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
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/15/2025 has been entered.
This Non-Final Office Action is in response to Applicant’s communication of 12/15/2025.
Claims 1, 3, 5-8, 11, 12, 14, 16, and 19-27 are pending, of which claims 1, 12, and 20 are independent.
In the most recent response, claims 1, 3, 5, 6, 11, 12, 14, 16, 19, and 20 are amended, claim 21-27 are new, claims 4, 9, 10, 15, 17 and 18 are currently cancelled, and claims 2 and 13 were previously cancelled.
All pending claims have been examined on the merits.
Claim Objections
Claims 5 is objected to because of the following informalities: “retraining parameters the one” should be amended to “retraining parameters of the one”. Appropriate correction is required.
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, and 19-27 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:
periodically obtain data associated with a current state of a current loan on a secured property of a user;
in response to obtaining the data, 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, and 19-27 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:
“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”.
Moreover, claims 1, 3, 5-8, 11, 12, 14, 16, and 19-27 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:
“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 a 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”, 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”, “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”, “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” 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. 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.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3, 5-8, 11, 12, 14, 16, and 19-27 are rejected under 35 U.S.C. 103 as being unpatentable over US-20220027984-A1 to Charton et al. (“Charton”. Eff. Filed on Jul. 22, 2020. Published on Jan. 27, 2022) in view of Official Notice, and further in view of US-2021/0035209-A1 to Peak (“Peak”. Eff. Filed on Jul. 31, 2019. Published on Feb. 4, 2021).
In regards to claim 1,
1. (Currently Amended) A method comprising:
periodically obtaining, by a computing system, data associated with a current state of a current loan on a secured property of a user;
(See Charton, para. [0020]: “In some embodiments, the context information 109 b can comprise client-specific information. Client-specific information can correspond to information relating to the individual client to which the proposed offer 109 a is directed. By way of example, client-specific information can include various information relating to the client's current behaviour, patterns, personal status, economic status, demographic segment, etc. such as financial transaction history, assets, account balances, credit score, website interaction data, social media interaction data, previous applications for financial products, home address, work address, occupation, title, professional affiliations, family and marital status, previous offers from competitors, cost to break an existing loan contract, among others.”)
in response to obtaining the data, automatically determining, by the computing system 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 …,
(See Charton, para. [0017]: “In the following disclosure, offers will be described in connection with concluding an agreement for a financial product between a client and a bank (the vendor). It is appreciated, however, that the teachings can be applied to other types of products as well and are not necessarily limited to the field of financial services. Moreover, in the embodiments of the present disclosure, the optimizing of offers will be described by adjusting a loan interest rate for a mortgage. It is appreciated, however, that an offer can include one or more other offer parameters, such as a loan term, amortization period, loan type (ex: open vs. closed, fixed vs. variable), guarantee length, amount, fees, etc., which can also be adjusted in order to optimize the offer. Finally, although optimizing a mortgage loan will be described, it is appreciated that the same principles can apply to other types of financial products, such as a commercial loan, insurance or a saving deposit account, or to any other type of product that also have negotiable parameters.”)
a predicted refinanced loan amount for the secured property at the predicted refinance rate and …, and
(See Charton, para. [0017]: “In the following disclosure, offers will be described in connection with concluding an agreement for a financial product between a client and a bank (the vendor). It is appreciated, however, that the teachings can be applied to other types of products as well and are not necessarily limited to the field of financial services. Moreover, in the embodiments of the present disclosure, the optimizing of offers will be described by adjusting a loan interest rate for a mortgage. It is appreciated, however, that an offer can include one or more other offer parameters, such as a loan term, amortization period, loan type (ex: open vs. closed, fixed vs. variable), guarantee length, amount, fees, etc., which can also be adjusted in order to optimize the offer. Finally, although optimizing a mortgage loan will be described, it is appreciated that the same principles can apply to other types of financial products, such as a commercial loan, insurance or a saving deposit account, or to any other type of product that also have negotiable parameters.”)
determining 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;
(See Charton, para. [0025]: “In the present embodiment, the AI module 103 comprises a machine learning model stored on computer-readable memory, and trained using an algorithm to classify offer data according to a positive client decision (i.e. offer acceptance) or a negative client decision (i.e. offer refusal). In particular, the machine learning model takes offer parameters and context information as an input and provides a prediction as an output in the form of a binary client decision (i.e. acceptance or refusal). The output can also include a corresponding probability or confidence of the prediction (for example in the form of a % or other numeric value). Different classifier algorithms can be used for this purpose, such as logistic regression, random forest classifier, and gradient boosting classifier. It is appreciated, however, that other supervised machine learning algorithms are also possible.”)
based on determining to present the offer for the refinanced loan to the user, generating and sending 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;
(See Charton, para. [0033]: “Once an optimized offer 115 is generated by the processing module, it can be transmitted for communication to the client via the output module 107. As can be appreciated, the output module 107 can be configured to transmit the optimized offer 115 via different mechanisms, such as via an e-mail, via a user interface, via an electronic message to an external system, etc. It is further appreciated that the optimized offer 115 can be transmitted directly to the client, or via an intermediary, such as a bank agent or employee, who can then communicate the optimized offer 115 to the client.”)
receiving a user response to the offer for the refinanced loan, wherein the user response indicates an acceptance or rejection of the offer;
labeling data of the offer as accepted or rejected to produce labeled data based on the user response to the offer; and
retraining parameters of the machine learning model based on the labeled data of the offer.
(See Charton, para. [0026]: “In order to predict future client decisions, the AI module 103 can be trained using historical offer data. As can be appreciated, offers that were previously presented to clients can have corresponding offer data stored in one or more historical data sources 113, such as databases, repositories, data stores, etc. This historical offer data can include parameters of historical offers previously presented to clients, along with historical context information associated with the historical offers. The historical offer data can further include client decisions associated with the historical offers, thus providing an indication of whether the client actually accepted or refused the offer. The actual client decisions can be used to label the historical offer parameters and context information for the purposes of training a classifier algorithm. In this fashion, the AI module 103 can be configured to learn from the outcome of previous offers in order to predict client decisions for future offers. As can be appreciated, the AI module 103 can learn from historical offers that were generated by traditional methods (ex: generated manually by a human analyst) and/or offers that were generated/optimized by the present system 100.”)
Moreover, Charton also teaches determining a “probability or confidence of the prediction”:
(See Charton, para. [0025]: “In the present embodiment, the AI module 103 comprises a machine learning model stored on computer-readable memory, and trained using an algorithm to classify offer data according to a positive client decision (i.e. offer acceptance) or a negative client decision (i.e. offer refusal). In particular, the machine learning model takes offer parameters and context information as an input and provides a prediction as an output in the form of a binary client decision (i.e. acceptance or refusal). The output can also include a corresponding probability or confidence of the prediction (for example in the form of a % or other numeric value). Different classifier algorithms can be used for this purpose, such as logistic regression, random forest classifier, and gradient boosting classifier. It is appreciated, however, that other supervised machine learning algorithms are also possible.”)
Moreover, under a conservative interpretation of Charton, it could be argued that Charton does not explicitly teach the following features, which recite determining “confidence scores”:
an associated first confidence score indicating a first level of certainty of the machine learning model that the predicted refinance rate is accurate;
an associated second confidence score indicating a second level of certainty of the machine learning model that the predicted refinanced loan amount is accurate;
an associated third confidence score indicating a third level of certainty of the machine learning model that the predicted risk value is accurate;
Official Notice It would have been obvious to a person having ordinary skill in the art (PHOSITA), before the effective filing date of the claimed invention, to include in the method for automating pricing desk operation, as taught by Charton above, with determining “confidence scores” for a plurality of parameters, because Charton para. [0025] teaches determining a “confidence score” for a prediction of a client decision, and determining “confidence scores” for other parameters is a multiplication of parts a design choice, and an obvious variation.
However, under a conservative interpretation of Charton, it could be argued that Charton does not explicitly teach the italicized portions below, which are taught by Peak:
a predicted risk value of the refinanced loan for the predicted refinanced loan amount at the predicted refinance rate and
(See Peak, para. [0060]: “In some embodiments, interest rate threshold and a lender risk score threshold may be used as parameters. For example, only interest rates determined to be above a particular threshold where the determined lender's risk does not exceed a particular threshold amount. As additional data becomes available, the machine learning model may be expanded to further refine the interest rate and risk score outputs.”)
(See Peak, para. [0061]: “FIG. 3 is a flowchart of an exemplary method for automatically predicting an interest rate within a lender's risk score threshold. The illustrative method provided in FIG. 3 may be implemented by unsecured lending platform 120 of FIG. 1.”)
It would have been obvious to a person having ordinary skill in the art (PHOSITA), before the effective filing date of the claimed invention, to include in the method for automating pricing desk operation, as taught by Charton above, with a method for managing unsecured lending transactions, as further taught by Peak above, because Peak teaches that this is an important parameter to calculate for the purposes of bank decision making regarding whether or not to offer the loan.
In regards to claim 2, it has been cancelled.
In regards to claim 3,
3. (Currently Amended) The method of claim 1,
further comprising generating a unique global user identifier for the user and associating the unique global user identifier with local user identifiers used at multiple data repositories;
wherein periodically obtaining receiving the data associated with the current state of the current loan on the secured property of the user comprises retrieving the data from the multiple data repositories based on the unique global identifier; and
(See Peak, para. [0035]: “In some implementations, unsecured lending platform 120, client computing devices 104, and/or external resources 130 may be operatively linked via one or more electronic communication links. For example, such electronic communication links may be established, at least in part, via a network 103 such as the Internet and/or other networks. It will be appreciated that this is not intended to be limiting, and that the scope of this disclosure includes implementations in which server(s) 126, client computing device(s) 104, and/or external resources 130 may be operatively linked via some other communication media.”)
The Examiner interprets that the claimed features are obvious and well-known features of Internet and/or other networked computer systems of the type disclosed in Peak, para. [0035].
further comprising associating and storing the data of relevant to the offer for the refinanced loan with the user using the unique global user identifier, wherein generating the offer for the refinanced loan includes using the unique global user identifier to determine data to provide to the one or more machine learning models.
(See Charton, para. [0026]: “In order to predict future client decisions, the AI module 103 can be trained using historical offer data. As can be appreciated, offers that were previously presented to clients can have corresponding offer data stored in one or more historical data sources 113, such as databases, repositories, data stores, etc. This historical offer data can include parameters of historical offers previously presented to clients, along with historical context information associated with the historical offers. The historical offer data can further include client decisions associated with the historical offers, thus providing an indication of whether the client actually accepted or refused the offer. The actual client decisions can be used to label the historical offer parameters and context information for the purposes of training a classifier algorithm. In this fashion, the AI module 103 can be configured to learn from the outcome of previous offers in order to predict client decisions for future offers. As can be appreciated, the AI module 103 can learn from historical offers that were generated by traditional methods (ex: generated manually by a human analyst) and/or offers that were generated/optimized by the present system 100.”)
In regards to claim 4, it has been cancelled.
In regards to claim 5,
5. (Currently Amended) The method of claim 1, further comprising obtaining information related to a user interaction with the message at a web page and wherein retraining parameters of the one or more machine learning models is further based on the information related to the user interaction with the message at the web page.
(See Charton, para. [0033]: “Once an optimized offer 115 is generated by the processing module, it can be transmitted for communication to the client via the output module 107. As can be appreciated, the output module 107 can be configured to transmit the optimized offer 115 via different mechanisms, such as via an e-mail, via a user interface, via an electronic message to an external system, etc. It is further appreciated that the optimized offer 115 can be transmitted directly to the client, or via an intermediary, such as a bank agent or employee, who can then communicate the optimized offer 115 to the client.”)
(See Peak, para. [0035]: “In some implementations, unsecured lending platform 120, client computing devices 104, and/or external resources 130 may be operatively linked via one or more electronic communication links. For example, such electronic communication links may be established, at least in part, via a network 103 such as the Internet and/or other networks. It will be appreciated that this is not intended to be limiting, and that the scope of this disclosure includes implementations in which server(s) 126, client computing device(s) 104, and/or external resources 130 may be operatively linked via some other communication media.”)
The Examiner interprets that the claimed feature of a “web page” is obvious in light of Charton para. [0033] teaching that the offer can be transmitted via an-mail or via a user interface, in combination with Peak para. [0035] teaching that the communication may be via Internet.
In regards to claim 6,
6. (Currently Amended) The method of claim 1, wherein determining whether to present the offer for the refinanced loan to the user comprises
transmitting the predicted refinance rate and the associated first confidence score, the predicted refinanced loan amount and the associated second confidence score, and the predicted risk value and the associated third confidence score to a second computing system configured to determine whether to present the offer for the refinanced loan to the user,
wherein the second computing system is configured to generate and approve the refinanced loan in accordance with the offer.
(See Charton, para. [0003]: “When seeking to enter into an agreement for a financial product, such as a mortgage, a client can be presented an initial offer from a bank that includes a proposed interest rate. If the client refuses the proposed interest rate, a negotiation process can ensue between the client and the bank in an effort to settle on an interest rate that would be acceptable to both parties. This negotiation process can include a multitude of offers and counteroffers made by either party.”)
(See Charton, para. [0004]: “In such a scenario, final approval by the bank for any proposed offer or counteroffer can be handled by a centralized team of analysts, referred to as a “pricing desk”. The pricing desk can have two main functions: (1) accepting or refusing a proposed offer (or counteroffer); and (2) if the offer is refused, proposing a new and final offer to present to the client.”)
In regards to claim 7,
7. (Original): The method of claim 6, wherein the second computing system enables an administrator to authorize the offer for the refinanced loan.
(See Charton, para. [0004]: “In such a scenario, final approval by the bank for any proposed offer or counteroffer can be handled by a centralized team of analysts, referred to as a “pricing desk”. The pricing desk can have two main functions: (1) accepting or refusing a proposed offer (or counteroffer); and (2) if the offer is refused, proposing a new and final offer to present to the client.”)
In regards to claim 8,
8. (Original): The method of claim 1, wherein the offer for the refinanced loan includes an offer restriction which the user must fulfill before the offer for the refinanced loan is valid.
(See Charton, para. [0017]: “In the following description, an “offer” will be described as a proposal that can be made in view of concluding an agreement between a client and a vendor in relation to a product. An offer comprises one or more offer parameters that can be negotiated between the client and vendor before coming to an agreement, such as a price of the product, or other terms/conditions attached to the product. In the following disclosure, offers will be described in connection with concluding an agreement for a financial product between a client and a bank (the vendor). It is appreciated, however, that the teachings can be applied to other types of products as well and are not necessarily limited to the field of financial services. Moreover, in the embodiments of the present disclosure, the optimizing of offers will be described by adjusting a loan interest rate for a mortgage. It is appreciated, however, that an offer can include one or more other offer parameters, such as a loan term, amortization period, loan type (ex: open vs. closed, fixed vs. variable), guarantee length, amount, fees, etc., which can also be adjusted in order to optimize the offer. Finally, although optimizing a mortgage loan will be described, it is appreciated that the same principles can apply to other types of financial products, such as a commercial loan, insurance or a saving deposit account, or to any other type of product that also have negotiable parameters.”)
In regards to claim 9, it has been cancelled.
In regards to claim 10, it has been cancelled.
In regards to claim 11,
11. (Currently Amended) The method of claim 1, further comprising:
periodically obtaining, by the computing system, additional data associated with current states of current loans on multiple additional secured properties;
in response to obtaining the additional data, determining, by the computing system using the one or more machine learning model models and based on the additional data, predicted information associated with refinanced loans on predicted refinance rates for the multiple additional secured properties and associated confidence scores that the predicted refinance rates are accurate;
determining whether to present one or more offers for the refinanced loans on one or more of the multiple additional secured properties based on the predicted information associated confidence scores and a determination of an advantage of the refinanced loans over current loans on the one or more of the multiple additional secured properties; and
based on determining to present the one or more offers, generating and sending messages including the one or more offers.
(See Charton, para. [0021]: “The context information 109 b can further include more general information that is not specific to the client but that may also impact the client's decision. For example, the context information 109 b can include market-related information that can correspond to information relating to market condition in which the proposed offer 109 a would be made. Such market-related information can include recently published competitor interest rates, current daily cost of funds, current pricing targets, among others. As another example, the context information 109 b can include product-related information that can correspond to information relating to the specific financial product associated with the offer. Such product-related information can include characteristics of the financial product, such as the object of the loan (ex: whether or not the loan is to finance the purchase of a high-end or luxury asset), a classification of the product (ex: whether the financial product is marketed as premium or deluxe), etc. As can be appreciated, such information can be indicators of competitiveness in the market and may affect what the client would consider acceptable.”)
(See Charton, para. [0044]: “A second subprocess 303 can comprise joining additional information. Although the request received in subprocess 301 can include some context information, additional information may be useful to construct a more complete picture of the offer context and/or to obtain more accurate results. Such additional context information may not be practical to include as part of the request (such as in an e-mail) and/or may only be accessible via external sources. Accordingly, subprocess 301 can comprise retrieving additional context information from external data sources 111, such as one or more databases. Such additional context information can include, for example, a current cost of funds, current competitor rates, current pricing targets, and/or other client-specific, market-related and/or product-related information. The combined data can subsequently be validated and formatted as required.”)
In regards to claim 12, it is rejected on the same grounds as claim 1.
In regards to claim 13, it has been cancelled.
In regards to claim 14, it is rejected on the same grounds as claim 3.
In regards to claim 15, it has been cancelled.
In regards to claim 16, it is rejected on the same grounds as claim 6.
In regards to claim 17, it has been cancelled.
In regards to claim 18, it has been cancelled.
In regards to claim 19, it is rejected on the same grounds as claim 11.
In regards to claim 20, it is rejected on the same grounds as claim 1.
In regards to claim 21,
21. (New): The method of claim 1, wherein periodically obtaining the data comprises automatically obtaining the data associated with the current state of the current loan from a plurality of data repositories on a fixed schedule.
(See Charton, para. [0020]: “In some embodiments, the context information 109 b can comprise client-specific information. Client-specific information can correspond to information relating to the individual client to which the proposed offer 109 a is directed. By way of example, client-specific information can include various information relating to the client's current behaviour, patterns, personal status, economic status, demographic segment, etc. such as financial transaction history, assets, account balances, credit score, website interaction data, social media interaction data, previous applications for financial products, home address, work address, occupation, title, professional affiliations, family and marital status, previous offers from competitors, cost to break an existing loan contract, among others.”)
The Examiner interprets that the “fixed schedule” of obtaining the client-specific information is an obvious variation of when the information is obtained.
In regards to claim 22,
22. (New): The method of claim 1, wherein periodically obtaining the data associated with the current state of the current loan triggers the automatic determination of the predicted information associated with the refinanced loan on the secured property in the absence of a request from the user.
(See Charton, para. [0023]: “The input module 101 can further be configured to receive the input offer data 109 from different sources. For example, the proposed offer 109 a and corresponding offer parameters can be received in the form of an e-mail, while some or all of the context information 109 b can reside on one or more external input data sources 111, such as databases, repositories, data stores, etc. Accordingly, the input module 101 can be configured to retrieve at least some context information 109 b from the one or more external data sources 111, and to join the external context information 109 b with the parameters of the proposed offer 109 a to form the input offer data 109.”)
The Examiner interprets that the “the automatic determination of the predicted information associated with the refinanced loan on the secured property in the absence of a request from the user” is an obvious variation of Charton, para. [0023] teaching of “Accordingly, the input module 101 can be configured to retrieve at least some context information 109 b from the one or more external data sources 111, and to join the external context information 109 b with the parameters of the proposed offer 109 a to form the input offer data 109”.
In regards to claim 23,
23. (New) The method of claim 1, wherein the data associated with the current state of the current loan includes at least location information of the secured property, a current loan amount, a current loan rate, data related to the user, and a current property value of the secured property.
(See Charton, para. [0020]: “In some embodiments, the context information 109 b can comprise client-specific information. Client-specific information can correspond to information relating to the individual client to which the proposed offer 109 a is directed. By way of example, client-specific information can include various information relating to the client's current behaviour, patterns, personal status, economic status, demographic segment, etc. such as financial transaction history, assets, account balances, credit score, website interaction data, social media interaction data, previous applications for financial products, home address, work address, occupation, title, professional affiliations, family and marital status, previous offers from competitors, cost to break an existing loan contract, among others.”)
In regards to claim 24,
24. (New) The method of claim 23, wherein periodically obtaining the data associated with the current state of the current loan on the secured property comprises periodically retrieving the location information of the secured property, the current loan amount, and the current loan rate from one or more data repositories internal to an enterprise network that hosts the computing system.
(See Charton, para. [0020]: “In some embodiments, the context information 109 b can comprise client-specific information. Client-specific information can correspond to information relating to the individual client to which the proposed offer 109 a is directed. By way of example, client-specific information can include various information relating to the client's current behaviour, patterns, personal status, economic status, demographic segment, etc. such as financial transaction history, assets, account balances, credit score, website interaction data, social media interaction data, previous applications for financial products, home address, work address, occupation, title, professional affiliations, family and marital status, previous offers from competitors, cost to break an existing loan contract, among others.”)
The Examiner interprets that the claimed “periodically retrieving the location information of the secured property, the current loan amount, and the current loan rate” is an obvious variation of Charton, para. [0020].
In regards to claim 25,
25. (New) The method of claim 23, wherein the computing system is hosted in an enterprise network of a lending institution, and wherein periodically obtaining the data related to the user comprises periodically retrieving data related to the user's creditworthiness, the user's account information, and the user's relationship with the lending institution from one or more data repositories internal to the enterprise network that hosts the computing system.
(See Charton, para. [0020]: “In some embodiments, the context information 109 b can comprise client-specific information. Client-specific information can correspond to information relating to the individual client to which the proposed offer 109 a is directed. By way of example, client-specific information can include various information relating to the client's current behaviour, patterns, personal status, economic status, demographic segment, etc. such as financial transaction history, assets, account balances, credit score, website interaction data, social media interaction data, previous applications for financial products, home address, work address, occupation, title, professional affiliations, family and marital status, previous offers from competitors, cost to break an existing loan contract, among others.”)
In regards to claim 26,
26. (New) The method of claim 23, wherein periodically obtaining the data associated with the current state of the current loan on the secured property comprises periodically sending, to a third-party source external to an enterprise network that hosts the computing system, a query for the current property value of the secured property based on the location information of the secured property.
(See Charton, para. [0020]: “In some embodiments, the context information 109 b can comprise client-specific information. Client-specific information can correspond to information relating to the individual client to which the proposed offer 109 a is directed. By way of example, client-specific information can include various information relating to the client's current behaviour, patterns, personal status, economic status, demographic segment, etc. such as financial transaction history, assets, account balances, credit score, website interaction data, social media interaction data, previous applications for financial products, home address, work address, occupation, title, professional affiliations, family and marital status, previous offers from competitors, cost to break an existing loan contract, among others.”)
In regards to claim 27,
27. (New) The method of claim 23, wherein the data associated with the current state of the current loan further includes a standard interest rate, and wherein the predicted refinance rate comprises a delta to the standard interest rate.
(See Charton, para. [0021]: “The context information 109 b can further include more general information that is not specific to the client but that may also impact the client's decision. For example, the context information 109 b can include market-related information that can correspond to information relating to market condition in which the proposed offer 109 a would be made. Such market-related information can include recently published competitor interest rates, current daily cost of funds, current pricing targets, among others. As another example, the context information 109 b can include product-related information that can correspond to information relating to the specific financial product associated with the offer. Such product-related information can include characteristics of the financial product, such as the object of the loan (ex: whether or not the loan is to finance the purchase of a high-end or luxury asset), a classification of the product (ex: whether the financial product is marketed as premium or deluxe), etc. As can be appreciated, such information can be indicators of competitiveness in the market and may affect what the client would consider acceptable.”)
The Examiner interprets that “the predicted refinance rate comprises a delta to the standard interest rate” is an obvious variation of Charton para. [0021] teaching of “Such market-related information can include recently published competitor interest rates”.
Response to Amendments
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 amended, as necessitated by Applicant’s amendments to the claims.
Conclusion
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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
March 7, 2026