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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 3/4/2025 was in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of Claims
This action is in reply to the application filed on 3/4/2025 which is a continuation of application 18-048795 issued as US 12,288,217, wherein:
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 of a first interactive data element comprising a first link to a first webpage, and a second interactive data element comprising a second link to a second webpage.
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 of a first interactive data element comprising a first link to a first webpage, and a second interactive data element comprising a second link to a second webpage.
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 of a first interactive data element comprising a first link to a first webpage, and a second interactive data element comprising a second link to a second webpage.
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.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a method, computer program produce, and apparatus for determining a debt solution which is considered a judicial exception because it falls under: 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. This judicial exception is not integrated into a practical application as discussed below and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception as discussed below.
This rejection follows the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed Reg 4, January 7, 2019, pp. 50-57 (“2019 PEG”)(MPEP 2106).
Analysis
Step 1 (Statutory Categories) – 2019 PEG pg. 53 (See MPEP 2106.03)
Claims 1-20 are directed to the statutory category of a process.
Step 2A, Prong 1 (Do the claims recite an abstract idea?) – 2019 PEG pg. 54 (See MPEP 2106.04(a)-(c))
For independent claims 1, 10, and 19, the claims recite an abstract idea of: determining a debt solution. The steps of independent claim 1 recite the abstract idea (in bold below) of: 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 of a first interactive data element comprising a first link to a first webpage, and a second interactive data element comprising a second link to a second webpage. Independent claims 10 and 19 recite similar steps that recite the abstract idea. Independent claims 1, 10, and 19, as drafted, are a process that, under the broadest reasonable interpretation, covers Certain Methods of Organizing Human Activity, since they recite: fundamental economic principles or practices including mitigating risk; and commercial or legal interactions including marketing, or sales activities, or behaviors. If the claim limitations, under the broadest reasonable interpretation, covers methods of organizing human activity but for the recitation of additional elements including generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Other than reciting the abstract idea, the independent claims recite additional elements including generic computer components such as “communications hardware, remote servers, a network interface, surrogate modeling circuitry, a plurality of surrogate models, an interest rate model, an approval likelihood model, optimizer modeling circuitry, an interactive user interface, a first interactive data element comprising a first link to a first webpage, a second interactive data element comprising a second link to a second webpage, an apparatus, and a computer program product comprising at least one non-transitory computer-readable storage medium storing software instructions executed by an apparatus”, and nothing in the claims precludes the steps from being performed as a method of organizing human activity. Accordingly, the independent claims recite an abstract idea.
Dependent claims 2-9, 11-18, and 20 recite similar limitations as claims 1, 10, and 19; and when analyzed as a whole are held to be patent ineligible under 35 U.S.C 101 because the additional recited limitations only refine the abstract idea further. Other than reciting the abstract idea, the dependent claims recite similar additional elements including generic computer components as the independent claims, such as “the optimizer modeling circuitry, the plurality of surrogate models, an approval likelihood surrogate model set, an interest rate surrogate model set, and a credit limit surrogate model set, a plurality of models, a logistic regression model, a shallow decision tree, a binary classifier, the apparatus, and the computer program product”. If a claim limitation, under its broadest reasonable interpretation, covers commercial or legal interactions, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.
Step 2A, Prong 2 (Does the claim recite additional elements that integrate the judicial exception into a practical application?) – 2019 PEG pg. 54 (See MPEP 2106.04(d)-(c))
This judicial exception is not integrated into a practical application. In particular, independent claims 1, 10, and 19 only recite the additional elements of “communications hardware, remote servers, a network interface, surrogate modeling circuitry, a plurality of surrogate models, an interest rate model, an approval likelihood model, optimizer modeling circuitry, an interactive user interface, a first interactive data element comprising a first link to a first webpage, a second interactive data element comprising a second link to a second webpage, an apparatus, and a computer program product comprising at least one non-transitory computer-readable storage medium storing software instructions executed by an apparatus”. A plain reading of the Figures and associated descriptions in the specification reveals that generic processors may be used to execute the claimed steps. The additional elements are recited at a high level of generality (i.e., as a generic processor performing generic computer functions) such that it amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)) and limits the judicial exception to a particular environment (See MPEP 2106.05(h)). Mere instructions to apply an exception using a generic computer component and limiting the judicial exception to a particular environment doesn’t integrate the abstract idea into a practical application in Step 2A. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Hence, independent claims 1, 10, and 19 are directed to an abstract idea.
Dependent claims 2-9, 11-18, and 20, recite similar additional elements as the independent claims including generic computer components, such as “the optimizer modeling circuitry, the plurality of surrogate models, an approval likelihood surrogate model set, an interest rate surrogate model set, and a credit limit surrogate model set, a plurality of models, a logistic regression model, a shallow decision tree, a binary classifier, the apparatus, and the computer program product”. The judicial exception is not integrated into a practical application because the additional elements in the dependent claims are also recited at a high-level of generality such that it amounts to more no more than mere instructions to apply the exception using generic computer components. Therefore, the additional elements do not integrate the abstract idea into a practical application because they also do not impose any meaningful limits on practicing the abstract idea. Also, the claims do not affect an improvement to another technology or technical field; the claims do not amount to an improvement of the functioning of a computer system itself; the claims do not effect a transformation or reduction of a particular article to a different state or thing; and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?) – 2019 PEG pg. 56 (See MPEP 2106.05)
Independent claims 1, 10, and 19 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the recited additional elements amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)) and limits the judicial exception to the particular environment of computers (See MPEP 2106.05(h)). The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the function of the elements when each is taken alone. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept in Step 2B.
In addition, the dependent claims 2-9, 11-18, and 20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the dependent claims to perform the claimed limitations, amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)). Similar to the independent claims, mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Also, for the same reasoning as the independent claims, the additional elements of the limitations of the dependent claims, when considered individually and as an ordered combination, together do not offer significantly more than the sum of the functions of the elements when each is taken alone and the dependent claims as a whole, do not amount to significantly more than the abstract idea itself. For these reasons, the dependent claims also are not patent eligible.
Subject Matter Overcoming 35 USC §102/§103
Claims 1-20 would be allowable if rewritten to overcome the rejections under 35 U.S.C. 101 set forth in this Office Action.
The following is an examiner’s statement of reasons for subject matter of independent clams 1, 10, and 19 overcoming the prior art rejections under 35 USC §102/§103.
The closest prior art of record is US 11,544,780 to Ben David et al. (hereinafter referred to as Ben David), US 2020/0074539 to Palaghita et al. (hereinafter referred to as Palaghita), US 20200082444 to Benkreira et al. (hereinafter Benkreira), US 11,145,005 to Brock et al. (hereinafter referred to as Brock), and US 2016/0232546 to Ranft et al. (hereinafter referred to as Ranft). Allowable subject matter is indicated because none of the prior art of record, alone or in combination, appears to teach or fairly suggest or render obvious the combination set forth in independent claims 1, 10, and 19. For independent claim 1, the prior art of Ben David, Palaghita, Benkreira, Brock, and Ranft specifically do not disclose: “generating by surrogate modeling circuitry, a user dataset by inputting 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 respective product of the 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; determining a scaled cost savings value for the recommended product portfolio by multiplying the cost savings value by the approval likelihood; causing presentation, by the communications hardware, of the debt service strategy solution via an interactive user interface by: simultaneously causing display of a first interactive data element comprising a first link to a first webpage, and a second interactive data element comprising a second link to a second webpage”. Independent claims 10 and 19 recite similar limitations as independent claim 1. Dependent claims 3-6, 8, 9, 12-15, 17, and 18 are allowable over the prior art by virtue of their dependency on an allowed claim.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cella (US 2020/0387965) teaches a system and method of automated debt management with machine learning.
Burrow et al. (US 8,533,092) teaches a financial evaluation process to evaluation an individual’s financial health.
VanLeeuwen (US 2009/0030819) teaches a method for analyzing a user’s finances and providing a plan for debt reduction.
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/PAUL S SCHWARZENBERG/Primary Examiner, Art Unit 3695 2/12/2025