DETAILED ACTION
The present application, filed on 11/16/2023 is being examined under the AIA first inventor to file provisions.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/24/2025 has been entered.
The following is a non-final Office Action on the Merits in response to Applicant’s submission.
a. Claims 1, 6, 11-12, 18 are amended
b. Claims 2-3, 13, 19 are cancelled
Overall, Claims 1, 4-12, 14-18, 20 are pending and have been considered below.
Claim Rejections - 35 USC § 101
35 USC 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, 4-12, 14-18, 20 are rejected under 35 USC 101 because the claimed invention is not directed to patent eligible subject matter. The claimed matter is directed to a judicial exception, i.e. an abstract idea, not integrated into a practical application, and without significantly more.
Per Step 1 of the multi-step eligibility analysis, claims 1-10 are directed to a computer implemented method, claims 11-17 are directed to a system, and claims 18-20 are directed to a computer implemented method.
Thus, on its face, each independent claim and the associated dependent claims are directed to a statutory category of invention.
[INDEPENDENT CLAIMS]
Per Step 2A.1. Independent Claim 1 is rejected under 35 USC 101 because the claim is directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The limitations of the independent claim 1 recite an abstract idea, shown in bold below:
[A] A computer-implemented method
[B] retrieving, by a first server, first transaction attributes, wherein the first transaction attributes correspond to a first transaction being processed by the first server;
[C] receiving, by the first server via a communication network, second transaction attributes from a second server, wherein the second transaction attributes correspond to a second transaction being processed by the second server;
[D] generating, by the first server, a confidence score by applying the first transaction attributes and the second transaction attributes to a machine learning model, wherein the trained machine learning model is trained via supervised learning using labeled transaction data, wherein the labeled transaction data comprises transaction attributes for sets of transactions labeled as either corresponding to the same transaction or different transactions, wherein the confidence score represents a likelihood that the first transaction and the second transaction are the same transaction;
[E] accepting, by the first server, that the first transaction and the second transaction are the same transaction based on the confidence score satisfying a predetermined threshold;
[F] generating, by the first server, a common transaction identifier (ID) for the first transaction and the second transaction by applying the first transaction attributes or the second transaction attributes or a combination thereof to a hashing algorithm; and
[G] transmitting, by the first server via the communication network, a signal comprising a
request for a risk score for the second transaction to the second server, wherein the risk score represents a probability of the second transaction being fraudulent, and wherein the request references the common transaction ID, wherein one of the first server or the second server is a transaction service provider server, and
[H] receiving, by the first server via the communication network, a signal comprising the risk score for the second transaction; and
[I] approving or denying, in real time, by the first server, the first transaction based on the
risk score for the second transaction, wherein the other one of the first server or the second server is an affiliate server.
Independent claim 1 recites: generating a confidence score ([D]); validating the transaction based on the confidence score ([E]); generating a common transaction identifier ([F]); requesting additional data about the second transaction ([G]); receiving a risk score ([H]); approving or denying a transaction ([I]) which, based on the claim language and in view of the application disclosure, represents a process aimed at: “evaluating payment transaction data for fraud and approving or denying the transaction based on the analysis result”.
This is a combination that, under its broadest reasonable interpretation, covers agreements in the form of sales activities or behaviors, business relationships (e-commerce), which falls under Certain Methods of Organizing Human Activity, i.e., Commercial or Legal Interactions grouping of abstract ideas (see MPEP 2106.04(a)(2)).
Accordingly, it is reasonable to conclude that independent claim 1 recites an abstract idea that corresponds to a judicial exception.
[INDEPENDENT CLAIMS – QUALIFIERS]
Per Step 2A.2. The identified abstract idea is not integrated into a practical application because the additional elements in the independent claims only amount to instructions to apply the judicial exception to a computer, or are a general link to a technological environment (see MPEP 2106.05(f); MPEP 2106.05(h)).
For example, the added elements “by the first server” recite computing elements at a high level of generality, generally linking the use of a judicial exception to a particular technological environment (see MPEP 2106.05(h)), or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). Further, the qualifiers wherein the trained machine learning model is trained via supervised learning using labeled transaction data, wherein the labeled transaction data comprises transaction attributes for sets of transactions labeled as either corresponding to the same transaction or different transactions, wherein the confidence score represents a likelihood that the first transaction and the second transaction are the same transaction; wherein the risk score represents a probability of the second transaction being fraudulent, and wherein the request references the common transaction ID, wherein one of the first server or the second server is a transaction service provider server, wherein the other one of the first server or the second server is an affiliate server, as applied to the confidence score, and the request references, are nothing more than (a) descriptive limitations of claim elements, such as describing the nature, structure and/or content of other claim elements, or (b) general links to the computing environment, which amount to instructions to “apply it,” or equivalent (MPEP 2106.05(f)).
These qualifiers do not preclude from carrying out the identified abstract idea “evaluating payment transaction data for fraud and approving or denying the transaction based on the analysis result”, and do not serve to integrate the identified abstract idea into a practical application.
[INDEPENDENT CLAIMS – ADDITIONAL STEPS]
The additional steps in the independent claims, shown not bolded above, recite: obtaining transaction attributes from a first server ([B]), obtaining transaction attributes from a first server ([C]), …. When considered individually, they amount to nothing more than receiving data, processing data, storing results or transmitting data, that serves merely to implement the abstract idea using computing components for performing computer functions (corresponding to the words “apply it” or an equivalent), or merely uses a computer as a tool to perform the identified abstract idea. Thus, it is reasonable to conclude that these claim elements do not integrate the identified abstract idea (“evaluating payment transaction data for fraud and approving or denying the transaction based on the analysis result”) into a practical application (see MPEP 2106.05(f)(2)).
Therefore, the additional steps of independent claim 1 do not integrate the identified abstract idea into a practical application and the claims remain a judicial exception.
Per Step 2B. Independent claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when the independent claim is reevaluated as a whole, as an ordered combination under the considerations of Step 2B, the outcome is the same like under Step 2A.2.
Therefore, it is concluded that independent claim 1 is deemed ineligible.
Independent Claim 11 is rejected under 35 USC 101 because the claim is directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The limitations of the independent claim 11 recite an abstract idea, shown in bold below:
[A] A transaction service provider system, comprising: a confidence model to:
[B] receive first transaction data for a first transaction being processed by the transaction service provider system;
[C] receive, via the communication network, second transaction data for a second transaction being processed by an affiliate system; and
[D] generate a confidence score based on the first transaction data and the second transaction data; wherein the confidence model is a machine learning model trained via supervised learning using labeled transaction data, wherein the labeled transaction data comprises transaction attributes for sets of transactions labeled as either corresponding to the same transaction or different transactions, wherein the confidence score represents a likelihood that the first transaction and the second transaction are the same transaction;
[E] a common transaction ID generator to: generate a common transaction ID for the first transaction and the second transaction based on the first transaction data; and
[F] a decision engine to: determine the confidence score satisfies a predetermined threshold; and
[G] transmit, via the communication network, a signal comprising a request for a risk score for the second transaction to the affiliate system, wherein the risk score represents a probability of the second transaction being fraudulent, and wherein the request comprises the common transaction ID
([H]) a fraud model to: receive the risk score for the second transaction from the affiliate system; and
([I]) approve or deny the first transaction based on the risk score for the second transaction.
Claim 11 recites: generating a confidence score ([D]); generating a common transaction ID ([E]); determining a confidence score ([F]); and requesting additional data ([G]), receive a risk score ([H]); approve or deny the transaction ([I]), which, based on the claim language and in view of the application disclosure, represents a process aimed at: “evaluating payment transaction data for fraud and approving or denying the transaction based on the analysis result”.
This is a combination that, under its broadest reasonable interpretation, covers agreements in the form of sales activities or behaviors, business relationships (e-commerce), which falls under Certain Methods of Organizing Human Activity, i.e., Commercial or Legal Interactions grouping of abstract ideas (see MPEP 2106.04(a)(2)).
Accordingly, it is reasonable to conclude that independent claim 11 recites an abstract idea that corresponds to a judicial exception.
[INDEPENDENT CLAIMS – QUALIFIERS]
Per Step 2A.2. The identified abstract idea is not integrated into a practical application because the additional elements in the independent claims only amount to instructions to apply the judicial exception to a computer, or are a general link to a technological environment (see MPEP 2106.05(f); MPEP 2106.05(h)).
For example, the added elements “a confidence model,” “a common transaction ID generator,” and “a decision engine” recite computing elements at a high level of generality, generally linking the use of a judicial exception to a particular technological environment (see MPEP 2106.05(h)), or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). Further, the qualifiers wherein the confidence model is a machine learning model trained via supervised learning using labeled transaction data, wherein the labeled transaction data comprises transaction attributes for sets of transactions labeled as either corresponding to the same transaction or different transactions, wherein the confidence score represents a likelihood that the first transaction and the second transaction are the same transaction; wherein the risk score represents a probability of the second transaction being fraudulent, and wherein the request comprises the common transaction ID as applied to the transactions, and to the request, are nothing more than (a) descriptive limitations of claim elements, such as describing the nature, structure and/or content of other claim elements, or (b) general links to the computing environment, which amount to instructions to “apply it,” or equivalent (MPEP 2106.05(f)).
These qualifiers do not preclude from carrying out the identified abstract idea “evaluating payment transaction data for fraud and approving or denying the transaction based on the analysis result”, and do not serve to integrate the identified abstract idea into a practical application.
[INDEPENDENT CLAIMS – ADDITIONAL STEPS]
The additional steps in the independent claims, shown not bolded above, recite: receive first transaction data for a first transaction being processed by the transaction service provider system ([B]) and receive second transaction data for a second transaction being processed by an affiliate system [C]. When considered individually, they amount to nothing more than receiving data, processing data, storing results or transmitting data, that serves merely to implement the abstract idea using computing components for performing computer functions (corresponding to the words “apply it” or an equivalent), or merely uses a computer as a tool to perform the identified abstract idea. Thus, it is reasonable to conclude that these claim elements do not integrate the identified abstract idea (“evaluating payment transaction data for fraud and approving or denying the transaction based on the analysis result”) into a practical application (see MPEP 2106.05(f)(2)).
Therefore, the additional steps of independent claim 11 do not integrate the identified abstract idea into a practical application and the claims remain a judicial exception.
Per Step 2B. Independent claim 11 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when the independent claim is reevaluated as a whole, as an ordered combination under the considerations of Step 2B, the outcome is the same like under Step 2A.2.
Therefore, it is concluded that independent claim 11 is deemed ineligible.
Independent Claim 18 is rejected under 35 USC 101 because the claim is directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The limitations of the independent claim 18 recite an abstract idea, shown in bold below:
[A] A computer-implemented method,
[B] receiving, by a first affiliate server via a communication network, a request to process a first transaction from a merchant server, wherein the request comprises first transaction data;
[C] sending, by the first affiliate server, the first transaction data to a confidence model; wherein the confidence model is a machine learning model trained via supervised learning using labeled transaction data, wherein the labeled transaction data comprises transaction attributes for sets of transactions labeled as either corresponding to the same transaction or different transactions, and wherein the confidence model is configured to output confidence scores representing likelihoods that sets of transactions are the same transactions based on receiving transaction attributes for the sets of transactions as inputs;
[D] receiving, by the first affiliate server, a confidence score for the first transaction and a second transaction being processed by a second affiliate server from the confidence model, wherein the confidence score represents a likelihood that the first transaction and [[a]] the second transaction are the same transaction;
[E] determining, by the first affiliate server, that the confidence score satisfies a predetermined threshold;
[F] generating, by the first affiliate server, a common transaction identification (ID) for the first transaction and the second transaction based on the first transaction data;
[G] transmitting, by the first affiliate server via a communication network, a signal comprising a request for a risk score for the second transaction to the second affiliate server, wherein the risk score represents a probability of the second transaction being fraudulent, and wherein the request references the common transaction ID;
[H] receiving, by the first affiliate server, a signal comprising the risk score for the second transaction;
[I] approving or denying, by the first affiliate server, the first transaction based on the the risk score for the second transaction.
Claim 18 recites: determining that the confidence score satisfies a predetermined threshold ([E]); generating a common transaction identification ([F]); and requesting and receiving additional data ([G], [H]), approving or denying the transaction ([I]), which, based on the claim language and in view of the application disclosure, represents a process aimed at: “evaluating payment transaction data for fraud and approving or denying the transaction based on the analysis result”.
This is a combination that, under its broadest reasonable interpretation, covers agreements in the form of sales activities or behaviors, business relationships (e-commerce), which falls under Certain Methods of Organizing Human Activity, i.e., Commercial or Legal Interactions grouping of abstract ideas (see MPEP 2106.04(a)(2)).
Accordingly, it is reasonable to conclude that independent claim 18 recites an abstract idea that corresponds to a judicial exception.
[INDEPENDENT CLAIMS – QUALIFIERS]
Per Step 2A.2. The identified abstract idea is not integrated into a practical application because the additional elements in the independent claims only amount to instructions to apply the judicial exception to a computer, or are a general link to a technological environment (see MPEP 2106.05(f); MPEP 2106.05(h)).
For example, the added elements “by the first affiliate server” recite computing elements at a high level of generality, generally linking the use of a judicial exception to a particular technological environment (see MPEP 2106.05(h)), or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). Further, the qualifiers wherein the confidence model is a machine learning model trained via supervised learning using labeled transaction data, wherein the labeled transaction data comprises transaction attributes for sets of transactions labeled as either corresponding to the same transaction or different transactions, and wherein the confidence model is configured to output confidence scores representing likelihoods that sets of transactions are the same transactions based on receiving transaction attributes for the sets of transactions as inputs; wherein the risk score represents a probability of the second transaction being fraudulent, and wherein the request references the common transaction ID; as applied to the confidence score, and the request, are nothing more than (a) descriptive limitations of claim elements, such as describing the nature, structure and/or content of other claim elements, or (b) general links to the computing environment, which amount to instructions to “apply it,” or equivalent (MPEP 2106.05(f)).
These qualifiers do not preclude from carrying out the identified abstract idea “evaluating payment transaction data for fraud and approving or denying the transaction based on the analysis result”, and do not serve to integrate the identified abstract idea into a practical application.
[INDEPENDENT CLAIMS – ADDITIONAL STEPS]
The additional steps in the independent claims, shown not bolded above, recite: receiving a transaction processing request ([B]), sending the confidence model ([C]), receiving a confidence score ([D]). When considered individually, they amount to nothing more than receiving data, processing data, storing results or transmitting data, that serves merely to implement the abstract idea using computing components for performing computer functions (corresponding to the words “apply it” or an equivalent), or merely uses a computer as a tool to perform the identified abstract idea. Thus, it is reasonable to conclude that these claim elements do not integrate the identified abstract idea (“evaluating payment transaction data for fraud and approving or denying the transaction based on the analysis result”) into a practical application (see MPEP 2106.05(f)(2)).
Therefore, the additional steps of independent claim 18 do not integrate the identified abstract idea into a practical application and the claims remain a judicial exception.
Per Step 2B. Independent claim 18 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when the independent claim is reevaluated as a whole, as an ordered combination under the considerations of Step 2B, the outcome is the same like under Step 2A.2.
Therefore, it is concluded that independent claim 18 is deemed ineligible.
[DEPENDENT CLAIMS]
Dependent claim 6 recites:
[A] training the machine learning model to output confidence scores representing likelihoods that sets of transactions are the same transaction based on receiving transaction attributes for the sets of transactions at inputs, wherein the training comprises supervised learning using the labeled transaction data, wherein the labeled transaction data comprises transaction attributes is from the first server and the second serve
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: “evaluating payment transaction data for fraud and approving or denying the transaction based on the analysis result”. The elements in this dependent claim are comparable to “Insignificant Extra-Solution (Pre-Solution and/or Post-Solution) Activity”, i.e. activities incidental to the primary process or product that are merely a nominal or tangential addition to the claims. Specifically, the claim elements are considered either pre-solution activity because they are mere gathering or pre-processing data/information in conjunction with the abstract idea, or post-solution activity because they are mere outputting or post-processing results from executing the abstract idea.
Thus, it is reasonable to conclude that these claim elements do not integrate the identified abstract idea (“evaluating payment transaction data for fraud and approving or denying the transaction based on the analysis result”) into a practical application (see MPEP 2106.05(g)).
The dependent claim elements have the same relationship to the underlying abstract idea (“evaluating payment transaction data for fraud and approving or denying the transaction based on the analysis result”) as outlined in the independent claims analysis above. Thus, it is readily apparent that the claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claim further elaborates on the previously identified abstract idea (“evaluating payment transaction data for fraud and approving or denying the transaction based on the analysis result”).
Therefore, dependent claim 6 is deemed ineligible.
Dependent claim 12 recites:
[A] approve or deny the first transaction based on a risk score for the first transaction and the risk score for the second transaction.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: “evaluating payment transaction data for fraud and approving or denying the transaction based on the analysis result”. This is a combination that, under its broadest reasonable interpretation, covers agreements in the form of sales activities or behaviors, business relationships (e-commerce), which falls under Certain Methods of Organizing Human Activity, i.e., Commercial or Legal Interactions grouping of abstract ideas (see MPEP 2106.04(a)(2)).
The dependent claim elements have the same relationship to the underlying abstract idea (“evaluating payment transaction data for fraud and approving or denying the transaction based on the analysis result”) as outlined in the independent claims analysis above. Thus, it is readily apparent that the claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claim further elaborates on the previously identified abstract idea (“evaluating payment transaction data for fraud and approving or denying the transaction based on the analysis result”).
Therefore, dependent claim 12 is deemed ineligible.
Dependent claim 16 recites:
[A] compare the transaction attributes of the first transaction data and the transaction attributes of the second transaction data.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: “evaluating payment transaction data for fraud and approving or denying the transaction based on the analysis result”. The elements in this dependent claim are comparable to “sorting information” i.e. comparing data, which has been recognized by a controlling court as "well-understood, routine and conventional computing functions" when claimed generically as they are in these dependent claims.
The dependent claim elements have the same relationship to the underlying abstract idea (“evaluating payment transaction data for fraud and approving or denying the transaction based on the analysis result”) as outlined in the independent claims analysis above. Thus, it is readily apparent that the claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claim further elaborates on the previously identified abstract idea (“evaluating payment transaction data for fraud and approving or denying the transaction based on the analysis result”).
Therefore, claim dependent 16 is deemed ineligible.
Dependent claims 4-5, 7-10, 14-15, 17, 20 recite:
wherein the affiliate server is a payment gateway server.
wherein the affiliate server is an authentication service provider server.
wherein the first transaction attributes and the second transaction attributes define types of attributes,
wherein the types of attributes comprise a primary account number (PAN), a transaction amount, a currency, a date and time, an authentication code, a transaction service provider transaction identifier (ID), or a merchant code, or a combination thereof.
wherein a first attribute of the first transaction attributes is different from a second attribute of the second transaction attributes,
wherein the first attribute and the second attribute are the same type of attribute.
wherein the predetermined threshold is a first predetermined threshold,
wherein the first transaction attributes comprise a transaction amount, and
wherein accepting that the first transaction and the second transaction are the same transaction is further based on the transaction amount satisfying a second predetermined threshold.
wherein accepting that the first transaction and the second transaction are the same transaction is further based on the types of attributes defined by the first transaction attributes and the second transaction attributes.
wherein the confidence model comprises a machine learning model trained via supervised learning using labeled data.
wherein the first transaction data and the second transaction data each comprise transaction attributes, and
wherein the transaction attributes comprise a primary account number (PAN), a transaction amount, a currency, a date and time, an authentication code, a transaction service provider transaction identifier (ID), or a merchant code, or a combination thereof.
wherein the common transaction ID generator comprises a hashing algorithm to generate the common transaction ID based on the transaction attributes of the first transaction data.
wherein the first transaction comprises a first transaction amount, and
wherein the predetermined threshold is based on the first transaction amount.
wherein the additional data related to the second transaction comprises a risk score for the second transaction.
wherein at least one of the first affiliate server and the second affiliate server is a payment gateway server.
These further elements in the dependent claims do not perform any claimed method steps. They describe the nature, structure and/or content of other claim elements – the server; the affiliate server; the transaction type; the attributes; the predetermined threshold; the transaction attributes; the acceptance of a transaction; the confidence model; the transaction data; the transaction ID generator; the predetermined threshold; the additional data; the affiliate server – and as such, cannot change the nature of the identified abstract idea (“evaluating payment transaction data for fraud and approving or denying the transaction based on the analysis result”), from a judicial exception into eligible subject matter, because they do not represent significantly more (see MPEP 2106.07). The nature, form or structure of the other claim elements themselves do not practically or significantly alter how the identified abstract idea would be performed and do not provide more than a general link to a technological environment.
Therefore, dependent claims 3-5, 7-10, 14-15, 17, 20 are deemed ineligible.
When the dependent claims are considered as a whole, as an ordered combination, the claim elements noted above appear to merely apply the abstract concept to a technical environment in a very general sense. The most significant elements, which form the abstract concept, are set forth in the independent claims. The fact that the computing devices and the dependent claims are facilitating the abstract concept is not enough to confer statutory subject matter eligibility, since their individual and combined significance do not transform the identified abstract concept at the core of the claimed invention into eligible subject matter. Therefore, it is concluded that the dependent claims of the instant application, considered individually, or as a as a whole, as an ordered combination, do not amount to significantly more (see MPEP 2106.07(a)II).
In sum, Claims 1, 4-12, 14-18, 20 are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The following is a 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 difference 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 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(a) are summarized as follows:
i. Determining the scope and contents of the prior art.
ii. Ascertaining the differences between the prior art and the claims at issue.
iii. Resolving the level of ordinary skill in the pertinent art.
iv. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4-10 are rejected under 35 U.S.C. 103 as being unpatentable over Crudelle et al (US 2022/0398583), in view of Lacross-Arnold et al (US 2018/0181953), in further view of Tay (US 2023/0196398), in further view of Juneja et al (US 2021/0304426)
Regarding Claim 1: Crudelle discloses: A computer-implemented method, comprising:
retrieving, by a first server, first transaction attributes, wherein the first transaction attributes correspond to a first transaction being processed by the first server; {see at least fig6, rc620, rc630, [0082] first transaction with transaction attributes}
receiving, by the first server via a communication network, second transaction attributes from a second server, wherein the second transaction attributes correspond to a second transaction being processed by the second server; {see at least fig6, rc620, rc630, [0082] first transaction with transaction attributes; fig7, rc731, rc732, rc710, [0089]-[0091] multiple servers (reads on second server); fig1, rc110, rc120, [0065] FIG. 1 illustrates a process 100 of reconciling transaction data from different sources in accordance with an example embodiment. In this example, two financial institutions are shown as the sources, but different sources and different number of sources may be used. Referring to FIG. 1, transaction data 111 from a first financial institution 110 may be input to a host platform 130. Likewise, transaction data 121 from a second financial institution 120 may be input to the host platform 130. Here, the transaction data 111 and 121 may include tabular data, spreadsheets, bank statements, XML documents (second financial institution (reads on second server) provides transaction data (reads on transaction attributes.}
generating, by the first server, a confidence score by applying the first transaction attributes and the second transaction attributes to a trained machine learning model {see at least [0052] machine learning, confidence score},
wherein the trained machine learning model is trained via supervised learning using labeled transaction data, {see at least [0052] machine learning model; [0019] supervised machine learning model}
wherein the labeled transaction data comprises transaction attributes for sets of transactions labeled as either corresponding to the same transaction or different transactions, and {see at least (0050]-[0052] between the two transaction (reads on different transactions}
wherein the confidence score represents a likelihood that the first transaction and the second transaction are the same transaction; {see at least [0052]-[0054] two recitation of the same transaction (reads on likelihood the transactions being the same)}
accepting, by the first server, that the first transaction and the second transaction are the same transaction based on the confidence score satisfying a predetermined threshold; {see at least [0052] below threshold. Crudelle does not explicitly disclose the confidence score satisfying the predetermined threshold. However, it is reasonable to assume that one of ordinary skills in the art will realize that once a predetermined threshold exists, the confidence score has to at list satisfy it – see MPEP 2123 and MPEP 2144.01}
generating, by the first server, a common transaction identifier (ID) for the first transaction and the second transaction {see at least fig6, rc640, [0082] file to correspond to common transaction (reads on common transaction ID)} by applying the first transaction attributes or the second transaction attributes or a combination thereof …; and {see at least [0084] payment attribute included in second transaction}
wherein the request references the common transaction ID. {see at least [0084]-[0085] second transaction has a different payment amount compared with first transaction (based on the broadest reasonable interpretation requirement (MPEP 2111), reads on request for additional data related to second transaction)}
Crudelle does not disclose, however, Lacross-Arnold discloses:
applying … to a hashing algorithm {see at least [0020] hash value by applying a hash algorithm}
It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Crudelle to include the elements of Lacross-Arnold. One would have been motivated to do so, in order to conceal the transaction attributes. In the instant case, Crudelle evidently discloses reconciling/ deduplicating transactions. Lacross-Arnold is merely relied upon to illustrate the functionality of a hashing algorithm in the same or similar context. Since both reconciling/ deduplicating transactions, as well as a hashing algorithm are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Crudelle, as well as Lacross-Arnold would function in the same manner in combination as they do in their separate embodiments, it would be reasonable to conclude that their resulting combination would be predictable. Accordingly, the claimed subject matter is obvious over Crudelle / Lacross-Arnold.
Crudelle, Lacross-Arnold does not disclose, however, Tay discloses:
wherein one of the first server or the second server is a transaction service provider server, and wherein the other one of the first server or the second server is an affiliate server. {see at least [0076] service server; fig3, rc30A, [abstract] affiliate server}
It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Crudelle, Lacross-Arnold to include the elements of Tay. One would have been motivated to do so, in order to ensure the computing infrastructure for the reconciling and deduplication of transactions. In the instant case, Crudelle, Lacross-Arnold evidently discloses reconciling/ deduplicating transactions. Tay is merely relied upon to illustrate the functionality of a transaction and an affiliate server in the same or similar context. Since both reconciling/ deduplicating transactions, as well as a transaction and an affiliate server are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Crudelle, Lacross-Arnold, as well as Tay would function in the same manner in combination as they do in their separate embodiments, it would be reasonable to conclude that their resulting combination would be predictable. Accordingly, the claimed subject matter is obvious over Crudelle, Lacross-Arnold / Tay.
Crudelle, Lacross-Arnold / Tay does not disclose, however, Juneja discloses:
transmitting, by the first server via a communication network, a signal comprising a request for a risk score the second transaction to the second server, {see at least fig3, [0061]-[0064] a risk score for the transaction}
wherein the risk score represents a probability of the second transaction being fraudulent, and {see at least [0062] The fraud risk score represents the likelihood that the transaction is fraudulent}
receiving, by the first server via the communication network, a signal comprising the risk score for the second transaction; and {see at least [0062] The fraud risk score represents the likelihood that the transaction is fraudulent}
approving or denying, in real time, by the first server, the first transaction based on the risk score for the second transaction, {see at least fig3, rc306, [0065] determine at least one authorization action}
It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Crudelle, Lacross-Arnold, Tay to include the elements of Juneja. One would have been motivated to do so, in order to prevent execution of fraudulent transactions. In the instant case, Crudelle, Lacross-Arnold, Tay evidently discloses reconciling/ deduplicating transactions. Juneja is merely relied upon to illustrate the functionality of approving/denying transactions based on a determined fraud risk score, in the same or similar context. Since both reconciling/ deduplicating transactions, as well as approving/denying transactions based on a determined fraud risk score are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Crudelle, Lacross-Arnold, Tay, as well as Juneja would function in the same manner in combination as they do in their separate embodiments, it would be reasonable to conclude that their resulting combination would be predictable. Accordingly, the claimed subject matter is obvious over Crudelle, Lacross-Arnold, Tay / Juneja.
Regarding Claim 4: Crudelle, Lacross-Arnold, Tay, Juneja discloses the limitations of Claim 1. Tay further discloses:
wherein the affiliate server is a payment gateway server. {see at least fig3, rc30A, [abstract] payment server}
it would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Crudelle, Lacross-Arnold, Tay, Juneja to include additional elements of Tay. One would have been motivated to do so, in order to ensure the computing infrastructure for the reconciling and deduplication of transactions. In the instant case, Crudelle, Lacross-Arnold, Tay, Juneja evidently discloses reconciling/ deduplicating transactions. Tay is merely relied upon to illustrate the additional functionality of a payment server in the same or similar context. Since the subject matter is merely a combination of old elements, and in the combination each element would have performed the same function it performed separately, one having ordinary skill in the art before the effective filing date would have recognized that the results of the combination were predictable.
Regarding Claim 5: Crudelle, Lacross-Arnold, Tay, Juneja discloses the limitations of Claim 1. Tay further discloses:
wherein the affiliate server is an authentication service provider server. {see at least [0076] service server}
It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Crudelle, Lacross-Arnold, Tay, Juneja to include additional elements of Tay. One would have been motivated to do so, in order to ensure the computing infrastructure for the reconciling and deduplication of transactions. In the instant case, Crudelle, Lacross-Arnold, Tay, Juneja evidently discloses reconciling/ deduplicating transactions. Tay is merely relied upon to illustrate the additional functionality of an affiliate server in the same or similar context. Since the subject matter is merely a combination of old elements, and in the combination each element would have performed the same function it performed separately, one having ordinary skill in the art before the effective filing date would have recognized that the results of the combination were predictable.
Regarding Claim 6: Crudelle, Lacross-Arnold, Tay, Juneja discloses the limitations of Claim 1. Crudelle further discloses:
training the machine learning model to output confidence scores representing likelihoods that sets of transactions are the same transaction based on receiving transaction attributes for the sets of transactions at inputs, wherein the training comprises supervised learning using the labeled transaction data {see at least [0018]-[0022] supervised learning; using labeled data; transaction records include amounts (reads on transaction attributes)}
wherein the labeled transaction data is from the first server and the second server. {see at least fig7, rc731, [0091] server distributed across multiple devices (based on BRI (MPEP2111), reads on first and second server)}
Regarding Claim 7: Crudelle, Lacross-Arnold, Tay, Juneja discloses the limitations of Claim 1. Crudelle further discloses:
wherein the first transaction attributes and the second transaction attributes define types of attributes, {see at least [0002] … the transaction record may have information separated into various parameters such as date, amount, payor, payee, transaction category (e.g. transfer, refund, income, ATM deposit, etc.), etc. and may or may not include a transaction string; [0016] The transaction record may include an identifier of the account (e.g., account number, last 4 digits of the account, etc.), a date of the transaction, an amount, and a transaction string.}
wherein the types of attributes comprise a primary account number (PAN), a transaction amount, a currency, a date and time, an authentication code, a transaction service provider transaction identifier (ID), or a merchant code, or a combination thereof. {see at least [0022] attributes include amounts}
Regarding Claim 8: Crudelle, Lacross-Arnold, Tay, Juneja discloses the limitations of Claim 7. Crudelle further discloses:
wherein a first attribute of the first transaction attributes is different from a second attribute of the second transaction attributes, wherein the first attribute and the second attribute are the same type of attribute. {see at least [0028] dates, transactions overlapping transactions of the same type … amounts, categories, transaction strings, (reads on different transaction attributes options)}
Regarding Claim 9: Crudelle, Lacross-Arnold, Tay, Juneja discloses the limitations of Claim 7. Crudelle further discloses:
wherein the predetermined threshold is a first predetermined threshold, {see at least [0052] predetermined threshold}
wherein the first transaction attributes comprise a transaction amount, and {see at least [0028] transaction amount}
wherein accepting that the first transaction and the second transaction are the same transaction is further based on the transaction amount satisfying a second predetermined threshold. {see at least [0052] predetermined threshold for the confidence score of a transaction for a certain amount}
Regarding Claim 10: Crudelle, Lacross-Arnold, Tay, Juneja discloses the limitations of Claim 9. Crudelle further discloses:
wherein accepting that the first transaction and the second transaction are the same transaction is further based on the types of attributes defined by the first transaction attributes and the second transaction attributes. {see at least [0019] deduplication (reads on same transactions; [0082]-[0083] type of attributes}
Claims 11-12, 14, 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Crudelle et al (US 2022/0398583), in view of Juneja et al (US 2021/0304206).
Regarding Claim 11: Crudelle: A transaction service provider system, comprising:
a confidence model to:
receive first transaction data for a first transaction being processed by the transaction service provider system; {see at least fig6, rc620, rc630, [0082] first transaction with transaction attributes}
receive, via a communication network, second transaction data for a second transaction being processed by an affiliate system; and {see at least fig6, rc620, rc630, [0082] first transaction with transaction attributes; fig7, rc731, rc732, rc710, [0089]-[0091] multiple servers (reads on second server); fig1, rc110, rc120, [0065] FIG. 1 illustrates a process 100 of reconciling transaction data from different sources in accordance with an example embodiment. In this example, two financial institutions are shown as the sources, but different sources and different number of sources may be used. Referring to FIG. 1, transaction data 111 from a first financial institution 110 may be input to a host platform 130. Likewise, transaction data 121 from a second financial institution 120 may be input to the host platform 130. Here, the transaction data 111 and 121 may include tabular data, spreadsheets, bank statements, XML documents (second financial institution (reads on second server) provides transaction data (reads on transaction attributes.}
generate a confidence score based on the first transaction data and the second transaction data; {see at least [0050]-[0052] between the two transaction (reads on first transaction data and second transaction data; machine learning model generates confidence scores for each transaction}
wherein the confidence model is a machine learning model trained via supervised learning using labeled transaction data, {see at least [0052] machine learning model; [0019] supervised machine learning model}
wherein the labeled transaction data comprises transaction attributes for sets of transactions labeled as either corresponding to the same transaction or different transactions, and {see at least (0050]-[0052] between the two transaction (reads on different transactions}
wherein the confidence score represents a likelihood that the first transaction and the second transaction are the same transaction; {see at least [0052]-[0054] confidence score … two recitation of the same transaction (reads on likelihood the transactions being the same)}
a common transaction ID generator to:
generate a common transaction ID for the first transaction and the second transaction based on the first transaction data; {see at least fig6, rc640, [0082] file to correspond to common transaction (reads on common transaction ID); [0084] payment attribute included in second transaction}
determine the confidence score satisfies a predetermined threshold; {see at least [0052] below threshold. Crudelle does not explicitly disclose the confidence score satisfying the predetermined threshold. However, it is reasonable to assume that one of ordinary skills in the art will realize that once a predetermined threshold exists, the confidence score has to at list satisfy it – see MPEP 2123 and MPEP 2144.01}
wherein the request comprises the common transaction ID. {see at least [0084]-[0085] second transaction has a different payment amount compared with first transaction (based on the broadest reasonable interpretation requirement (MPEP 2111), reads on request for additional data related to second transaction and the request contains the common transaction ID)}
Crudelle does not disclose, however, Juneja discloses:
a decision engine to:
transmit, via a communication network, a signal comprising a request for a risk score the second transaction to the affiliate system, {see at least fig3, [0061]-[0064] a risk score for the transaction; [0056] more transactions (reads on second transaction); [0060] two separate fraud scoring systems (reads on affiliate system)}
wherein the risk score represents a probability of the second transaction being fraudulent, {see at least [0062] The fraud risk score represents the likelihood that the transaction is fraudulent}
a fraud model to:
receive the risk score for the second transaction from the affiliate system; and {see at least [0060] two separate fraud scoring systems (reads on affiliate system)}
approve or deny the first transaction based on the risk score for the second transaction. {see at least fig3, rc306, [0065] determine at least one authorization action}
It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Crudelle, to include the elements of Juneja. One would have been motivated to do so, in order to prevent execution of fraudulent transactions. In the instant case, Crudelle, evidently discloses reconciling/ deduplicating transactions. Juneja is merely relied upon to illustrate the functionality of approving/denying transactions based on a determined fraud risk score, in the same or similar context. Since both reconciling/ deduplicating transactions, as well as approving/denying transactions based on a determined fraud risk score are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Crudelle as well as Juneja would function in the same manner in combination as they do in their separate embodiments, it would be reasonable to conclude that their resulting combination would be predictable. Accordingly, the claimed subject matter is obvious over Crudelle / Juneja.
Regarding Claim 12: Crudelle, Juneja discloses the limitations of Claim 11. Juneja further discloses: further comprising: wherein the fraud model is configured to:
receive the first transaction data; {see at least fig3, [0061]-[0062] receive transaction data}
generate a first risk score for the first transaction; {see at least fig3, [0061]-[0062] generate a fraud risk score}
receive the additional data related to the second transaction from the affiliate system, wherein the additional data comprises a second risk score for the second transaction; and {see at least fig3, [0061]-[0064] a risk score for the transaction; [0056] more transactions (reads on second transaction); [0060] two separate fraud scoring systems (reads on affiliate system)}
approve or deny the first transaction based on a risk score for the first transaction and the risk score for the second transaction. {see at least fig3, rc306, [0065] determine at least one authorization action}
It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Crudelle, Juneja to include additional elements of Juneja. One would have been motivated to do so, in order to prevent fraudulent transactions to be processed. In the instant case, Crudelle, Juneja evidently discloses reconciling/deduplicating transactions and identifying fraudulent transactions. Juneja is merely relied upon to illustrate the additional functionality of authorizing transactions based on the analysis results in the same or similar context. Since the subject matter is merely a combination of old elements, and in the combination each element would have performed the same function it performed separately, one having ordinary skill in the art before the effective filing date would have recognized that the results of the combination were predictable.
Regarding Claim 14: Crudelle, Juneja discloses the limitations of Claim 11. Crudelle further discloses:
wherein the first transaction data and the second transaction data each comprise transaction attributes, and {see at least [0002] … the transaction record may have information separated into various parameters such as date, amount, payor, payee, transaction category (e.g. transfer, refund, income, ATM deposit, etc.), etc. and may or may not include a transaction string; [0016] The transaction record may include an identifier of the account (e.g., account number, last 4 digits of the account, etc.), a date of the transaction, an amount, and a transaction string.}
wherein the transaction attributes comprise a primary account number (PAN), a transaction amount, a currency, a date and time, an authentication code, a transaction service provider transaction identifier (ID), or a merchant code, or a combination thereof. {see at least [0022] attributes include amounts}
Regarding Claim 17: Crudelle, Juneja discloses the limitations of Claim 11. Crudelle further discloses:
wherein the first transaction comprises a first transaction amount, and {see at least {see at least fig6, rc620, rc630, [0082] first transaction with transaction attributes; [0022] attributes include amounts}
wherein the predetermined threshold is based on the first transaction amount. {see at least [0028] transaction amount}
Regarding Claim 18: Crudelle discloses: A computer-implemented method, comprising:
receiving, by a first affiliate server via a communication network, a request to process a first transaction from a merchant server, wherein the request comprises first transaction data; {see at least fig6, rc620, rc630, [0082] first transaction with transaction attributes; [0028] transaction amount (reads on transaction data)}
sending, by the first affiliate server, the first transaction data to a confidence model; {see at least {see at least [0052] machine learning (based on the broadest reasonable interpretation requirement (MPEP 2111), reads on confidence model; confidence score}
wherein the confidence model is a machine learning model trained via supervised learning using labeled transaction data, {see at least [0052] machine learning model; [0019] supervised machine learning model}
wherein the labeled transaction data comprises transaction attributes for sets of transactions labeled as either corresponding to the same transaction or different transactions, and {see at least (0050]-[0052] between the two transaction (reads on different transactions}
wherein the confidence model is configured to output confidence scores representing likelihoods that sets of transactions are the same transactions based on receiving transaction attributes for the sets of transactions as inputs; {see at least fig6, rc620, rc630, [0082] first transaction with transaction attributes; fig7, rc731, rc732, rc710, [0089]-[0091] multiple servers (reads on second server); fig1, rc110, rc120, [0065] FIG. 1 illustrates a process 100 of reconciling transaction data from different sources in accordance with an example embodiment. In this example, two financial institutions are shown as the sources, but different sources and different number of sources may be used. Referring to FIG. 1, transaction data 111 from a first financial institution 110 may be input to a host platform 130. Likewise, transaction data 121 from a second financial institution 120 may be input to the host platform 130. Here, the transaction data 111 and 121 may include tabular data, spreadsheets, bank statements, XML documents (second financial institution (reads on second server) provides transaction data (reads on transaction attributes.}
receiving, by the first affiliate server, a confidence score for the first transaction and a second transaction being processed by a second affiliate server from the confidence model, {see at least [0050]-[0052] machine learning model generates confidence scores for each transaction}
wherein the confidence score represents a likelihood that the first transaction and the second transaction are the same transaction; {see at least [0052]-[0054] two recitation of the same transaction (reads on likelihood the transactions being the same)}
determining, by the first affiliate server, that the confidence score satisfies a predetermined threshold; {see at least [0052] below threshold. Crudelle does not explicitly disclose the confidence score satisfying the predetermined threshold. However, it is reasonable to assume that one of ordinary skills in the art will realize that once a predetermined threshold exists, the confidence score has to at list satisfy it – see MPEP 2123 and MPEP 2144.01}
generating, by the first affiliate server, a common transaction identification (ID) for the first transaction and the second transaction based on the first transaction data; {see at least fig6, rc640, [0082] file to correspond to common transaction (reads on common transaction ID)}
wherein the request references the common transaction ID; {see at least [0084]-[0085] second transaction has a different payment amount compared with first transaction (based on the broadest reasonable interpretation requirement (MPEP 2111), reads on request for additional data related to second transaction)}
Crudelle does not disclose, however, Juneja discloses:
transmitting, by the first affiliate server via a communication network, a signal comprising a request for a risk score the second transaction to the second affiliate server, {see at least fig3, [0061]-[0064] a risk score for the transaction}
wherein the risk score represents a probability of the second transaction being fraudulent, and {see at least [0062] The fraud risk score represents the likelihood that the transaction is fraudulent}
receiving, by the first affiliate server via a communication network, a signal comprising the risk score for the second transaction {see at least [0062] The fraud risk score represents the likelihood that the transaction is fraudulent}
approving or denying, by the first affiliate server, the first transaction based on the risk score for the second transaction. {see at least fig3, rc306, [0065] determine at least one authorization action}
It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Crudelle, to include the elements of Juneja. One would have been motivated to do so, in order to prevent execution of fraudulent transactions. In the instant case, Crudelle, evidently discloses reconciling/ deduplicating transactions. Juneja is merely relied upon to illustrate the functionality of approving/denying transactions based on a determined fraud risk score, in the same or similar context. Since both reconciling/ deduplicating transactions, as well as approving/denying transactions based on a determined fraud risk score are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Crudelle as well as Juneja would function in the same manner in combination as they do in their separate embodiments, it would be reasonable to conclude that their resulting combination would be predictable. Accordingly, the claimed subject matter is obvious over Crudelle / Juneja.
Claims 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Crudelle et al (US 2022/0398583), in view of Juneja (US 2021/0304206), in further view of Lacross-Arnold et al (US 2018/0181953).
Regarding Claim 15: Crudelle, Juneja discloses the limitations of Claim 14. Crudelle, Juneja does not disclose, however, Lacross-Arnold discloses:
wherein the common transaction ID generator comprises a hashing algorithm to generate the common transaction ID based on the transaction attributes of the first transaction data. {see at least [0020] hash value by applying a hash algorithm. The claim element “to generate the common transaction ID based on the transaction attributes of the first transaction data” consists entirely of language disclosing at most a reason to have performed earlier method steps (intended use or field of use), but does not affect the functions in a manipulative sense (see MPEP 2103 I C) and imparts neither structure nor functionality to the claimed method (see MPEP 2111.05, MPEP 2114 and authorities cited therein), so it is considered but given no patentable weight. The reference is provided for the purpose of compact prosecution.}
It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Crudelle, Juneja to include the elements of Lacross-Arnold. One would have been motivated to do so, in order to conceal the transaction attributes. In the instant case, Crudelle, Juneja evidently discloses reconciling/ deduplicating transactions. Lacross-Arnold is merely relied upon to illustrate the functionality of a hashing algorithm in the same or similar context. Since both reconciling/ deduplicating transactions, as well as a hashing algorithm are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Crudelle, Juneja, as well as Lacross-Arnold would function in the same manner in combination as they do in their separate embodiments, it would be reasonable to conclude that their resulting combination would be predictable. Accordingly, the claimed subject matter is obvious over Crudelle, Juneja / Lacross-Arnold.
Regarding Claim 16: Crudelle, Juneja, Lacross-Arnold discloses the limitations of Claim 15. Crudelle further discloses: wherein the decision engine is to
compare the transaction attributes of the first transaction data and the transaction attributes of the second transaction data. {see at least [0028] dates, transactions overlapping transactions of the same type … amounts, categories, transaction strings, (reads on comparing different transaction attributes/data)}
Claims 20 are rejected under 35 U.S.C. 103 as being unpatentable over Crudelle et al (US 2022/0398583), in view of Juneja (US 2021/0304206), in further view of Tay (US 2023/0196398).
Regarding Claim 20: Crudelle, Juneja discloses the limitations of Claim 18. Crudelle, Juneja does not disclose, however, Tay discloses:
wherein at least one of the first affiliate server and the second affiliate server is a payment gateway server. {see at least fig3, rc30A, [abstract] payment server}
it would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Crudelle, Juneja to include the elements of Tay. One would have been motivated to do so, in order to ensure the computing infrastructure for the reconciling and deduplication of transactions. In the instant case, Crudelle, Juneja evidently discloses reconciling/ deduplicating transactions. Tay is merely relied upon to illustrate the functionality of a payment server in the same or similar context. Since both reconciling/ deduplicating transactions, as well as a payment server are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Crudelle, Juneja, as well as Tay would function in the same manner in combination as they do in their separate embodiments, it would be reasonable to conclude that their resulting combination would be predictable. Accordingly, the claimed subject matter is obvious over Crudelle, Juneja / Tay.
The prior art made of record and not relied upon which, however, is considered pertinent to applicant's disclosure:
US 20200250747 A1 Padmanabhan; Prithvi Krishnan SYSTEMS, METHODS, AND APPARATUSES FOR DYNAMICALLY ASSIGNING NODES TO A GROUP WITHIN BLOCKCHAINS BASED ON TRANSACTION TYPE AND NODE INTELLIGENCE USING DISTRIBUTED LEDGER TECHNOLOGY (DLT) - Systems, methods, and apparatuses for dynamically assigning nodes to a group within blockchains based on transaction type and node intelligence using Distributed Ledger Technology (DLT) in conjunction with a cloud based computing environment. For example, according to one embodiment there is a system having at least a processor and a memory therein executing within a host organization, in which such a system includes means for operating a blockchain interface to the blockchain on behalf of a plurality of tenants of the host organization, in which each one of the plurality of tenants operate as a participating node with access to the blockchain; creating a consensus group on the blockchain and associating the consensus group with a specific transaction type for transactions to be processed via the blockchain; assigning a subset of the participating nodes to the consensus group; granting increased weight consensus voting rights to any participating nodes assigned to the consensus group; receiving a transaction at the blockchain having a transaction type matching the specific transaction type associated with the consensus group; and determining consensus for the transaction based on the consensus votes of the participating nodes assigned to the consensus group. Other related embodiments are disclosed.
US 20240020648 A1 Robinson; Jason et al. BENEFIT ADMINISTRATION PLATFORM - Provided are systems and methods for verification and management of benefit administration. The system can determine the eligibility of users to receive basic income and other forms of benefits, grants, aid, etc. Furthermore, the system can automate and manage the distribution of such benefits while creating an immutable/auditable trail of the disbursements. Accordingly, the verification system described herein can prevent risk and other forms of malfeasance within the benefit administration process.
US 20240013168 A1 Oei; Jonathan Andrew et al. Systems and Methods for Reconciling Virtual Bank Account Transactions - In one embodiment, a method includes storing a set of first transaction entries comprising real-time transaction data, identifying identical pairs of a first transaction entry and a second transaction entry from the set of first transaction entries and a set of second transaction entries derived from the historical transaction data, wherein the first and second transaction entries are excluded from the remaining sets of first and second transaction entries for each identical pair, identifying matching pairs of a first and a second transaction entries for each matching cycle from the remaining sets of first and second transaction entries, and wherein the first and second transaction entries are excluded from the remaining sets of first and second transaction entries for each matching pair, and analyzing the remaining sets of first and second transaction entries to identify incongruous transaction entries and determine remediations for some of the incongruous transaction entries.
US 20250005682 A1 MUELLER-EBERSTEIN; Mark EXTENSIBILITY MODEL FOR BLOCKCHAIN ANALYSIS PLATFORM - Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing an extensibility model for a blockchain analysis platform are disclosed. In one aspect, a method includes the actions of accessing digital transaction data that reflects a digital transaction on an asset. The actions further include determining a first set of actions for the digital transaction by parsing the digital transaction data, wherein each action of the first set of actions is decomposable into operation primitives of a set of operation primitives. The actions further include receiving a second set of actions, wherein each action in the second set of actions is decomposable into operation primitives of the set of operation primitives. The actions further include determining a first candidate cost basis of the asset. The actions further include determining a second candidate cost basis. The actions further include selecting a cost basis.
US 20210350382 A1 LOPES; Maria et al. MACHINE LEARNING SYSTEM FOR TRANSACTION RECONCILIATION - A device may receive transaction data associated with transactions. The transaction data may be associated with transaction entries that are associated with the transactions. The device may process, using a matching model, the transaction entries to classify the transaction entries into a set of matched transaction entries and a set of unmatched transaction entries. The device may update a transaction grouping model based on the set of matched transaction entries to create an updated transaction grouping model. The device may determine, using the updated transaction grouping model, that a subset of the set of unmatched transaction entries are associated with a same transaction. The device may classify the subset of the set of unmatched transaction entries as grouped transaction entries. The device may provide an indication that the grouped transaction entries and the set of matched transaction entries are reconciled transactions.
US 20190073666 A1 ORTIZ; Edison U. et al. METHODS AND SYSTEMS FOR DIGITAL REWARD PROCESSING - Embodiments generally relate to the field of reward processing, and more particularly, systems, methods, and computer readable media for digital reward processing utilizing distributed ledger technology. Distributed ledger technology is utilized wherein distributed ledgers are stored on a plurality of node computing devices, the distributed ledgers including sequential entries that are cryptographically linked to one another.
Response to Amendments/Arguments
Applicant’s submitted remarks and arguments have been fully considered.
Applicant disagrees with the Office Action conclusions and asserts that the presented claims fully comply with the requirements of 35 U.S.C. § 101 regrading judicial exceptions. Further, Applicant is of the opinion that the prior art fails to teach Applicant’s invention.
Examiner respectfully disagrees in both regards.
With respect to Applicant’s Remarks as to the claims being rejected under 35 USC § 112(a).
The rejection is withdrawn, as a result of the amendments.
With respect to Applicant’s Remarks as to the claims being rejected under 35 USC § 101.
Applicant submits:
a. The pending claims are not directed to an abstract idea.
b. The identified abstract idea is integrated into a practical application.
c. The pending claims amount to significantly more.
Furthermore, Applicant asserts that the Office has failed to meet its burden to identify the abstract idea and to establish that the identified abstract idea is not integrated into a practical application and that the pending claims do not amount to significantly more.
Examiner responds – The arguments have been considered in light of Applicants’ amendments to the claims. The arguments ARE NOT PERSUASIVE. Therefore, the rejection is maintained.
The pending claims, as a whole, are directed to an abstract idea not integrated into a practical application. This is because (1) they do not effect improvements to the functioning of a computer, or to any other technology or technical field (see MPEP 2106.05 (a)); (2) they do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or a medical condition (see the Vanda memo); (3) they do not apply the abstract idea with, or by use of, a particular machine (see MPEP 2106.05 (b)); (4) they do not effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05 (c)); (5) they do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the identified abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designated to monopolize the exception (see MPEP 2106.05 (e) and the Vanda memo).
In addition, the pending claims do not amount to significantly more than the abstract idea itself.
As such, the pending claims, when considered as a whole, are directed to an abstract idea not integrated into a practical application and not amounting to significantly more.
More specific:
Applicant submits “Claims 1, 11, and 18 recite additional elements that integrate any allegedly-recited abstract idea into a practical application such that they are not directed to the abstract idea under Step 2A Prong 2 and provide "significantly more" than the abstract idea under Step 2B.”
Examiner has carefully considered, but doesn’t find Applicant’s arguments persuasive.
First, MPEP 2106.04(d)(1) discloses:
An important consideration to evaluate when determining whether the claim as a whole integrates a judicial exception into a practical application is whether the claimed invention improves the functioning of a computer or other technology .... In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art .... Second, if the specification sets forth an improvement in technology. the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. (Emphasis added)
That is, the claimed invention may integrate the judicial exception into a practical application by demonstrating that it improves the relevant existing technology although it may not be an improvement over well-understood, routine, conventional activity. (Emphasis added)
Second, Per Step 2B. Independent claims 1, 11, 18 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when the independent claim is reevaluated as a whole, as an ordered combination under the considerations of Step 2B, the outcome is the same like under Step 2A.2.
Thus, the rejection is proper and has been maintained.
Applicant submits “Specifically, the claims provide unconventional technical solution over prior approaches to fraud prevention in the technical field of payment network system transaction processing by enabling different entities of a payment network to share risk scores for determining whether to approve or deny a transaction, in real time.”
Examiner has carefully considered, but doesn’t find Applicant’s arguments persuasive.
MPEP 2106.05(a) discloses that the additional claim elements bring about “improvements to the functioning of a computer, or any other technology or technical field.” Making payments is a pure BUSINESS problem, rather than a technology or technical field problem. As such, the limitations which have not been deemed as being part of the identified abstract idea, i.e., the “additional limitations,” do not integrate the identified abstract idea into a practical application, as disclosed by MPEP 2106.05(a).
Thus, the rejection is proper and has been maintained.
Applicant submits “The Specification further provides an unconventional technical solution to enable entities to share fraud analysis results (risk scores) for real-time during transaction processing by generating a common transaction ID to identify transactions between the entities that are actually the same transaction, thus enabling the entities to improve fraud detection and approve or deny transaction in real time based on the fraud detection.”
Examiner has carefully considered, but doesn’t find Applicant’s arguments persuasive.
See response immediately above.
Thus, the rejection is proper and has been maintained.
Applicant submits “An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art.”
Examiner has carefully considered, but doesn’t find Applicant’s arguments persuasive.
See responses here above.
Thus, the rejection is proper and has been maintained.
Applicant submits “However, as noted above, referring to technological field improvements that provide subject matter eligibility,”
Examiner has carefully considered, but doesn’t find Applicant’s arguments persuasive.
As noted in the quoted Advisory Action, the “technological solution” in itself is not an eligibility criterion. Improvements “to the functioning of a computer or any other technology or technical field” have the potential to integrate the identified abstract idea into a practical application, thus reversing the status of the judicial exception (see MPEP 2106.05(a)
Thus, the rejection is proper and has been maintained.
Applicant submits “Applicant's remarks above clearly lay out a technical problem and unconventional technical solution to the problem provided by the specification and reflected by Claim 1.”
Examiner has carefully considered, but doesn’t find Applicant’s arguments persuasive.
See responses here above.
Thus, the rejection is proper and has been maintained.
It follows from the above that there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself. Therefore, the rejection under 35 U.S.C. § 101 is maintained.
With respect to Applicant’s Remarks as to the claims being rejected under 35 USC § 103.
Applicant submits remarks and arguments geared toward the amendments. Examiner has carefully reviewed and considered Applicant’s remarks, however they ARE MOOT in light of the fact that they are geared towards the amendments. Nevertheless, Juneja discloses:
transmitting, by the first server via a communication network, a signal comprising a request for a risk score the second transaction to the second server, {see at least fig3, [0061]-[0064] a risk score for the transaction}
wherein the risk score represents a probability of the second transaction being fraudulent, and {see at least [0062] The fraud risk score represents the likelihood that the transaction is fraudulent}
receiving, by the first server via the communication network, a signal comprising the risk score for the second transaction; and {see at least [0062] The fraud risk score represents the likelihood that the transaction is fraudulent}
approving or denying, in real time, by the first server, the first transaction based on the risk score for the second transaction, {see at least fig3, rc306, [0065] determine at least one authorization action}
The other arguments presented by Applicant continually point back to the above arguments as being the basis for the arguments against the other 103 rejections, as the other arguments are presented only because those claims depend from the independent claims, and the main argument above is presented against the independent claims. Therefore, it is believed that all arguments put forth have been addressed by the points above.
Examiner has reviewed and considered all of Applicant’s remarks. The changes of the grounds for rejection, if any, have been necessitated by Applicant’s extensive amendments to the claims. Therefore, the rejection is maintained, necessitated by the extensive amendments and by the fact that the rejection of the claims under 35 USC § 101 has not been overcome.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Radu Andrei whose telephone number is 313.446.4948. The examiner can normally be reached on Monday – Friday 8:30am – 5pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patrick McAtee can be reached at 571.272.7575. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http:/www.uspto.gov/interviewpractice.
As disclosed in MPEP 502.03, communications via Internet e-mail are at the discretion of the applicant. Without a written authorization by applicant in place, the USPTO will not respond via Internet e-mail to any Internet correspondence which contains information subject to the confidentiality requirement as set forth in 35 U.S.C. 122. A paper copy of such correspondence will be placed in the appropriate patent application. The following is a sample authorization form which may be used by applicant:
“Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with me concerning any subject matter of this application by electronic mail. I understand that a copy of these communications will be made of record in the application file.”
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center information webpage. Status information for unpublished applications is available to registered users through Patent Center information webpage only.
To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov.
Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (in USA or CANADA) or 571-272-1000.
Any response to this action should be mailed to:
Commissioner of Patents and Trademarks
P.O. Box 1450
Alexandria, VA 22313-1450
or faxed to 571-273-8300
/Radu Andrei/
Primary Examiner, AU 3698