DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the communications filed on 09/04/2026.
Claims 1, 11, and 17 have been amended and are hereby entered.
Claims 1-20 are currently pending and have been examined.
This action is made Non-Final.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for future claim amendments to avoid U.S.C 112(a) issues that can arise. The Examiner thanks the Applicant in advance.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/04/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09/04/2026 has been entered.
Claim Objections
Claims 1, 11, and 17 are objected to because of the following informalities:
Claims 1, 11, and 17 recite “the first purchase based on the second portion of the risk,, wherein, upon credit utilization for the first purchase.” It appears there is a grammatical mistake because there are two commas (,,) after the word ‘risk’. For compact examination purposes, Examiner interpreted the instances recited in Claims 1, 11, and 17 as “the first purchase based on the second portion of the risk; wherein, upon credit utilization for the first purchase.” Appropriate corrections are required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of determining that a user is authorized to use the services of an entity based on a plurality of retrieved metrics associated with the user; without significantly more.
Claim 1 is directed to one or more non-transitory computer storage media, which is one of the statutory categories of invention; Claim 11 is directed to a method, which is one of the statutory categories of invention; and Claim 17 is directed to a system, which is one of the statutory categories of invention. (Step 1: YES).
Claim 1 is directed to one or more non-transitory computer storage media storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform operations for a user application, the operations comprising: training a machine learning model by: extracting a set of historical features from historical data comprising past user histories that have been labeled as inspected or not inspected; converting the set of historical features into one or more representative feature vectors; and processing the one or more representative feature vectors using the machine learning model to identify a vector space representative of the set of historical features; subsequent to training the machine learning model: extracting, by the one or more processors, a set of features from a plurality of applicant histories, wherein each applicant history of the plurality of applicant histories comprises a feature of an applicant for using a service provided by a first entity; converting, by the one or more processors, the set of features into one or more feature vectors; processing, by the one or more processors, the one or more feature vectors using the machine learning model to generate a prediction for a risk metric associated the applicant, wherein the machine learning model generates the prediction based at least in part on determining a distance, in the vector space, between the one or more feature vectors and the one or more representative feature vectors; automatically determining, by the one or more processors, based on the risk metric, that the applicant is authorized to use a first portion of the service of the first entity with a second entity assuming a first portion of a risk for the service provided by the first entity and the first entity assuming a second portion of the risk for the service provided by the first entity; assigning a first recourse percentage to a first purchase based on the first portion of the risk and a second recourse percentage to the first purchase based on the second portion of the risk,, wherein, upon credit utilization for the first purchase: a risk assessment is conducted by the one or more processors to determine a percentage of risk assumed by the second entity and a percentage of risk assumed by the first entity when the first purchase occurs; the first recourse percentage is calculated by the one or more processors based on the percentage of risk assumed by the second entity, and the second recourse percentage is calculated by the one or more processors based on the percentage of risk assumed by the first entity; and the first recourse percentage and the second recourse percentage are stored by the one or more processors in association with a credit transaction record for the first purchase; receiving a second request to use a second portion of services provide by the first entity; automatically determining based on the risk metric, that the applicant is authorized to use the second portion of the service provide by the first entity, with the second entity assuming a third portion of a risk for the second portion of service provided by the first entity and the first entity assuming a fourth portion of the risk for the second portion of the service provided by the first entity; and assigning a third recourse percentage for a second purchase based on the third portion of the risk and a fourth recourse percentage based on the fourth portion of the risk. These series of steps describe the abstract idea of determining that a user is authorized to use the services of an entity based on a plurality of retrieved metrics associated with the user (with the exception of the italicized and bolded terms above), which is mitigating risk of an unauthorized user using services provided by an entity; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the use of services provided by an entity of processing merchant credit applications, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. The system limitations, e.g., one or more processors, user application, and machine learning model do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 1 recites an abstract idea (Step 2A-Prong 1: YES).
This judicial exception is not integrated into a practical application because the additional elements of one or more processors, user application, and machine learning model are no more than simply applying the abstract idea using generic computer elements. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Thus, claim 1 does not integrate the abstract idea into a practical application (Step 2A-Prong 2: NO).
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of one or more processors, user application, and machine learning model are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment (Step 2B: NO). Thus, claim 1 is not patent eligible.
Dependent claims 2-10 are directed to one or more non-transitory computer storage media, which perform the steps that describe the abstract idea of determining that a user is authorized to use the services of an entity based on a plurality of retrieved metrics associated with the user, which is mitigating risk of an unauthorized user using services provided by an entity. Furthermore, dependent claim 4 is directed to one or more non-transitory computer storage media, which performs the step: “wherein the automatically determining that the applicant is authorized to use a portion of the service includes a secondary validation process to double-check the risk metric against external credit risk databases.” The series of steps of claims 2-10 describe the abstract idea of determining that a user is authorized to use the services of an entity based on a plurality of retrieved metrics associated with the user (with the exception of the italicized and bolded terms above), which is mitigating risk of an unauthorized user using services provided by an entity; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the use of services provided by an entity of processing merchant credit applications, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Thus, claims 2-10 recite an abstract idea. The additional elements of one or more processors, user application, machine learning model, and external credit risk databases are no more than simply applying the abstract idea using generic computer elements. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Furthermore, the additional elements: one or more processors, user application, machine learning model, and external credit risk databases, do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment.
Claim 11 is directed to a method for an application performed by one or more processors, the method comprising: processing one or more feature vectors using a machine learning model to generate a risk metric associated an applicant; automatically determining, by the one or more processors, based on the risk metric, that the applicant is authorized to use a first portion of a service of a first entity with a second entity assuming a first portion of a risk for the service provided by the first entity and the first entity assuming a second portion of the risk for the service provided by the first entity; assigning a first recourse percentage to a first purchase based on the first portion of the risk and a second recourse percentage to the first purchase based on the second portion of the risk,, wherein, upon credit utilization for the first purchase: a risk assessment is conducted by the one or more processors to determine a percentage of risk assumed by the second entity and a percentage of risk assumed by the first entity when the first purchase occurs; the first recourse percentage is calculated by the one or more processors based on the percentage of risk assumed by the second entity, and the second recourse percentage is calculated by the one or more processors based on the percentage of risk assumed by the first entity; and the first recourse percentage and the second recourse percentage are stored by the one or more processors in association with a credit transaction record for the first purchase; receiving a second request to use a second portion of service provide by the first entity; automatically determining based on the risk metric, that the applicant is authorized to use the second portion of the service provide by the first entity, with the second entity assuming a third portion of a risk for the second portion of service provided by the first entity and the first entity assuming a fourth portion of the risk for the second portion of the service provided by the first entity; and assigning a third recourse percentage for a second purchase based on the third portion of the risk and a fourth recourse percentage based on the fourth portion of the risk. These series of steps describe the abstract idea of determining that a user is authorized to use the services of an entity based on a plurality of retrieved metrics associated with the user (with the exception of the italicized and bolded terms above), which is mitigating risk of an unauthorized user using services provided by an entity; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the use of services provided by an entity of processing merchant credit applications, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. The system limitations, e.g., an application, one or more processors, and machine learning model, do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 11 recites an abstract idea (Step 2A-Prong 1: YES).
This judicial exception is not integrated into a practical application because the additional elements of an application, one or more processors, and machine learning model are no more than simply applying the abstract idea using generic computer elements. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Thus, claim 1 does not integrate the abstract idea into a practical application (Step 2A-Prong 2: NO).
Claim 11 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of an application, one or more processors, and machine learning model are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment (Step 2B: NO). Thus, claim 11 is not patent eligible.
Dependent claims 12-16 are directed to a method, which recites a series of steps that describe the abstract idea of determining that a user is authorized to use the services of an entity based on a plurality of retrieved metrics associated with the user, which is mitigating risk of an unauthorized user using services provided by an entity. Furthermore, dependent claim 13 is directed to a method, which recites the step: “wherein the machine learning model is specifically a neural network configured to process temporal and transactional data of the applicant to generate the risk metric.” The series of steps of claim 12-16 describe the abstract idea of determining that a user is authorized to use the services of an entity based on a plurality of retrieved metrics associated with the user (with the exception of the italicized and bolded terms above), which is mitigating risk of an unauthorized user using services provided by an entity; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the use of services provided by an entity of processing merchant credit applications, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Thus, claims 12-16 recite an abstract idea. The additional elements of an application, one or more processors, machine learning model, and neural network are no more than simply applying the abstract idea using generic computer elements. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). The computer network limitations are a field of use limitations (MPEP 2106.05(h)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Furthermore, the additional elements: an application, one or more processors, machine learning model, and neural network, do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment.
Claim 17 is directed to a system comprising: one or more processors; and one or more computer storage media storing computer-readable instructions thereon that, when executed by the one or more processors, cause the one or more processors to: generate a risk metric associated an applicant based on a plurality of metrics associated with the applicant; automatically determine based on the risk metric, that the applicant is authorized to use a first portion of a service of a first entity with a second entity assuming a first portion of a risk for the service provided by the first entity and the first entity assuming a second portion of the risk for the service provided by the first entity; assigning a first recourse percentage to a first purchase based on the first portion of the risk and a second recourse percentage to the first purchase based on the second portion of the risk,, wherein, upon credit utilization for the first purchase: a risk assessment is conducted by the one or more processors to determine a percentage of risk assumed by the second entity and a percentage of risk assumed by the first entity when the first purchase occurs; the first recourse percentage is calculated by the one or more processors based on the percentage of risk assumed by the second entity, and the second recourse percentage is calculated by the one or more processors based on the percentage of risk assumed by the first entity; and the first recourse percentage and the second recourse percentage are stored by the one or more processors in association with a credit transaction record for the first purchase; receiving a second request to use a second portion of services provide by the first entity; automatically determine based on the risk metric, that the applicant is authorized to use the second portion of a service provide by the first entity, with the second entity assuming a third portion of a risk for the second portion of service provided by the first entity and the first entity assuming a fourth portion of the risk for the second portion of the service provided by the first entity; and assign a third recourse percentage for a second purchase based on the third portion of the risk and a fourth recourse percentage based on the fourth portion of the risk. These series of steps describe the abstract idea of d determining that a user is authorized to use the services of an entity based on a plurality of retrieved metrics associated with the user (with the exception of the italicized and bolded terms above), which is mitigating risk of an unauthorized user using services provided by an entity; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the use of services provided by an entity of processing merchant credit applications, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. The system limitations, e.g., one or more processors and one or more computer storage media do not necessarily restrict the claim from reciting an abstract idea. Thus, claim 17 recites an abstract idea (Step 2A-Prong 1: YES).
This judicial exception is not integrated into a practical application because the additional elements of one or more processors and one or more computer storage media, are no more than simply applying the abstract idea using generic computer elements. The additional elements listed above are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Thus, claim 17 does not integrate the abstract idea into a practical application (Step 2A-Prong 2: NO).
Claim 17 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of one or more processors and one or more computer storage media are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment (Step 2B: NO). Thus, claim 17 is not patent eligible.
Dependent claims 18-20 are directed to a system , which perform the steps that describe the abstract idea of determining that a user is authorized to use the services of an entity based on a plurality of retrieved metrics associated with the user, which is mitigating risk of an unauthorized user using services provided by an entity; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the use of services provided by an entity of processing merchant credit applications, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Thus, claims 18-20 recite an abstract idea. The additional elements of one or more processors and one or more computer storage media are no more than simply applying the abstract idea using generic computer elements. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. Furthermore, the additional elements: one or more processors and one or more computer storage media, do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment.
Dependent claims 2-10, 12-16, and 18-20 have further defined the abstract idea that is present in their respective independent claims: Claims 1, 11, and 17; and thus correspond to Certain Methods of Organizing Human Activity and are abstract in nature for the reason presented above. The dependent claims 2-10, 12-16, and 18-20 do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, claims 2-10, 12-16, and 18-20 are directed to an abstract idea without significantly more.
Thus, claims 1-20 are not patent-eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
26. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Fidanza (U.S. Patent Application Publication No. US 2020/0349641 A1; hereinafter “Fidanza”), in view of Wang (C.N. Patent Publication No. CN-114186126-A; hereinafter “Wang”).
Regarding Claims 1, 11, and 17:
Fidanza teaches:
A method for an application performed by one or more processors, the method comprising: (Fidanza, See, Para. 9; Abstract);
a system comprising: a processor; and [one or more computer storage media storing computer-readable instructions thereon] that, when executed by the at least one processor, cause the at least one processor to: (Fidanza, See, Para. 4-8; Abstract);
training a machine learning model by: extracting a set of historical features from historical data comprising past user histories that have been labeled as inspected or not inspected; (Fidanza, It may be used to issue loans to consumers for personal needs and requirements. The loan issuance system has a platform that may combine a machine learning algorithm and may use a cloud-based data architecture to allow the system to access a data stream from the online banking of a user and within milliseconds, seemingly "instantly" to a client, approve the client up to a maximum allowed credit (See, Para. 29); Wrapper methods may include those methods that consider the selection of a set of features as a search problem, where different combinations are prepared, evaluated and compared to other combinations. An example is the recursive feature elimination algorithm. Embedded methods may be related to how the loan issuance system 30 and a machine learning module as part of the server processor 52 or loan scoring engine 64 may learn which features best contribute to the accuracy of the model while the model is created. A common type of embedded feature selection methods are regularization methods (See, Para. 50; Fig. 1); Usually transaction data is pulled multiple times per day and notifications may be made. The authentication may authenticate accounts for ACH and eliminate the requirement for clients to enter account and routing numbers or deal with micro-deposits (See, Para. 118));
subsequent to training the machine learning model: extracting, by the one or more processors, a set of features from a plurality of applicant histories, wherein each applicant history of the plurality of applicant histories comprises a feature of an applicant for using a service provided by a first entity; (Fidanza, It may be used to issue loans to consumers for personal needs and requirements. The loan issuance system has a platform that may combine a machine learning algorithm and may use a cloud-based data architecture to allow the system to access a data stream from the online banking of a user and within milliseconds, seemingly "instantly" to a client, approve the client up to a maximum allowed credit (See, Para. 29); transaction data may be user-authorized and received for credit in depository-type accounts via the transactions endpoint, and the data may be standardized across financial institutions (See, Para.122; Fig. 1); Information that is input to these algorithms 65,66 via a first Application Programming Interface (API), i.e., application layer 68 may come from different sources, including a third-party banking information provider 70, which in FIG. 1 corresponds to the financial services data provider 60, a credit history 72, and publicly available data 74 (See, Para.40; Fig. 1-2));
processing, by the one or more processors, the [one or more feature vectors] using the machine learning model to generate a prediction for a risk metric associated the applicant, wherein the machine learning model generates the prediction based at least in part on [determining a distance, in the vector space, between the one or more feature vectors and the one or more representative feature vectors]; (Fidanza, Transaction data may be user-authorized and received for credit in depository-type accounts via the transactions endpoint, and the data may be standardized across financial institutions. There may be a unique ID of a transaction corresponding to a transaction ID and the ID of the account in which the transaction occurred such as an account ID ( See, Para. 122; Fig. 1); It may be used to issue loans to consumers for personal needs and requirements. The loan issuance system has a platform that may combine a machine learning algorithm and may use a cloud-based data architecture to allow the system to access a data stream from the online banking of a user and within milliseconds, seemingly "instantly" to a client, approve the client up to a maximum allowed credit (See, Para. 29); Information that is input to these algorithms 65,66 via a first Application Programming Interface (API), i.e., application layer 68 may come from different sources, including a third-party banking information provider 70, which in FIG. 1 corresponds to the financial services data provider 60, a credit history 72, and publicly available data 74 (See, Para.40; Fig. 1-2); The loan issuance server receives a private access token and limited identity data regarding a bank account associated with the client. A credit score engine receives public data associated with the client and income and transactional data of the client bank account and applies a machine learning model to create an initial credit score that is indicative of the maximum allowed credit for the client (See, Abstract; Para. 52));
automatically determining, by the one or more processors, based on the risk metric, that the applicant is authorized to use a first portion of the service of the first entity with a second entity assuming a first portion of a risk for the service provided by the first entity and the first entity assuming a second portion of the risk for the service provided by the first entity; (Fidanza, Once a pre-approved amount 76 is determined, the application layer 68 provides for further processing and possible user input, (See, Para. 40; Fig. 2) Note: The second entity assuming risk for the services provided by the first entity in the art is the bank);
assigning a first recourse percentage to a first purchase based on the first portion of the risk and a second recourse percentage to the first purchase based on the second portion of the risk; (Fidanza, There are different authentication paths for a user operating on a client computing device 32 to verify authentication numbers such as an instant authentication where the client enters their credentials and are authenticated immediately, or an instant match technique where a client enters their credentials, account and routing numbers, and the financial services data provider 60 may match user input and authenticate immediately (See, Para. 122; Fig. 1); The loan issuance server 50 operates in what appears to be almost instantaneously and its server processor 52 may estimate a pre-approved loan amount using the machine learning model with the associated credit risk by obtaining and processing publicly available information about the client and banking or financial data retrieved from a banking institution 62 associated with the client via a third-party financial services data provider 60. This risk measure is then combined with payment capacity forecasting based on the same sources of information, e.g., the banking or financial data and publicly available information credit data may be taken into account (See, Para. 32; Fig. 1); Transaction data may be user-authorized and received for credit in depository-type accounts via the transactions endpoint, and the data may be standardized across financial institutions. There may be a unique ID of a transaction corresponding to a transaction ID and the ID of the account in which the transaction occurred such as an account ID ( See, Para. 122));
wherein, upon credit utilization for the first purchase: (Fidanza, There are different authentication paths for a user operating on a client computing device 32 to verify authentication numbers such as an instant authentication where the client enters their credentials and are authenticated immediately, or an instant match technique where a client enters their credentials, account and routing numbers, and the financial services data provider 60 may match user input and authenticate immediately (See, Para. 122; Fig. 1); The loan issuance server 50 operates in what appears to be almost instantaneously and its server processor 52 may estimate a pre-approved loan amount using the machine learning model with the associated credit risk by obtaining and processing publicly available information about the client and banking or financial data retrieved from a banking institution 62 associated with the client via a third-party financial services data provider 60. This risk measure is then combined with payment capacity forecasting based on the same sources of information, e.g., the banking or financial data and publicly available information credit data may be taken into account (See, Para. 32; Fig. 1); Transaction data may be user-authorized and received for credit in depository-type accounts via the transactions endpoint, and the data may be standardized across financial institutions. There may be a unique ID of a transaction corresponding to a transaction ID and the ID of the account in which the transaction occurred such as an account ID ( See, Para. 122));
a risk assessment is conducted by the one or more processors to determine a percentage of risk assumed by the second entity and a percentage of risk assumed by the first entity when the first purchase occurs; (Fidanza, The loan issuance system….calculate the credit…..This instant credit applies both to consumers, but preferably to businesses, and can be delivered as an instant credit at a point-of-sale (retail), instant credit at a checkout (e-commerce) to finance a purchase, or as instant cash as credit deposited on the borrower's bank account…...The loan issuance system 30 incorporates a machine learning model that aims to calculate the maximum amount of money to lend a client as a preferred business loan while minimizing risk based on “instant” credit information ….. the algorithm may work in real-time to deliver decision making information and should balance the risk exposition with the calculated indebtedness capacity. (See, Abstract; Para. 7, 18, 30-33, 43, 48, 49 Fig. 1, 5); The loan issuance system 30 may automate the feature ranking to make use of a standard methodology to feed the model with the best predictor variables for the model to forecast the bad debt. According to a recursive feature elimination, the best features for bad debt discrimination from the most correlated to the least correlated are ….. incomes/spending ratio ( See, 102-105, 116, 124; Fig. 1, 5); Note: The second entity assuming risk for the services provided by the first entity in the art is the bank);
the first recourse percentage is calculated by the one or more processors based on the percentage of risk assumed by the second entity, and the second recourse percentage is calculated by the one or more processors based on the percentage of risk assumed by the first entity; and (Fidanza, The loan issuance system….calculate the credit…..This instant credit applies both to consumers, but preferably to businesses, and can be delivered as an instant credit at a point-of-sale (retail), instant credit at a checkout (e-commerce) to finance a purchase, or as instant cash as credit deposited on the borrower's bank account…...The loan issuance system 30 incorporates a machine learning model that aims to calculate the maximum amount of money to lend a client as a preferred business loan while minimizing risk based on “instant” credit information ….. the algorithm may work in real-time to deliver decision making information and should balance the risk exposition with the calculated indebtedness capacity. (See, Abstract; Para. 7, 18, 30-33, 43, 48, 49 Fig. 1, 5); The loan issuance system 30 may automate the feature ranking to make use of a standard methodology to feed the model with the best predictor variables for the model to forecast the bad debt. According to a recursive feature elimination, the best features for bad debt discrimination from the most correlated to the least correlated are ….. incomes/spending ratio ( See, 102-105, 116, 124; Fig. 1, 5) ); Note: The second entity assuming risk for the services provided by the first entity in the art is the bank);
the first recourse percentage and the second recourse percentage are stored by the one or more processors in association with a credit transaction record for the first purchase; (Fidanza, The loan issuance system….calculate the credit…..This instant credit applies both to consumers, but preferably to businesses, and can be delivered as an instant credit at a point-of-sale (retail), instant credit at a checkout (e-commerce) to finance a purchase, or as instant cash as credit deposited on the borrower's bank account…...The loan issuance system 30 incorporates a machine learning model that aims to calculate the maximum amount of money to lend a client as a preferred business loan while minimizing risk based on “instant” credit information ….. the algorithm may work in real-time to deliver decision making information and should balance the risk exposition with the calculated indebtedness capacity. (See, Abstract; Para. 7, 18, 30-33, 43, 48, 49 Fig. 1, 5); The loan issuance system 30 may automate the feature ranking to make use of a standard methodology to feed the model with the best predictor variables for the model to forecast the bad debt. According to a recursive feature elimination, the best features for bad debt discrimination from the most correlated to the least correlated are ….. incomes/spending ratio ( See, 102-105, 116, 124; Fig. 1, 5); In a non-limiting example only, the features extracted from third-party banking information or financial services provider may include: (1) available income, which is computed as a sum of client incomes, weighted by the confidence of income occurrence ( See, Para. 52); Referring now to FIG. 1, the loan issuance system is illustrated generally at 30 and shows the client computing device 32 that may include a display 34, processor 36 connected to the display, and database 38 connected to the processor. A data entry 40 permits the client or consumer to enter data such as for initial registration with a loan issuance program (See, Para. 31; Fig. 1); The loan issuance server 50 operates in what appears to be almost instantaneously and its server processor 52 may estimate a pre-approved loan amount using the machine learning model with the associated credit risk by obtaining and processing publicly available information about the client and banking or financial data retrieved from a banking institution 62 associated with the client via a third-party financial services data provider 60. This risk measure is then combined with payment capacity forecasting based on the same sources of information, e.g., the banking or financial data and publicly available information credit data may be taken into account (See, Para. 32; Fig. 1));
receiving a second request to use a second portion of services provide by the first entity; (Fidanza, In a non-limiting example only, the features extracted from third-party banking information or financial services provider may include: (1) available income, which is computed as a sum of client incomes, weighted by the confidence of income occurrence ( See, Para. 52); Referring now to FIG. 1, the loan issuance system is illustrated generally at 30 and shows the client computing device 32 that may include a display 34, processor 36 connected to the display, and database 38 connected to the processor. A data entry 40 permits the client or consumer to enter data such as for initial registration with a loan issuance program (See, Para. 31; Fig. 1); The loan issuance server 50 operates in what appears to be almost instantaneously and its server processor 52 may estimate a pre-approved loan amount using the machine learning model with the associated credit risk by obtaining and processing publicly available information about the client and banking or financial data retrieved from a banking institution 62 associated with the client via a third-party financial services data provider 60. This risk measure is then combined with payment capacity forecasting based on the same sources of information, e.g., the banking or financial data and publicly available information credit data may be taken into account (See, Para. 32; Fig. 1));
automatically determining based on the risk metric, that the applicant is authorized to use the second portion of the service provide by the first entity, with the second entity assuming a third portion of a risk for the second portion of service provided by the first entity and the first entity assuming a fourth portion of the risk for the second portion of the service provided by the first entity; and (Fidanza, Once a pre-approved amount 76 is determined, the application layer 68 provides for further processing and possible user input, (See, Para. 40; Fig. 2); There are different authentication paths for a user operating on a client computing device 32 to verify authentication numbers such as an instant authentication where the client enters their credentials and are authenticated immediately, or an instant match technique where a client enters their credentials, account and routing numbers, and the financial services data provider 60 may match user input and authenticate immediately (See, Para. 122; Fig. 1); Usually transaction data is pulled multiple times per day and notifications may be made. The authentication may authenticate accounts for ACH and eliminate the requirement for clients to enter account and routing numbers or deal with micro-deposits (See, Para. 118));
assigning a third recourse percentage for a second purchase based on the third portion of the risk and a fourth recourse percentage based on the fourth portion of the risk. (Fidanza, There are different authentication paths for a user operating on a client computing device 32 to verify authentication numbers such as an instant authentication where the client enters their credentials and are authenticated immediately, or an instant match technique where a client enters their credentials, account and routing numbers, and the financial services data provider 60 may match user input and authenticate immediately (See, Para. 122; Fig. 1); The loan issuance server 50 operates in what appears to be almost instantaneously and its server processor 52 may estimate a pre-approved loan amount using the machine learning model with the associated credit risk by obtaining and processing publicly available information about the client and banking or financial data retrieved from a banking institution 62 associated with the client via a third-party financial services data provider 60. This risk measure is then combined with payment capacity forecasting based on the same sources of information, e.g., the banking or financial data and publicly available information credit data may be taken into account (See, Para. 32; Fig. 1); Transaction data may be user-authorized and received for credit in depository-type accounts via the transactions endpoint, and the data may be standardized across financial institutions. There may be a unique ID of a transaction corresponding to a transaction ID and the ID of the account in which the transaction occurred such as an account ID ( See, Para. 122)).
Fidanza does not specifically teach one or more non-transitory computer storage media storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform operations for a user application, the operations comprising; converting the set of historical features into one or more representative feature vectors; processing the one or more representative feature vectors using the machine learning model to identify a vector space representative of the set of historical features; converting, by the one or more processors, the set of features into one or more feature vectors; one or more feature vectors; and determining a distance, in the vector space, between the one or more feature vectors and the one or more representative feature vectors.
However, Wang further teaches the following limitations:
One or more non-transitory computer storage media storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform operations for a user application, the operations comprising; (Wang, See, Page (“P.”): 5: Para. 13-14; Abstract);
converting the set of historical features into one or more representative feature vectors; and processing the one or more representative feature vectors using the machine learning model to identify a vector space representative of the set of historical features; (Wang, training the preset machine learning model, obtaining the training vector space representation model in the process, the preset machine learning model is determined as the current machine learning model; inputting the operation log data of the second account into the current machine learning model, obtaining the prediction feature vector corresponding to the second account; determining the sequencing index prediction data according to the prediction feature vector of the second account and the plurality of operation prediction data corresponding to the history recommendation object; based on the sequencing index prediction data and the actual operation data, determining the loss value; when the loss value is greater than the preset threshold value, performing reverse propagation based on the loss value, updating the current machine learning model to obtain the updated machine learning model, re-determining the updated machine learning model as the current machine learning model; repeat step: inputting the operation log data of the second account into the current machine learning model, obtaining the prediction feature vector corresponding to the second account; when the loss value is less than or equal to the preset threshold value, the current machine learning model is determined as vector space representation model (See, entire P. 4, 13, 14));
converting, by the at least one computer processor, the set of features into one or more feature vectors; (Wang, training the preset machine learning model, obtaining the training vector space representation model in the process, the preset machine learning model is determined as the current machine learning model; inputting the operation log data of the second account into the current machine learning model, obtaining the prediction feature vector corresponding to the second account; determining the sequencing index prediction data according to the prediction feature vector of the second account and the plurality of operation prediction data corresponding to the history recommendation object; based on the sequencing index prediction data and the actual operation data, determining the loss value; when the loss value is greater than the preset threshold value, performing reverse propagation based on the loss value, updating the current machine learning model to obtain the updated machine learning model, re-determining the updated machine learning model as the current machine learning model; repeat step: inputting the operation log data of the second account into the current machine learning model, obtaining the prediction feature vector corresponding to the second account; when the loss value is less than or equal to the preset threshold value, the current machine learning model is determined as vector space representation model (See, entire P. 4, 13, 14));
one or more feature vectors (Wang, a training sub-module, configured to perform the operation log data of the second account input preset machine learning model, obtaining the prediction feature vector corresponding to the second account; the prediction feature vector comprises a plurality of prediction weight information; a plurality of prediction weight information corresponding to a plurality of operation prediction data; determining the sequencing index prediction data according to the prediction feature vector of the second account and the plurality of operation prediction data corresponding to the history recommendation object; based on the sequencing index prediction data and the actual operation data, training the preset machine learning model, obtaining the vector space representation model after training. (See, P. 4: Para. 12)).
determining a distance, in the vector space, between the one or more feature vectors and the one or more representative feature vectors; (Wang, training the preset machine learning model, obtaining the training vector space representation model in the process, the preset machine learning model is determined as the current machine learning model…… determining the sequencing index prediction data according to the prediction feature vector of the second account and the plurality of operation prediction data corresponding to the history recommendation object; based on the sequencing index prediction data and the actual operation data, determining the loss value; when the loss value is greater than the preset threshold value, performing reverse propagation based on the loss value, updating the current machine learning model to obtain the updated machine learning model, re-determining the updated machine learning model as the current machine learning model; repeat step: inputting the operation log data of the second account into the current machine learning model, obtaining the prediction feature vector corresponding to the second account; when the loss value is less than or equal to the preset threshold value, the current machine learning model is determined as vector space representation model (See, P. 14: Para. 2)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Fidanza with the features of Wang’s system because “a training sub-module, configured to perform the operation log data of the second account input preset machine learning model, obtaining the prediction feature vector corresponding to the second account; the prediction feature vector comprises a plurality of prediction weight information; a plurality of prediction weight information corresponding to a plurality of operation prediction data; determining the sequencing index prediction data according to the prediction feature vector of the second account and the plurality of operation prediction data corresponding to the history recommendation object; based on the sequencing index prediction data and the actual operation data, training the preset machine learning model, obtaining the vector space representation model after training.” (Wang, P.4: Para. 12).
Regarding Claim 2:
Fidanza teaches:
wherein the set of historical features includes data points selected from a group consisting of payment history, credit usage, account balances, and duration of credit history. (Fidanza, Information that is input to these algorithms 65,66 via a first Application Programming Interface (API), i.e., application layer 68 may come from different sources, including a third-party banking information provider 70, which in FIG. 1 corresponds to the financial services data provider 60, a credit history 72, and publicly available data 74 (See, Para.40; Fig. 1-2); It may be used to issue loans to consumers for personal needs and requirements. The loan issuance system has a platform that may combine a machine learning algorithm and may use a cloud-based data architecture to allow the system to access a data stream from the online banking of a user and within milliseconds, seemingly "instantly" to a client, approve the client up to a maximum allowed credit (See, Para. 29)).
Regarding Claim 3:
Fidanza teaches:
further comprising updating the machine learning model periodically based on one or more additional applicant histories. (Fidanza, Information that is input to these algorithms 65,66 via a first Application Programming Interface (API), i.e., application layer 68 may come from different sources, including a third-party banking information provider 70, which in FIG. 1 corresponds to the financial services data provider 60, a credit history 72, and publicly available data 74 (See, Para. 40; Fig. 1-2); It may be used to issue loans to consumers for personal needs and requirements. The loan issuance system has a platform that may combine a machine learning algorithm and may use a cloud-based data architecture to allow the system to access a data stream from the online banking of a user and within milliseconds, seemingly "instantly" to a client, approve the client up to a maximum allowed credit (See, Para. 29)).
Regarding Claim 4:
Fidanza teaches:
wherein the automatically determining that the applicant is authorized to use a portion of the service includes a secondary validation process to double-check the risk metric against external credit risk databases. (Fidanza, Usually transaction data is pulled multiple times per day and notifications may be made. The authentication may authenticate accounts for ACH and eliminate the requirement for clients to enter account and routing numbers or deal with micro-deposits (See, Para. 118); There are different authentication paths for a user operating on a client computing device 32 to verify authentication numbers such as an instant authentication where the client enters their credentials and are authenticated immediately, or an instant match technique where a client enters their credentials, account and routing numbers, and the financial services data provider 60 may match user input and authenticate immediately (See, Para. 122; Fig. 1); Usually transaction data is pulled multiple times per day and notifications may be made. The authentication may authenticate accounts for ACH and eliminate the requirement for clients to enter account and routing numbers or deal with micro-deposits (See, Para. 118); (Fidanza, Wrapper methods may include those methods that consider the selection of a set of features as a search problem, where different combinations are prepared, evaluated and compared to other combinations. An example is the recursive feature elimination algorithm. Embedded methods may be related to how the loan issuance system 30 and a machine learning module as part of the server processor 52 or loan scoring engine 64 may learn which features best contribute to the accuracy of the model while the model is created. A common type of embedded feature selection methods are regularization methods (See, Para. 50; Fig. 1); Information that is input to these algorithms 65,66 via a first Application Programming Interface (API), i.e., application layer 68 may come from different sources, including a third-party banking information provider 70, which in FIG. 1 corresponds to the financial services data provider 60, a credit history 72, and publicly available data 74 (See, Para. 40; Fig. 2)).
Regarding Claim 5:
Fidanza teaches:
wherein the service provide by the first entity includes financial credit services. (Fidanza, The loan issuance server receives a private access token and limited identity data regarding a bank account associated with the client. A credit score engine receives public data associated with the client and income and transactional data of the client bank account and applies a machine learning model to create an initial credit score that is indicative of the maximum allowed credit for the client (See, Abstract)).
Regarding Claim 6:
Fidanza teaches:
further comprising an automated appeal process if the applicant is initially denied service usage. (Fidanza, Wrapper methods may include those methods that consider the selection of a set of features as a search problem, where different combinations are prepared, evaluated and compared to other combinations. An example is the recursive feature elimination algorithm. Embedded methods may be related to how the loan issuance system 30 and a machine learning module as part of the server processor 52 or loan scoring engine 64 may learn which features best contribute to the accuracy of the model while the model is created. A common type of embedded feature selection methods are regularization methods (See, Para. 50; Fig. 1); Usually transaction data is pulled multiple times per day and notifications may be made. The authentication may authenticate accounts for ACH and eliminate the requirement for clients to enter account and routing numbers or deal with micro-deposits (See, Para. 118); Information that is input to these algorithms 65,66 via a first Application Programming Interface (API), i.e., application layer 68 may come from different sources, including a third-party banking information provider 70, which in FIG. 1 corresponds to the financial services data provider 60, a credit history 72, and publicly available data 74 (See, Para. 40; Fig. 2)).
Regarding Claim 7:
Fidanza teaches:
wherein the first entity is a financial institution and the second entity is a merchant. (Fidanza, Referring now to FIG. 1, the loan issuance system is illustrated generally at 30 and shows the client computing device 32 that may include a display 34, processor 36 connected to the display, and database 38 connected to the processor. A data entry 40 permits the client or consumer to enter data such as for initial registration with a loan issuance program (See, Para. 31; Fig. 1)).
Regarding Claim 8:
Fidanza teaches:
further comprising recalculating the risk metric and updating a service authorization if a financial behavior of the applicant changes. (Fidanza, Information that is input to these algorithms 65,66 via a first Application Programming Interface (API), i.e., application layer 68 may come from different sources, including a third-party banking information provider 70, which in FIG. 1 corresponds to the financial services data provider 60, a credit history 72, and publicly available data 74 (See, Para. 40; Fig. 2); Wrapper methods may include those methods that consider the selection of a set of features as a search problem, where different combinations are prepared, evaluated and compared to other combinations. An example is the recursive feature elimination algorithm. Embedded methods may be related to how the loan issuance system 30 and a machine learning module as part of the server processor 52 or loan scoring engine 64 may learn which features best contribute to the accuracy of the model while the model is created. A common type of embedded feature selection methods are regularization methods (See, Para. 50; Fig. 1)).
Regarding Claim 9:
Fidanza teaches:
further comprising assigning risk portions and recourse percentages for one or more additional purchases beyond the first purchase, based on the risk metric, where the risk and percentages are dynamically adjusted according to an ongoing applicant behavior. (Fidanza, Information that is input to these algorithms 65,66 via a first Application Programming Interface (API), i.e., application layer 68 may come from different sources, including a third-party banking information provider 70, which in FIG. 1 corresponds to the financial services data provider 60, a credit history 72, and publicly available data 74 (See, Para. 40; Fig. 2); Wrapper methods may include those methods that consider the selection of a set of features as a search problem, where different combinations are prepared, evaluated and compared to other combinations. An example is the recursive feature elimination algorithm. Embedded methods may be related to how the loan issuance system 30 and a machine learning module as part of the server processor 52 or loan scoring engine 64 may learn which features best contribute to the accuracy of the model while the model is created. A common type of embedded feature selection methods are regularization methods (See, Para. 50; Fig. 1); There are different authentication paths for a user operating on a client computing device 32 to verify authentication numbers such as an instant authentication where the client enters their credentials and are authenticated immediately, or an instant match technique where a client enters their credentials, account and routing numbers, and the financial services data provider 60 may match user input and authenticate immediately (See, Para. 122; Fig. 1); This risk measure is then combined with payment capacity forecasting based on the same sources of information, e.g., the banking or financial data and publicly available information credit data may be taken into account (See, Para. 32; Fig. 1)).
Regarding Claim 10:
Fidanza teaches:
further comprising the first entity and the second entity assuming a bad debt following a default by the applicant based on one or more assigned risk portions to each of the first entity and the second entity. (Fidanza, The model captures relationships among factors to allow assessment of bad debt risk or the potential of that consumer and associated with a particular set of conditions. This helps guide automatic decision-making in the system so that the system determines when the consumer requires an increase in the maximum allowed credit and the risk involved with increasing the maximum allowed credit. Thresholds can be set of the model outcome (See, Para. 93); Once a pre-approved amount 76 is determined, the application layer 68 provides for further processing and possible user input, (See, Para. 40; Fig. 2); There are different authentication paths for a user operating on a client computing device 32 to verify authentication numbers such as an instant authentication where the client enters their credentials and are authenticated immediately, or an instant match technique where a client enters their credentials, account and routing numbers, and the financial services data provider 60 may match user input and authenticate immediately (See, Para. 122; Fig. 1); Usually transaction data is pulled multiple times per day and notifications may be made. The authentication may authenticate accounts for ACH and eliminate the requirement for clients to enter account and routing numbers or deal with micro-deposits (See, Para. 118)).
Regarding Claim 12:
Fidanza teaches:
wherein the risk metric is based on a weighted evaluation of the plurality of metrics associated with the applicant. (Fidanza, In a non-limiting example only, the features extracted from third-party banking information or financial services provider may include: (1) available income, which is computed as a sum of client incomes, weighted by the confidence of income occurrence ( See, Para. 52); Referring now to FIG. 1, the loan issuance system is illustrated generally at 30 and shows the client computing device 32 that may include a display 34, processor 36 connected to the display, and database 38 connected to the processor. A data entry 40 permits the client or consumer to enter data such as for initial registration with a loan issuance program (See, Para. 31; Fig. 1); The loan issuance server 50 operates in what appears to be almost instantaneously and its server processor 52 may estimate a pre-approved loan amount using the machine learning model with the associated credit risk by obtaining and processing publicly available information about the client and banking or financial data retrieved from a banking institution 62 associated with the client via a third-party financial services data provider 60. This risk measure is then combined with payment capacity forecasting based on the same sources of information, e.g., the banking or financial data and publicly available information credit data may be taken into account (See, Para. 32; Fig. 1)).
Regarding Claim 13:
Fidanza teaches:
wherein the machine learning model is specifically a neural network configured to process temporal and transactional data of the applicant to generate the risk metric. (Fidanza, It may be used to issue loans to consumers for personal needs and requirements. The loan issuance system has a platform that may combine a machine learning algorithm and may use a cloud-based data architecture to allow the system to access a data stream from the online banking of a user and within milliseconds, seemingly "instantly" to a client, approve the client up to a maximum allowed credit (See, Para. 29)).
Regarding Claim 14:
Fidanza teaches:
wherein the [one or more feature vectors] include data derived from historical data for the applicant, including payment timeliness, frequency of service usage, and financial transaction amounts. (Fidanza, Information that is input to these algorithms 65,66 via a first Application Programming Interface (API), i.e., application layer 68 may come from different sources, including a third-party banking information provider 70, which in FIG. 1 corresponds to the financial services data provider 60, a credit history 72, and publicly available data 74 (See, Para.40; Fig. 1-2); The loan issuance server 50 operates in what appears to be almost instantaneously and its server processor 52 may estimate a pre-approved loan amount using the machine learning model with the associated credit risk by obtaining and processing publicly available information about the client and banking or financial data retrieved from a banking institution 62 associated with the client via a third-party financial services data provider 60. This risk measure is then combined with payment capacity forecasting based on the same sources of information, e.g., the banking or financial data and publicly available information credit data may be taken into account (See, Para. 32; Fig. 1)).
Fidanza does not specifically teach one or more feature vectors.
However, Wang further teaches the following limitation:
one or more feature vectors (Wang, a training sub-module, configured to perform the operation log data of the second account input preset machine learning model, obtaining the prediction feature vector corresponding to the second account; the prediction feature vector comprises a plurality of prediction weight information; a plurality of prediction weight information corresponding to a plurality of operation prediction data; determining the sequencing index prediction data according to the prediction feature vector of the second account and the plurality of operation prediction data corresponding to the history recommendation object; based on the sequencing index prediction data and the actual operation data, training the preset machine learning model, obtaining the vector space representation model after training. (See, P. 4: Para. 12)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention to have modified Fidanza with the features of Wang’s system because “a training sub-module, configured to perform the operation log data of the second account input preset machine learning model, obtaining the prediction feature vector corresponding to the second account; the prediction feature vector comprises a plurality of prediction weight information; a plurality of prediction weight information corresponding to a plurality of operation prediction data; determining the sequencing index prediction data according to the prediction feature vector of the second account and the plurality of operation prediction data corresponding to the history recommendation object; based on the sequencing index prediction data and the actual operation data, training the preset machine learning model, obtaining the vector space representation model after training.” (Wang, P.4: Para. 12).
Regarding Claim 15:
Fidanza teaches:
wherein the second entity assumes the risk by guaranteeing a payment or assuming a liability for an authorized user. (Fidanza, Referring now to FIGS. 8-15, there are illustrated screen shots of examples for process steps that are described generally in FIGS. 7A and 7B. The potential client makes initial contact with the loan insurance server 50 and accesses the loan issuance program (See, Para. 112; Fig. 7A-B, 8-15)).
Regarding Claim 16:
Fidanza teaches:
wherein the authorization decision is determined based on predefined risk thresholds set by the second entity. (Fidanza, The loan issuance server 50 operates in what appears to be almost instantaneously and its server processor 52 may estimate a pre-approved loan amount using the machine learning model with the associated credit risk by obtaining and processing publicly available information about the client and banking or financial data retrieved from a banking institution 62 associated with the client via a third-party financial services data provider 60. This risk measure is then combined with payment capacity forecasting based on the same sources of information, e.g., the banking or financial data and publicly available information credit data may be taken into account (See, Para. 32; Fig. 1)).
Regarding Claim 18:
Fidanza teaches:
further comprising monitoring an authorized user's usage of the service and adjusting the authorization decision based on their behavior and compliance with one or more service terms. (Fidanza, Information that is input to these algorithms 65,66 via a first Application Programming Interface (API), i.e., application layer 68 may come from different sources, including a third-party banking information provider 70, which in FIG. 1 corresponds to the financial services data provider 60, a credit history 72, and publicly available data 74 (See, Para. 40; Fig. 2)).
Regarding Claim 19:
Fidanza teaches:
wherein the plurality of metrics associated with the applicant is periodically updated. (Fidanza, Wrapper methods may include those methods that consider the selection of a set of features as a search problem, where different combinations are prepared, evaluated and compared to other combinations. An example is the recursive feature elimination algorithm. Embedded methods may be related to how the loan issuance system 30 and a machine learning module as part of the server processor 52 or loan scoring engine 64 may learn which features best contribute to the accuracy of the model while the model is created. A common type of embedded feature selection methods are regularization methods (See, Para. 50; Fig. 1)).
Regarding Claim 20:
Fidanza teaches:
wherein the authorization decision is automatically reevaluated in response to a change in the plurality of metrics associated with the applicant. (Fidanza, Wrapper methods may include those methods that consider the selection of a set of features as a search problem, where different combinations are prepared, evaluated and compared to other combinations. An example is the recursive feature elimination algorithm. Embedded methods may be related to how the loan issuance system 30 and a machine learning module as part of the server processor 52 or loan scoring engine 64 may learn which features best contribute to the accuracy of the model while the model is created. A common type of embedded feature selection methods are regularization methods (See, Para. 50; Fig. 1)).
Response to Arguments
With respect to the objections of claims 1 and 11, the objections are withdrawn in view of Applicant’s arguments/remarks made in an amendment filed on 09/04/2026.
Applicant's arguments filed on 09/04/2026 have been fully considered, but are not persuasive due to the following reasons:
With respect to the rejection of claims 1-20 under 35 U.S.C. 101, Applicant arguments are moot in view of the grounds of rejections presented above in this office action. The arguments are addressed to the extent they apply to the amended claims.
Applicant argues that “under part 1 of the Alice analysis, the claims are not directed to an abstract idea. Beginning with part 1, Applicant respectfully submits that the present claims are not directed merely to determining whether a user is authorized to use services based on retrieved metrics. As amended, the claims recite a specific computer-implemented transaction-processing sequence in which, upon utilization of credit for a particular purchase, processors determine respective percentages of risk assumed by multiple entities, calculate corresponding recourse percentages, and store those recourse percentages in association with a credit transaction record for the purchase. The pending claims should be found to be patent-eligible under the Federal Circuit’s McRO decision……Similarly here, the claimed invention incorporates specific processor-executed operations that…respond to utilization of credit for a particular purchase; … determine a percentage of risk assumed by each of two entities when the purchase occurs; … calculate separate recourse percentages based on the respective entity-specific risk percentages; … and store the calculated recourse percentages in association with the credit transaction record for that particular purchase ... The ordered combination therefore does more than merely evaluate an applicant’s risk or determine whether credit should be authorized. The claimed system generates transaction-specific, entity-specific risk-allocation data and preserves that allocation in association with the corresponding credit transaction record. This distinction is significant. A conventional risk score or credit-limit determination indicates whether an applicant presents an acceptable level of risk or how much credit may be extended. The amended claim additionally requires the processors, when credit is utilized for a particular purchase, to determine how the risk associated with that purchase is distributed between corresponding entities, calculate recourse percentages reflecting that allocation, and store those percentages in association with the transaction record. Accordingly, the claims recite a concrete computer-implemented mechanism for transaction-level risk processing and record management rather than merely the abstract concept of financial decision-making. The claimed operations bind processor-generated risk-allocation values to the particular transaction to which those values apply, thereby preserving transaction-specific information for subsequent processing. For at least these reasons, Applicant respectfully submits that the pending claims are not directed to an abstract idea and are therefore directed to patent-eligible subject matter. Applicant respectfully requests withdrawal of the rejection of all pending claims under 35 U.S.C. § 101. "
Examiner respectfully disagrees.
Under Step 2A: Prong 1, as previously discussed, Examiner respectfully notes that claims 1, 11, and 17, as amended, are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of determining that a user is authorized to use the services of an entity based on a plurality of retrieved metrics associated with the user; without significantly more. The series of steps recited in claims 1, 11, and 17, as amended, describe the abstract idea of determining that a user is authorized to use the services of an entity based on a plurality of retrieved metrics associated with the user, which is mitigating risk of an unauthorized user using services provided by an entity; therefore, corresponding to a fundamental economic principle or practice (including mitigating risk). Hence, a fundamental economic principle or practice (mitigating risk) is a Certain Methods of Organizing Human Activity. The abstract idea is also the use of services provided by an entity of processing merchant credit applications, which is a commercial interaction. Therefore, a commercial interaction is also a Certain Methods of Organizing Human Activity. Furthermore, the system limitations ( claim 1), e.g., one or more processors, user application, and machine learning model, do not necessarily restrict the claim from reciting an abstract idea.
Moreover, Examiner respectfully notes that the claims are first analyzed in the absence of technology to determine if it recites an abstract idea. As previously discussed, the additional limitations of technology are then considered to determine if it restricts the claim from reciting an abstract idea. Unlike the referenced Federal Circuit’s McRO decision ( “McRO”), it is determined that the additional limitations of technology do not necessarily restrict claims 1, 11, and 17, as amended, from reciting an abstract idea. Furthermore, unlike McRO, Examiner respectfully notes that the recited features in the limitations: “one or more non-transitory computer storage media storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform operations for a user application, the operations comprising: training a machine learning model by: extracting a set of historical features from historical data comprising past user histories that have been labeled as inspected or not inspected; converting the set of historical features into one or more representative feature vectors; and processing the one or more representative feature vectors using the machine learning model to identify a vector space representative of the set of historical features; subsequent to training the machine learning model: extracting, by the one or more processors, a set of features from a plurality of applicant histories, wherein each applicant history of the plurality of applicant histories comprises a feature of an applicant for using a service provided by a first entity; converting, by the one or more processors, the set of features into one or more feature vectors; processing, by the one or more processors, the one or more feature vectors using the machine learning model to generate a prediction for a risk metric associated the applicant, wherein the machine learning model generates the prediction based at least in part on determining a distance, in the vector space, between the one or more feature vectors and the one or more representative feature vectors; automatically determining, by the one or more processors, based on the risk metric, that the applicant is authorized to use a first portion of the service of the first entity with a second entity assuming a first portion of a risk for the service provided by the first entity and the first entity assuming a second portion of the risk for the service provided by the first entity; assigning a first recourse percentage to a first purchase based on the first portion of the risk and a second recourse percentage to the first purchase based on the second portion of the risk,, wherein, upon credit utilization for the first purchase: a risk assessment is conducted by the one or more processors to determine a percentage of risk assumed by the second entity and a percentage of risk assumed by the first entity when the first purchase occurs; the first recourse percentage is calculated by the one or more processors based on the percentage of risk assumed by the second entity, and the second recourse percentage is calculated by the one or more processors based on the percentage of risk assumed by the first entity; and the first recourse percentage and the second recourse percentage are stored by the one or more processors in association with a credit transaction record for the first purchase; receiving a second request to use a second portion of services provide by the first entity; automatically determining based on the risk metric, that the applicant is authorized to use the second portion of the service provide by the first entity, with the second entity assuming a third portion of a risk for the second portion of service provided by the first entity and the first entity assuming a fourth portion of the risk for the second portion of the service provided by the first entity; and assigning a third recourse percentage for a second purchase based on the third portion of the risk and a fourth recourse percentage based on the fourth portion of the risk” are simply making use of a computer and the computer limitations do not necessarily restrict the claim from reciting an abstract idea as discussed above under Step 2A-Prong 1 of the 35 U.S.C. 101 rejection.
Hence, Examiner has also considered each and every arguments under Step 2A-Prong 1 and concludes that these arguments are not persuasive. For example, under Step 2A-Prong 1, Examiner considers each and every limitation to determine if the claim recites an abstract idea. In this case, it is determined that the claim recites an abstract idea and the additional limitations of a computer device does not necessarily restrict the claim from reciting an abstract idea. The recited steps, as amended, are abstract in nature as there are no technical/technology improvements as a result of these steps. Thus, the claim recites an abstract idea. Whether the claim integrates the abstract idea into a practical application by providing technical/technology improvements are considered under Step 2A-Prong 2.
Applicant argues that “under part 2 of the Alice analysis, even if the claims were directed to an abstract idea, the claims recite features sufficient to ensure the claims amount to significantly more than the abstract idea, itself. … Nevertheless, even if the claims were found to be directed to an abstract idea, Applicant respectfully submits that the claims recite additional limitations that integrate any alleged abstract idea into a practical application and amount to significantly more than the alleged judicial exception..…. These limitations require more than generic computer implementation of financial decision-making. They require a defined processor-executed sequence tied to a particular credit transaction: a transaction event triggers an entity-specific risk assessment; the resulting risk percentages are used to calculate corresponding recourse percentages; and those processor-generated values are stored in association with the credit transaction record for the purchase. The amended limitations therefore meaningfully constrain how the claimed risk allocation is performed and used. The claim does not merely recite determining risk and displaying or communicating a result. Rather, the processors generate two corresponding transaction-specific values and maintain those values in association with the record for the transaction to which they apply. These additional elements, considered both individually and as an ordered combination, provide a specific practical application of the claimed risk information and amount to significantly more than merely determining whether an applicant should receive credit. The present amendments further distinguish the claims from a generalized financial practice because the claimed recourse percentages are not merely abstract values representing financial risk. They are generated through a defined processor sequence and maintained in association with the specific transaction record to which the allocation applies. Accordingly, even if the claims were considered to recite an abstract idea, the additional limitations integrate that alleged abstract idea into a practical application and provide significantly more than the alleged judicial exception. Independent claims 11 and 17 recite corresponding features and subject matter as independent claim 1. For at least the same reasons provided above with respect to claim 1, independent claims 11 and 17 are likewise directed to patent-eligible subject matter. Each of claims 2–10, 12–16, and 18–20 depends, either directly or indirectly, from one of independent claims 1, 11, or 17. Accordingly, by virtue of their dependency, these dependent claims incorporate the patent-eligible features of their respective independent claims and are similarly directed to patent-eligible subject matter. Applicant respectfully requests withdrawal of the § 101 rejection of claims 1–20. Claims 1–20 are believed to be in condition for allowance, and favorable consideration is respectfully requested."
Examiner respectfully disagrees.
Under Step 2A: Prong II, as previously discussed, Examiner respectfully notes that there is no improved technology in simply receiving, extracting, training, converting, processing, identifying, generating, determining, predicting, assigning, calculating, providing, authorizing, selecting, storing, updating, and outputting data (i.e., historical features, historical data, user histories, plurality of applicant histories, applicant risk metrics, entity data, purchase data, recourse data, and etc.). The disclosed invention cannot be equated to improvement to technological practices or computers. There is no technical improvement at all. Instead, Applicant recites “one or more non-transitory computer storage media storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform operations for a user application, the operations comprising: training a machine learning model by: extracting a set of historical features from historical data comprising past user histories that have been labeled as inspected or not inspected; converting the set of historical features into one or more representative feature vectors; and processing the one or more representative feature vectors using the machine learning model to identify a vector space representative of the set of historical features; subsequent to training the machine learning model: extracting, by the one or more processors, a set of features from a plurality of applicant histories, wherein each applicant history of the plurality of applicant histories comprises a feature of an applicant for using a service provided by a first entity; converting, by the one or more processors, the set of features into one or more feature vectors; processing, by the one or more processors, the one or more feature vectors using the machine learning model to generate a prediction for a risk metric associated the applicant, wherein the machine learning model generates the prediction based at least in part on determining a distance, in the vector space, between the one or more feature vectors and the one or more representative feature vectors; automatically determining, by the one or more processors, based on the risk metric, that the applicant is authorized to use a first portion of the service of the first entity with a second entity assuming a first portion of a risk for the service provided by the first entity and the first entity assuming a second portion of the risk for the service provided by the first entity; assigning a first recourse percentage to a first purchase based on the first portion of the risk and a second recourse percentage to the first purchase based on the second portion of the risk,, wherein, upon credit utilization for the first purchase: a risk assessment is conducted by the one or more processors to determine a percentage of risk assumed by the second entity and a percentage of risk assumed by the first entity when the first purchase occurs; the first recourse percentage is calculated by the one or more processors based on the percentage of risk assumed by the second entity, and the second recourse percentage is calculated by the one or more processors based on the percentage of risk assumed by the first entity; and the first recourse percentage and the second recourse percentage are stored by the one or more processors in association with a credit transaction record for the first purchase; receiving a second request to use a second portion of services provide by the first entity; automatically determining based on the risk metric, that the applicant is authorized to use the second portion of the service provide by the first entity, with the second entity assuming a third portion of a risk for the second portion of service provided by the first entity and the first entity assuming a fourth portion of the risk for the second portion of the service provided by the first entity; and assigning a third recourse percentage for a second purchase based on the third portion of the risk and a fourth recourse percentage based on the fourth portion of the risk.” The recited features in the limitations do not result in computer functionality or technical improvement. Examiner respectfully notes that Applicant is simply using a computer to input, process, and output data. The recited features in the limitations, as amended, does not disclose a technical solution to technical problem, but simply a business solution. Specifically, the recited steps, as amended, are merely managing/processing data (MPEP 2106.05(d)(II)) and do not result in computer functionality or technical improvement. Thus, , as previously discussed, Applicant has simply provided a business method practice of processing data (historical features, historical data, user histories, plurality of applicant histories, applicant risk metrics, entity data, purchase data, recourse data, and etc.), and no technical solution or improvement has been disclosed.
Moreover, there is no technology/technical improvement as a result of implementing the abstract idea. The recited limitations in the pending claims simply amount to the abstract idea of determining that a user is authorized to use the services of an entity based on a plurality of retrieved metrics associated with the user. There is no computer functionality improvement or technology improvement. The claim does not provide a technical solution to a technical problem. If there is an improvement, it is to the abstract idea and not to technology. Additionally, Examiner notes that it is important to keep in mind that an improvement in the judicial exception itself (e.g., recited fundamental economic principle or practice and/or commercial interaction) is not an improvement in technology (See, MPEP 2106.05(a)(II)). Thus, the claim does not integrate the abstract idea into a practical application; and these arguments are not persuasive.
Additionally, Claims 1, 11, and 17, as amended, recites steps at a high level of generality. In addition, all uses of the recited judicial exceptions require such data gathering and outputting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering, processing, and outputting. See MPEP 2106.05. As previously discussed, the claim simply makes use of a computer as a tool to apply the abstract idea without transforming the abstract idea into a patent eligible subject matter. Thus, these arguments are not persuasive. The recited steps in claim 1, as amended, are recited as being performed by one or more processors, user application, and machine learning model, and at least one computer processor. The additional elements: one or more processors, user application, and machine learning model are recited at a high level of generality, and are used as a tool to perform the generic computer function of receiving, processing, and outputting data. See MPEP 2106.05(f). Additionally, the claims, as amended, recites one or more processors, user application, and machine learning model, which are used to perform an abstract idea, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Specifically, the recitation of “one or more processors, user application, and machine learning model” in the limitations of the claims, as amended, merely indicates a field of use or technological environment in which the judicial exception is performed. Specifically, as previously discussed, the additional elements are all recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computing arrangement. Merely invoking the above listed additional elements is similar to invoking software and software components. The presence of a generic computer arrangement is nothing more than to implement the claimed invention (MPEP 2106.05(f)). Therefore, the recitations of additional elements do not meaningfully apply the abstract idea and hence do not integrate the abstract idea into a practical application. The claim, as amended, merely confines the use of the abstract idea to a particular technological environment; and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception. Hence, claims 1-20, as amended, do not integrate the abstract idea into a practical application.
Furthermore, under Step 2B, Examiner respectfully notes that all of Applicant's arguments have been reviewed, and the inventive concept cannot be furnished by a judicial exception. The improvements argued are to the abstract idea and not to technology. The technical limitations are simply utilized as a tool to implement the abstract idea without adding significantly more. Thus, the claim is directed to an abstract idea, and hence these arguments are not persuasive. The presence of a computer does not make the claimed solution necessarily rooted in computer technology. As noted above, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of one or more processors, user application, and machine learning model (Claim 1) are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. The additional elements when considered separately and as an ordered combination do not amount to add significantly more as these limitations provide nothing more than to simply apply the exception in a generic computer environment. Furthermore, as explained above with respect to Step 2A, Prong II, the additional elements: one or more processors, user application, and machine learning model (Claim 1), are at best mere instructions to “apply” the abstract idea, which cannot provide an inventive concept. See MPEP 2106.05(f). As discussed in Step 2A, Prong II above, the claims’ limitations are recited at a high level of generality. These elements simply amount to receiving and outputting data and are well-understood, routine, conventional activity. See MPEP 2106.05(d)(II). As discussed in Step 2A, Prong II above, the recitation of a computer/processor to perform recited limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept.
Hence, Examiner respectfully declines Applicant’s request to withdraw the 35 U.S.C. 101 rejection of claims 1-20.
With respect to the rejection of claims 1-20 under 35 U.S.C. 103, Applicant arguments are moot in view of cited language in previously used prior art, as presented above in this office action. The arguments are addressed to the extent they apply to the amended claims.
Applicant argues that “the combination of references fails to teach or suggest the newly-recited features of amended independent claim 1….. the newly-amended claim requires a different operation. Upon utilization of credit for the first purchase, the processors conduct a risk assessment that separately determines a percentage of risk assumed by the second entity and a percentage of risk assumed by the first entity when that purchase occurs. The processors then calculate a first recourse percentage based on the risk assumed by the second entity and a second recourse percentage based on the risk assumed by the first entity. Those recourse percentages are thereafter stored in association with the credit transaction record for the particular purchase ….. Neither Fidanza nor Wang teaches or suggests this purchase-triggered, multi-entity recourse operation. Nor does the cited combination provide a reason why a person of ordinary skill would modify Fidanza’s applicant-level credit evaluation system to determine separate entity-specific risk percentages, calculate corresponding recourse percentages, and store those percentages in association with an individual credit transaction record. Wang’s recommendation-weighting architecture provides no teaching directed to such financial-risk allocation. Accordingly, the cited references, individually or in combination, fail to teach or suggest the newly-recited limitations of amended claim 1. The proposed combination therefore does not render claim 1 obvious. Independent claims 11 and 17 recite corresponding features of the claimed invention and, for at least the reasons set forth above with respect to independent claim 1, the cited references fail to teach or suggest the features of independent claims 11 and 17. Each of claims 2–10, 12–16, and 18–20 depends, either directly or indirectly, from one of independent claims 1, 11, and 17. Accordingly, by virtue of their dependency, the cited references likewise fail to teach or suggest the features of these claims. Applicant respectfully requests withdrawal of the rejection of claims 1–20 under 35 U.S.C. § 103.”
Examiner respectfully disagrees.
Examiner respectfully notes that Fidanza teaches “wherein, upon credit utilization for the first purchase: a risk assessment is conducted by the one or more processors to determine a percentage of risk assumed by the second entity and a percentage of risk assumed by the first entity when the first purchase occurs; the first recourse percentage is calculated by the one or more processors based on the percentage of risk assumed by the second entity, and the second recourse percentage is calculated by the one or more processors based on the percentage of risk assumed by the first entity; and the first recourse percentage and the second recourse percentage are stored by the one or more processors in association with a credit transaction record for the first purchase.” Specifically, Fidanza recites that “the loan issuance system….calculate the credit…..This instant credit applies both to consumers, but preferably to businesses, and can be delivered as an instant credit at a point-of-sale (retail), instant credit at a checkout (e-commerce) to finance a purchase, or as instant cash as credit deposited on the borrower's bank account…...The loan issuance system 30 incorporates a machine learning model that aims to calculate the maximum amount of money to lend a client as a preferred business loan while minimizing risk based on “instant” credit information ….. the algorithm may work in real-time to deliver decision making information and should balance the risk exposition with the calculated indebtedness capacity. (See, Abstract; Para. 7, 18, 30-33, 43, 48, 49 Fig. 1, 5); The loan issuance system 30 may automate the feature ranking to make use of a standard methodology to feed the model with the best predictor variables for the model to forecast the bad debt. According to a recursive feature elimination, the best features for bad debt discrimination from the most correlated to the least correlated are ….. incomes/spending ratio ( See, 102-105, 116, 124; Fig. 1, 5); In a non-limiting example only, the features extracted from third-party banking information or financial services provider may include: (1) available income, which is computed as a sum of client incomes, weighted by the confidence of income occurrence ( See, Para. 52); Referring now to FIG. 1, the loan issuance system is illustrated generally at 30 and shows the client computing device 32 that may include a display 34, processor 36 connected to the display, and database 38 connected to the processor. A data entry 40 permits the client or consumer to enter data such as for initial registration with a loan issuance program (See, Para. 31; Fig. 1); The loan issuance server 50 operates in what appears to be almost instantaneously and its server processor 52 may estimate a pre-approved loan amount using the machine learning model with the associated credit risk by obtaining and processing publicly available information about the client and banking or financial data retrieved from a banking institution 62 associated with the client via a third-party financial services data provider 60. This risk measure is then combined with payment capacity forecasting based on the same sources of information, e.g., the banking or financial data and publicly available information credit data may be taken into account (See, Para. 32; Fig. 1). , There are different authentication paths for a user operating on a client computing device 32 to verify authentication numbers such as an instant authentication where the client enters their credentials and are authenticated immediately, or an instant match technique where a client enters their credentials, account and routing numbers, and the financial services data provider 60 may match user input and authenticate immediately (See, Para. 122; Fig. 1); The loan issuance server 50 operates in what appears to be almost instantaneously and its server processor 52 may estimate a pre-approved loan amount using the machine learning model with the associated credit risk by obtaining and processing publicly available information about the client and banking or financial data retrieved from a banking institution 62 associated with the client via a third-party financial services data provider 60. This risk measure is then combined with payment capacity forecasting based on the same sources of information, e.g., the banking or financial data and publicly available information credit data may be taken into account (See, Para. 32; Fig. 1); Transaction data may be user-authorized and received for credit in depository-type accounts via the transactions endpoint, and the data may be standardized across financial institutions. There may be a unique ID of a transaction corresponding to a transaction ID and the ID of the account in which the transaction occurred such as an account ID ( See, Para. 122)).”
Hence, Examiner respectfully declines Applicant’s request to withdraw the 35 U.S.C. 103 rejection of claims 1-20.
Conclusion
27. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure are the following:
Pletz (U.S. Patent Application Publication No. US 2014/0297307-A1) “System and Method for Processing Qualified Healthcare Account Related Financial Transactions”
Foley (U.S. Patent No. US 10,248,915-B2) “Risk profiling for enterprise risk management”
Cannon (U.S. Patent Application Publication No. US 2016/0125412-A1) “Method and system for preventing identity theft and increasing security on all systems”
Matthew (U.S. Patent Application Publication No. US 2020/0167863-A1) “Method and System for Determining a Supplemental Credit Metric”
Soh Aik Guan (U.S. Patent Application Publication No. US 2023/0206320-A1) “Method and system for generating a financial infographic of a user through a financing platform”
Dhodapkar (U.S. Patent Application Publication No. US 2023/0252470-A1) “Verification and Approval Controls for Secondary Accounts”
Koupanou (U.S. Patent Application Publication No. US 2024/0046347-A1) “Machine-learning model to predict likelihood of events impacting a product”
Huke (U.S. Patent Application Publication No. US 2024/0078869-A1) “Real time action of interest notification system”
Overby (U.S. Patent Application Publication No. US 2024/0135445-A1) “User application approval”
Parker (U.S. Patent Application Publication No. US 2025/0054007-A1) “Affordability sweet spot identification”
Yu (U.S. Patent Application Publication No. US 2019/0258818-A1) “Smart access control system for implementing access restrictions of regulated database records based on machine learning of trends”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED H MUSTAFA whose telephone number is (571)270-7978. The examiner can normally be reached M-F 8:00 - 5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael W Anderson can be reached on 571-270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MOHAMMED H MUSTAFA/Examiner, Art Unit 3693
/ELIZABETH H ROSEN/Primary Examiner, Art Unit 3693
