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 .
DETAILED ACTION
This Office Action is in response to Applicant’s communication filed on January 24, 2025 for the patent application 18/998,455. Claims 1 – 20 are pending in the application.
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.
Claim(s) 1 – 20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 - 20 are either directed to a method or system or computer readable medium, which are statutory categories of invention. (Step 1: YES).
The Examiner has identified method claim 1 as the claim that represents the claimed invention for analysis and is similar to method claim 7 and system claim 14. Claim 1 recites the limitations of:
( A ) ( a. ) electronically receiving a query for a financial transaction between a borrower and a lender, wherein the financial transaction is a loan application;
( B ) ( b. ) aggregating a plurality of financial records, wherein the financial records are connected with the borrower, and the financial records include debits and credits;
( C ) ( c. ) classifying, by utilizing instructions from a memory that are executed by a processor, the debits into discretionary and non-discretionary expenses; and
( D ) ( d. ) scoring, by utilizing the instructions from the memory that are executed by the processor, the cash flow of the borrower based on the debits and credits, wherein the debits associated with discretionary expenses are given a discount value.
These limitations without the bolded limitations above, cover performance of the limitations as certain methods of organizing human activity under their broadest reasonable interpretation.
More specifically, these limitations cover performance of the limitations as a fundamental economic practice.
In summary, if claim 1 limitations, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic practice, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Claims 7 and 14 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract).
The use of the processor or any of the bolded limitations in claim 1 are just applying generic computer components to the recited abstract limitations. Similar arguments apply to claims 7 and 14.
Therefore, the above mentioned judicial exception is not integrated into a practical application by merely applying generic computer components (bolded elements).
Furthermore, the “receiving” and “aggregating” steps are recited at a high level of generality and amounts to mere data gathering/transmitting, which are forms of insignificant extra-solution activity (See MPEP 2106.05(g): CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011); and OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)).
In addition, supported by specification, the computer hardware are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component., see MPEP 2106.05(f), where applying a computer or using a computer is not indicative of a practical application).
Claim 1, limitation ( C ) and ( D ) above in Applicant’s specification para [0051], which discloses “In certain embodiments, the first user device 102 may include a memory 103 that includes instructions, and a processor 104 that executes the instructions from the memory 103 to perform the various operations that are performed by the first user device 102. In certain embodiments, the processor 104 may be hardware, software, or a combination thereof. The first user device 102 may also include an interface 105 (e.g., screen, monitor, graphical user interface, etc.) that may enable the first user 101 to interact with various applications executing on the first user device 102 and to interact with the system 100. In certain embodiments, the first user device 102 may be and/or may include a computer, any type of sensor, a laptop, a set-top-box, a tablet device, a phablet, a server, a mobile device, a smartphone, a smart watch, a voice-controlled-personal assistant, a physical monitoring device (e.g., camera, etc.), an internet of things device (IoT), appliances, an autonomous vehicle, and/or any other type of computing device. Illustratively, the first user device 102 is shown as a computer in FIG. 1. In certain embodiments, the first user device 102 may be utilized by the first user 101 to control, access, and/or provide some or all of the operative functionality of the system 100.“.
Also, claim 1, limitation ( C ) and ( D ) above in Applicant’s specification para [0060], which discloses “Notably, the functionality of the system 100 may be supported and executed by using any combination of the servers 140, 145, 150, and 160. The servers 140, 145, and 150 may reside in communications network 135, however, in certain embodiments, the servers 140, 145, 150 may reside outside communications network 135. The servers 140, 145, and 150 may provide and serve as a server service that performs the various operations and functions provided by the system 100. In certain embodiments, the server 140 may include a memory 141 that includes instructions, and a processor 142 that executes the instructions from the memory 141 to perform various operations that are per-formed by the server 140. The processor 142 may be hardware, software, or a combination thereof. Similarly, the server 145 may include a memory 146 that includes instructions, and a processor 147 that executes the instructions from the memory 146 to perform the various operations that are performed by the server 145. Furthermore, the server 150 may include a memory 151 that includes instructions, and a processor 152 that executes the instructions from the memory 151 to perform the various operations that are performed by the server 150. In certain embodiments, the servers 140, 145, 150, and 160 may be network servers, routers, gateways, switches, media distribution hubs, signal transfer points, service control points, service switching points, firewalls, routers, edge devices, nodes, computers, mobile devices, or any other suitable computing device, or any combination thereof. In certain embodiments, the servers 140, 145, 150 may be communicatively linked to the communications network 135, any network, any device in the system 100, or any combination thereof.“. Similar arguments apply to claims 7 and 14.
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Therefore, Claims 1, 7 and 14 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application).
The claims 1, 7 and 14 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements (bolded elements above) amount to no more than mere instructions to apply the abstract idea using generic computer components. In conclusion, merely "applying" the exception using generic computer components cannot provide an inventive concept. Therefore, the claims 1, 7, and 14 are not patent eligible under 35 USC 101. (Step 2B: NO. The claims do not provide significantly more).
Dependent Claims
Dependent claims 2 – 6, 8 - 13 and 15 - 20 are also rejected under 35 U.S.C. 101. Dependent claims 2 – 6, 8 - 13 and 15 - 20 are further define the abstract idea or further define the extra-solution activities that are present in independent claim 1 thus abstract idea correspond to certain methods of organizing human activity as presented above. Claims 2 – 6, 8 - 13 and 15 - 20 clearly further define the abstract idea as stated above and further define extra-solution activities such as presenting data and transmitting/receiving data.
Furthermore, dependent claims 2 – 6, 8 - 13 and 15 - 20 do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination.
Regarding claim 2, this claim merely recite additional steps that amount to no more than insignificant extra-solution activity. Specifically, claim 2 states “wherein non-discretionary expenses comprise rent payment history, mortgage payment history, utility payment history, or a combination thereof.”. These steps amount to no more than mere data gathering/analysis, which is a form of insignificant extra- solution activity (See M PEP 2016.05(g): CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011); and GIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)). Such limitations do not integrate the abstract idea into a practical application, or amount to significantly than the abstract idea, because the courts have found the concept of data gathering to be well-understood, routine, and conventional activity (See MPEP 2106.05(d): GIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, (Fed. Cir. 2014)).
Regarding claim 3, this claim merely recite, "wherein non-discretionary expenses comprise mortgage loan obligations, car loan obligations, school loan obligations, or a combination thereof.“. These limitation merely recites storing data in a server which amounts to no more than gathering/storing data which is a form of insignificant extra-solution activity (See MPEP 2106.0S(g)(3)(iii): GIP Technologies, 788 F.3d at 1363). This does not integrate the abstract idea into a practical application because it has been determined, by the courts, that the concept of storing data is well-understood, routine, and conventional activity (See MPEP 2106.0S(d)(II): Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015)).
Regarding claim 4, this claim merely provide further detail regarding further, recited in claim 1. Merely stating, “wherein the non-discretionary expenses are provided with an undiscounted value or weight.”. This does not integrate the abstract idea into a practical application because it does not impose any meaningful limitation on practicing the abstract idea.
Regarding claim 5, this claim merely recite, "wherein the cash flow is based on non-discretionary expenses.“. These limitation merely recites storing data in a server which amounts to no more than gathering/storing data which is a form of insignificant extra-solution activity (See MPEP 2106.0S(g)(3)(iii): GIP Technologies, 788 F.3d at 1363). This does not integrate the abstract idea into a practical application because it has been determined, by the courts, that the concept of storing data is well-understood, routine, and conventional activity (See MPEP 2106.0S(d)(II): Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015)).
Regarding claims 6 and 13, these claims merely provide further detail regarding the processing the new claim, recited in claims 1 and 7. Merely stating “wherein the aggregation of the plurality of financial transaction records comprises computer-executable instructions causing the processor to perform operations through one or more application programming interfaces (APIs) provided by a financial institution computing system.". These limitation merely recites storing data in a server which amounts to no more than gathering/storing data which is a form of insignificant extra-solution activity (See MPEP 2106.0S(g)(3)(iii): GIP Technologies, 788 F.3d at 1363). This does not integrate the abstract idea into a practical application because it has been determined, by the courts, that the concept of storing data is well-understood, routine, and conventional activity (See MPEP 2106.0S(d)(II): Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015)). Similar arguments can be made for claim 13.
Regarding claim 8, This claim merely add further description to the process of “further comprising classifying the debits and credits according to at least one debit type and at least one credit type respectively,”, which amounts to no more than gathering/storing data which is a form of insignificant extra-solution activity (See MPEP 2106.0S(g)(3)(iii): GIP Technologies, 788 F.3d at 1363). This does not integrate the abstract idea into a practical application because it has been determined, by the courts, that the concept of storing data is well-understood, routine, and conventional activity (See MPEP 2106.0S(d)(II): Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015)).
Regarding claims 9 and 15, these claims merely add further description to the process of “calculating a total amount of credits for each time period over a timeframe; and, calculating a total amount of debits for each time period over the timeframe based on the non-discretionary expenses and the adjusted discretionary expenses.”. This does not integrate the abstract idea into a practical application because it has been determined, by the courts, that the concept of storing data is well-understood, routine, and conventional activity (See MPEP 2106.0S(d)(II): Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015)). Similar arguments can be made for claim 15.
Regarding claims 10 and 16, these claims merely recite, "rejecting the total amount of credits for each time period for which information associated with the portion of the total amount of credits is insufficient, is atypical, or a combination thereof, thereby resulting in remaining total amount of credits for each remaining time period; and, rejecting the total amount of debits for each time period for which information associated with the portion of the total amount of debits is insufficient, is atypical, or a combination thereof, thereby resulting in remaining total amount of debits for each remaining time period.“. These limitation merely recites storing data in a server which amounts to no more than gathering/storing data which is a form of insignificant extra-solution activity (See MPEP 2106.0S(g)(3)(iii): GIP Technologies, 788 F.3d at 1363). This does not integrate the abstract idea into a practical application because it has been determined, by the courts, that the concept of storing data is well-understood, routine, and conventional activity (See MPEP 2106.0S(d)(II): Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015)). Similar arguments can be made for claim 16.
Regarding claims 11 and 17, these claims merely provide further detail regarding the processing recited in claims 1 and 14. Merely stating “calculating a mean total income for reach remaining time period based on the remaining total amount of credits for each remaining time period; and b. calculating a mean adjusted residual income for each remaining time period, wherein the mean adjust residual income is calculated based on subtracting the remaining total amount of debits for each remaining time period from the remaining total amount of credits for each remaining time period to result in a residual income for each remaining time period and the averaging the residual income for each remaining time period.". This does not integrate the abstract idea into a practical application because it does not impose any meaningful limitation on practicing the abstract idea. Similar arguments can be made for claim 17.
Regarding claims 12 and 18, These claims merely add further description to the process of “calculating a mean adjusted residual income to expense ratio, wherein the mean adjusted residual income to expense ratio is calculated based on dividing the remaining total amount of credits for each remaining time period by the remaining total amount of debits for each remaining time period to result in a residual income to expense ratio for each remaining time period and averaging the residual income to expense ratio for each remaining time period; and b. calculating a threshold score associated with the borrower for each remaining time period.”. This amount to no more than mere data gathering/outputting as described in reference to claims 1, 7 and 14 (see analysis above). Merely describing the comparing the new claim information does not integrate the abstract idea into a practical application, or amount to significantly more than the judicial exception, because it does not impose any meaningful limitations on practicing the abstract idea. Similar arguments can be made for claim 18.
Regarding claims 13 and 19, these claims merely add further description to the process of “computing the ability-to-pay score based on the mean total income, the mean adjusted residual income, the mean adjusted income to expense ratio, the threshold score and an offset.”, which amounts to no more than gathering/storing data which is a form of insignificant extra-solution activity (See MPEP 2106.0S(g)(3)(iii): GIP Technologies, 788 F.3d at 1363). This does not integrate the abstract idea into a practical application because it has been determined, by the courts, that the concept of storing data is well-understood, routine, and conventional activity (See MPEP 2106.0S(d)(II): Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015)). Similar arguments can be made for claim 19.
Regarding claim 20, this claim merely recite, "wherein the processor is further configured to determine whether the borrower is suitable for the transaction based on the ability-to-pay score.“. These limitation merely recites storing data in a server which amounts to no more than gathering/storing data which is a form of insignificant extra-solution activity (See MPEP 2106.0S(g)(3)(iii): GIP Technologies, 788 F.3d at 1363). This does not integrate the abstract idea into a practical application because it has been determined, by the courts, that the concept of storing data is well-understood, routine, and conventional activity (See MPEP 2106.0S(d)(II): Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015)).
As a result, such limitations do not overcome the requirements as described above. Therefore, claims 2 – 6, 8 - 13 and 15 - 20 are directed to an abstract idea. Thus, claims 1 - 20 are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of pre-AIA 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(b) the invention was patented or described in a printed publication in this or a foreign country or in public use or on sale in this country, more than one year prior to the date of application for patent in the United States.
Claims 1 – 12, 14 – 18 and 20 are rejected under 35 U.S.C. 102(b) as being anticipated by Jason Hubard et al. (Pub. # US 2022/0122171 A1 – herein referred to as Hubard).
Re: Claim 1, Hubard discloses a method for evaluating creditworthiness, the method comprising:
( a. ) electronically receiving a query for a financial transaction between a borrower and a lender, wherein the financial transaction is a loan application (Hubard, [0040], [0045], [0052] - Referring now to FIG. 4, the overall process of generating the happy money score 399 based on a borrower's transactions is now described. One or more credit bureau reports 402 are received and the FICO score is removed to avoid undue influence. The borrower's transactions data 404 is received. Additional reported data 406 is received, including that provided in a loan application by the borrower. Additional reported data 406 may be psychometric data, obtained through a psychometric query process,);
( b. ) aggregating a plurality of financial records, wherein the financial records are connected with the borrower, and the financial records include debits and credits (Hubard, Fig. 2, [0042] - The loan origination engine 200 receives an input application from the borrower/debtor 102 indicating the debtor's monthly income and expenses as well as preexisting liabilities (debts) and assets (bank/savings accounts). The loan origination engine 200 is in communication with pre-existing lenders Ll - Lm 104 to place loans with the debtors 102.);
( c. ) classifying, by utilizing instructions from a memory that are executed by a processor, the debits into discretionary and non-discretionary expenses (Hubard, [0054] - At block 412, an artificial intelligence engine uses artificial intelligence and/or machine learning model executed by a processor to perform an analysis of data features over the parsed transactions data (e.g., liabilities, income, cash flow, savings and investments), behavioral data, and psychological data, if available. One or more of the data features can be predetermined data features that show adverse credit risks, such as checking account volatility, credit card paydown behavior, number of overdrafts of bank accounts, discretionary spending amounts, and obligatory spending amounts, for example. One or more of the data features can be predetermined data features that show positive credit risks in contrast with adverse credit risk, such as savings account balance, saving/investing behavior, and checking account balance, for example.); and
( d. ) scoring, by utilizing the instructions from the memory that are executed by the processor, the cash flow of the borrower based on the debits and credits, wherein the debits associated with discretionary expenses are given a discount value (Hubard, Fig. 5, [0073], [0074], [0115] - At block 510, disregarding the descriptions of the transactions as to where the money is coming and going, the overall periodic cash flow (e.g., weekly, biweekly, monthly, bi-monthly, quarterly, semiannually, annually) for each available period can be computed by the server system. The algorithm broadly looks at how much money is coming in and going out and the various patterns of cash flow. These computed overall cash flows for each predetermined period are saved as overall cashflow features for the risk model.).
Re: Claim 2, Hubard discloses the method of claim 1,
wherein non-discretionary expenses comprise rent payment history, mortgage payment history, utility payment history, or a combination thereof (Hubard, [0070] - At block 504, the transactions data is tagged using a tagging algorithm to distinguish the type of transaction. The transaction descriptions are analyzed, and the transaction is assigned to a predetermined income or expense category, such as paycheck, fast food purchase/expense, or rent/housing expense. With the tagged and categorized income and expense transactions, a monthly/annual income can be estimated, and a monthly/annual spending can be estimated.).
Re: Claim 3, Hubard discloses the method of claim 1,
wherein non-discretionary expenses comprise mortgage loan obligations, car loan obligations, school loan obligations, or a combination thereof (Hubard, [0190] - A happy personality 2604 enables a borrower to have a clearer picture of how they can reduce their default probability (e.g., reduce happy money score or transaction score) and thereby be a better candidate for loans. A happy personality 2604 provides user interfaces that display a borrower's cash flow allocation 2601, credit cost reduction 2602, and/or other expense reduction 2603, among other things. Cash flow allocation 2601 may include, for example, advice for managing discretionary spending, building savings and investments, and/or paying down debt, among other things. Credit cost reduction 2602 may include, for example, advice for refinancing credit cards, refinancing other unsecured debt, refinancing mortgages, and/or refinancing student loans, among other things. Other expense reduction 2603 may include, for example, advice for reducing duplicate charges and forgotten subscriptions, reducing mobile phone bills, reducing Internet service bills, and/or reducing auto/home insurance, among other things.).
Re: Claim 4, Hubard discloses the method of claim 1,
wherein the non-discretionary expenses are provided with an undiscounted value or weight (Hubard, [0115] - The borrower's financial features are ranked in importance by assigning a weight to each feature. A positive/negative weight indicates that the corresponding feature informed of a higher/lower probability of defaulting (0 is no default probability and 1 is a certain default probability). Weights are assigned according to the following criterion: The weight of a feature is defined as the change in the prediction induced by knowing the value taken by that feature. The values of the weights so obtained are called SHAP (SHapley Additive exPlanations) values.).
Re: Claim 5, Hubard discloses the method of claim 1,
wherein the cash flow is based on non-discretionary expenses (Hubard, [0054] - At block 412, an artificial intelligence engine uses artificial intelligence and/or machine learning model executed by a processor to perform an analysis of data features over the parsed transactions data (e.g., liabilities, income, cash flow, savings and investments), behavioral data, and psychological data, if available. One or more of the data features can be predetermined data features that show adverse credit risks, such as checking account volatility, credit card paydown behavior, number of overdrafts of bank accounts, discretionary spending amounts, and obligatory spending amounts, for example. One or more of the data features can be predetermined data features that show positive credit risks in contrast with adverse credit risk, such as savings account balance, saving/investing behavior, and checking account balance, for example.).
Re: Claim 6, Hubard discloses the method of claim 1,
wherein the aggregation of the plurality of financial transaction records comprises computer-executable instructions causing the processor to perform operations through one or more application programming interfaces (APIs) provided by a financial institution computing system (Hubard, [0106], [0193] - A computer, as well as a computer server, includes one or more processors and a storage device storing instructions executable by the one or more processors. When implemented in software, the elements of the embodiments are essentially the code segments (instructions) of a program executed by a processor to perform the necessary tasks. The program or code segments (instructions) can be stored in a processor readable storage medium (storage device). The processor readable storage medium may include any medium that can store information. Examples of the processor readable storage medium include an electronic circuit, a semiconductor memory device, a read only memory (ROM), a flash memory, an erasable programmable read only memory (EPROM), a magnetic media, a magnetic disk, a floppy disk, a magnetic hard disk, an optical media, an optical disk, a compact disk (CD), a digital versatile disk (DVD), or a Blu-Ray disk (BD). One or more of the code segments (instructions) of the software can be downloaded into a computer using computer data signals through computer networks such as the Internet, Intranet, etc. and temporarily stored in a storage device.).
Re: Claim 7, Hubard discloses a method, comprising:
( a. ) electronically receiving a query for a transaction between a borrower and a lender (Hubard, [0040], [0045], [0052] - Referring now to FIG. 4, the overall process of generating the happy money score 399 based on a borrower's transactions is now described. One or more credit bureau reports 402 are received and the FICO score is removed to avoid undue influence. The borrower's transactions data 404 is received. Additional reported data 406 is received, including that provided in a loan application by the borrower. Additional reported data 406 may be psychometric data, obtained through a psychometric query process,);
( b. ) receiving a plurality of data associated with the borrower, wherein the data includes debits and credits (Hubard, Fig. 2, [0042] - The loan origination engine 200 receives an input application from the borrower/debtor 102 indicating the debtor's monthly income and expenses as well as preexisting liabilities (debts) and assets (bank/savings accounts). The loan origination engine 200 is in communication with pre-existing lenders Ll - Lm 104 to place loans with the debtors 102.);
( c. ) classifying the debits as discretionary or non-discretionary expenses (Hubard, [0054], [0079] - At block 412, an artificial intelligence engine uses artificial intelligence and/or machine learning model executed by a processor to perform an analysis of data features over the parsed transactions data (e.g., liabilities, income, cash flow, savings and investments), behavioral data, and psychological data, if available. One or more of the data features can be predetermined data features that show adverse credit risks, such as checking account volatility, credit card paydown behavior, number of overdrafts of bank accounts, discretionary spending amounts, and obligatory spending amounts, for example. One or more of the data features can be predetermined data features that show positive credit risks in contrast with adverse credit risk, such as savings account balance, saving/investing behavior, and checking account balance, for example.);
( d. ) adjusting the discretionary expenses by an amount based on at least one type associated with the discretionary expenses to generate adjusted discretionary expenses (Hubard, [0054], [0079], [0115] - At block 412, an artificial intelligence engine uses artificial intelligence and/or machine learning model executed by a processor to perform an analysis of data features over the parsed transactions data (e.g., liabilities, income, cash flow, savings and investments), behavioral data, and psychological data, if available. One or more of the data features can be predetermined data features that show adverse credit risks, such as checking account volatility, credit card paydown behavior, number of overdrafts of bank accounts, discretionary spending amounts, and obligatory spending amounts, for example. One or more of the data features can be predetermined data features that show positive credit risks in contrast with adverse credit risk, such as savings account balance, saving/investing behavior, and checking account balance, for example.);
( e. ) computing, based on the adjusted discretionary expenses and the credits, an ability-to-pay score for the borrower (Hubard, [0067] - The GBM model leverages the same features/attributes for each borrower for the purpose of scoring a person's creditworthiness. Some of the key features/attributes used by the GBM model to determine the Happy money score 399.);
( f. ) synthesizing a report comprising the ability-to-pay score for the borrower (Hubard, [0067] - The GBM model leverages the same features/attributes for each borrower for the purpose of scoring a person's creditworthiness. Some of the key features/attributes used by the GBM model to determine the Happy money score 399.)); and
( g. ) transmitting the report to the lender in response to the query (Hubard, [0119] - At step 716, the happy money score and the one or more model factor codes are returned to the system and can be presented to the borrower if just a happy money score was requested. Otherwise, the process can further go onto loan origination in the case that the happy money score for the given borrower indicated a level of risk worth taking for the use of the loan. If there is an adverse action, the SHAP values mapped to the model factor codes can be used to provide advice to the applicant borrower to improve his happy money score in the future.).
Re: Claim 8, Hubard discloses the method of claim 7,
further comprising classifying the debits and credits according to at least one debit type and at least one credit type respectively (Hubard, [0070] - At block 504, the transactions data is tagged using a tagging algorithm to distinguish the type of transaction. The transaction descriptions are analyzed, and the transaction is assigned to a predetermined income or expense category, such as paycheck, fast food purchase/expense, or rent/housing expense. With the tagged and categorized income and expense transactions, a monthly/annual income can be estimated, and a monthly/annual spending can be estimated.).
Re: Claim 9, Hubard discloses the method of claim 7, further comprising:
( a. ) calculating a total amount of credits for each time period over a timeframe (Hubard, [0071], [0121] - At block 506, an estimate of total monthly (periodic) income can be made by summing the credit / income transactions for the prior months, looking for the weekly, biweekly, or monthly paychecks and other income, such as interest on investments. Other income computations for other predetermined periods (week, biweekly, bi-monthly, annual, quarterly, semiannual) can be made. The tagged income transactions can be grouped together with the computed income totals as income features along with the income total for the risk model.); and
( b. ) calculating a total amount of debits for each time period over the timeframe based on the non-discretionary expenses and the adjusted discretionary expenses (Hubard, [0072], [0121] - At block 508, concurrently in parallel with income, an estimate of total monthly (periodic) expenses can be made by summing together the prior monthly expense/ payment transactions. Other computations of expenses can be made for other predetermined periods (week, biweekly, bi-monthly, annual, quarterly, semiannual). The tagged expense transactions can be grouped together with the computed expense totals as expense/spending features for the risk model.).
Re: Claim 10, Hubard discloses the method of claim 9, further comprising:
( a. ) rejecting the total amount of credits for each time period for which information associated with the portion of the total amount of credits is insufficient, is atypical, or a combination thereof, thereby resulting in remaining total amount of credits for each remaining time period (Hubard, [0143] - However, with real-world data, the system may come across data that does not always fit the rule. For example, in the FIGS. 12A-12B, Group 4 may have an unexpected result and/or an anomaly (e.g., an abnormal value that is outside a statistically normal range). Specifically, Group 4 has a lower savings than Group 5 while having a lower charge-off rate (unexpected result and/or anomaly). Group 4 thereby defies the notion that a lower savings tends to be associated a higher charge-off rate. Accordingly, the system may discover that real world data does not always strictly follow a predetermined rule. Like in FIGS. 12A-12B, the system has discovered Group 4 with a lower savings while also has a lower charge-off rate, even though such a finding may not be typical. An anomaly in the data is a reason for the system to run a transaction model with many features (e.g., 2 or more predictors), so the system can weed out (e.g., average out) the anomaly from a transaction score. If there are significant unexpected results in the real-world data (e.g., more unexpected results than a predetermined threshold, more than one group has unexpected data, an anomaly far outside the statistical norm, or any other threshold, etc.), then the system can use the unexpected results to refine, update, and/or calibrate the transaction model. A savings account balance is one of many features (predictors) that the system can use in running a transaction model.); and
( b. ) rejecting the total amount of debits for each time period for which information associated with the portion of the total amount of debits is insufficient, is atypical, or a combination thereof, thereby resulting in remaining total amount of debits for each remaining time period (Hubard, [0143] - However, with real-world data, the system may come across data that does not always fit the rule. For example, in the FIGS. 12A-12B, Group 4 may have an unexpected result and/or an anomaly (e.g., an abnormal value that is outside a statistically normal range). Specifically, Group 4 has a lower savings than Group 5 while having a lower charge-off rate (unexpected result and/or anomaly). Group 4 thereby defies the notion that a lower savings tends to be associated a higher charge-off rate. Accordingly, the system may discover that real world data does not always strictly follow a predetermined rule. Like in FIGS. 12A-12B, the system has discovered Group 4 with a lower savings while also has a lower charge-off rate, even though such a finding may not be typical. An anomaly in the data is a reason for the system to run a transaction model with many features (e.g., 2 or more predictors), so the system can weed out (e.g., average out) the anomaly from a transaction score. If there are significant unexpected results in the real-world data (e.g., more unexpected results than a predetermined threshold, more than one group has unexpected data, an anomaly far outside the statistical norm, or any other threshold, etc.), then the system can use the unexpected results to refine, update, and/or calibrate the transaction model. A savings account balance is one of many features (predictors) that the system can use in running a transaction model.).
Re: Claim 11, Hubard discloses the method of claim 10, further comprising:
( a. ) calculating a mean total income for reach remaining time period based on the remaining total amount of credits for each remaining time period (Hubard, [0075] - FIG. 6, consisting of FIGS. 6A-6B, illustrates a flow chart of the process of income verification of a borrower performed by the income verifier 312. The income verifier 312 receives the raw transactions data and the input loan application data (applicant data) in order to calculate a net income for one or more periods and generate an income stability score.); and
( b. ) calculating a mean adjusted residual income for each remaining time period, wherein the mean adjust residual income is calculated based on subtracting the remaining total amount of debits for each remaining time period from the remaining total amount of credits for each remaining time period to result in a residual income for each remaining time period and the averaging the residual income for each remaining time period (Hubard, [0025] - FIGS. 19A and 19B are user interfaces of charts illustrating a ratio of the mean or average credit card balance over (divided by) the average or mean monthly discretionary spending (credit card debt to discretionary spending or income) associated with a borrower.).
Re: Claim 12, Hubard discloses the method of claim 11, further comprising:
( a. ) calculating a mean adjusted residual income to expense ratio, wherein the mean adjusted residual income to expense ratio is calculated based on dividing the remaining total amount of credits for each remaining time period by the remaining total amount of debits for each remaining time period to result in a residual income to expense ratio for each remaining time period and averaging the residual income to expense ratio for each remaining time period (Hubard, [0020] - FIGS. 14A-14B are example user interfaces of charts illustrating the spending-to-income ratios (spending divided by income).); (Hubard, [0025] - FIGS. 19A and 19B are user interfaces of charts illustrating a ratio of the mean or average credit card balance over (divided by) the average or mean monthly discretionary spending (credit card debt to discretionary spending or income) associated with a borrower.); and
( b. ) calculating a threshold score associated with the borrower for each remaining time period (Hubard, [0029] - FIG. 23 is a diagram illustrating a logistic regression algorithm that can be used to model the probability of default based on a debt balance to income (BTI) ratio.).
Re: Claim 14, Claim 14 is a system claim corresponding to method claim 7. Therefore, claim 14 is analyzed and rejected as previously discussed with respect to claim 7.
Re: Claim 15, Claim 15 is a system claim corresponding to method claim 9. Therefore, claim 15 is analyzed and rejected as previously discussed with respect to claim 9.
Re: Claim 16, Claim 16 is a system claim corresponding to method claim 10. Therefore, claim 16 is analyzed and rejected as previously discussed with respect to claim 10.
Re: Claim 17, Claim 17 is a system claim corresponding to method claim 11. Therefore, claim 17 is analyzed and rejected as previously discussed with respect to claim 11.
Re: Claim 18, Claim 18 is a system claim corresponding to method claim 12. Therefore, claim 18 is analyzed and rejected as previously discussed with respect to claim 12.
Re: Claim 19, Claim 19 is a system claim corresponding to method claim 13. Therefore, claim 19 is analyzed and rejected as previously discussed with respect to claim 13.
Re: Claim 20, Hubard discloses the system of claim 14,
wherein the processor is further configured to determine whether the borrower is suitable for the transaction based on the ability-to-pay score (Hubard, [0067] - The GBM model leverages the same features / attributes for each borrower for the purpose of scoring a person's creditworthiness. Some of the key features/attributes used by the GBM model to determine the Happy money score 399.); (Hubard, [0114] – A base model is created based on a subset of the original dataset which is used to make predictions on the whole dataset. Errors are calculated and observations which are incorrectly predicted, are given higher weights. Another model is created which tries to correct the errors from the previous model. Similarly, multiple models are created, each correcting the errors of the previous model. The final model (strong learner) is the weighted mean of all the models (weak learners). The model shown in FIG. 27 can be considered a weak learner. A plurality of these weak models can be generated based on a training set and assembled together as a strong model, an ensemble model. Each financial, credit, income, and cash feature can have a plurality of weak learning models that are weighted and assembled together to form an overall machine learning model for the happy money score. In this manner, the happy money score can be generated for a borrower based on the borrower debt, cash spending, and income cash inputs on a loan application. The process then goes on to step 712.).
Claim Rejections – 35 USC §103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 13 and 19 are rejected under 35 U.S.C. 103 as being obvious over Jason Hubard et al. (Pub. # US 2022/0122171 A1 – herein referred to as Hubard).
Re: Claim 13, Hubard discloses the method of claim 12.
further comprising computing the ability-to-pay score based on the mean total income, the mean adjusted residual income, the mean adjusted income to expense ratio, the threshold score and an offset (Hubard, [0088] - At block 620, a gradient boosted tree model is used with the income stability features 618 to generate an income stability score 699. The gradient boosted tree model is a risk model, Given the features of a borrower's income, the gradient boosted tree model generates an income stability score indicating how credit worthy or risky the given borrower is based on income.); (Hubard, [0120] – FIG. 8 is a user interface of an example chart 800 illustrating financial transaction features of interest and their contribution strength (t-value) to a probability of default. An administrator/user can use chart 800 to build, inspect, update, and/or calibrate a transaction model. The system can run a transaction score model by using financial transaction features of interest to form a happy money score (aka, fused score or combined score). A feature of interest may be referred to as a predictor. Example features of interest include, without limitation, checking balance volatility (e.g., FIG. l0A), overdraft count (e.g., FIG. 11A), savings balance (e.g., FIG. 12A), number of income sources (e.g., FIG. 13A) spending/income ratio (e.g., FIG. 14A), and so on. A user interface is the location on a computer device where a user can interact with a computer, website, and/or software application.).
Therefore, in light of the teachings of Hubard, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the method of Hubard, motivation according to one KSR Exemplary Rationale where a known technique is used to improve similar methods and systems in the same way by derive a mathematical relationship o expense ratio, the threshold score
between on the mean total income, the mean adjusted residual income, the mean adjusted income and an offset, since where the general conditions of the claim are disclosed in the prior art, discovering the optimum equation involves
routine skill in the art and would be effective in improving loan origination to more credit worthy borrowers (Happy [0037]).
Conclusion
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/John H. Holly/Primary Examiner, Art Unit 3696
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