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 .
The following is a Final Office Action in response to communications received 11/14/2025. Claim(s) 3, 5, 10, 14 have been canceled. Claims 1, 8, 11-13 and 15 have been amended. New claim 23 has been added. Therefore, claims 1-2, 4, 6-9, 11-13 and 15-23 are pending and addressed below.
Priority
Application 17877423 filed 07/29/2022 and having 1 RCE-type filing therein 4. Claims Priority from Provisional Application 63227218 , filed 07/29/2021
Applicant Name/Assignee: Early Warning Services, LLC
Inventor(s): Loganathan, Ravi; Panjwani, Aniket; Gwinn, Rachel; Alcorn, Ronald Scott; Burke, Chris; Dean, Jesse; Aswal, Deepakshi
Response to Amendment/Amendments
Claim Rejections - 35 USC § 101
Applicant's arguments filed 11/14/2025 have been fully considered but they are not persuasive.
In the remarks applicant points to the USPTO 2019 PEG section III (A)(2) with respect to step 2A prong 2, reciting the amended limitations:
the cash flow data comprising a typical net cashflow of the debtor over a predetermined period of time; and
the typical net cashflow of the debtor is based at least in part on the recurring expenses;
generate a suggested credit limit based on the typical net cashflow of the debtor, the suggested credit limit comprising one or both of a total credit amount and a periodic payment that the debtor is able to afford;
providing the lending score and the suggested credit limit to the requesting financial institution.
Applicant argues that the present claim amended limitations describe typical net cashflow of debtor over time used to generate suggested credit limit including a total credit amount and periodic payment that debtor can afford. This provides lending institutions suggestions to enable lending institutions to make informed lending decisions. Such features establish a framework for providing a technical solution that addresses problems associated with conventional credit scoring techniques integrating the claims into a practical application. Applicant’s argument is not persuasive. Applicant is arguing problems related to the abstract idea of providing credit with risk of repayment. The criteria for patent eligibility under step 2A prong 2, is not to provide a solution to a problem rooted in the abstract idea but rather in technology. The cashflow process claimed is not a technical solution to a problem rooted in technology, rather the limitations are directed toward a business process to address a problem rooted in risk of repayment in credit provided by lenders. The rejection is maintained.
Claim Rejections - 35 USC § 103
Applicant's arguments filed 11/14/2025 have been fully considered but they are not persuasive.
In the remarks applicant argues the prior art references fails to teach “reconciling the account data from the one or more financial accounts and the data from the one or more third party data sources” of claim 1. The references do not disclose multiple forms for entered data, such as account data from the one or more financial accounts and the data from the one or more third party data sources, reconciled by the deduplication module. The examiner respectfully disagrees. The prior art combination Flowers in view of Dotter teaches data received from a plurality of data sources (see Fig. 1A-B ) and teaches deduplication module entering financial transaction based on metadata records where the module combines, consolidates and/or delete data for a duplicate transaction matching and teaches comparing attributes (names, dates, accounting categories or the like to ensure accounts are accurate and reconciled) -para 0188-0189. In the preceding aggregating limitation the prior art Dotter teaches that the third party data is from a plurality of distinct financial institutions (para 0049) and financial accounts. The examiner maintains the rejection.
In the remarks applicant argues the prior art references fails to teach “parsing account data to identify inflow transactions”, the examiner respectfully disagrees. The prior art combination Flowers in view of Dotter teaches in para 0049 the transaction module categorize and classify aggregated transaction data and map/and or vectors form category and/or classification the transaction data to an accounting category G/L code, in at least para 0062-0064 the prior art teaches parsing the financial transaction data to generate metadata records comprising transaction amount, date, spending category, entity identifier, classification as to whether recurring transaction, transaction type…. The prior art in para 0063 discloses transaction data may include payment amount, sale amount, purchase amount, deposit amount, paycheck amount, deposit amount, loan amount, accounts receivable [inflow data]; credit amount, debit amount, withdrawal amount, accounts payable [outflow data]. The rejection is maintained.
In the remarks applicant argues the prior art references fails to teach “a credit limit based on typical net cashflow of the debtor, the suggested credit limit comprising one or both of a total credit amount and a periodic payment that the debtor is able to afford”, the examiner respectfully disagrees. The prior art combination Flowers in view of Dotter teaches para 0054 wherein the prior art teaches interest payments, loan payments, insurance payments, para 0055-0056 wherein the prior art teaches repeating transaction at specified time periods, para 0063-0064, para 0104-0106 wherein the prior art teaches determining accounting categories for each transaction provides real-time view of entity’s financial profile allowing module to provide financial products such as a loan for financing a purchase and select a financial product based on predicted event by monitoring A/C and A/P accounts, balances, losses, to predict a shortfall or future breach of financial covenant or future excess of funds, para 0107 wherein the prior art teaches the module selects financial product in response to prediction of shortfall of funds the offer select amount of loan to cover predicted future shortfall and to be paid back in the contingent offer for up to a predetermined period of time selected accounts receivable payment. The rejection is maintained.
The prior art combination Flowers in view of Wellman teaches the typical net cashflow of the debtor is based at least in part on the recurring expenses --- in at least para 0040 wherein the prior art teaches providing input data for a learning model from financial accounting metadata, from a bank/multiple banks, general ledger, the data including loan applications cashflow statistics; para 0050 wherein the institution data may include accounts receivable, accounts payable, income statement, financial liability, net income during a time period, loans, para 0053 wherein the prior art teaches total amount of liabilities, an amount of cashflow of actual payments received, an amount of scheduled cashflow,
In the remarks applicant argues the prior art references fails to teach “determination of whether a particular transaction would exceed an existing balance/limit”, Applicant is arguing limitations not claimed.
In the remarks applicant argues the prior art references fails to teach “providing the lending score and the suggested credit limit to the requesting financial institution.” the examiner respectfully disagrees. The prior art combination Flowers in view of Dotter teaches an offer module para 0178 and para 0198 which determines and outputs offers which include risk analysis, credit score or other indicator of credit worthiness that can be transmitted to another entity and teaches para 0107 wherein the prior art teaches the module selects financial product in response to prediction of shortfall of funds the offer select amount of loan to cover predicted future shortfall and to be paid back in the contingent offer for up to a predetermined period of time selected accounts receivable payment, para 0113 wherein the prior art teaches display module displaying financial product offer, risk analysis, credit scores and other indicators of credit worthiness. The rejection is maintained.
In the remarks applicant argues that based on the deficit of the prior art references to teach the limitations argued above, the amended claim 1 and dependent claims 2, 4 and 6-7 and independent claims 8 and 15 with respective dependent claims 9, 11 and 16-20 are allowable over the prior art references. The examiner respectfully disagrees. See response above. The rejection is maintained.
In the remarks applicant argues that the prior art references of the independent claims in view of Stubbs applied to claims 12 and 13 do not cure the deficiencies of the rejection of the independent claim 8 and therefore are allowable over the prior art references. The examiner respectfully disagrees. See response above. The rejection is maintained.
In the remarks applicant argues that the prior art references of the independent claims in view of Bjornerud applied to claims 21-22 do not cure the deficiencies of the rejection of the independent claim 1 and therefore are allowable over the prior art references. The examiner respectfully disagrees. See response above. The rejection is maintained.
In the remarks applicant argues that the prior art references of the independent claims in view of Bjornerud and Tom applied to claims 21-22 do not cure the deficiencies of the rejection of the independent claim 1 and therefore are allowable over the prior art references. The examiner respectfully disagrees. See response above. The rejection is maintained.
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 1-2, 4, 6-13 and 15-23 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
In reference to Claims 1-2, 4, 6-7 and 21-23:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a method, as in independent Claim 1 and the dependent claims. Such methods fall under the statutory category of "process." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The claimed invention is directed to an abstract idea without significantly more. Method claim 1 recites a method steps 1) receive data (identifier) 2) identify one or more accounts 3) access account data 4) match the identifier of debtor with data from one or more third party sources 5) access data form one or more third party sources, 6) aggregate account data 7) reconcile …account data from one or more financial accounts and data from one or more third parties 8) parse …account data and data from third party sources 9) using a tag of each item of data to categorize item of data 10) generate balance sheet from inflow/outflow transaction 11) categorize expenses 12) generate cash flow data (13) generating suggested credit limit (14) train a model to generate scores 15) provide prior balance sheet/cashflow data associated with plurality of debtors 16) identify plurality of risk factors 17) provide cash flow data 18) generate lending score 19) provide score. The claimed limitations which under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer. That is other than reciting “risk management network” to perform the abstract idea and using an ML model to generate scores and identifying risk factors to perform the limitations, and using the tag to categorize data, nothing in the claimed elements precludes the limitations from practically being performed in the mind. For example the human mind is capable receiving identifiers, identifying financial accounts associated with financial institutions, accessing account data, matching identifiers, aggregating account data, using pen and paper to reconcile account data, generate a balance sheet, generating cash flow, generating suggested credit limit via an accounting practice. The mere nominal mention of a “risk management network” does not take the claim limitations out of mental process grouping. The claimed limitations as cited can be performed in the mind. The steps recite steps that can easily be performed in the human mind as mental processes because the steps of receiving data and accessing data mimic mental processes of observation. The recited steps identifying accounts, parsing data, categorizing expenses, identifying risk factors, matching identifiers, aggregating data, reconciling data and generating a balance sheet and generating lending score mimic mental processes of analysis and evaluation, where the data interpretation is perceptible only in the human mind. The generation of a balance sheet can be implemented via mental analysis and with the use of pen and paper. The limitations “providing balance sheet/cashflow data and providing lending score mimic communication of data. See In re TLI Commc'ns LLC Patent Litig., 823 F.3d 607, 611 (Fed. Cir. 2016); FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1093-94 (Fed. Cir. 2016)
The specification discloses when determining whether a debtor is eligible for a line of credit/loan, conventional credit reporting only takes into account past performance of debtors which may insufficiently represent debtor’s credit worthiness. Typical credit reporting fails to consider debtors current/recent cash flow/ or actual ability to make a payment on a loan/credit line which leads to unnecessary risk to lenders and that there is a need to improve resources for credit/lending decisions (spec ¶ 0002). The specification discloses using account data to generate balances sheets/cashflow data to provide in-depth understanding of debtors financial situation. Accordingly, the claim limitations, in light of the specification, when considered as a whole the claimed subject matter is directed toward receiving and analyzing financial information, generating a balance sheet and lending score to allow lenders to mitigate risk. Such concepts can be found in the abstract category of commercial activity and fundamental economic activity. These concepts are enumerated in Section I of the 2019 revised patent subject matter eligibility guidance published in the federal register (84 FR 50) on January 7, 2019) is directed toward abstract category of mental processes and methods of organizing human activity.
STEP 2A Prong 2: The identified judicial exception is not integrated into a practical application because the claims fail to provide indications of patent eligible subject matter that integrate the alleged abstract idea into a practical application. The additional elements recited in the claim beyond the abstract idea include “risk management network” and “machine learning model”.
The “risk management network” applied to perform the operation “receiving…an identifier”, which According to MPEP 2106.05(d) II (see also MPEP 2106.05(g)) the courts have recognized the following computer functions are claimed in a merely generic manner (e.g., at a high level of generality) where technology is merely applied to perform the abstract idea or as insignificant extra-solution activity.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014)
The claim limitations “receiving” is recited at a high level of generality without details of technical implementation and thus are insignificant extra solution activity.
The “risk management network” applied to perform the operation “identifying …accounts”, “matching …the identifier of the debtor…”, “aggregating …the account data…”, “reconciling …account data”, “parsing…the account data”, “generating…a balance sheet”, “generating …cash flow data…”, “generating a suggested credit limit…” which are processes directed toward organizing and analyzing financial data in order to determine a suggested credit limit which is a business practice. Technology is not the focus of the claimed operations of the “risk management network”.
The “machine learning model” applied to perform the operation “identifying…a plurality of risk factors…” and “generating…a lending score…” and “providing…the lending score and …suggested credit limit”. The machine learning model provides no technical details and merely amounts to no more than mere instructions to identify risk factors related to lending, generating a lending score and outputting the results.
The wherein clause does not further limit the match or access functions of the risk management system, instead merely limiting the data content retrieved/read from the third party sources and the data acted upon and limits the third party sources from financial institutions where the data from the third party sources comprise a tag indicating data type. The steps are is recited at a high-level of generality and is so high level as to be performed by any and all known means using generic technology. Taking the claim elements separately, the operation performed by the system at each step of the process is purely in terms of results desired and devoid of implementation of details. Technology is not integral to the process as the claimed subject matter is silent with respect to a technical process or environment Furthermore, the claimed functions do not provide an operation that could be considered as sufficient to provide a technological implementation or application of/or improvement to this concept (i.e. integrated into a practical application).
When the claims are taken as a whole, as an ordered combination, the combination of limitations 1-6 are directed toward receiving data and analyzing and accumulating data used to analyzing debtor data- directed toward risk mitigation. The combination of limitations 1-6 and 7-13 are directed toward performing accounting process reconciling, parsing data using data tags which identify data type to categorized data for generation of balance sheets, cash flow and suggested credit limit based on the data collected and analyzed of limitations 1-6. The combination of limitations 1-13 and 14-15 are directed toward applying a ML model trained to generate scores, identify risk factors based on accounting data in order to generate risk scores that is outputted to lenders- applying generic technology to analyze and generate risk scores and output the results. The combination of limitations 16-19 is directed toward identifying risk factors, provide cash flow data, generate lending score and providing the score of limitations 1-15. Accordingly, the combinations of parts is not directed toward any technical process or technological technique or technological solution to a problem rooted in technology, but instead risk analysis for risk mitigation. The recitation in the claim of the business process being performed by a “risk management network”
In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps not integrate the judicial exception into a practical application as the claim process fails to impose meaningful limits upon the abstract idea. The recitation of the additional element “risk management network” that performs the abstract idea is recited at a high level of generality and merely automates the financial data analysis, therefore, acting as a generic computer operating in its ordinary capacity and does not use the judicial exception in a manner that imposes meaningful limits on the exception. The additional limitations (i.e. machine learning model) is no more than mere instructions to apply the exception using a “risk management network” (i.e. computer). The recited “training machine learning” model lacks technical disclosure instead the limitation is recited at a high level of generality limited by the data acted upon used to generate lending scores for risk mitigation. Accordingly, the claimed subject matter fails to provide additional elements or combination or elements to apply or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The functions recited in the claims recite the concept of accessing and aggregating account data and generating a balance sheet and cash flow which is a process directed toward a business practice. The claim limitations when considered individually fail to provide any indications of patent eligible subject matter, according to MPEP guidance (see MPEP 2106.05 (a)-(c), (e )-(h).
(i) an improvement to the functioning of a computer;
(ii) an improvement to another technology or technical field;
(iii) an application of the abstract idea with, or by use of, a
particular machine;
(iv) a transformation or reduction of a particular article to a
different state or thing; or
(v) other meaningful limitations beyond generally linking the
use of the abstract idea to a particular technological environment.
The integration of elements do not improve upon technology or improve upon computer functionality or capability in how computers carry out one of their basic functions. The integration of elements do not provide a process that allows computers to perform functions that previously could not be performed. The integration of elements do not provide a process which applies a relationship to apply a new way of using technology. The instant application, therefore, still appears only to implement the abstract idea without any need of specific computer functionality in the related arts. The steps are still a combination made generate a balance sheet based on aggregated account data and does not provide any of the determined indications of patent eligibility set forth in the 2019 USPTO 101 guidance. The additional steps only add to those abstract ideas using generic functions, and the claims do not show improved ways of, for example, an particular technical function for performing the abstract idea that imposes meaningful limits upon the abstract idea. Moreover, Examiner was not able to identify any specific technological processes that goes beyond merely confining the abstract idea in a particular technological environment, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. The additional element recited in the claim beyond the abstract idea include a “risk management network” to perform the steps to “receiving data”, “identifying one or more accounts”, “accessing account data”, “aggregating account data”, “matching identifiers” , “parsing data”, “reconciling data”, “generating balance sheet from aggregated account data”, “generating cash flow data” and “generating lending score” which are some of the most basic functions applied for business process as the limitations fail to recite any details of technical implementation that go beyond the recitation of the function to perform a process for the implementing the abstract idea. Taking the claim elements separately, the function performed by the computer at each step of the process is purely conventional. When the claims are taken as a whole, as an ordered combination, the combination of steps does not add “significantly more” by virtue of considering the steps as a whole, as an ordered combination. All of these computer functions are generic, routine, conventional computer activities that are performed only for their conventional uses. See Elec. Power Grp. v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016). Also see In re Katz Interactive Call Processing Patent Litigation, 639 F.3d 1303, 1316 (Fed. Cir. 2011) Absent a possible narrower construction of the terms “receiving data”, “identifying one or more accounts”, “accessing account data”, “categorizing expenses”, “aggregating account data”, “parsing account data”, “matching identifiers”, “parsing data”, “reconciling”, “training a model based on probabilities”, “generating balance sheet from aggregated account data”, “providing balance sheet data”, “identifying risk factors”, “providing data” and “generating cash flow data”... are functions can be achieved by any general purpose computer without special programming". None of these activities are used in some unconventional manner nor do any produce some unexpected result. Applicants do not contend they invented any of these activities. In short, each step does no more than require a generic computer to perform generic computer functions.
As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. Invest Pic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018). Considered as an ordered combination, the computer components of Applicant’s claimed functions add nothing that is not already present when the steps are considered separately. The sequence of data reception-analysis modification-transmission is equally generic and conventional. See Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715 (Fed. Cir. 2014) (sequence of receiving, selecting, offering for exchange, display, allowing access, and receiving payment recited as an abstraction), Inventor Holdings, LLC v. Bed Bath & Beyond, Inc., 876 F.3d 1372, 1378 (Fed. Cir. 2017) (sequence of data retrieval, analysis, modification, generation, display, and transmission), Two-Way Media Ltd. v. Comcast Cable Communications, LLC, 874 F.3d 1329, 1339 (Fed. Cir. 2017) (sequence of processing, routing, controlling, and monitoring). The ordering of the steps is therefore ordinary and conventional. We conclude that the claims do not provide an inventive concept because the additional elements recited in the claims do not provide significantly more than the recited judicial exception.
According to 2106.05 well-understood and routine processes to perform the abstract idea is not sufficient to transform the claim into patent eligibility. As evidence the examiner provides:
The specification describes the “risk management network” in terms of the business process results performed by the functions of the risk management network (see para 0003, para 0015, 0022-0028, para 0031, para 0033-0034, para 0038)
The specification discloses the instructions for the network to perform as it relates to performing the abstract idea applying basic computer components
[0006] Some embodiments of the present technology may encompass risk management
networks. The risk management networks may include one or more processors. The risk
25 management networks may include a memory having instructions stored thereon. When
executed, the instructions may cause the one or more processors to receive, from a requesting financial institution, an identifier associated with a request for funds. The instructions may cause the one or more processors to identify one or more financial accounts associated with a debtor who initiated the request for funds based on the identifier. The instructions may cause the one or 30 more processors to access account data from the one or more financial accounts associated with the debtor. The instructions may cause the one or more processors to aggregate the account data from the one or more financial accounts. The instructions may cause the one or more processors to generate a balance sheet from the aggregated account data. The balance sheet may provide data associated with income and expenses of the debtor. (see also para 0019-0021).
[0049] In the embodiments described above, for the purposes of illustration, processes may have been described in a particular order. It should be appreciated that in alternate embodiments, the methods may be performed in a different order than that described. It should also be appreciated that the methods and/or system components described above may be performed by hardware and/or software components (including integrated circuits, processing units, and the like), or may be embodied in sequences of machine-readable, or computer-readable, instructions, which may be used to cause a machine, such as a general-purpose or special-purpose processor or logic circuits programmed with the instructions to perform the methods. These machine readable instructions may be stored on one or more machine-readable mediums, such as CDROMs or other type of optical disks, floppy disks, ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, flash memory, or other types of machine-readable mediums suitable for storing electronic instructions. Alternatively, the methods may be performed by a combination of hardware and software.
With respect to the limitation machine learning and the training thereof, the specification lacks technical disclosure and is trivial mentioned:
[0025]…. In some embodiments, the risk management network 106 may include and/or be in communication with a machine learning model that is trained to identify transaction details based on different transaction data.
[0029] In some embodiments, the risk management network 108 may include one or more
machine learning models (such as a Gradient Boosted Trees model) that has been trained to
generate lending scores based on the probability that a debtor 102 with a given set of balance
sheet information and/or cashflow data will present a high risk of default or other nonpayment of a given credit offer. For example, the risk model may be provided with prior balance sheet information and/or cashflow data associated with a number of prior debtors 102 as input
variables, along with indications of whether each prior debtor 102 successfully paid off a given
line of credit, defaulted, missed payments, and/or had another credit outcome. The risk model
may be trained to identify various risk factors (including prior balance sheet information and/or
cashflow data such as, but not limited to, net cashflow, average cashflow, average account
balances over a given period of time, etc.) that may be indicative of risk for nonpayment of a
given amount of credit The prior balance sheet information and/or cashflow data may be
analyzed by the machine learning model in view of whether each prior debtor 102 was associated with a positive or negative lending outcome, enabling the machine learning model to generate a number of sets of risk factors that are indicative of a high risk for a given type, term, and/or amount of credit. When a new debtor 102 is analyzed, the relevant prior balance sheet information and/or cashflow data may be supplied to the machine learning model, which may identify risk associated with the new debtor 102 to determine whether the new debtor 102 is likely to present a risk for a given type, term, and/or amount of credit.
[0030] In some embodiments, the risk model may behave deterministically (e.g., an inquiry
with the same information scored by the model with the same feature values will always produce
the same score). In other embodiments the risk model can be updated/retrained multiple times
(e.g., the model can change upon retraining of the model, when the model goes through model
governance, and/or when a new version of the model is deployed).
With respect to financial institution providing identifiers of the debtor the examiner provides US Pub No. 2015/0186990 by Joseph -para 0077; US Pub No. 2011/0184857 A1 by Shakkarwar – para 0048; US Patent No. 7,254,557 B1 by Gillin et al – Col 13 lines 55-Col 14 lines 1-3;
The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is generic components and functions in the related arts. The claim is not patent eligible.
The remaining dependent claims—which impose additional limitations—also fail to claim patent-eligible subject matter because the limitations cannot be considered statutory. In reference to claims 2, 4, 6-7 and 21-23 these dependent claim have also been reviewed with the same analysis as independent claim 1. Dependent claim 2 is directed toward debtors- business practice. Dependent claim 4 is directed toward financial metrics of debtor- business practices. Dependent claim 6 is directed toward accounts maintained in plurality of financial institutions- common business practice. Dependent claim 7 is directed toward financial account types- common business practice. Dependent claim 21 is directed toward an ML model that is trained using financial data lacking technical disclosure- risk mitigation analysis using models. Dependent claim 22 is directed toward the ml model to identify risk factors- applying technology to mitigate risk- lacks technical disclosure. Dependent claim 23 is directed toward reconciling account data and data from data sources which is an accounting process.
The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1. Where all claims are directed to the same abstract idea, “addressing each claim of the asserted patents [is] unnecessary.” Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat 7 Ass ’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims 2, 4, 6-7 and 21-23 are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter.
In reference to Claims 8-13:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a management network, as in independent Claim 8 and the dependent claims. Such networks fall under the statutory category of "machine." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The functions of machine claim 8 corresponds to the steps of method claim 1. Therefore, claim 8 has been analyzed and rejected as being directed toward an abstract idea of the categories of concepts directed toward mental processes and methods of organizing human activity previously discussed with respect to claim 1.
STEP 2A Prong 2 The functions of machine claim 8 corresponds to the steps of method claim 1. Therefore, claim 8 has been analyzed and rejected as failing to provide limitations that are indicative of integration into a practical application, as previously discussed with respect to claim 1.
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. The additional elements beyond the abstract idea include a management network comprising a memory having instructions and one or more processors–is purely functional and generic. Nearly every technical machine for implementing a process will include a “memory” and “one or more processors” capable of performing the basic computer functions -of “receive”, “identify”, “access”, “match”, “reconcile”, “parse”, “aggregate”, “generate”. As a result, none of the hardware recited by the network claim limitations offers a meaningful limitation beyond generally linking the use of the method to a particular technological environment, that is, implementation via computers.
The functions of machine claim 8 corresponds to the steps of method claim 1. Therefore, claim 8 has been analyzed and rejected as failing to provide additional elements that amount to an inventive concept –i.e. significantly more than the recited judicial exception. Furthermore, as previously discussed with respect to claim 1, the limitations when considered individually, as a combination of parts or as a whole fail to provide any indication that the elements recited are unconventional or otherwise more than what is well understood, conventional, routine activity in the field.
According to 2106.05 well-understood and routine processes to perform the abstract idea is not sufficient to transform the claim into patent eligibility. As evidence the examiner provides:
The specification discloses:
[0006] Some embodiments of the present technology may encompass risk management networks. The risk management networks may include one or more processors. The risk management networks may include a memory having instructions stored thereon. When executed, the instructions may cause the one or more processors to receive, from a requesting financial institution, an identifier associated with a request for funds. The instructions may cause the one or more processors to identify one or more financial accounts associated with a debtor who initiated the request for funds based on the identifier. The instructions may cause the one or 30 more processors to access account data from the one or more financial accounts associated with the debtor. The instructions may cause the one or more processors to aggregate the account data from the one or more financial accounts. The instructions may cause the one or more processors to generate a balance sheet from the aggregated account data. The balance sheet may provide data associated with income and expenses of the debtor.
[0038] A computer system as illustrated in FIG. 4 may be incorporated as part of the previously described computerized devices. For example, computer system 400 can represent some of the components of computing devices, such as financial institutions 100, user device 20 102, risk management network 106, third party data sources 108, network 104, and/or other computing devices described herein. FIG. 4 provides a schematic illustration of one embodiment of a computer system 400 that can perform the methods provided by various other embodiments, as described herein. FIG. 4 is meant only to provide a generalized illustration of various components, any or all of which may be utilized as appropriate. FIG. 4, therefore, broadly illustrates how individual system elements may be implemented in a relatively separated or relatively more integrated manner.
[0040] The computer system 400 may further include (and/or be in communication with) one or more non-transitory storage devices 425, which can comprise, without limitation, local and/or network accessible storage, and/or can include, without limitation, a disk drive, a drive array, an 10 optical storage device, a solid-state storage device such as a random access memory ("RAM") and/or a read-only memory ("ROM"), which can be programmable, flash-updateable and/or the like. Such storage devices may be configured to implement any appropriate data stores, including without limitation, various file systems, database structures, and/or the like.
[0049] In the embodiments described above, for the purposes of illustration, processes may have been described in a particular order. It should be appreciated that in alternate embodiments, the methods may be performed in a different order than that described. It should also be appreciated that the methods and/or system components described above may be performed by hardware and/or software components (including integrated circuits, processing units, and the like), or may be embodied in sequences of machine-readable, or computer-readable, instructions, which may be used to cause a machine, such as a general-purpose or special-purpose processor or logic circuits programmed with the instructions to perform the methods. These machine readable instructions may be stored on one or more machine-readable mediums, such as CDROMs or other type of optical disks, floppy disks, ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, flash memory, or other types of machine-readable mediums suitable for storing electronic instructions. Alternatively, the methods may be performed by a
combination of hardware and software.
With respect to the limitation machine learning and the training thereof, the specification lacks technical disclosure and is trivial mentioned:
[0025]…. In some embodiments, the risk management network 106 may include and/or be in communication with a machine learning model that is trained to identify transaction details based on different transaction data.
[0029] In some embodiments, the risk management network 108 may include one or more
machine learning models (such as a Gradient Boosted Trees model) that has been trained to
generate lending scores based on the probability that a debtor 102 with a given set of balance
sheet information and/or cashflow data will present a high risk of default or other nonpayment of a given credit offer. For example, the risk model may be provided with prior balance sheet information and/or cashflow data associated with a number of prior debtors 102 as input
variables, along with indications of whether each prior debtor 102 successfully paid off a given
line of credit, defaulted, missed payments, and/or had another credit outcome. The risk model
may be trained to identify various risk factors (including prior balance sheet information and/or
cashflow data such as, but not limited to, net cashflow, average cashflow, average account
balances over a given period of time, etc.) that may be indicative of risk for nonpayment of a
given amount of credit The prior balance sheet information and/or cashflow data may be
analyzed by the machine learning model in view of whether each prior debtor 102 was associated with a positive or negative lending outcome, enabling the machine learning model to generate a number of sets of risk factors that are indicative of a high risk for a given type, term, and/or amount of credit. When a new debtor 102 is analyzed, the relevant prior balance sheet information and/or cashflow data may be supplied to the machine learning model, which may identify risk associated with the new debtor 102 to determine whether the new debtor 102 is likely to present a risk for a given type, term, and/or amount of credit.
[0030] In some embodiments, the risk model may behave deterministically (e.g., an inquiry
with the same information scored by the model with the same feature values will always produce
the same score). In other embodiments the risk model can be updated/retrained multiple times
(e.g., the model can change upon retraining of the model, when the model goes through model
governance, and/or when a new version of the model is deployed).
US Pub No. 2008/0015982 A1 by Sokolic et al, US Pub No. 2002/0091635 A1 by Dilip et al and US Pub No. 2005/0187862 A1 by Dheer et al- discloses collecting and aggregating data for generating balance sheet and balance sheet analysis. US Pub No. 2004/0143464 A1 by Houle et al- disclose receiving financial data and generating balance sheets and profit loss statements.
With respect to financial institution providing identifiers of the debtor the examiner provides US Pub No. 2015/0186990 by Joseph -para 0077; US Pub No. 2011/0184857 A1 by Shakkarwar – para 0048; US Patent No. 7,254,557 B1 by Gillin et al – Col 13 lines 55-Col 14 lines 1-3;
The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is generic components and functions in the related arts. The claim is not patent eligible.
The remaining dependent claims—which impose additional limitations—also fail to claim patent-eligible subject matter because the limitations cannot be considered statutory. In reference to claims 9-14 these dependent claim have also been reviewed with the same analysis as independent claim 8. Dependent claim 9 is directed toward accessing account data- insignificant extra solution activity and common business practice. Dependent claim 10 is directed toward generating cash flow data and suggested credit lending limits- common business practice. Dependent claim 11 is directed toward generating suggested lending limits based on account balances – common business practice. Dependent claim 12 is directed toward generating plurality of suggested credit limits and associated with different lending scores – a business practice. Dependent claim 13 is directed toward provide suggested credit limits- a business practice.
The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 8. Where all claims are directed to the same abstract idea, “addressing each claim of the asserted patents [is] unnecessary.” Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat 7 Ass ’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims 9-14 are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter.
In reference to Claims 15-20:
STEP 1. Per Step 1 of the two-step analysis, the claims are determined to include a non-transitory computer-readable medium, as in independent Claim 15 and the dependent claims. Such manufacture fall under the statutory category of "manufacture." Therefore, the claims are directed to a statutory eligibility category.
STEP 2A Prong 1. The instructions of machine claim 15 corresponds to the steps of method claim 1. Therefore, claim 15 has been analyzed and rejected as being directed toward an abstract idea of the categories of concepts directed toward mental processes and methods of organizing human activity previously discussed with respect to claim 1.
STEP 2A Prong 2 The instructions of machine claim 15 corresponds to the steps of method claim 1. Therefore, claim 15 has been analyzed and rejected as failing to provide limitations that are indicative of integration into a practical application, as previously discussed with respect to claim 1.
STEP 2B; The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to concepts of the abstract idea into a practical application. The additional elements beyond the abstract idea include a non-transitory computer-readable medium having instructions executed by one or more processors–is purely functional and generic. Nearly every manufacture for implementing a process will include a medium having instruction executable by processors capable of performing the basic computer functions -of “receive”, “identify”, “access”, “aggregate”, “match”, “parse”, “reconcile”, “generate”. As a result, none of the hardware recited by the processors executing the limitations offers a meaningful limitation beyond generally linking the use of the method to a particular technological environment, that is, implementation via computers.
The functions of instructions of medium claim 15 corresponds to the steps of method claim 1. Therefore, claim 15 has been analyzed and rejected as failing to provide additional elements that amount to an inventive concept –i.e. significantly more than the recited judicial exception. Furthermore, as previously discussed with respect to claim 1, the limitations when considered individually, as a combination of parts or as a whole fail to provide any indication that the elements recited are unconventional or otherwise more than what is well understood, conventional, routine activity in the field.
According to 2106.05 well-understood and routine processes to perform the abstract idea is not sufficient to transform the claim into patent eligibility. As evidence the examiner provides:
The specification discloses:
[0043] A set of these instructions and/or code might be stored on a computer-readable storage
medium, such as the storage device(s) 425 described above. In some cases, the storage medium might be incorporated within a computer system, such as computer system 400. In other embodiments, the storage medium might be separate from a computer system (e.g., a removable medium, such as a compact disc), and/or provided in an installation package, such that the storage medium can be used to program, configure and/or adapt a special purpose computer with the instructions/code stored thereon. These instructions might take the form of executable code, which is executable by the computer system 400 and/or might take the form of source and/or installable code, which, upon compilation and/or installation on the computer system 400 (e.g., using any of a variety of available compilers, installation programs, compression/decompression utilities, etc.) then takes the form of executable code.
With respect to the limitation machine learning and the training thereof, the specification lacks technical disclosure and is trivial mentioned:
[0025]…. In some embodiments, the risk management network 106 may include and/or be in communication with a machine learning model that is trained to identify transaction details based on different transaction data.
[0029] In some embodiments, the risk management network 108 may include one or more
machine learning models (such as a Gradient Boosted Trees model) that has been trained to
generate lending scores based on the probability that a debtor 102 with a given set of balance
sheet information and/or cashflow data will present a high risk of default or other nonpayment of a given credit offer. For example, the risk model may be provided with prior balance sheet information and/or cashflow data associated with a number of prior debtors 102 as input
variables, along with indications of whether each prior debtor 102 successfully paid off a given
line of credit, defaulted, missed payments, and/or had another credit outcome. The risk model
may be trained to identify various risk factors (including prior balance sheet information and/or
cashflow data such as, but not limited to, net cashflow, average cashflow, average account
balances over a given period of time, etc.) that may be indicative of risk for nonpayment of a
given amount of credit The prior balance sheet information and/or cashflow data may be
analyzed by the machine learning model in view of whether each prior debtor 102 was associated with a positive or negative lending outcome, enabling the machine learning model to generate a number of sets of risk factors that are indicative of a high risk for a given type, term, and/or amount of credit. When a new debtor 102 is analyzed, the relevant prior balance sheet information and/or cashflow data may be supplied to the machine learning model, which may identify risk associated with the new debtor 102 to determine whether the new debtor 102 is likely to present a risk for a given type, term, and/or amount of credit.
[0030] In some embodiments, the risk model may behave deterministically (e.g., an inquiry
with the same information scored by the model with the same feature values will always produce
the same score). In other embodiments the risk model can be updated/retrained multiple times
(e.g., the model can change upon retraining of the model, when the model goes through model
governance, and/or when a new version of the model is deployed).
With respect to financial institution providing identifiers of the debtor the examiner provides US Pub No. 2015/0186990 by Joseph -para 0077; US Pub No. 2011/0184857 A1 by Shakkarwar – para 0048; US Patent No. 7,254,557 B1 by Gillin et al – Col 13 lines 55-Col 14 lines 1-3;
The remaining dependent claims—which impose additional limitations—also fail to claim patent-eligible subject matter because the limitations cannot be considered statutory. In reference to claims 16-20 these dependent claim have also been reviewed with the same analysis as independent claim 15. Dependent claim 16 is directed toward defining an identifier related to a debtor for use- a common business practice. Dependent claim 17 is directed toward retrieving data- insignificant extra solution activity common business practice. Dependent claim 18 is directed toward aggregating data and data content- data manipulation and common business practice. Dependent claim 19 is directed toward generating cash flow data, identifying recurring expenses and generating lending score – a business practice. Dependent claim 20 is directed toward categorize data- business practice. The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 15. Where all claims are directed to the same abstract idea, “addressing each claim of the asserted patents [is] unnecessary.” Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat 7 Ass ’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims 16-20 are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter.
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 (i.e., changing from AIA to pre-AIA ) 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, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2, 4, 6-9, 11, 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub No. 2017/0323345 A1 by Flowers (Flowers), in view of US Pub No. 2020/0074565 A1 by Dotter (Dotter) and further in view of US Pub No. 2022/0335518 A1 by Wellmann et al. (Wellmann)
In reference to Claim 1:
Flowers teaches:
(Currently Amended) A computerized method of generating a credit/lending balance sheet [income/expense profile] ((Flowers) in at least Abstract), comprising
receiving [downloading] , by a risk management network and from a requesting financial institution, an identifier of a debtor associated with a request for funds, wherein the risk management network has access to account data associated with a plurality of different financial institutions ((Flowers) in at least para 0036, para 0050, para 0052-0053, para 0082);
identifying, by the risk management network, one or more financial accounts associated with the debtor who initiated the request for funds based on the identifier, wherein at least one account of the one or more financial accounts is associated with a financial institution of the plurality of different financial institutions other than the requesting financial institution ((Flowers) in at least para 0024, para 0033-0034 wherein the prior art teaches tracking accounts and detecting in one or more accounts of different institution insufficient and/or surplus balance in order to anticipate overdraft or fund transfer, para 0052-0053, para 0075, para 0078-0079 wherein the prior art teaches determining most likely account user will use accessing account balances and other aspects);
accessing account data from the one or more financial accounts associated with the debtor ((Flowers) in at least para 0063, para 0071, para 0078, para 0084, para 0090); …
aggregating, by the risk management network, the account data from the one or more financial accounts and the data from one or more third party data sources((Flowers) in at least para 0024, para 0056 wherein the prior art teaches additional data from additional data sources such as third party data providers or other data sources used for input into the model , para 0058 wherein the prior art teaches financial data profiles include aggregation of data accessed including the exemplary data of para 0056-0057, para 0071-0073);…
generating, by the risk management network, a balance sheet [income/expense profile] from the inflow and outflow transactions, the balance sheet [income/expense profile]providing data associated with income and expenses of debtor ((Flowers) in at least para 0023-0024, para 0071, para 0075 wherein the prior art teaches tracked account balance of account data received and stored as part of user profile; para 0084 wherein the prior art teaches calculating average account balance based on previous account activity (deposit frequencies, day to day balances) associated with financial account associated with user profile; para 0087, para 0089 wherein the prior art teaches financial data profiles generated to perform calculations which include storing any suitable number and/or type of data to facilitate calculations/predictions; para 0091-0092, para 0094- 0096, para 0102-0103, para 0105, para 0169, para 0191);
categorizing as at least some of expenses as … expenses (para 0057);
generating, by the risk management network, cash flow data associated with the one or more financial accounts ((Flowers) in at least para 0057, para 0071, para 0084, para 0094, para 0100, para 0127, para 0132, para 0161, para 0182, para 0184), wherein:
the cash flow data comprising comprises the recurring expenses;
the cash flow data comprising a typical net cashflow of the debtor over a predetermined period of time ((Flowers) in at least para 0071, para 0084, para 0182); and
the typical net cashflow of the debtor is based at least in part on the recurring expenses ((Flowers) in at least para 0182);…
training a machine learning model to generate …[financial behavior] based on a probability that a given debtor with a given set of balance sheet [income/expense profile]information and a given set of cashflow data will present a high risk of default or non-payment of a given credit offer ((Flowers) in at least para 0024, para 0050, para 0058, para 0064 wherein the prior art teaches cognitive computing that learn, reason from interactions; para 0066-0067, para 0069, para 0072, para 0074, para 0076-0079 wherein the prior art teaches predicting likelihood of overdraft or negative financial outcomes for user, para 0082, para 0179 wherein the prior art teaches applying learning algorithm to determine based on financial profiles risk level for customer and ability to pay back): by
providing prior balance sheet [income/expense profile]information and cashflow data associated with a plurality of debtors to the machine learning model as input variables, wherein the prior balance sheet [income/expense profile]information and cashflow data associated with a given debtor comprises an indication of a … [financial behavior] outcome associated with the given debtor ((Flowers) in at least para 0024, para 0050, para 0058, para 0067, para 0069, para 0072, para 0074, para 0076-0079 wherein the prior art teaches predicting likelihood of overdraft or negative financial outcomes for user, para 0082, para 0084 wherein the prior art teaches calculating average account balance based on previous account activity (deposit frequencies, day to day balances) associated with financial account associated with user profile; para 0087, para 0089 wherein the prior art teaches financial data profiles generated to perform calculations which include storing any suitable number and/or type of data to facilitate calculations/predictions; para 0091-0092, para 0094- 0096, para 0102-0103, para 0105, para 0169, para 0191); and
identifying, by the machine learning model, a plurality of risk factors that are indicative of risk for nonpayment of a given amount of credit based on the prior balance sheet [income/expense profile]information and cashflow data associated with the plurality of debtors and the credit outcomes ((Flowers) in at least para 0076-0079 wherein the prior art teaches predicting likelihood of overdraft or negative financial outcomes for user, para 0082, para 0179 wherein the prior art teaches applying learning algorithm to determine based on financial profiles risk level for customer and ability to pay back), wherein:
the plurality of risk factors comprise at least one of:
a net cashflow of the given debtor;
an average cashflow of the given debtor; or
average account balances of the given debtor over a given period of time ((Flowers) in at least para 0024, para 0076-0079, para 0081-0082, para 0084-0085, para 0091, para 0096-0097, para 0103-0108, para 0110-0111);
providing the cash flow data associated with the one or more financial accounts to the machine learning model ((Flowers) in at least para 0065-0068, para 0084 wherein the prior art teaches calculating average account balance based on previous account activity (deposit frequencies, day to day balances) associated with financial account associated with user profile; para 0087, para 0089, para 0091-0092, para 0094- 0096, para 0102-0103, para 0105, para 0149, para 0179 wherein the prior art teaches applying learning algorithm to determine based on financial profiles risk level for customer and ability to pay back);
generating, by the machine learning model, a …[statistical behavior probability] based on the recurring expenses and on information from the balance sheet [income/expense profile]and the cash flow data using the machine learning model ((Flowers) in at least para 0023-0024, para 0056, para 0074, para 0079-0080, para 0084-0085, para 0100, para 0102-0108, para 0179 wherein the prior art teaches applying learning algorithm to determine based on financial profiles risk level for customer and ability to pay back); and…
providing the …suggested credit limit to the requesting financial institution ((Flowers) in at least para 0179)
According to KSR, common sense rationale obviousness includes when the prior art provides some teaching, suggestion or motivation one of ordinary skill in the art would have been led to modify the prior art reference to arrive at the claimed invention. The prior art Flowers, does not explicitly describe the financial profile record as “balance sheet”, however, Flowers does teach the financial data stored in the user profile to be a record that comprises, income, expenses, average incomes which is similar in its purpose as a balance sheet. It is general knowledge and available in the field of endeavor to one of ordinary skill in the art that a balance sheet is a financial statement that details income and expenses. Accordingly, one of ordinary skill in the art in light of the prior art which provides suggestion of a balance sheet to modify the financial record of expenses, income and cashflow to be a balance sheet with a reasonable expectation of success.
Flowers does not explicitly teach:
…balance sheet…
matching, by the risk management network, the identifier of the debtor with information from one or more third party data sources to identify and access data from the one or more third party data sources that is associated with the debtor, wherein:
the data from the one or more third party data sources comprises at least one of utility bill payment information, payroll check information, payroll deposition information, or rent payment information;
the data from the one more third party data sources comprises a tag indicating a data type of each item of the data; and
the one or more third party data sources are distinct from the plurality of financial institutions;
reconciling, by the risk management network, the account data from the one or more financial accounts and the data from the one or more third party data sources to prevent duplicative data from being double counted
parsing, by the risk management network, the account data and the data from the one or more third party data sources to identify inflow and outflow transaction associated with each of the one or more financial accounts, wherein parsing the data from the one or more third party data sources comprises using the tag of each item of data within the data from the one or more third party data sources to categorize a particular item of data as an inflow transaction or an outflow transaction
categorizing as at least some of expenses as recurring expenses;
generating a suggested credit limit based on the typical net cashflow of the debtor, the suggested credit limit comprising one or both of a total credit amount and a periodic payment that the debtor is able to afford;
…generate lending scores…
generating, by the machine learning model, a lending score
providing the lending score and the suggested credit limit to the requesting financial institution
Dotter teaches:
receiving, by a risk management network and from a requesting financial institution, an identifier of a debtor associated with a request for funds, wherein the risk management network has access to account data associated with a plurality of different financial institutions ((Dotter) in at least FIG. 1A-B; para 0036, para 0050, para 0065, para 0113, para 0119, para 0123, para 0133);
identifying, by the risk management network, one or more financial accounts associated with the debtor who initiated the request for funds based on the identifier, wherein at least one account of the one or more financial accounts is associated with a financial institution of the plurality of different financial institutions other than the requesting financial institution ((Dotter) in at least FIG. 1A-B; para 0065, para 0123;
accessing account data from the one or more financial accounts associated with the debtor ((Dotter) in at least para 0140, para 0154, para 0184);
matching, by the risk management network, the identifier of the debtor with information from one or more third party data sources to identify and access data from the one or more third party data sources that is associated with the debtor ((Dotter) in at least FIG. 1A-B; para 0071, para 0084-0085, para 0187, para 0189), wherein:
the data from the one or more third party data sources comprises at least one of utility bill payment information, payroll check information, payroll deposition information, or rent payment information ((Dotter) in at least para 0046, para 0048, para 0054, para 0063);
the data from the one more third party data sources comprises a tag [GL code or indicator (para 0064)] indicating a data type of each item of the data ((Dotter) in at least para 0049, para 0051-0052, para 0063-0064, para 0065, para 0103, para 0192, para 0195; and
the one or more third party data sources are distinct from the plurality of financial institutions ((Dotter) in at least para 0049 wherein the prior art teaches third party have GL code for transaction type and accounting category/classification);
aggregating, by the risk management network, the account data from the one or more financial accounts and the data from the one or more third party data sources ((Dotter) in at least FIG. 1A-B wherein the prior art illustrate system for data aggregation; FIG. 5-6; Abstract; para 0004, para 0006 -0008, para 0033-0034, para 0036, para 0040, para 0048-0050, para 0053, para 0058-0059, para 0061, para 0191);
reconciling, by the risk management network, the account data from the one or more financial accounts and the data from the one or more third party data sources to prevent duplicative data from being double counted ((Dotter) in at least FIG. 1A-B; Fig. 3; para 0188-0189);
parsing, by the risk management network, the account data and the data from the one or more third party data sources to identify inflow and outflow transactions associated with each of the one or more financial accounts, wherein parsing the data from the one or more third party data sources comprises using the tag of each item of data within the data from the one or more third party data sources to categorize a particular item of data as an inflow transaction or an outflow transaction ((Dotter) in at least para 0049, para 0062-0064, para 0067-0068, para 0103, para 0175);
generating, by the risk management network, a balance sheet from the inflow and outflow transactions, the balance sheet providing data associated with income and expenses of the debtor ((Dotter) in at least para 0034, para 0037, para 0104-0105, para 0111);
categorizing at least some of the expenses as recurring expenses ((Dotter) in at least para 0175);
generating, by the risk management network, cash flow data associated with the one or more financial accounts, the cash flow data comprising the recurring expenses ((Dotter) in at least para 0048, para 0053-0054, para 0063, para 0105-0106, para 0139, para 0175, para 0177);
generating, by the risk management network, cash flow data ((Wellmann) in at least para 0040) associated with the one or more financial accounts, wherein:
the cash flow data comprising comprises the recurring expenses;
the cash flow data comprising a typical net cashflow of the debtor over a predetermined period of time ((Dotter) in at least para 0048, para 0054, para 0063); and
the typical net cashflow of the debtor is based at least in part on the recurring expenses ((Dotter) in at least para 0054 wherein the prior art teaches interest payments, loan payments, insurance payments, para 0055-0056 wherein the prior art teaches repeating transaction at specified time periods, para 0063-0064)
generating a suggested credit limit based on the typical net cashflow of the debtor, the suggested credit limit comprising one or both of a total credit amount and a periodic payment that the debtor is able to afford ((Dotter) in at least para 0048, para 0054 wherein the prior art teaches interest payments, loan payments, insurance payments, para 0055-0056 wherein the prior art teaches repeating transaction at specified time periods, para 0063-0064, para 0104-0106 wherein the prior art teaches determining accounting categories for each transaction provides real-time view of entity’s financial profile allowing module to provide financial products such as a loan for financing a purchase and select a financial product based on predicted event by monitoring A/C and A/P accounts, balances, losses, to predict a shortfall or future breach of financial covenant or future excess of funds, para 0107 wherein the prior art teaches the module selects financial product in response to prediction of shortfall of funds the offer select amount of loan to cover predicted future shortfall and to be paid back in the contingent offer for up to a predetermined period of time selected accounts receivable payment);
providing the lending score and the suggested credit limit to the requesting financial institution ((Dotter) in at least para 0107 wherein the prior art teaches the module selects financial product in response to prediction of shortfall of funds the offer select amount of loan to cover predicted future shortfall and to be paid back in the contingent offer for up to a predetermined period of time selected accounts receivable payment, para 0113 wherein the prior art teaches display module displaying financial product offer, risk analysis, credit scores and other indicators of credit worthiness)
Both Flowers and Dotter teach collecting income/expense data in order to perform user spending analysis. Dotter teaches the motivation of an aggregate of collected data and accounting system as financial reports are often incomplete or inaccurately transcribed which includes determining account categories for each of a plurality of transaction from a plurality of both local and third party sources in order to generate a balance sheet and monthly cash flow analysis in order to predict account balances. It would have been obvious to one having ordinary skill at the time of effective filing the invention to expand the cashflow and average balance financial records of Flowers to be in the aggregation and accounting system of Dotter since Dotter teaches the motivation of an aggregate of collected data and accounting system as financial reports are often incomplete or inaccurately transcribed which includes determining account categories for each of a plurality of transaction from a plurality of both local and third party sources in order to generate a balance sheet and monthly cash flow analysis in order to predict account balances..
Wellmann teaches:
generating, by the risk management network, cash flow data ((Wellmann) in at least para 0040) associated with the one or more financial accounts, wherein:
the cash flow data comprising comprises the recurring expenses;
the cash flow data comprising a typical net cashflow of the debtor over a predetermined period of time; and
the typical net cashflow of the debtor is based at least in part on the recurring expenses ((Wellmann) in at least para 0040 wherein the prior art teaches providing input data for a learning model from financial accounting metadata, from a bank/multiple banks, general ledger, the data including loan applications cashflow statistics; para 0050 wherein the institution data may include accounts receivable, accounts payable, income statement, financial liability, net income during a time period, loans, para 0053 wherein the prior art teaches total amount of liabilities, an amount of cashflow of actual payments received, an amount of scheduled cashflow, para 0056, para 0063, para 0065, para 0073, para 0078, para 0083);
training a machine learning model to generate lending scores based on a probability that a given debtor with a given set of balance sheet information and a given set of cashflow data will present a high risk of default or nonpayment of a given credit offer ((Wellmann) in at least Abstract; para 0006-0008, para 0040, para 0058, para 0060, para 0072, para 0077-0078, para 0087) by:
providing prior balance sheet information and cashflow data associated with a plurality of debtors to the machine learning model as input variables, wherein the prior balance sheet information and cashflow data associated with a given debtor comprises an indication of a credit outcome associated with the given debtor ((Wellmann) in at least para 0031, para 0038 wherein the prior art teaches inputting into model general ledger data; para 0040, para 0053, para 0077); and
identifying, by the machine learning model, a plurality of risk factors that are indicative of risk for nonpayment of a given amount of credit based on the prior balance sheet information and cashflow data associated with the plurality of debtors and the credit outcomes ((Wellmann) in at least FIG. 7C; para 0006, para 0022, para 0052, para 0054, para 0056, para 0059-0063), wherein: the plurality of risk factors comprise at least one of:
a net cashflow of the given debtor ((Wellmann) in at least para 0052, para 0069);
an average cashflow of the given debtor ((Wellmann) in at least para 0051-0052, para 0054, para 0057, para 0059); or
average account balances of the given debtor over a given period of time ((Wellmann) in at least para 0054-0055);
providing the cash flow data associated with the one or more financial accounts to the machine learning model ((Wellmann) in at least para 0031, para 0038; para 0040, para 0053-0054, para 0077);
generating, by the machine learning model, a lending score based on the recurring expenses and on information from the balance sheet and the cash flow data using the machine learning model ((Wellmann) in at least FIG. 7B; para 0058, para 0061-0062, para 0065); and
providing the lending score to the requesting financial institution ((Wellmann) in at least para 0058-0059, para 0062, para 0064-0065).
Both Flowers and Wellman (¶ 0062) teach collecting and analyzing financial data which includes evaluating financial information in order to provide statistical analysis of user financial behavior using computer algorithms. Wellmann teaches the motivation that there is a need for institutional risk management to provide customized risk analysis and teaches the motivation of training and applying ML models to perform the risk scoring analysis in order for the learning algorithm to learn through iterative feedback in order for the analysis to improve over time based on feedback for more accurate analysis for probability of default and then providing to the pertinent financial institutions the scoring results. It would have been obvious to one having ordinary skill at the time of effective filing the invention to expand the calculations of the learning algorithm determine statistical behavior analysis to determine based on financial profiles risk level for customer and ability to pay back of Flowers to include training customized learning model for use in the analysis as taught by Wellman since Wellmann teaches the motivation that there is a need for institutional risk management to provide customized risk analysis and teaches the motivation of training and applying ML models to perform the risk scoring analysis in order for the learning algorithm to learn through iterative feedback in order for the analysis to improve over time based on feedback for more accurate analysis for probability of default and then providing to the pertinent financial institutions the scoring results .
In reference to Claim 2:
The combination of Flowers, Dotter and Wellman the limitations of independent claim 1. Flowers further discloses the limitations of dependent claim 2
(Original) The computerized method of generating a credit/lending balance sheet of claim 1 (see rejection of claim 1 above), wherein:
the debtor comprises an individual or a business entity. ((Flowers) in at least para 0006, para 0049, para 0150)
In reference to Claim 4:
The combination of Flowers, Dotter and Wellman discloses the limitations of independent claim 1. Flowers further discloses the limitations of dependent claim 4
(Previously Presented) The computerized method of generating a credit/lending balance sheet of claim 1 (see rejection of claim 1 above), wherein: the cash flow data comprises one or more financial metrics of the debtor selected from a group consisting of:
average monthly expenses, average monthly income, recurring monthly expenses, average monthly balance across the one or more financial accounts, highest monthly balance across the one or more financial accounts, lowest monthly balance across the one or more financial accounts, and average monthly net cash flow across the one or more financial accounts.((Flowers) in at least para 0024, para 0076-0079, para 0081-0082, para 0084-0085, para 0091, para 0096-0097, para 0103-0108, para 0110-0111)
In reference to Claim 6:
The combination of Flowers, Dotter and Wellman discloses the limitations of independent claim 1. Flowers further discloses the limitations of dependent claim 6
(Original) The computerized method of generating a credit/lending balance sheet of claim 1 (see rejection of claim 1 above), wherein:
the one or more financial accounts are maintained by a plurality of financial institutions. ((Flowers) in at least para 0036, para 0050, para 0052-0053, para 0082)
In reference to Claim 7:
The combination of Flowers, Dotter and Wellman discloses the limitations of independent claim 1. Flowers further discloses the limitations of dependent claim 7
(Original) The computerized method of generating a credit/lending balance sheet of claim 1 (see rejection of claim 1 above), wherein: the one or more financial accounts are selected from a group consisting of:
a checking account, a savings account, a brokerage account, and a credit card account. ((Flowers) in at least para 0078-0079, para 0082, para 0102, para 0157)
In reference to Claim 23:
The combination of Flowers, Dotter and Wellman discloses the limitations of independent claim 1. Flowers further discloses the limitations of dependent claim 23
(New) The computerized method of generating a credit/lending balance sheet of claim 1 (see rejection of claim 1 above), wherein:
Flowers does not explicitly teach:
reconciling the account data from the one or more financial accounts and the data from the one or more third party data sources comprises checking multiple accounts of the one or more financial accounts for a plurality of deposits that match payroll information from the one or more third party data sources.
Dotter teaches:
reconciling the account data from the one or more financial accounts and the data from the one or more third party data sources comprises checking multiple accounts of the one or more financial accounts for a plurality of deposits that match payroll information from the one or more third party data sources.((Dotter) in at least para 0004-0007, para 0048, para 0054, para 0063, para 0071, para 0082, para 0087-0098, para 0105, para 0188-0189)
Both Flowers and Dotter teach collecting income/expense data in order to perform user spending analysis. Dotter teaches the motivation of an aggregate of collected data and accounting system as financial reports are often incomplete or inaccurately transcribed which includes determining account categories for each of a plurality of transaction from a plurality of both local and third party sources in order to generate a balance sheet and monthly cash flow analysis in order to predict account balances. It would have been obvious to one having ordinary skill at the time of effective filing the invention to expand the cashflow and average balance financial records of Flowers to be in the aggregation and accounting system of Dotter since Dotter teaches the motivation of an aggregate of collected data and accounting system as financial reports are often incomplete or inaccurately transcribed which includes determining account categories for each of a plurality of transaction from a plurality of both local and third party sources in order to generate a balance sheet and monthly cash flow analysis in order to predict account balances.
In reference to Claim 8:
The combination of Flowers, Dotter and Wellman discloses the limitations of independent claim 8.
Claim 8 functional processes correspond to the method steps of method claim 1. The additional limitations recited in claim 8 that go beyond the limitations of claim 1 include the management network ((Flowers) in at least abstract) to perform the operation that correspond to claim 1 include the structure to perform the operations corresponding to claim 1 comprising:
one or more processors ((Flowers) in at least FIG. 1; para 0006, para 0060, para 0071, para 0074, para 0081-0082); and
a memory having instructions stored thereon that, when executed, cause the one or more processors ((Flowers) in at least FIG. 1-2, para 0074, para 0083) to:
Therefore, claim 8 has been analyzed and rejected as previously discussed with respect to claim 1.
In reference to Claim 9:
The combination of Flowers, Dotter and Wellman discloses the limitations of independent claim 8. Flowers further discloses the limitations of dependent claim 9
(Original) The risk management network of claim 8 (see rejection of claim 8 above), wherein: accessing account data from the one or more financial accounts comprises
performing an entity resolution process. ((Flowers) in at least para 0034, para 0125-0126, para 0131)
In reference to Claim 11:
The combination of Flowers, Dotter and Wellman discloses the limitations of dependent claim 10. Flowers further discloses the limitations of dependent claim 11.
(Currently Amended) The risk management network of claim 9(see rejection of claim 9 above),
wherein: generating the suggested credit limit is further based on an average account balance across the one or more financial accounts. ((Flowers) in at least para 0137-0138, para 0142, para 0179)
In reference to Claim 15:
The combination of Flowers, Dotter and Wellman discloses the limitations of independent claim 15.
Non-transitory computer readable medium Claim 15 executed instructions correspond to the method steps of method claim 1. The additional limitations recited in claim 15 that go beyond the limitations of claim 1 include: Non-transitory computer readable medium having instructions executable by one or more processors to perform the operation that correspond to claim 1 ((Flowers) in at least para 0194)
Therefore, claim 15 has been analyzed and rejected as previously discussed with respect to claim 1.
In reference to Claim 16:
The combination of Flowers, Dotter and Wellman discloses the limitations of independent claim 15. Flowers further discloses the limitations of dependent claim 16
(Original) The non-transitory computer-readable medium of claim 15 (see rejection of claim 15 above), wherein:
the identifier comprises personally identifiable information data associated with the debtor ((Flowers) in at least para 0050, para 0062, para 0119)
the personally identifiable information data is used to look up the account data ((Flowers) in at least para 0062, para 0119 wherein the prior art teaches using password and logon information in order to access account data).
In reference to Claim 17:
The combination of Flowers, Dotter and Wellman discloses the limitations of independent claim 15. Flowers further discloses the limitations of dependent claim 17
(Original) The non-transitory computer-readable medium of claim 15 (see rejection of claim 15 above), wherein the instructions further cause the one or more processors to:
Flowers does not explicitly teach:
retrieve one or both of income data and expense data from at least one third party data source that is not a financial institution associated with the account data
Dotter teaches:
retrieve one or both of income data and expense data from at least one third party data source that is not a financial institution associated with the account data ((Dotter) in at least para 0033-0034, para 0036-0037, para 0039-0040, para 0046, para 0048-0050, para 0053-0054, para 0063, para 0106-0107. Both Flowers and Dotter teach collecting and analyzing financial data which includes evaluating financial information in order to provide statistical analysis of user financial behavior using computer algorithms. Dotter teaches the motivation of receiving a plurality of financial data in order to apply different financial data for analysis of user profile data where the financial data for analysis can include income/transactional data obtained from third party sources based on geolocation data which has risk elements that are used for determining association of transaction data is associated with transactions for a plurality of spending categories for customized risk analysis. It would have been obvious to one having ordinary skill at the time of effective filing the invention to expand the financial profile data used for calculations of the learning algorithm determine financial behavior analysis to determine based on financial profiles risk level for customer and ability to pay back of Flowers to include financial data retrieved from third party resources of Dotter since Dotter teaches the motivation of receiving a plurality of financial data in order to apply different financial data for analysis of user profile data where the financial data for analysis can include income/transactional data obtained from third party sources based on geolocation data which has risk elements that are used for determining association of transaction data is associated with transactions for a plurality of spending categories for customized risk analysis
In reference to Claim 18:
The combination of Flowers, Dotter and Wellmann discloses the limitations of dependent claim 17. Flowers further discloses the limitations of dependent claim 18.
(original) The non-transitory computer-readable medium of claim 17 (see rejection of claim 17), wherein aggregating the account data from the one or more financial accounts comprises:
Flowers does not explicitly teach:
aggregating the one or both of income data and expense data and reconciling the one or both of income data and expense data with the account data to ensure that the one or both of income data and expense data is not doubled counted; and the balance sheet further comprises the one or both of income data and expense data.
Dotter teaches:
aggregating the one or both of income data and expense data and reconciling the one or both of income data and expense data with the account data to ensure that the one or both of income data and expense data is not doubled counted; and the balance sheet further comprises the one or both of income data and expense data.((Dotter) in at least FIG. 3; FIG. 5-6; Abstract; para 0004, para 0006 -0008, para 0033-0034, para 0036, para 0040, para 0048-0050, para 0053, para 0058-0059, para 0061, para 0188-0189, para 0191 )
Both Flowers and Dotter are directed toward collecting and aggregating transaction data in order to analyze financial reports. Dotter teaches the motivation of evaluating whether duplicate transactions have been entered in order to combine, consolidate or delete data for duplicate transactions ensuring records are accurate and reconciled. It would have been obvious to one having ordinary skill at the time of effective filing the invention to expand the financial profile data analysis of Flowers to include evaluating whether duplicate transactions have been presented of Dotter since Dotter teaches the motivation of evaluating whether duplicate transactions have been entered in order to combine, consolidate or delete data for duplicate transactions ensuring records are accurate and reconciled.
In reference to Claim 19:
The combination of Flowers, Dotter and Wellman discloses the limitations of independent claim 15. Flowers further discloses the limitations of dependent claim 19
(Previously Presented) The non-transitory computer-readable medium of claim 15 (see rejection of claim 15 above), wherein the instructions further cause the one or more processors to:
generate cash flow data associated with the one or more financial accounts
Flowers does not explicitly teach:
identify recurring expenses based on the cash flow data; and
generate a lending score based at least in part on the recurring expenses.
Dotter teaches:
identify recurring expenses based on the cash flow data ((Dotter) in at least para 0056, para 0058, para 0062, para 0064, para 0103, para 0175); and
Both Flowers and Dotter are directed toward collecting and analyzing financial data of a user in order to provide a user financial behavior analysis based on a plurality of income and spending behavior. Dotter teaches the motivation of identifying reoccurring expenses in order to classify the type of expense when determining user spending behavior. It would have been obvious to one having ordinary skill at the time of effective filing the invention to expand the calculations of the learning algorithm determine behavior analysis to determine based on financial profiles risk level for customer and ability to pay back of Flowers to include identifying payment behavior of reoccurring payments of Dotter since Dotter teaches the motivation of identifying reoccurring expenses in order to classify the type of expense when determining user spending behavior.
Wellmann teaches:
identify recurring expenses based on the cash flow data ((Wellman) in at least FIG, 7C; para 0052-0053)
generate a lending [trust] score based at least in part on the recurring expenses. ((Wellmann) in at least FIG. 7B; para 0058, para 0061-0062, para 0065)
Both Flowers and Wellman teach collecting and analyzing financial data which includes evaluating financial information in order to provide analysis of user financial behavior using computer algorithms. Wellmann teaches the motivation of identifying monthly payment being paid on time or late in order to generate a lending score reflecting payment behaviors . It would have been obvious to one having ordinary skill at the time of effective filing the invention to expand the calculations of the learning algorithm determine behavior analysis to determine based on financial profiles risk level for customer and ability to pay back of Flowers to include identifying payment behavior of reoccurring payments of Wellman since Wellmann teaches the motivation of identifying monthly payment being paid on time or late in order to generate a lending score reflecting payment behaviors . .
In reference to Claim 20:
The combination of Flowers, Dotter and Wellmann discloses the limitations of dependent claim 19. Flowers further discloses the limitations of dependent claim 20:
(Original) The non-transitory computer-readable medium of claim 19 , wherein the instructions further cause the one or more processors (see rejection of claim 19 above) to:
Flowers does not explicitly teach:
categorize the expenses into a plurality of categories, wherein generating the lending score is based at least in part on the categorized expenses
Wellmann teaches:
categorize the expenses into a plurality of categories, wherein generating the lending score is based at least in part on the categorized expenses ((Wellmann) in at least para 0040, para 0057-0058, para 0060-0062, para 0065, para 0074)
Both Flowers and Wellmann teach collecting and analyzing financial data which includes evaluating financial information in order to provide analysis of user financial behavior using computer algorithms. Wellmann teaches the motivation of categorizing expenses and income in order to weight different categories of expenses/incomes when calculating the risk score so as to determine when applying a risk threshold the appropriate financial institutions to inform of the scoring results. It would have been obvious to one having ordinary skill at the time of effective filing the invention to expand the calculations of the learning algorithm determine statistical behavior analysis to determine based on financial profiles risk level for customer and ability to pay back of Flowers to include categorizing expense/spending of Wellmann since Wellmann teaches the motivation of categorizing expenses and income in order to weight different categories of expenses/incomes when calculating the risk score so as to determine when applying a risk threshold the appropriate financial institutions to inform of the scoring results.
Claim(s) 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub No. 2017/0323345 A1 by Flowers (Flowers), in view of US Pub No. 2020/0074565 A1 by Dotter (Dotter) in view of US Pub No. 2022/0335518 A1 by Wellmann et al. (Wellmann) as applied to claim 10 above, and further in view of US Pub No. 2013/0346284 A1 by Stubbs et al. (Stubbs)
In reference to Claim 12:
The combination of Flowers, Dotter and Wellmann discloses the limitations of dependent claim 10. Flowers further discloses the limitations of dependent claim 12.
(Currently Amended) The risk management network of claim 10, wherein: generating the suggested credit limit (see rejection of claim 10 above) comprises
generating a plurality of suggested credit limits ((Flowers) in at least para 0179); and
Flowers does not explicitly teach:
each of the plurality of suggested credit limits is associated with different lending score.
Stubbs teaches:
generating a plurality of suggested credit limits ((Stubbs) in at least para 0017); and
each of the plurality of suggested credit limits is associated with different lending score. ((Stubbs) in at least FIG. 2, FIG. 5-6; para 0028, para 0035)
Both Flowers and Stubbs teaches collecting and analyzing financial data which includes evaluating financial information in order to provide financial offers. Stubbs teaches the motivation of calculating a risk score using financial data in order to determine maximum loan/credit limits recommended based on calculated risk. It would have been obvious to one having ordinary skill at the time of effective filing the invention to expand the suggestions for financial offers according to customer statistical profile of Flowers to include correlate suggestions for maximum loan amount with respect to a calculated risk score as taught by Stubbs since Stubbs teaches the motivation of calculating a risk score using financial data in order to determine maximum loan/credit limits recommended based on calculated risk.
In reference to Claim 13:
The combination of Flowers, Dotter and Wellmann discloses the limitations of dependent claim 12. Flowers further discloses the limitations of dependent claim 13.
(Currently Amended) The risk management network of claim 12 (see rejection of claim 12 above),
wherein the instructions further cause the one or more processors to: provide the plurality of suggested credit limits to the requesting financial institution. ((Flowers) in at least para 0179)
Stubbs teaches:
wherein the instructions further cause the one or more processors to: provide the plurality suggested credit limits to the requesting financial institution. ((Stubbs) in at least FIG. 4; para 0021 wherein the prior art teaches score transferred to lender, para 0033 wherein the prior art teaches outputting score to lender 490; para 0035, para 0039)
Both Flowers and Stubbs teaches collecting and analyzing financial data which includes evaluating financial information in order to provide financial offers. Stubbs teaches the motivation of providing lenders the calculated a risk score using financial data in order to determine maximum loan/credit limits recommended in order to permit banks to safely loan money. It would have been obvious to one having ordinary skill at the time of effective filing the invention to expand the suggestions for financial loan offers of Flowers to include providing risk scores and corresponding loan limits to lenders as taught by Stubbs since Stubbs teaches the motivation of providing lenders the calculated a risk score using financial data in order to determine maximum loan/credit limits recommended in order to permit banks to safely loan money.
Claim(s) 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub No. 2017/0323345 A1 by Flowers (Flowers), in view of US Pub No. 2020/0074565 A1 by Dotter (Dotter) in view of US Pub No. 2022/0335518 A1 by Wellmann et al. (Wellmann) as applied to Claim 1 above, and further in view of US Patent No. 5,832,465 A by Tom ( Tom) and US Pub No. 2019/0102835 A1 by Bjornerud et al (Bjornerud)
In reference to Claim 21:
The combination of Flowers, Dotter and Wellman discloses the limitations of independent claim 1. Flowers further discloses the limitations of dependent claim 21
(Previously Presented) The computerized method of generating a credit/lending balance sheet of claim 1, wherein:
Flowers does not explicitly teach:
the machine learning model is trained using prior balance sheet information and prior cashflow data associated with a number of prior debtors and a credit outcome associated with each of the prior debtors as input variables.
Tom teaches:
the machine learning model is trained using prior … sheet information and prior cashflow data associated with a number of prior debtors and a credit outcome associated with each of the prior debtors as input variables.((Tom) in at least FIG. 7A-B; Col 1 lines 66-Col 2 lines 1-60, Col 3 lines 57-Col 4 lines 1-37, lines 55-Col 5 lines 1-33, Col 10 lines 51-Col 11 lines 1-4)
Both Flowers and Tom are directed toward using collected cash flow data for risk analysis of loan offers using machine learning models. Tom teaches the motivation of applying a machine learning analysis trained on inputted financial data that in order to determine whether the applicant is good, bad, poor etc…that appropriate information includes amount of debtors and credit outcomes. It would have been obvious to one having ordinary skill at the time of effective filing the invention to expand the data analyzed of Flowers to include the data analyzed using ML model of Tom since Tom teaches the motivation of applying a machine learning analysis trained on inputted financial data that in order to determine whether the applicant is good, bad, poor ect…that appropriate information includes amount of debtors and credit outcomes.
Bjornerud teaches:
the machine learning model is trained using prior balance sheet information and prior cashflow data associated with a … debtors and a credit outcome associated with each of the prior debtors as input variables ((Bjornerud) in at least FIG. 2; para 0005, para 0044, para 0047-0048, para 0056).
Both Flowers and Bjornerud are directed toward using collected cash flow data for risk analysis of loan offers using machine learning algorithms. Bjornerud teaches the motivation of using artificial intelligence models in order to analyze and learn from data collected in order to determined loan deals for borrowers according to patterns in credit facilities for credit request/offers and that it is a matter of due diligence to financial institutions to collect prior balance sheet information in order to determine information such as cash flows and the identification of factors of the borrower both positive/negative in order to determine credit strength of the borrower. It would have been obvious to one having ordinary skill at the time of effective filing the invention to expand the data analyzed of Flowers to include the data analyzed using ML model and balance sheets and other financial data of Bjornerud since Bjornerud teaches the motivation of using artificial intelligence models in order to analyze and learn from data collected in order to determined loan deals for borrowers according to patterns in credit facilities for credit request/offers and that it is a matter of due diligence to financial institutions to collect prior balance sheet information in order to determine information such as cash flows and the identification of factors of the borrower both positive/negative in order to determine credit strength of the borrower.
In reference to Claim 22:
The combination of Flowers, Park, Dotter, Wellman and Bjornerud discloses the limitations of dependent claim 21. Flowers further discloses the limitations of dependent claim 22
(Previously Presented) The computerized method of generating a credit/lending balance sheet of claim 21 (see rejection of claim 21 above),
wherein: the machine learning model is trained to identify various risk factors that are indicative of risk for nonpayment of a given amount of credit.((Flowers) in at least para 0179)
Tom teaches:
wherein: the machine learning model is trained to identify various risk factors that are indicative of risk for nonpayment of a given amount of credit. .((Tom) in at least FIG. 7A-B; Col 1 lines 66-Col 2 lines 1-60, Col 3 lines 57-Col 4 lines 1-37, lines 55-Col 5 lines 1-33, Col 10 lines 51-Col 11 lines 1-4)
Both Flowers and Tom are directed toward collecting financial data for risk analysis of loan applications. Tom teaches the motivation of applying a machine learning analysis trained on inputted financial data that in order to determine whether the applicant is good, bad, poor etc…that appropriate information includes amount of debtors and credit outcomes. It would have been obvious to one having ordinary skill at the time of effective filing the invention to expand the data analyzed of Dheer to include the data analyzed using ML model of Tom since Tom teaches the motivation of applying a machine learning analysis trained on inputted financial data that in order to determine whether the applicant is good, bad, poor ect…that appropriate information includes amount of debtors and credit outcomes.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US Pub No. 2021/0350340 A1 by Lai et al; US Patent No. 11,094020 B1 by Lee et al; US Patent No. 8,423,435 B1 by Poteet et al; US Pub. No. 2010/0198707 A1 by Handa et al; US Pub No. 2003/0149660 A1 by Canfield; US Patent No. 8,751,346 B2 by Maisonneuve; US Patent No. 7,689,494 B2 by Torre et al; US Patent No. 7,383,223 B1 by Dilip et al; CA 3162046 by Sutherland; WO 2013012531 A2 by Vysogorets
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARY M GREGG whose telephone number is (571)270-5050. The examiner can normally be reached M-F 9am-5pm.
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/MARY M GREGG/Examiner, Art Unit 3695
/CHRISTINE M Tran/Supervisory Patent Examiner, Art Unit 3695