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 .
Priority
Priority is recognized based on the provisional application filed on December 23, 2019.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8 and 11-20 are rejected under 35 U.S.C. 101 because the claimed invention discloses an abstract idea without significantly more.
Step 1: Whether a Claim is to a Statutory Category
In the instant case, claims 1-8 recites method/process claims and claims 11-15 recite system/machine claims; and claims 16-20 recite computer program/manufacture claims that are performing a series of functions. Therefore, these claims fall within the four statutory categories of invention of a process and a machine. Step 1 is satisfied.
Step2A – Prong 1: Does the Claim Recite a Judicial Exception
Exemplary claim 1 (and similarly claims 11 and 16) recites the following abstract concepts that are found to include an enumerated “abstract idea”:
A computer-implemented method, comprising:
receiving, with at least one processor, historical transaction data comprising authorization record data associated with a plurality of merchants and clearing record data associated with the plurality of merchants;
training, with at least one processor, a machine learning model based on the historical transaction data to produce a trained machine learning model, the trained machine learning model configured to output an estimated clearing delay based at least partly on an input of a merchant identifier, the estimated clearing delay comprising an estimated period of time to receive a clearing record from a merchant that is associated with the merchant identifier being input to the trained machine learning model;
receiving, with at least one processor, a clearing batch file comprising a plurality of clearing records, each clearing record of the plurality of clearing records comprising at least one key field, the plurality of clearing records being associated with a plurality of payment transactions that were completed in a payment transaction processing network, and the plurality of clearing records being associated with the plurality of merchants;
determining, with at least one processor, a first merchant identifier of a first merchant of the plurality of merchants based on the at least one key field of a first clearing record of the plurality of clearing records;
generating, with at least one processor, a first confidence score comprising a first value based on a first merchant transaction clearing delay indicative of a likelihood of the first clearing record being a force-post payment transaction, wherein the first merchant transaction clearing delay comprises an estimated period of time to receive a clearing record from the first merchant after receiving a corresponding authorization record, and wherein generating the first confidence score comprises:
inputting the first merchant identifier of the first merchant to the trained machine learning model;
determining a first estimated clearing delay based on a first output of the trained machine learning model; and
generating the first confidence score based on the first estimated clearing delay;
determining, with at least one processor, that the first confidence score satisfies a
confidence threshold, the confidence threshold associated with a likelihood of a clearing record being associated with a force-post payment transaction;
in response to determining that the first confidence score satisfies the confidence threshold, processing, with at least one processor, the first clearing record as a force- post payment transaction;
determining, with at least one processor, a second merchant identifier of a second merchant of the plurality of merchants based on the at least one key field of a second clearing record of the plurality of clearing records;
generating, with at least one processor, a second confidence score comprising a second value based on a second merchant transaction clearing delay indicative of a likelihood of the second clearing record being a force-post payment transaction, wherein the second merchant transaction clearing delay comprises an estimated period of time to receive a clearing record from the second merchant after receiving a corresponding authorization record, and wherein generating the second confidence score comprises:
inputting the second merchant identifier of the second merchant to the trained machine learning model;
determining a second estimated clearing delay based on a second output of the trained machine learning model; and
generating the second confidence score based on the second estimated clearing delay;
determining, with at least one processor, that the second confidence score does not satisfy the confidence threshold;
in response to determining that the second confidence score does not satisfy the confidence threshold, comparing, with at least one processor, a value associated with a first key field of the second clearing record to a value associated with a first key field of one more authorization records associated with payment transactions that were authorized in the payment transaction processing network, the first key field of the second clearing record corresponding to the first key field of the one or more authorization records, wherein the one or more authorization records are associated with an authorization request for a payment transaction of the payment transactions that were authorized;
determining, with at least one processor, that the second clearing record corresponds to an authorization record from among the one or more authorization records based on comparing the value associated with the first key field of the second clearing record to the value associated with the first key field of the one or more authorization records;
generating, with at least one processor, an updated clearing record based on determining that the second clearing record corresponds to the authorization record;
generating, with at least one processor, an updated clearing batch file based on the clearing batch file and the updated clearing record;
determining, with at least one processor, an issuer system that is associated with the authorization record; and
transmitting, with at least one processor, the updated clearing batch file to the issuer system.
[Emphasis added to show the abstract idea being executed by additional elements that do not meaningfully limit the abstract idea]
This method claim is grouped within the "mathematical concepts” grouping of abstract ideas in prong one of step 2A of the Alice/Mayo test because the claims involve a series of steps of mathematical calculations by using a machine learning model to estimate a clearing delay of clearing records being associated with a plurality of payment transactions which is a process that is encompassed by the abstract idea of mathematical concepts. Nowhere in the claims or specification of the instant application is there a clear training process disclosed for the claimed machine learning model that shows how said model is trained nor is there clear disclosure of a particular machine learning model beyond the general use of a machine learning model. See e.g. MPEP 2106.04(a)(2)(I)(C) and July 2024 Subject Matter Eligibility Example 47 claim 2. Accordingly, claim 1 (and similarly claims 11 and 16) are found to recite abstract idea(s).
Step2A – Prong 2: Does the Claim Recite Additional Elements that Integrate the Judicial Exception into a Practical Application
The abstract idea of claim 1 (and similarly claims 11 and 16) is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test, the additional elements of the claims such as a computer, processor, transaction processing network and issuer system merely use a computer as a tool to perform an abstract idea and/or generally link the use of a judicial exception to a particular technological environment. Specifically, the computer, processor, transaction processing network and issuer system perform the steps or functions of mathematical calculations by using a machine learning model to estimate a clearing delay of clearing records being associated with a plurality of payment transactions. The use of a processor/computer as a tool to implement the abstract idea and/or generally linking the use of the abstract idea to a particular technological environment does not integrate the abstract idea into a practical application because it requires no more than a computer (or technical elements disclosed at a high level of generality such as computer, processor, transaction processing network and issuer system) performing functions of receiving, training, producing, comparing, determining, generating, inputting and transmitting that correspond to acts required to carry out the abstract idea (MPEP 2106.05(f) and (h)). Accordingly, the additional elements of claim 1 do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
Step2B: Does the Claim Amount to Significantly More
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test, the additional elements of computer, processor, transaction processing network and issuer system are being used to perform the steps of receiving, training, producing, comparing, determining, generating, inputting and transmitting amounts to no more than using a computer or processor to automate and/or implement the abstract idea of mathematical calculations by using a machine learning model to estimate a clearing delay of clearing records being associated with a plurality of payment transactions. As discussed above, taking the claim elements separately, computer, processor, transaction processing network and issuer system perform the steps or functions of mathematical concepts of mathematical calculations by using a machine learning model to estimate a clearing delay of clearing records being associated with a plurality of payment transactions. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of mathematical concepts of mathematical calculations by using a machine learning model to estimate a clearing delay of clearing records being associated with a plurality of payment transactions because said combination of elements remains disclosed at a high level of generality. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(l)(A)(f) & (h)). Therefore, the claims are not patent eligible.
Independent claims 11 and 16 describe a system and computer program performing the functions of receiving, training, producing, comparing, determining, generating, inputting and transmitting relating to mathematical concepts without additional elements beyond technical elements disclosed at a high level of generality such as a server, computer, non-transitory computer readable medium, processor, network and issuer system that provide significantly more than the abstract idea of mathematical concepts of mathematical calculations by using a machine learning model to estimate a clearing delay of clearing records being associated with a plurality of payment transactions as noted above regarding claim 1. Therefore, these independent claims are also not patent eligible.
Dependent claims 2, 12 and 17 further limit their respective independent claims 1, 11 and 16 by adding functions of normalizing and converting, however, these steps do not show how the machine learning model of the respective independent claims 1, 11 and 16 is trained, therefore dependent claims 2, 12 and 17 remain as performed by technical elements disclosed at a high level of generality and are not significantly more than the abstract idea of mathematical concepts of mathematical calculations by using a machine learning model to estimate a clearing delay of clearing records being associated with a plurality of payment transactions.
Dependent claims 3, 13 and 18 further limit their respective independent claims 1, 11 and 16 by adding functions of comparing and determining, however, these steps do not show how the machine learning model of the respective independent claims 1, 11 and 16 is trained, therefore dependent claims 3, 13 and 18 remain as performed by technical elements disclosed at a high level of generality and are not significantly more than the abstract idea of mathematical concepts of mathematical calculations by using a machine learning model to estimate a clearing delay of clearing records being associated with a plurality of payment transactions.
Dependent claims 4, 14 and 19 further limit their respective independent claims 1, 11 and 16 by adding functions of determining, however, these steps do not show how the machine learning model of the respective independent claims 1, 11 and 16 is trained, therefore dependent claims 4, 14 and 19 remain as performed by technical elements disclosed at a high level of generality and are not significantly more than the abstract idea of mathematical concepts of mathematical calculations by using a machine learning model to estimate a clearing delay of clearing records being associated with a plurality of payment transactions.
Dependent claim 5 further limits dependent claim 3, which further limits independent claim 1 by adding functions of determining, however, these steps do not show how the machine learning model of independent claim 1 is trained, therefore dependent claim 5 remains as performed by technical elements disclosed at a high level of generality and are not significantly more than the abstract idea of mathematical concepts of mathematical calculations by using a machine learning model to estimate a clearing delay of clearing records being associated with a plurality of payment transactions.
Dependent claim 6 further limits dependent claim 3, which further limits independent claim 1 by adding functions of determining, however, these steps do not show how the machine learning model of independent claim 1 is trained, therefore dependent claim 5 remains as performed by technical elements disclosed at a high level of generality and are not significantly more than the abstract idea of mathematical concepts of mathematical calculations by using a machine learning model to estimate a clearing delay of clearing records being associated with a plurality of payment transactions.
Dependent claims 7, 15 and 20 further limit their respective independent claims 1, 11 and 16 by adding functions of modifying, however, these steps do not show how the machine learning model of the respective independent claims 1, 11 and 16 is trained, therefore dependent claims 7, 15 and 20 remain as performed by technical elements disclosed at a high level of generality and are not significantly more than the abstract idea of mathematical concepts of mathematical calculations by using a machine learning model to estimate a clearing delay of clearing records being associated with a plurality of payment transactions.
Dependent claim 8 further limits dependent claim 7, which further limits independent claim 1 by adding functions of modifying, however, these steps do not show how the machine learning model of independent claim 1 is trained, therefore dependent claim 8 remains as performed by technical elements disclosed at a high level of generality and are not significantly more than the abstract idea of mathematical concepts of mathematical calculations by using a machine learning model to estimate a clearing delay of clearing records being associated with a plurality of payment transactions.
Response to Arguments
Applicant's arguments filed 04/23/2025 have been fully considered but they are not persuasive.
Rejection under 35 U.S.C. § 101:
In consideration of the amended claims and applicant’s remarks asserting that the invention is related to improving the processing of transaction records in electronic payment networks by resolving the ambiguity between authorization and clearing records when no explicit index links them, thereby improving predictive accuracy and avoiding triggering data workflows where they are not needed, the rejection under 35 U.S.C. § 101 is maintained. The functional steps of the claim 1, individually and as a whole, remain as executed by technical elements disclosed at a high level of generality such that said claim amounts to not more than computer implementation of the abstract idea noted above in the current rejection under 35 U.S.C. § 101. Therefore, any improvement shown in the claim is to the abstract idea itself and not to the underlying technology. The applicant is further reminded that the specification of an instant application is not read into the claims during examination.
While the amended independent claims 1, 11 and 16 do recite a “clearing batch file” that is updated by way of applying the unique and dynamic process as required by said independent claims, this updating of a file amounts to mere data processing because the technical elements that are reflected in the claims remain disclosed at a high level of generality such that said claim amounts to not more than computer implementation of the abstract idea and any improvement shown in the claim is to the abstract idea itself, not to the underlying technology.
Contrary to the applicant’s assertion that the present specification provides the intrinsic evidence supporting the technical improvements of the instant application with consideration of Uniloc USA, the specification of the instant application as filed does not specifically disclose the technical elements that are performing the claimed functions as they are currently limited. The issue is that the functions of said claim include multiple determining, generating, processing and comparing steps that all rely on a trained machine learning model to output an estimated clearing delay based on historical transaction data. Nowhere in the claims or specification of the instant application is there a clear training process disclosed for the claimed machine learning model that shows how said model is trained, nor is there clear disclosure of a particular machine learning model itself. Without this disclosure, the machine learning model amounts to a technical element that is merely “applied” to the invention of the instant application and does not show integration into a practical application. Consequently, this machine learning model and the processor performing the multiple determining, generating, processing and comparing steps leave the claim as a whole as not significantly more than the abstract idea of mathematical concepts through mathematical calculations because said claim remains executed by technical elements disclosed at a high level of generality such that said claim amounts to not more than computer implementation of the abstract idea. Therefore, the Office has met the requirement of considering the claims as a whole. Further, the Office has not erred in its conclusion that there are not sufficient elements remaining for a practical application.
Independent claims 11 and 16 are similar in scope to independent claim 1 and are remain rejected under 35 U.S.C. § 101 for the same reasons as noted above regarding claim 1. Dependent claims 2-8, 12-15 and 17-20 also do not cure the deficiencies or their respective independent claims and do not show significantly more than an abstract idea as noted above in the current rejection under 35 U.S.C. § 101.
Rejection under 35 U.S.C. § 103:
In response to the amended claims and the applicant’s remarks, the previous rejection under 35 U.S.C. § 103 is withdrawn. The claimed invention as limited by claims 1-8 and 11-20 are allowable over prior art.
The prior art combination of Belanger, Howe and Mori does not teach in an obvious manner, the estimated clearing delay comprising an estimated period of time to receive a clearing record from a merchant that is associated with the merchant identifier being input to the trained machine learning model… to show a first merchant transaction clearing delay indicative of a likelihood of the first clearing record being a force-post payment transaction as required by amended independent claims 1, 11 and 16. Dependent claims 2-8, 12-15 and 17-20 are also allowable for their dependency on their respective independent claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW S WERONSKI whose telephone number is (571)272-5802. The examiner can normally be reached M-F 8 am - 5 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fahd A. Obeid can be reached at 5712703324. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW S WERONSKI/Examiner, Art Unit 3627
/FAHD A OBEID/Supervisory Patent Examiner, Art Unit 3627
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