, 12, 14, 16, 19, 20, and 23-27 recite “Mathematical Concepts", specifically “Mathematical Relationships”, “Mathematical Formulas or Equations”, and “Mathematical Calculations”, as discussed in MPEP §2106.04(a)(2) Part (IV), and in the 2019 Revised Patent Subject Matter Eligibility Guidance.
The mathematic elements include the “machine learning model”, which is a mathematical construct, which “automatically” determines “a predicted refinance rate”, “a predicted refinanced loan amount”, and “a predicted risk value of the refinanced loan”, and which is claimed as follows:
“automatically determine, using a machine learning model, predicted information associated with a refinanced loan on the secured property, the predicted information including:”
“a predicted refinance rate for the secured property and an associated first confidence score indicating a first level of certainty of the machine learning model that the predicted refinance rate is accurate”
“a predicted refinanced loan amount for the secured property at the predicted refinance rate and an associated second confidence score indicating a second level of certainty of the machine learning model that the predicted refinanced loan amount is accurate”
“a predicted risk value of the refinanced loan for the predicted refinanced loan amount at the predicted refinance rate and an associated third confidence score indicating a third level of certainty of the machine learning model that the predicted risk value is accurate”
“determine whether to present an offer for the refinanced loan on the secured property for the predicted refinanced loan amount at the predicted refinance rate to the user based on whether the first confidence score, the second confidence score, and the third confidence score satisfy corresponding thresholds and a determination of an advantage of the refinanced loan over the current loan on the secured property”.
The “additional elements” include: “one or more memories” and “processing circuitry in communication with the one or more memories”.
The “additional extra-solution elements” include: “periodically obtain data associated with a current state of a current loan on a secured property of the user”, “automatically retrieve the data from the multiple data repositories using the unique global user identifier on a fixed schedule”, “generate and send a message including an indication of the offer for the refinanced loan for the predicted refinanced loan amount at the predicted refinance rate to a user device of the user”, “receive a user response to the offer for the refinanced loan”, and “retrain parameters of the machine learning model based on the labeled data of the offer”.
This abstract idea is not integrated into a practical application, because:
The claim is directed to an abstract idea with additional generic computer elements. The generically recited computer elements (“one or more memories” and “processing circuitry in communication with the one or more memories”) do not add a meaningful limitation to the abstract idea, because they amount to simply implementing the abstract idea on a computer. The claim amounts to adding the words "apply it" (or an equivalent) with the abstract idea, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea.
The extra-solution activities (“periodically obtain data”, “automatically retrieve the data from the multiple data repositories using the unique global user identifier on a fixed schedule”, “generate and send a message … to a user device of the user”, “receive a user response”, and “retrain parameters of the machine learning model”) do not add a meaningful limitation to the method, as they are insignificant extra-solution activity;
The combination of the abstract idea with the additional elements (generically recited computer elements), and/or with the extra-solution activities, does not integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea, because:
When considering the elements "alone and in combination" (“one or more memories” and “processing circuitry in communication with the one or more memories”), they do not add significantly more (also known as an "inventive concept") to the exception, because they amount to simply implementing the abstract idea on a computer. Instead, they merely add the words "apply it" (or an equivalent) with the abstract idea, or mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea.
In regards to the extra solution activities (“periodically obtain data associated with a current state of a current loan on a secured property of the user”, “automatically retrieve the data from the multiple data repositories using the unique global user identifier on a fixed schedule”, “generate and send a message … to a user device of the user”, “receive a user response”, and “retrain parameters of the machine learning model”), these are recognized as such by the court decisions listed in MPEP § 2106.05(d).
More specifically, in regards to the “receive a user response”, ““automatically retrieve the data”, and “generate and send a message … to a user device of the user” steps, see the court cases OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network) and (presenting offers and gathering statistics), OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93; buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
The Examiner holds that the independent claims “use a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data)” or “simply add a general purpose computer or computer components after the fact to an abstract idea”.
More specifically, in regards to the “retrain parameters of the machine learning model based on the labeled data of the offer” step, the Examiner holds that this feature is an “apply it” application of an inherent feature of machine learning algorithms.
In regards to “apply it” (applying the abstract idea on a general purpose computer), the 35 USC § 101 rejections are based on the CAFC decision in Recentive Analytics, Inc. v. Fox Corp. April 18, 2025 (https://www.cafc.uscourts.gov/opinions-orders/23-2437.OPINION.4-18-2025_2500790.pdf).
The Recentive Analytics decision states (see page 10 of the verdict): “This case presents a question of first impression: whether claims that do no more than apply established methods of machine learning to a new data environment are patent eligible. We hold that they are not.”
The Examiner holds that Applicant’s description of the neural network merely describes “apply it” uses of a generic neural network.
Also, this feature merely generally links the use of the abstract idea to a particular technological environment or field of use (e.g. electric grid data is not claiming the actual electric grid)- see MPEP 2106.05(h).
Independent claims 12 and 20 are rejected on the same grounds as independent claim 1. Independent claim 20 is also rejected on the grounds that it recites a computer-readable medium, which is merely another generic computer component.
All dependent claims are also rejected, because they merely further define the abstract idea.
Response to Amendments
Re: Claim Objections
The objection to claim 5 has been withdrawn, as necessitated by Applicant’s amendments to the claim.
Re: Claim Rejections - 35 USC § 101
The 35 U.S.C. 101 rejection has been amended, as necessitated by Applicant’s amendments to the claims.
Re: Claim Rejections - 35 USC § 103
The 35 U.S.C. 103 rejection has been withdrawn, as necessitated by Applicant’s amendments to the claims.
Conclusion
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 should be directed to Examiner Ayal Sharon, whose telephone number is (571) 272-5614, and fax number is (571) 273-1794. The Examiner can normally be reached from Monday to Friday between 9 AM and 6 PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SPE Christine Behncke can be reached at (571) 272-8103 or at christine.behncke@uspto.gov. The fax number for the organization where this application or proceeding is assigned is 571-273-8300.
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Sincerely,
/Ayal I. Sharon/
Examiner, Art Unit 3695
August 26, 2026
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