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 03/02/2026.
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 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 statements (IDS) submitted on 05/07/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 1 and 11 are objected to because of the following informalities:
Claim 1: line 2 recites the limitation “One or more processors.” Claim 1: lines 13, 17, and 19 recites “at least one computer processor.” Furthermore, Claim 1: line 26 recites the limitation “the processor”. “One or more processors,” “at least one computer processor” and “processor” are interchangeably used. Is the “one or more processors” recited in Claim 1: line 2 different that the “at least one computer processor” recited in Claim 1: lines 13, 17, and 19, or “the processor” as recited Claim 1: line 26? It appears there is a typographical mistake since the specification only points to one processor for this interpretation. For examination purposes, Examiner interpreted the instances recited in Claim 1: lines 13, 17, 19, and 26 as “one or more processors.” Appropriate corrections are required.
Claim 1: line 26 recites “between the one or more feature vectors and the one or more representative feature vectors.” There is no semicolon ( ; ) after this claim limitation. Each claim limitation needs a semicolon after it, until the end of the sentence. Furthermore, Claim 1: line 45 and Claim 11: line 22 recite “the first entity assuming a fourth portion of the risk for the second portion of the service provided by the first entity.” ‘And’ is not recited after this claim limitation, which is the second to last claim limitation in Claims 1 and 11. After the second to last claim limitation needs an “and”. 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 at least one computer processor, 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 at least one computer processor, the set of features into one or more feature vectors; processing, by the at least one computer processor, 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 processor, 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 a recourse percentage is a transaction-specific percentage calculated and assigned by the at least one computer processor for a purchase, the recourse percentage indicates a percentage of risk assumed by a corresponding entity at the time of 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; 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, machine learning model, and at least one computer processor 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, machine learning model, and at least one computer processor 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, machine learning model, and at least one computer processor 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, at least one computer processor, 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, at least one computer processor, 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 processor, 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 a recourse percentage is a transaction-specific percentage calculated and assigned by the at least one computer processor for a purchase, the recourse percentage indicates a percentage of risk assumed by a corresponding entity at the time of 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; 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, machine learning model, and at least one computer processor, 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, machine learning model, and at least one computer processor 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, machine learning model, and at least one computer processor 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, at least one computer processor, 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, at least one computer processor, 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: 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: 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 a recourse percentage is a transaction-specific percentage calculated and assigned by the at least one computer processor for a purchase, the recourse percentage indicates a percentage of risk assumed by a corresponding entity at the time of 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., a processor, one or more computer storage media, and at least one computer processor 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 a processor, one or more computer storage media, and at least one computer processor, 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 a processor, one or more computer storage media, and at least one computer processor 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 a processor, one or more computer storage media, and at least one computer processor, 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: a processor, one or more computer storage media, and at least one computer processor, 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 Claim 1:
Fidanza teaches:
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 at least one computer processor, 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 at least one computer processor, 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 processor, 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 a recourse percentage is a transaction-specific percentage calculated and assigned by the at least one computer processor for a purchase, the recourse percentage indicates a percentage of risk assumed by a corresponding entity at the time of 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));
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; (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; 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; converting, by the at least one computer processor, 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 11:
Fidanza teaches:
A method for an application performed by one or more processors, the method comprising: (Fidanza, See, Para. 9; Abstract);
processing [one or more feature vectors] using a machine learning model to generate a risk metric associated an applicant; (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)); 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));
automatically determining, by the processor, 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; (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 a recourse percentage is a transaction-specific percentage calculated and assigned by the at least one computer processor for a purchase, the recourse percentage indicates a percentage of risk assumed by a corresponding entity at the time of 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));
receiving a second request to use a second portion of service 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; (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 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 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 17:
Fidanza teaches:
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);
generate a risk metric associated an applicant based on a plurality of metrics associated with the applicant; (Fidanza, When the loan is requested, an initial credit score may be computed 78 using the credit score engine 64 followed by a request for a transactions history of financial data 80, (See, Para.42; Fig. 3));
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; (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 a recourse percentage is a transaction-specific percentage calculated and assigned by the at least one computer processor for a purchase, the recourse percentage indicates a percentage of risk assumed by a corresponding entity at the time of 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));
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 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 (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));
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. (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 computer storage media storing computer-readable instructions thereon.
However, Wang further teaches the following limitation:
one or more computer storage media storing computer-readable instructions thereon; (Wang, See, P: 5: Para. 13-15; Abstract);
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 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 objection of claim 11: line 3, the objection is withdrawn in view of Applicant’s arguments/remarks made in an amendment filed on 03/02/2026. However, the objections of Claim 1: lines 13, 17, 19, 26, and 45 and Claim 11: line 22 are not withdrawn because the noted typographical mistakes were not appropriately corrected. In view of the grounds for the claim objections presented above in this office action, appropriate corrections are required.
Applicant's arguments filed on 03/02/2026 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, Applicants respectfully submit that the present claims are not directed to a judicial exception as suggested by the Office. As presented herein, the claims are directed to a computer-implemented system that dynamically calculates and assigns transaction-specific recourse percentages to a purchase, where each recourse percentage represents a percentage of risk assumed by a corresponding entity at the time of 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 rules that: determine risk portions assumed by distinct entities, compute corresponding recourse percentages for a particular purchase, and bind those recourse percentages to the transaction at the time of purchase. The ordered combination of claimed steps—processing applicant data, determining risk portions, calculating transaction-specific recourse percentages, and assigning those percentages to the purchase—improves the technological process of distributed authorization and liability management within a computer-implemented credit system. Rather than merely evaluating risk as a mental process, the claims require a processor to generate a purchase-bound numerical allocation of liability between entities at transaction time. This constitutes a specific technological mechanism for transaction-level risk allocation. As such, the claims improve the functioning of a credit authorization system by replacing static, issuer-centric credit approval with dynamic, transaction-specific allocation of risk portions between multiple entities. For at least these reasons, Applicant respectfully submits that part 1 of the patent eligibility analysis indicates that the pending claims are not directed to an abstract idea, and therefore are directed to patent eligible subject matter. Thus, Applicants respectfully request withdrawal of the rejection of all pending claims under 35 U.S.C. § 101. "
Examiner respectfully disagrees.
Under Step 2A: Prong 1, 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, machine learning model, and at least one computer processor 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. 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 at least one computer processor, 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 at least one computer processor, the set of features into one or more feature vectors; processing, by the at least one computer processor, 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 processor, 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 a recourse percentage is a transaction-specific percentage calculated and assigned by the at least one computer processor for a purchase, the recourse percentage indicates a percentage of risk assumed by a corresponding entity at the time of 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; 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. … But even if the claims were found to be directed to an abstract idea under part 1, Applicants respectfully submit that the claims nevertheless recite additional features sufficient to ensure the claims amount to significantly more than an abstract idea, itself. Claims 1–20 have been presented herein to include additional elements that amount to significantly more than the alleged judicial exception. In particular, the additional computer elements do not merely provide conventional computer functions but further add meaningful limits to practicing the invention. …. These elements, when viewed in combination, amount to significantly more than abstract financial decision-making because they define a specific computer-implemented architecture that dynamically binds liability allocation values to transaction records. The claims therefore impose concrete technical constraints that confine the invention to a particular machine-based implementation. Moreover, as discussed above, the claims: (1) improve credit authorization system technology by enabling transaction-level, multi-entity risk allocation; (2) improve system operation by dynamically computing and assigning recourse percentages at purchase time rather than relying on static credit limits; and (3) add specific limitations that are not well-understood, routine, or conventional in the field, including the processor-based generation and assignment of transaction-specific recourse percentages reflecting distinct risk portions of multiple entities. Accordingly, for the reasons discussed above, Applicant respectfully requests withdrawal of the § 101 rejection of the claims. Independent claims 11 and 17 recite similar features and subject matter as independent claim 1, which has been relied upon above to illustrate that the claims are directed to patent-eligible subject matter under 35 U.S.C. § 101. For at least the same reasons provided with respect to claim 1, independent claims 11 and 17 are likewise directed to patent-eligible subject matter and should also be found eligible. 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 eligible features of their respective independent claims and are similarly directed to patent-eligible subject matter. Applicant respectfully requests withdrawal of the § 101 rejections 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, 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 at least one computer processor, 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 at least one computer processor, the set of features into one or more feature vectors; processing, by the at least one computer processor, 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 processor, 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 a recourse percentage is a transaction-specific percentage calculated and assigned by the at least one computer processor for a purchase, the recourse percentage indicates a percentage of risk assumed by a corresponding entity at the time of 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; 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, 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. 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, machine learning model, and at least one computer processor. The additional elements: one or more processors, user application, machine learning model, and at least one computer processor 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, machine learning model, and at least one computer processor, 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, machine learning model, and at least one computer processor” 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, the additional elements, as listed above, 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, machine learning model, and at least one computer processor (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, machine learning model, and at least one computer processor (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 features of amended independent claim 1 (e.g., “wherein a recourse percentage is a transaction-specific percentage calculated and assigned by the at least one computer processor for a purchase, the recourse percentage indicates a percentage of risk assumed by a corresponding entity at the time of purchase.”) …. In summary, while Fidanza and Wang disclose risk evaluation and machine learning model training, the combination fails to teach or suggest calculating and assigning a transaction-specific recourse percentage that indicates the percentage of risk assumed by a corresponding entity at the time of purchase, as required by amended claim 1. The references do not contemplate or enable such transaction-level liability allocation, and the proposed combination lacks both motivation and reasonable expectation of success. Accordingly, the cited art does not render the amended claims obvious. Independent claims 11 and 17 recite novel 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. As such, by virtue of their dependency, it is respectfully submitted that the cited references fail to teach or suggest the features of these claims, for at least the reasons set forth above. Applicant respectfully requests withdrawal of the rejection of claims 2–10, 12–16, and 18–20. Claims 1–20 are believed to be in condition for allowance, and favorable action is respectfully requested. ”
Examiner respectfully disagrees.
Examiner respectfully notes that Fidanza teaches “wherein a recourse percentage is a transaction-specific percentage calculated and assigned by the at least one computer processor for a purchase, the recourse percentage indicates a percentage of risk assumed by a corresponding entity at the time of 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 (percentage) (See, 102-105, 116, 124; Fig. 1, 5)” which teaches a recourse percentage is a transaction-specific percentage calculated and assigned by the at least one computer processor for a purchase, the recourse percentage indicates a percentage of risk assumed by a corresponding entity at the time of purchase. (See, Abstract; Para. 96-97).
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 (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”
28. THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
30. A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
31. 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.
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/MOHAMMED H MUSTAFA/Examiner, Art Unit 3693
/Mike Anderson/ Supervisory Patent Examiner, Art Unit 3693
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