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
Claims 1-20 are pending in this application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/31/25 filed is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 9-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Although the specification specifically excludes signal per se from computer storage media (paragraph 49), it also states that the memory can include “computer storage media in the form of volatile” memory (paragraph 51). Volatile memory is a type of computer memory that needs a continuous power supply to maintain its stored data. This would involve electric signals to power the memory. The applicant may wish to clarify or limit the scope of the claim.
The United States Patent and Trademark Office (USPTO) is obliged to give claims their broadest reasonable interpretation consistent with the specification during proceedings before the USPTO. See In re Zletz, 893 F.2d 319 (Fed. Cir. 1989) (during patent examination the pending claims must be interpreted as broadly as their terms reasonably allow). The broadest reasonable interpretation of a claim drawn to a computer readable medium (also called machine readable medium and other such variations) typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. See MPEP 2111.01. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. § 101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter) and Interim Examination Instructions for Evaluating Subject Matter Eligibility Under 35 U.S.C. §101, Aug. 24, 2009; p. 2.
The USPTO recognizes that applicants may have claims directed to computer readable media that cover signals per se, which the USPTO must reject under 35 U.S.C. § 101 as covering both non-statutory subject matter and statutory subject matter. In an effort to assist the patent community in overcoming a rejection or potential rejection under 35 U.S.C. § 101 in this situation, the USPTO suggests the following approach. A claim drawn to such a computer readable medium that covers both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 U.S.C. § 101 by adding the limitation “non-transitory” to the claim. Cf. Animals - Patentability, 1077 Off. Gaz. Pat. Office 24 (April 21, 1987) (suggesting that applicants add the limitation “non-human” to a claim covering a multi-cellular organism to avoid a rejection under 35 U.S.C. § 101). Such an amendment would typically not raise the issue of new matter, even when the specification is silent because the broadest reasonable interpretation relies on the ordinary and customary meaning that includes signals per se. The limited situations in which such an amendment could raise issues of new matter occur, for example, when the specification does not support a non-transitory embodiment because a signal per se is the only viable embodiment such that the amended claim is impermissibly broadened beyond the supporting disclosure. See, e.g., Gentry Gallery, Inc. v. Berkline Corp., 134 F.3d 1473 (Fed. Cir. 1998). Appropriate correction is required.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-20 are directed to a system, which are/is one of the statutory categories of invention. (Step 1: YES”).
The Examiner has identified independent system claim 1 as the claim that represents the claimed invention for analysis. Claim 1 recites the limitations of approving merchant’s credit request by checking merchant’s name and credit score.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. Receiving user’s (i.e., merchant’s) request for authorization; querying external database… to receive a list of entities; receiving a selection of business from merchant; normalizing data format; retrieving user’s credit score from external database; decision logic model trained using prior application based on credit score; generating decision for authorization; and transmitting the decision to the new merchant, – specifically, the claim recites “receiving… a request from a new merchant to authorize the new merchant for merchant credit application; querying… an external database… to retrieve a list of similar business entities corresponding to the new merchant based on identifiers associated with the new merchant; receiving, from the new merchant, a selection of a first business entity from the list of similar business entities; normalizing… heterogeneous data formats of the first business entity into a standardized data structure in a centralized integration data store; retrieving, from a first external source, a commercial credit score associated with the first business entity; a decision logic model trained using prior credit application outcomes to determine, based on the commercial credit score and the standardized data structure, an authorization decision for the new merchant; generating… a decision message including the authorization decision; and transmitting the decision message to the new merchant”, recites a fundamental economic practice, directed to mitigating risk.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic practice or commercial interactions, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The “at least one processor”, “one or more computer storage media”, “a user interface”, “an integration server”, “an external database”, “an application interface (API)”, “a decision server”, “a decision logic model”, and “a message server”, in claim 1; and the additional technical element of “memory” in claim 9, are just applying generic computer components to the recited abstract limitations. The recitation of generic computer components in a claim does not necessarily preclude that claim from reciting an abstract idea. Claims 9 and 15 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims recite an abstract idea)
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of: a computer such as at least one processor, an integration server, a decision server, and a message server; a communication device such as a user interface, an application interface (API); a storage unit such as one or more computer storage media and an external database; and software module and algorithm such as a decision logic model. The computer hardware/software is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, claims 1, 9, and 15 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Thus, claims 1, 9, and 15 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims further define the abstract idea that is present in their independent claims 1, 9, and 15 are and thus correspond to Certain Methods of Organizing Human Activity, and hence are abstract for the reasons presented above.
Dependent claim 2 discloses the limitation of retrieving, from a second external source, a personal credit score for each of a set owner associated with the new merchant, which further narrows the abstract idea. Note that the technical element “a second external source” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 3 discloses the limitation of identifying a set of owners associated with the new merchant; and determining, based on the set of owners, that the request includes information for each owner of the new merchant, which further narrows the abstract idea.
Dependent claim 4 discloses the limitation of the operations further comprising normalizing personal credit score data with commercial credit score data into the standardized data structure stored in the centralized integration data store, which further narrows the abstract idea. Note that the technical element “the centralized integration data store” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 5 discloses the limitation of the operations further comprising executing a trained decision-logic model that determines, based at least in part on both the commercial credit score and a normalized personal credit score data, the decision message for the new merchant, which further narrows the abstract idea. Note that the technical element “a trained decision-logic model” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 6 discloses the limitation of the operations further comprising assigning a confidence level to each similar entity in the list of similar business entities, the confidence level representing a degree of similarity based on the identifiers associated with the new merchant, which further narrows the abstract idea.
Dependent claim 7 discloses the limitation of the operations further comprising filtering out a business entity from the list of similar business entities having a confidence level below a predetermined threshold, which further narrows the abstract idea.
Dependent claim 8 discloses the limitation of generating and transmitting, to cause display via the user interface, a message including the authorization decision and one or more status indicators of the merchant credit application, which further narrows the abstract idea. Note that the technical element “the user interface” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 10 discloses the limitation of retrieving, from a second external source, a personal credit score for each of a set owner associated with the new merchant, which further narrows the abstract idea. Note that the technical element “a second external source” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 11 discloses the limitation of identifying a set of owners associated with the new merchant; and determining, based on the set of owners, that the request includes information for each owner of the new merchant, which further narrows the abstract idea.
Dependent claim 12 discloses the limitation of normalizing personal credit score data with commercial credit score data into the standardized data structure stored in the centralized integration data store, which further narrows the abstract idea. Note that the technical element “the centralized integration data store” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 13 discloses the limitation of executing a trained decision-logic model that determines, based at least in part on both the commercial credit score and a normalized personal credit score data, the decision message for the new merchant, which further narrows the abstract idea. Note that the technical element “a trained decision-logic model” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 14 discloses the limitation of assigning a confidence level to each similar entity in the list of similar business entities, the confidence level representing a degree of similarity based on the identifiers associated with the new merchant, which further narrows the abstract idea.
Dependent claim 16 discloses the limitation of retrieving, from a second external source, a personal credit score for each of a set owner associated with the new merchant, which further narrows the abstract idea. Note that the technical element “a second external source” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 17 discloses the limitation of identifying a set of owners associated with the new merchant; and determining, based on the set of owners, that the request includes information for each owner of the new merchant, which further narrows the abstract idea.
Dependent claim 18 discloses the limitation of normalizing personal credit score data with commercial credit score data into the standardized data structure stored in the centralized integration data store, which further narrows the abstract idea. Note that the technical element “the centralized integration data store” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 19 discloses the limitation of, executing a trained decision-logic model that determines, based at least in part on both the commercial credit score and a normalized personal credit score data, the decision message for the new merchant which further narrows the abstract idea. Note that the technical element “a trained decision-logic model” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 20 discloses the limitation of assigning a confidence level to each similar entity in the list of similar business entities, the confidence level representing a degree of similarity based on the identifiers associated with the new merchant, which further narrows the abstract idea.
Thus, the dependent claims 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, the dependent claims are directed to an abstract idea. Thus, the 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Tomich (20220180429) in view of Abreu (20220327542).
Regarding claim 1, Tomich discloses
a system comprising: at least one 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 executing operations comprising
(“[0076] The computer system 1600 may additionally include a computer-readable storage media reader 1612, a communications system 1614 (e.g., a modem, a network card (wireless or wired), an infra-red communication device, etc.), and working memory 1618, which may include RAM and ROM devices as described above. In some embodiments, the computer system 1600 may also include a processing acceleration unit 1616, which can include a digital signal processor DSP, a special-purpose processor, and/or the like”).
receiving, via a user interface, a request from a new merchant to authorize the new merchant for merchant credit application
(“[0056] FIG. 8 is a diagram of the borrower request and admin review process flow of the present invention... Borrower information 804 includes: the company name; the owner's name; email address; contact phone number”).
querying, by an integration server, an external database [through an application interface (API)] to retrieve a list of similar business entities corresponding to the new merchant based on identifiers associated with the new merchant
(“[0048] FIG. 1 is a diagram showing the data used to obtain the credit score for the present invention. In accordance with the preferred embodiment of the present invention, the process of obtaining a credit score 100 requires data from B2B Merchants 102, credit network data 104, and external data services 106. Data from B2B Merchants 102 includes non-public transaction history; borrower profiles; and merchant data insights. Data obtained from the credit network 104 includes: transaction history stored within the database of the present invention; sector and industry risk; and loan performance”).
receiving, from the new merchant, a selection of a first business entity from the list of similar business entities
(“[0015] ERP data is derived from: the service provider or retailer etc.; from its client; Social Security number or a portion of a Social Security number (or applicable Tax Identification Number, either alone or in conjunction with a Social Security Number) ... Merchants have ERP data derived from their respective customer relationships, and that ERP data may be operated upon and integrated into business lending decisions according to the present invention”).
(“[0051] FIG. 4 is a process flow diagram of the known borrower underwriting model of the present invention... the Known Borrower (ERP, or Enterprise Resource Planning) underwriting model 400 requires the applicant to input personal details 402 such as: the applicant's first and last name; home address; phone number; SSN; and date of birth, as well as Business details 404, such as: the legal business name and DBA; the business address; the EIN; the business phone number; and Gross Annual Revenue (GAR)”).
retrieving, from a first external source, a commercial credit score associated with the first business entity
(“[0048] Data obtained from external services 106 includes: collaborative fraud screening; ID verification; and credit risk scores”).
executing, by a decision server, [a decision logic model trained using prior credit application outcomes to determine], based on the commercial credit score and the standardized data structure, an authorization decision for the new merchant
See FIG. 3A for Company FICOs as basis for credit approval.
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generating, by a message server, a decision message including the authorization decision; and transmitting the decision message to the new merchant via the user interface
(“[0050] FIG. 3A is a rendering of the admin portal, displaying an overview of all applications and their current status of approval”).
“[0052] FIG. 5A is a rendering of the borrower viewing screen that shows all currently active loans, including the total outstanding loan balance, the available credit, and the due date for the next payment. FIG. 5B is a rendering of the borrower portal that displays incomplete applications. FIG. 5C is a rendering of the borrower portal that displays both the currently active loans as well as incomplete applications”.
Tomich does not disclose, however, Abreu teaches
[through an application interface (API)]
(“[0046] For example, client 220 may be an application or set of applications operated by a financial institution which processes requests for new credit cards made by customers of that financial institution”).
(“[0047] Application interface 120 may comprise one or more application programming interfaces (APIs) that permit applications associated with client 220 to communicate with FSS 100”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Tomich to include [through an application interface (API)] as taught by Abreu to use application programming interfaces (APIs) to facilitate communications between devices and parties – see “[0047] Clients 220 interact with FSS 100 such that data may be communicated between them via application interface 120 and such that FSS 100 may process fraud score requests made by clients 220 with regard to one or more of the above types of applications made by individuals or entities such as organizations. Application interface 120 may comprise one or more application programming interfaces (APIs) that permit applications associated with client 220 to communicate with FSS 100”.
Tomich does not disclose, however, Abreu teaches
normalizing, by the integration server, heterogeneous data formats of the first business entity into a standardized data structure in a centralized integration data store
(“[0055] In a preferred embodiment of the present invention, cleansing module 310 cleanses feedback data received from clients 220 such that variations in data format and classification can be normalized and used by FSE 300 to update one or more pending rules bases 420 to perform more effectively”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Tomich to include normalizing, by the integration server, heterogeneous data formats of the first business entity into a standardized data structure in a centralized integration data store as taught by Abreu to normalized data format to facilitate the data processing – see “[0055] In a preferred embodiment of the present invention, cleansing module 310 cleanses feedback data received from clients 220 such that variations in data format and classification can be normalized and used by FSE 300 to update one or more pending rules bases 420 to perform more effectively”).
Tomich does not disclose, however, Abreu teaches
[a decision logic model trained using prior credit application outcomes to determine]
(“[0046] For example, client 220 may be an application or set of applications operated by a financial institution which processes requests for new credit cards made by customers of that financial institution”).
(“[0079] In some further embodiments of the present invention, other processing steps may also be implemented to optimize the accuracy of machine learning when generating one or more scoring regimes. These regimes are highly dependent on a training data dependent variable distribution, selected independent variables and the final chosen machine learning algorithm. When updating models in real-time, dependent variable distributions, independent variables and final machine learning algorithms will vary. This creates a challenge for those using the scores”).
(“[0090] The machine learning model can be trained with supervised learning and use training data that can be obtained from a history of transaction data (as defined by feedback data). More specifically, each item of the training data can include an instance of a prior transaction matched to one or more determinations (i.e. reasons) for a fraudulent occurrence”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Tomich to include [a decision logic model trained using prior credit application outcomes to determine] as taught by Abreu to use algorithm trained on prior data and analysis to help with analyzing new data/transactions to improve performance or reduce undesired transactions – see “[0090] The machine learning model can be trained with supervised learning and use training data that can be obtained from a history of transaction data (as defined by feedback data). More specifically, each item of the training data can include an instance of a prior transaction matched to one or more determinations (i.e. reasons) for a fraudulent occurrence. The matching can be performed according to a predetermined algorithm configured to receive transaction data from a historical record and pair it with results of analysis of the record, such as what types fraud occurred (e.g., improper PII, forged ID, etc.)”.
Regarding claim 2, the combination of Tomich and Abreu, as shown in the rejection above, discloses the limitations of claim 1.
Tomich further discloses
retrieving, from a second external source, a personal credit score for each of a set owner associated with the new merchant
See FIG. 2, below. Note In the context of Equifax, BPR stands for Business Principal Report. This report provides information about the credit history of a business's principal(s), including business owners, principal guarantors, and their associations and financial issues. It essentially delves into the creditworthiness of the individuals who are key players in the business.
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Regarding claim 3, the combination of Tomich and Abreu, as shown in the rejection above, discloses the limitations of claim 1.
Tomich further discloses
identifying a set of owners associated with the new merchant; and determining, based on the set of owners, that the request includes information for each owner of the new merchant
See FIG. 2, below. Note In the context of Equifax, BPR stands for Business Principal Report. This report provides information about the credit history of a business's principal(s), including business owners, principal guarantors, and their associations and financial issues. It essentially delves into the creditworthiness of the individuals who are key players in the business.
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Regarding claim 4, the combination of Tomich and Abreu, as shown in the rejection above, discloses the limitations of claim 1.
Tomich does not disclose, however, Abreu further discloses
normalizing personal credit score data with commercial credit score data into the standardized data structure stored in the centralized integration data store
(“[0046] For example, client 220 may be an application or set of applications operated by a financial institution which processes requests for new credit cards made by customers of that financial institution”).
(“[0055] In a preferred embodiment of the present invention, cleansing module 310 cleanses feedback data received from clients 220 such that variations in data format and classification can be normalized and used by FSE 300 to update one or more pending rules bases 420 to perform more effectively”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Tomich to include normalizing personal credit score data with commercial credit score data into the standardized data structure stored in the centralized integration data store as taught by Abreu to normalized data format to facilitate the data processing – see “[0055] In a preferred embodiment of the present invention, cleansing module 310 cleanses feedback data received from clients 220 such that variations in data format and classification can be normalized and used by FSE 300 to update one or more pending rules bases 420 to perform more effectively”).
Regarding claim 5, the combination of Tomich and Abreu, as shown in the rejection above, discloses the limitations of claim 1.
Tomich further discloses
executing a trained decision-logic model that determines, based at least in part on both the commercial credit score and a normalized personal credit score data, the decision message for the new merchant
See FIG. 3A for Company FICOs as basis for credit approval.
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Regarding claim 6, the combination of Tomich and Abreu, as shown in the rejection above, discloses the limitations of claim 1.
Tomich further discloses
assigning a confidence level to each similar entity in the list of similar business entities, the confidence level representing a degree of similarity based on the identifiers associated with the new merchant
See FIG. 3A for confidence/approval credit level for the various companies being different (here, dependent on the FICO scores).
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Regarding claim 7, the combination of Tomich and Abreu, as shown in the rejection above, discloses the limitations of claim 1.
Tomich further discloses
filtering out a business entity from the list of similar business entities having a confidence level below a predetermined threshold
(“[0049] the underwriting process processes data from the B2B module 200 of the present invention, such as data from the individual applying for credit as well as business information… it is compared with the requirements for the location 206, the Gross Annual Revenue (GAR) 210… If the submitted data does not meet these requirements, the application is declined 214. If the application passes the Pre-Flight checks the application then proceeds to being processed through the Loan Origination factors in the core decision-making 216, which includes assessing factors such as: fraud screening approval…and minimum revolving balance; and Equifax BPR. If these Loan Origination factors are not met, the application is declined 218… Alternatively, if certain criteria are in a specific threshold, the application is submitted for further review 220. Where the Equifax BPR is between 550 and 599 score 222, the application is submitted to sub-prime checks 224. Where credit history of more than two years, and 20 percent availability of existing credit exists, the application is approved 226”).
Regarding claim 8, the combination of Tomich and Abreu, as shown in the rejection above, discloses the limitations of claim 1.
Tomich further discloses
generating and transmitting, to cause display via the user interface, a message including the authorization decision and one or more status indicators of the merchant credit application
(“[0050] FIG. 3A is a rendering of the admin portal, displaying an overview of all applications and their current status of approval”).
“[0052] FIG. 5A is a rendering of the borrower viewing screen that shows all currently active loans, including the total outstanding loan balance, the available credit, and the due date for the next payment. FIG. 5B is a rendering of the borrower portal that displays incomplete applications. FIG. 5C is a rendering of the borrower portal that displays both the currently active loans as well as incomplete applications”.
Claim 9 is rejected using the same rationale that was used for the rejection of claim 1.
Claim 10 is rejected using the same rationale that was used for the rejection of claim 2.
Claim 11 is rejected using the same rationale that was used for the rejection of claim 3.
Claim 12 is rejected using the same rationale that was used for the rejection of claim 4.
Claim 13 is rejected using the same rationale that was used for the rejection of claim 5.
Claim 14 is rejected using the same rationale that was used for the rejection of claim 6.
Claim 15 is rejected using the same rationale that was used for the rejection of claim 1.
Claim 16 is rejected using the same rationale that was used for the rejection of claim 2.
Claim 17 is rejected using the same rationale that was used for the rejection of claim 3.
Claim 18 is rejected using the same rationale that was used for the rejection of claim 4.
Claim 19 is rejected using the same rationale that was used for the rejection of claim 5.
Claim 20 is rejected using the same rationale that was used for the rejection of claim 6.
Response to Arguments
Applicant's arguments filed 11/7/25 have been fully considered but they are not persuasive.
In response to applicant's argument that:
“35 U.S.C 101… Claim 1 is not abstract. Rather, it is directed to a specific, technological improvement in computer functionality-namely, the normalization and orchestration of heterogeneous merchant data across distributed servers to enable interoperable, real-time authorization processing,”
the examiner respectfully disagrees. The examiner has determined that the claims are directed to the abstract idea of approving merchant’s credit request by checking merchant’s name and credit score. And the claims do not disclose any improvement to the computer technology. The normalization and processing of data is carried out by “generic computer”.
In response to applicant's argument that:
“Claim 1 recites a non-generic, multi-server architecture comprising an integration server, decision server, and message server, each performing distinct data-processing operations. The integration server receives a merchant authorization request, queries multiple external databases via application programming interfaces (APIs), and normalizes heterogeneous data formats into a standardized schema stored in a centralized integration data,”
the examiner respectfully disagrees. These servers – integration server, decision server, and message server – are not positively recited by the claims. The claimed invention system claimed “at least one processor; and one or more computer storage media storing computer-readable instructions”. Thus, as written, it is not a “multi-server architecture”. The “at least one processor” calls on the various servers – outside the claimed invention – to carry out the business model/process.
In response to applicant's argument that:
“The system of Claim 1 further enables … application programming interfaces (APIs),”
the examiner respectfully disagrees. The “application programming interfaces” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
In response to applicant's argument that:
“Schema normalization and conversion of heterogeneous data formats into a standardized structure,”
the examiner respectfully disagrees. Data normalization is part of a business process. A data format was chosen to be used. Subsequent data needs to be of similar format for the system to operate more efficiently.
In response to applicant's argument that:
“Execution, by the decision server, of a trained decision-logic model using the standardized data to generate an authorization decision,”
the examiner respectfully disagrees. The “trained decision-logic model” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
In response to applicant's argument that:
“Generation, by the message server, of structured decision messages and application status updates transmitted through the user interface,”
the examiner respectfully disagrees. This is a business decision/process of sending information to the user (new merchant).
In response to applicant's argument that:
“Claim 1 is not directed to a mental process, fundamental economic practice, or method of organizing human activity. Rather, it recites specific computing operations performed by distinct technical components,”
the examiner respectfully disagrees. As stated above, the claims is merely applying generic computer components to the recited abstract limitations. The recitation of generic computer components in a claim does not necessarily preclude that claim from reciting an abstract idea. See Claim Rejections - 35 USC § 101 above.
In response to applicant's argument that:
“is fully integrated into a practical computer-implemented application. The claimed multi-server architecture enables the automated orchestration of API-level ingestion, schema normalization, and machine-learning based decisioning-each performed by dedicated system components and producing a structured, interoperable dataset for downstream analysis,”
the examiner respectfully disagrees. Again, the technical elements (e.g., API and machine-learning module) are recited at such a high level of generality that they do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See Claim Rejections - 35 USC § 101 above.
In response to applicant's argument that:
“it recites an inventive concept that transforms it into patent-eligible subject matter… normalization engine… machine-learning logic… communication workflow (emphasis original’s),”
the examiner respectfully disagrees. Again, data normalization and workflow are part of a business process. And the “machine-learning logic” is recited at a high level of generality. It does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
In response to applicant's argument that:
“35 U.S.C. 102(a)(2)… the combination of references fails to teach or suggest the features of amended independent claim I-particularly the recited operation of: "normalizing, by the integration server, heterogeneous data formats of the first business entity into a standardized data structure in a centralized integration data store,”
Applicant’s arguments have been considered but are moot because the arguments do not apply to the new analysis in view of the additional reference being used in the current rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.
Accordingly, THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK H GAW whose telephone number is (571)270-0268. The examiner can normally be reached Mon-Fri: 9am -5pm.
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/MARK H GAW/Examiner, Art Unit 3693