DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the amended claims filed on 5/20/2026 in Application 19/070398 which is a continuation of application 18-048795 issued as US 12,288,217, wherein:
Claim 1, 10, and 19 have been amended;
Claims 2-9, 11-18, and 20 remain as original; and
Claims 1-20 are currently pending and have been examined.
Claim Interpretation
The following claim limitations have been interpreted in accordance with the specification and drawings as follows:
An apparatus is defined in para. 0033 as “system device 204 of the debt optimization system 202 may be embodied by one or more computing devices or servers, shown as apparatus 300 in fig. 3. As illustrated in fig. 3, the apparatus 300 may include processor 302, memory 304, communications hardware 306, interface generation circuitry 308, surrogate modeling circuitry 310, and optimizer modeling circuitry 312”. For examination purposes, an apparatus will be interpreted as a computing device or server.
Communications hardware is defined in para. 0033 as “As illustrated in fig. 3, the apparatus 300 may include processor 302, memory 304, communications hardware 306, interface generation circuitry 308, surrogate modeling circuitry 310, and optimizer modeling circuitry 312”. Para. 0037 of the specification further states that communication hardware “may include one or more network interface cards, antennas, buses, switches, routers, modems, and supporting hardware and/or software, or any other device suitable for enabling communications via a network”. Para. 0038 further states communications hardware “may comprise an interface, such as a display, and may further comprise the components that govern use of the interface, such as a web browser, mobile application, dedicated client device, or the like. In some embodiments, the communications hardware 306 may include a keyboard, a mouse, a touch screen, touch areas, soft keys, a microphone, a speaker, and/or other input/output mechanisms”. For examination purposes, communications hardware will be interpreted as an interface or component of the computing device or server;
An interactive user interface (UI) is defined in the specification in para. 0039 as “apparatus 300 further comprises interface generation circuitry 308 that generates an interactive user interface (UI) comprising a plurality of UI elements”. For examination purposes, interactive user interface (UI) comprising a plurality of UI elements will be interpreted as part of the interface generation circuitry which is part of the computing device or server;
Surrogate modeling circuitry is defined in para. 0033 as “As illustrated in fig. 3, the apparatus 300 may include processor 302, memory 304, communications hardware 306, interface generation circuitry 308, surrogate modeling circuitry 310, and optimizer modeling circuitry 312. For examination purposes, surrogate modeling circuitry will be interpreted as part of the computing device or server;
A plurality of surrogate models is defined in para. 0040 of the specification as “In some embodiments, example surrogate models of the surrogate modeling circuitry 310 may include an approval likelihood surrogate model set, an interest rate surrogate model set, and a credit limit surrogate model set…In some embodiments, the surrogate modeling circuitry 310 may comprise multiple surrogate models, such as machine learning (ML) models (e.g., supervised or unsupervised), artificial intelligence (AI) reasoning models, logistic regression models, quantile regression models, and/or the like which are utilized to generate output data (e.g., a parameter estimation set) based on corresponding input data provided to the models.” For examination purposes surrogate models will be interpreted as machine learning, artificial intelligence, logistic regression, quantile regression or some other trained algorithm performed by the computing device or server;
An approval likelihood surrogate model set is defined in para. 0040 of the specification as “In some embodiments, example surrogate models of the surrogate modeling circuitry 310 may include an approval likelihood surrogate model set, an interest rate surrogate model set, and a credit limit surrogate model set.” For examination purposes an approval likelihood surrogate model set is interpreted as one of the surrogate models which interpreted as machine learning, artificial intelligence, logistic regression, quantile regression or some other trained algorithm performed by the computing device or server;
An interest rate surrogate model set is defined in para. 0040 of the specification as “In some embodiments, example surrogate models of the surrogate modeling circuitry 310 may include an approval likelihood surrogate model set, an interest rate surrogate model set, and a credit limit surrogate model set.” For examination purposes an interest rate surrogate model set is interpreted as one of the surrogate models which will be interpreted as machine learning, artificial intelligence, logistic regression, quantile regression or some other trained algorithm performed by the computing device or server;
A credit limit surrogate model set is defined in para. 0040 of the specification as “In some embodiments, example surrogate models of the surrogate modeling circuitry 310 may include an approval likelihood surrogate model set, an interest rate surrogate model set, and a credit limit surrogate model set.” For examination purposes a credit limit surrogate model set is interpreted as one of the surrogate models which will be interpreted as machine learning, artificial intelligence, logistic regression, quantile regression or some other trained algorithm performed by the computing device or server;
a plurality of models is defined in para. 0040 of the specification as “In some embodiments, example surrogate models of the surrogate modeling circuitry 310 may include an approval likelihood surrogate model set, an interest rate surrogate model set, and a credit limit surrogate model set.” For examination purposes a plurality of models will be interpreted as the surrogate models which are interpreted as machine learning, artificial intelligence, logistic regression, quantile regression or some other trained algorithm performed by the computing device or server; and
An optimizer modeling circuitry, is defined in para. 0033 of the specification as “As illustrated in FIG. 3, the apparatus 300 may include processor 302, memory 304, communications hardware 306, interface generation circuitry 308, surrogate modeling circuitry 310, and optimizer modeling circuitry 312” For examination purposes an optimizer modeling circuitry will be interpreted as part of the computing device or server.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 of Application 19/070398 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of US Patent No. 12,288,217. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the ‘217 Patent recite all the limitations of claims 1-20 of the instant Application No. 19/070398 as indicated in the comparison table below.
Claims of 19/070398
Claims of US Patent No. 12,288,217
1. A method for developing an optimized debt service strategy solution utilizing products across multiple product categories and multiple entities, the method comprising:
receiving, by communications hardware from a plurality of remote servers via a network interface, (i) user financial information comprising a FICO score or a debt-to-income ratio, and (ii) product information for one or more products offered by the multiple entities,
wherein each of the plurality of remote servers is associated with a respective entity of the multiple entities;
generating, by surrogate modeling circuitry, one or more parameters by inputting, at least in part, the user financial information into a plurality of surrogate models comprising an interest rate model and an approval likelihood model;
determining, by optimizer modeling circuitry and based on the product information the one or more parameters, a debt service strategy solution for a first entity of the multiple entities, by:
determining a baseline strategy solution comprising suggested values for existing products of a user,
generating a product portfolio comprising (i) a product of a plurality of products, and (ii) the existing products of the user,
determining, a recommended product portfolio comprising a cost savings value that is equal to, or greater than, the baseline strategy solution,
determining an approval likelihood for the product of the recommended product portfolio, and
determining a scaled cost savings value for the recommended product portfolio by multiplying the cost savings value by the approval likelihood; and
causing presentation, by the communications hardware, of the debt service strategy solution via an interactive user interface by:
simultaneously causing display, in a first portion of the interactive user interface, of a first interactive data element comprising a first link to a first webpage associated with a first debt service strategy solution,
and causing display, in a second portion of the interactive user interface, a second interactive data element comprising a second link to a second webpage associated with a second debt service strategy solution.
1. A method for developing an optimized debt service strategy solution utilizing products across multiple product categories and multiple entities, the method comprising:
receiving, by communications hardware from a plurality of remote servers via a network interface, (i) user financial information comprising a FICO score or a debt-to-income ratio, and (ii) product information for products offered by the multiple entities,
wherein each of the plurality of remote servers is associated with a respective entity of the multiple entities;
generating, by surrogate modeling circuitry, a user dataset by inputting, at least in part, the user financial information into a plurality of surrogate models comprising an interest rate model and an approval likelihood model;
generating, by the surrogate modeling circuitry and based on the user dataset, a parameter estimation set;
determining, by optimizer modeling circuitry and based on the product information, the user dataset, and the parameter estimation set, a respective debt service strategy solution for each respective entity, wherein the respective debt service strategy solution comprises at least one product of a first product category from a plurality of products associated with the multiple product categories, wherein determining the respective debt service strategy solution for each respective entity comprises:
determining, by the optimizer modeling circuitry and based on the user dataset, a baseline strategy solution comprising suggested values for existing products of a user in accordance with a constraint factor set,
generating, by the optimizer modeling circuitry and for each product in the plurality of products, a product portfolio comprising (i) a respective product of the plurality of products and (ii) the existing products,
determining, by the optimizer modeling circuitry and using the interest rate model, a recommended product portfolio set comprising one or more recommended product portfolios, wherein each recommended product portfolio has a respective cost savings value that is equal to, or greater than, the baseline strategy solution,
determining, by the surrogate modeling circuitry and using the approval likelihood model, an approval likelihood for each product associated with each recommended product portfolio based, at least in part, on the user financial information,
determining, by the optimizer modeling circuitry, a respective scaled cost savings value for each of the one or more recommended product portfolios by multiplying the respective cost savings value by a respective approval likelihood,
ranking, by the optimizer modeling circuitry, the one or more recommended product portfolios based on the respective scaled cost savings value for each of the one or more recommended product portfolios, and
selecting, by the optimizer modeling circuitry, a top-ranked recommended product portfolio as the respective debt service strategy solution for each respective entity; and
causing presentation, by the communications hardware, of the respective debt service strategy solution for each respective entity via an interactive user interface by:
simultaneously causing display of a first interactive data element associated with a first debt service strategy solution of a first entity in a first portion of the interactive user interface, wherein the first interactive data element comprises a first link to a first webpage of the first entity,
and a second interactive data element associated with a second debt service strategy solution of a second entity in a second portion of the interactive user interface, wherein the second interactive data element comprises a second link to a second webpage of the second entity.
2. The method of claim 1, wherein the one or more parameters comprises a constraint factor set comprising at least one of a budgetary constraint factor, an existing debt constraint factor, or a savings constraint factor.
2. The method of claim 1, wherein the user dataset comprises the constraint factor set comprising at least one of: a budgetary constraint factor, an existing debt constraint factor, and a savings constraint factor,
wherein the respective debt service strategy solution for each respective entity is determined such that the respective debt service strategy solution for each respective entity satisfies constraint factors of the constraint factor set.
3. The method of claim 2, wherein the debt service strategy solution for the first entity is determined such that the debt service strategy solution satisfies one or more constraint factors of the constraint factor set.
2. …wherein the respective debt service strategy solution for each respective entity is determined such that the respective debt service strategy solution for each respective entity satisfies constraint factors of the constraint factor set.
4. The method of claim 1, wherein the plurality of surrogate models comprise an approval likelihood surrogate model set, an interest rate surrogate model set, and a credit limit surrogate model set.
3. The method of claim 1, wherein the plurality of surrogate models comprise an approval likelihood surrogate model set, an interest rate surrogate model set, and a credit limit surrogate model set.
5. The method of claim 4, wherein each of the approval likelihood surrogate model set, the interest rate surrogate model set, and the credit limit surrogate model set comprise a respective plurality of models trained to predict an estimated value for a respective product.
4. The method of claim 3, wherein each of the approval likelihood surrogate model set, the interest rate surrogate model set, and the credit limit surrogate model set comprise a respective plurality of models trained to predict an estimated value for a respective product.
6. The method of claim 1, wherein the approval likelihood model comprises a logistic regression model and a shallow decision tree,
wherein the method further comprises: training the shallow decision tree as a binary classifier for approval predictions of the logistic regression model.
8. The method of claim 1, wherein the approval likelihood model comprises a logistic regression model and a shallow decision tree,
wherein the method further comprises: training the shallow decision tree as a binary classifier for approval predictions of the logistic regression model.
7. The method of claim 6, wherein the approval likelihood model further comprises a cut- off criteria,
wherein the method further comprises: hard-coding the cut-off criteria into the approval likelihood model, wherein the cut-off criteria is configured to mitigate a false expectation of approval.
18. The method of claim 8, wherein the approval likelihood model further comprises a cut-off criteria,
wherein the method further comprises: hard-coding the cut-off criteria into the approval likelihood model, wherein the cut-off criteria is configured to mitigate a false expectation of approval.
8. The method of claim 7, further comprising: applying the logistic regression model for data space beyond one or more partitions defined by the cut-off criteria.
19. The method of claim 18, further comprising:
applying the logistic regression model for data space beyond one or more partitions defined by the cut-off criteria.
9. The method of claim 8, wherein the cut-off criteria comprises a respective predefined threshold for each of the multiple product categories,
wherein the multiple product categories comprises two or more of a credit card category, a personal loan category, or a home loan category,
wherein the cut-off criteria comprises a predefined FICO score threshold for at least one of the credit card category and the personal loan category, wherein the cut-off criteria comprises a debt-to-income ratio threshold for the home loan category.
20. The method of claim 19, wherein the cut-off criteria comprises a respective predefined threshold for each of the multiple product categories,
wherein the multiple product categories comprises two or more of a credit card category, a personal loan category, or a home loan category,
wherein the cut-off criteria comprises a predefined FICO score threshold for at least one of the credit card category and the personal loan category, wherein the cut-off criteria comprises a debt-to-income ratio threshold for the home loan category.
10. An apparatus for developing an optimized debt service strategy solution utilizing products across multiple product categories and multiple entities, the apparatus comprising:
communications hardware configured to receive, from a plurality of remote servers via a network interface, (i) user financial information comprising a FICO score or a debt-to-income ratio, and
(ii) product information for one or more products offered by the multiple entities, wherein each of the plurality of remote servers is associated with a respective entity of the multiple entities;
surrogate modeling circuitry configured to generate one or more parameters by inputting, at least in part, the user financial information into a plurality of surrogate models comprising an interest rate model and an approval likelihood model;
and optimizer modeling circuitry configured to determine, based on the product information, the one or more parameters a debt service strategy solution for a first entity of the multiple entities,
by: determining a baseline strategy solution comprising suggested values for existing products of a user,
generating a product portfolio comprising (i) a product of a plurality of products, and (ii) the existing products of the user,
determining a recommended product portfolio comprising a cost savings value that is equal to, or greater than, the baseline strategy solution,
determining an approval likelihood for the product of the recommended product portfolio, and
determining a scaled cost savings value for the recommended product portfolio by multiplying the cost savings value by the approval likelihood,
wherein the communications hardware is further configured to cause presentation of the debt service strategy solution via an interactive user interface by:
simultaneously causing display, in a first portion of the interactive user interface, of a first interactive data element comprising a first link to a first webpage associated with a first debt service strategy solution, and causing display, in a second portion of the interactive user interface,
a second interactive data element comprising a second link to a second webpage associated with a second debt service strategy solution.
9. An apparatus for developing an optimized debt service strategy solution utilizing products across multiple product categories and multiple entities, the apparatus comprising:
communications hardware configured to receive, from a plurality of remote servers via a network interface, (i) user financial information comprising a FICO score or a debt-to-income ratio, and
(ii) product information for products offered by the multiple entities, wherein each of the plurality of remote servers is associated with a respective entity of the multiple entities;
surrogate modeling circuitry configured to:
generate a user dataset by inputting, at least in part, the user financial information into a plurality of surrogate models comprising an interest rate model and an approval likelihood model, and
generate, based on the user dataset, a parameter estimation set;
and optimizer modeling circuitry configured to determine, based on the product information, the user dataset, and the parameter estimation set, a respective debt service strategy solution for each respective entity, wherein the respective debt service strategy solution comprises at least one product of a first product category from a plurality of products associated with the multiple product categories, wherein determining the respective debt service strategy solution for each respective entity comprises:
determining, based on the user dataset, a baseline strategy solution comprising suggested values for existing products of a user in accordance with a constraint factor set,
generating, for each product in the plurality of products, a product portfolio comprising (i) a respective product of the plurality of products and (ii) the existing products,
determining, using the interest rate model, a recommended product portfolio set comprising one or more recommended product portfolios, wherein each recommended product portfolio has a respective cost savings value that is equal to, or greater than, the baseline strategy solution,
determining, using the approval likelihood model, an approval likelihood for each product associated with each recommended product portfolio based, at least in part, on the user financial information,
determining a respective scaled cost savings value for each of the one or more recommended product portfolios by multiplying the respective cost savings value by a respective approval likelihood,
ranking the one or more recommended product portfolios based on the respective scaled cost savings value for each of the one or more recommended product portfolios, and selecting a top-ranked recommended product portfolio as the respective debt service strategy solution for each respective entity,
wherein the communications hardware is further configured to cause presentation of the respective debt service strategy solution for each respective entity via an interactive user interface by:
simultaneously causing display of a first interactive data element associated with a first debt service strategy solution of a first entity in a first portion of the interactive user interface, wherein the first interactive data element comprises a first link to a first webpage of the first entity, and
a second interactive data element associated with a second debt service strategy solution of a second entity in a second portion of the interactive user interface, wherein the second interactive data element comprises a second link to a second webpage of the second entity.
11. The apparatus of claim 10, wherein the one or more parameters comprises a constraint factor set comprising at least one of a budgetary constraint factor, an existing debt constraint factor, and a savings constraint factor.
10. The apparatus of claim 9, wherein the user dataset comprises the constraint factor set comprising at least one of: a budgetary constraint factor, an existing debt constraint factor, and a savings constraint factor,…
12. The apparatus of claim 11, wherein the optimizer modeling circuitry determines the debt service strategy solution for the first entity such that the debt service strategy solution satisfies one or more constraint factors of the constraint factor set.
10…wherein the optimizer modeling circuitry determines the respective debt service strategy solution for each respective entity such that the respective debt service strategy solution for each respective entity satisfies constraint factors of the constraint factor set.
13. The apparatus of claim 10, wherein the plurality of surrogate models comprise an approval likelihood surrogate model set, an interest rate surrogate model set, and a credit limit surrogate model set.
11. The apparatus of claim 9, wherein the plurality of surrogate models comprise an approval likelihood surrogate model set, an interest rate surrogate model set, and a credit limit surrogate model set.
14. The apparatus of claim 13, wherein each of the approval likelihood surrogate model set, the interest rate surrogate model set, and the credit limit surrogate model set comprise a respective plurality of models trained to predict an estimated value for a respective product.
12. The apparatus of claim 11, wherein each of the approval likelihood surrogate model set, the interest rate surrogate model set, and the credit limit surrogate model set comprise a respective plurality of models trained to predict an estimated value for a respective product.
15. The apparatus of claim 10, wherein the approval likelihood model comprises a logistic regression model and a shallow decision tree,
wherein the approval likelihood model is configured to train the shallow decision tree as a binary classifier for approval predictions of the logistic regression model.
8. The method of claim 1, wherein the approval likelihood model comprises a logistic regression model and a shallow decision tree,
wherein the method further comprises: training the shallow decision tree as a binary classifier for approval predictions of the logistic regression model.
16. The apparatus of claim 15, wherein the approval likelihood model further comprises a cut-off criteria,
wherein the approval likelihood model is further configured to hard-code the cut-off criteria into the approval likelihood model, wherein the cut-off criteria is configured to mitigate a false expectation of approval.
18. The method of claim 8, wherein the approval likelihood model further comprises a cut-off criteria,
wherein the method further comprises:
hard-coding the cut-off criteria into the approval likelihood model, wherein the cut-off criteria is configured to mitigate a false expectation of approval.
17. The apparatus of claim 16, wherein the approval likelihood model is further configured to applying the logistic regression model for data space beyond one or more partitions defined by the cut-off criteria.
19. The method of claim 18, further comprising:
applying the logistic regression model for data space beyond one or more partitions defined by the cut-off criteria.
18. The apparatus of claim 17, wherein the cut-off criteria comprises a respective predefined threshold for each of the multiple product categories, wherein the multiple product categories comprises two or more of a credit card category, a personal loan category, or a home loan category, wherein the cut-off criteria comprises a predefined FICO score threshold for at least one of the credit card category and the personal loan category, wherein the cut-off criteria comprises a debt-to-income ratio threshold for the home loan category.
20. The method of claim 19, wherein the cut-off criteria comprises a respective predefined threshold for each of the multiple product categories, wherein the multiple product categories comprises two or more of a credit card category, a personal loan category, or a home loan category, wherein the cut-off criteria comprises a predefined FICO score threshold for at least one of the credit card category and the personal loan category, wherein the cut-off criteria comprises a debt-to-income ratio threshold for the home loan category.
19. A computer program product for developing an optimized debt service strategy solution utilizing products across multiple product categories and multiple entities, the computer program product comprising
at least one non-transitory computer-readable storage medium storing software instructions that, when executed, cause an apparatus to:
receive, from a plurality of remote servers via a network interface, (i) user financial information comprising a FICO score or a debt-to-income ratio, and (ii) product information for one or more products offered by the multiple entities,
wherein each of the plurality of remote servers is associated with a respective entity of the multiple entities;
generate, one or more parameters by inputting, at least in part, the user financial information into a plurality of surrogate models comprising an interest rate model and an approval likelihood model;
determine, based on the product information the one or more parameters, a debt service strategy solution for a first entity of the multiple entities, by:
determining a baseline strategy solution comprising suggested values for existing products of a user,
generating a product portfolio comprising (i) a product of a plurality of products, and (ii) the existing products of the user,
determining a recommended product portfolio comprising a cost savings value that is equal to, or greater than, the baseline strategy solution,
determining an approval likelihood for the product of the recommended product portfolio, and
determining a scaled cost savings value for the recommended product portfolio by multiplying the cost savings value by the approval likelihood; and
cause presentation of the debt service strategy solution via an interactive user interface by:
simultaneously causing display, in a first portion of the interactive user interface, of a first interactive data element comprising a first link to a first webpage associated with a first debt service strategy solution, and
causing display, in a second portion of the interactive user interface, a second interactive data element comprising a second link to a second webpage associated with a second debt service strategy solution.
16. A computer program product for developing an optimized debt service strategy solution utilizing products across multiple product categories and multiple entities, the computer program product comprising
at least one non-transitory computer-readable storage medium storing software instructions that, when executed, cause an apparatus to:
receive, from a plurality of remote servers via a network interface, (i) user financial information comprising a FICO score or a debt-to-income ratio, and (ii) product information for products offered by the multiple entities,
wherein each of the plurality of remote servers is associated with a respective entity of the multiple entities;
generate, a user dataset by inputting, at least in part, the user financial information into a plurality of surrogate models comprising an interest rate model and an approval likelihood model; generate, based on the user dataset, a parameter estimation set;
determine, based on the product information, the user dataset, and the parameter estimation set, a respective debt service strategy solution for each respective entity, wherein the respective debt service strategy solution comprises at least one product of a first product category from a plurality of products associated with the multiple product categories, wherein determining the respective debt service strategy solution for each respective entity comprises:
determining, based on the user dataset, a baseline strategy solution comprising suggested values for existing products of a user in accordance with a constraint factor set,
generating, for each product in the plurality of products, a product portfolio comprising (i) a respective product of the plurality of products and (ii) the existing products,
determining, using the interest rate model, a recommended product portfolio set comprising one or more recommended product portfolios, wherein each recommended product portfolio has a respective cost savings value that is equal to, or greater than, the baseline strategy solution,
determining, using the approval likelihood model, an approval likelihood for each product associated with each recommended product portfolio based, at least in part, on the user financial information,
determining a respective scaled cost savings value for each of the one or more recommended product portfolios by multiplying the respective cost savings value by a respective approval likelihood,
ranking the one or more recommended product portfolios based on the respective scaled cost savings value for each of the one or more recommended product portfolios, and
selecting a top-ranked recommended product portfolio as the respective debt service strategy solution for each respective entity; and
cause presentation of the respective debt service strategy solution for each respective entity via an interactive user interface by: simultaneously causing display of a first interactive data element associated with a first debt service strategy solution of a first entity in a first portion of the interactive user interface, wherein the first interactive data element comprises a first link to a first webpage of the first entity, and
a second interactive data element associated with a second debt service strategy solution of a second entity in a second portion of the interactive user interface, wherein the second interactive data element comprises a second link to a second webpage of the second entity.
20. The computer program product of claim 19, wherein the one or more parameters comprises a constraint factor set comprising at least one of a budgetary constraint factor, an existing debt constraint factor, and a savings constraint factor,
wherein the debt service strategy solution for the first entity is determined such that the debt service strategy solution satisfies one or more constraint factors of the constraint factor set.
17. The computer program product of claim 16,
wherein the user dataset comprises the constraint factor set comprising at least one of: a budgetary constraint factor, an existing debt constraint factor, and a savings constraint factor,
wherein the respective debt service strategy solution for each respective entity is determined such that the respective debt service strategy solution for each respective entity satisfies constraint factors of the constraint factor set.
Allowable Subject Matter
Claims 1-20 would be allowable if rewritten to overcome the nonstatutory double patenting rejections set forth in this Office Action.
Examiner’s statement of reasons for indicating Patent-eligible subject matter over the prior art for the claims was previously given in the Non-final Rejection dated 3/2/2026 and are hence not repeated here.
The following is an examiner’s statement of reasons for indicating Patent-eligible subject matter in view of 35 USC § 101.
The claims recite an abstract idea for determining a debt solution. The claimed limitations cover Certain Methods of Organizing Human Activity such as fundamental economic principles or practices, including mitigating risk; and commercial or legal interactions, including marketing or sales activities or behaviors. Under Step 2A, Prong 2, the claimed invention has been deemed to recite limitations that integrate the abstract idea into a practical application; and under Step 2B the claimed invention has been deemed to recite limitations that are indicative of an inventive concept (aka “significantly more”) The steps in independent claims 1, 10, and 19 for “A method for developing an optimized debt service strategy solution utilizing products across multiple product categories and multiple entities, the method comprising: receiving, by communications hardware from a plurality of remote servers via a network interface, (i) user financial information comprising a FICO score or a debt-to-income ratio, and(ii) product information for one or more products offered by the multiple entities, wherein each of the plurality of remote servers is associated with a respective entity of the multiple entities; generating, by surrogate modeling circuitry, one or more parameters by inputting, at least in part, the user financial information into a plurality of surrogate models comprising an interest rate model and an approval likelihood model; determining, by optimizer modeling circuitry and based on the product information the one or more parameters, a debt service strategy solution for a first entity of the multiple entities, by: determining a baseline strategy solution comprising suggested values for existing products of a user, generating a product portfolio comprising (i) a product of a plurality of products, and (ii) the existing products of the user, determining, a recommended product portfolio comprising a cost savings value that is equal to, or greater than, the baseline strategy solution, determining an approval likelihood for the product of the recommended product portfolio, and determining a scaled cost savings value for the recommended product portfolio by multiplying the cost savings value by the approval likelihood; and causing presentation, by the communications hardware, of the debt service strategy solution via an interactive user interface by: simultaneously causing display, in a first portion of the interactive user interface, of a first interactive data element comprising a first link to a first webpage associated with a first debt service strategy solution, and causing display, in a second portion of the interactive user interface, a second interactive data element comprising a second link to a second webpage associated with a second debt service strategy solution” are limitations, which when considered as an ordered combination, integrates the method of organizing human activity into a practical application and are indicative of an inventive concept.
Specifically, the claimed limitations of the independent claims address technical problems of current electronic debt management technologies which do not have the ability to tailor a specific debt solution using multiple product categories from multiple entities for the particular financial situation of an individual. The claimed limitations resolve the technical problem by providing an improvement in electronic debt management technology that: leverages trained surrogate models to analyze users’ financial information acquired from remote servers to determine a parameter estimation set; using optimizer modeling circuitry to determine a baseline strategy solution comprising existing products of a user and recommended product portfolios comprising cost savings equal to or greater than the baseline, determining using the surrogate modeling circuitry an approval likelihood for each of the recommended products and scaled cost savings, determining a scaled cost savings for the recommended product portfolio, and presenting a first and second debt service strategy solution to users on two portions of an interactive user interface simultaneously with interactive data elements comprising links to the webpages of the entities (see specification paras. 0003-0006, 0024-0026, and 0090). Additionally, the elements in the amended independent claims, as an ordered combination, are not well-understood, routine, conventional activities for providing debt solutions. For these reasons, independent claims 1, 10, and 19 are deemed patent eligible under 35 USC 101. Dependent claims 2-9, 11-18, and 20 are deemed patent eligible by virtue of dependency on a patent eligible claim.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been fully considered by the Examiner. In regards to the rejections of the claims under 35 USC 101, the Examiner agrees that Applicant’s amendments that the amended independent claims are deemed patent eligible under 35 USC 101 as further indicated above. The non-statutory double patenting rejections of claims 1-20 are maintained.
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Paul Schwarzenberg whose telephone number is (313) 446-6611. The examiner can normally be reached on Monday-Thursday (7:30-6:30).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christine Tran, can be reached on (571) 272-8103. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/PAUL S SCHWARZENBERG/Primary Examiner, Art Unit 3695 6/3/2026
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