DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 7/27/2026 has been entered.
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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because, while the claims herein are directed to a method and/or system, which could be classified under one of the listed statutory classifications (i.e., 2019 Revised Patent Subject Matter Eligibility Guidance (hereinafter “PEG”) “PEG” Step 1=Yes), the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding claims 1, 8, 15, the claims recite, in part, generating a first model by training, using first training data, to output predictions of whether input transactions will settle, wherein the first training data comprises correlations between historical transaction data and indications of whether each transaction of the historical transaction data was settled; generating a second model by training. using second training data. to output predictions of posted amounts, wherein the second training data comprises correlations between historical transaction authorized amounts and final posted transaction amounts; receiving, from a user, a rewards notification threshold; monitoring user interaction with one or more web elements on an online shopping website; determining, based on the user interaction, an indication of an authorization of a card-based transaction to be finalized at a future time by the user; based on the authorization of the card-based transaction, providing, as input to the first model. at least a portion of the transaction data that indicates transaction data corresponding to the card-based transaction; receiving, as output from the first model, output indicating a likelihood that the card-based transaction will settle; providing, as input to the second model, at least a portion of the transaction data that indicates an authorized amount of the transaction; receiving, as output from the second model, output indicating that a predicted posted amount of the card-based transaction is lower than the authorized amount of the card-based transaction; causing, based on comparing the predicted posted amount of the card-based transaction to the rewards notification threshold based on the likelihood that the card-based transaction will settle, and based on determining that the card-based transaction has not yet completed, output, before the future time, of a notification indicating a predicted quantity of rewards, wherein the predicted quantity of rewards is based on the predicted posted amount; based on user input received in response to the notification, causing to store an association between an account of the user and the predicted quantity of rewards, wherein the predicted quantity of rewards are immediately usable by the user during a period between authorization and posting of the card-based transaction; and based on determining that the card-based transaction has completed: determining an actual posted amount of the card-based transaction, based on a difference between the predicted posted amount and the actual posted amount; modifying the association to indicate a final quantity of rewards; and further training, using additional training data indicating that the card-based transaction was settled and the actual posted amount, at least one of the first model and the second model to improve prediction accuracy for future transactions.
The limitations, as drafted and detailed above, recites determining and issuance of a predicted and final quantity of rewards based on transaction data and historical transaction data, which falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, and more specifically commercial interactions including sales activities or behaviors. Accordingly, the claim recites an abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes).
This judicial exception is not integrated into a practical application. In particular, the claims only recite the additional elements of computing device (claims 1, 15), one or more processors (claims 1, 15), memory (claim 1), first trained machine learning model (claims 1, 8, 15, merely software used at an “apply it” level), first artificial neural network (claims 1, 8, 15, just like the machine learning model, merely software used at an “apply it” level); second trained machine learning model (claims 1, 8, 15, merely software used at an “apply it” level); second artificial neural network (claims 1, 8, 15, just like the machine learning model, merely software used at an “apply it” level); mobile device associated with a user (claims 1, 8, 15), user interface of the mobile device (claims 1, 8, 15). database (claims 1, 8, 15, not actively claimed as part of the invention, merely the passive target of operations), and one or more non-transitory computer-readable media (claim 15). The additional technical elements above are recited at a high-level of generality (i.e. as a generic processor performing a generic computer function of generating, receiving, monitoring, determining, processing, providing, causing output, causing…to store, modifying, and further training) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. There are no additional functional limitations to be considered under prong two.
Accordingly, the additional technical elements above do not integrate the abstract idea/judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e) and Vanda memo).
Rather, the limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), or generally link the use of the
judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Thus, the claim is “directed to” an abstract idea (i.e. “PEG” Revised Step 2A Prong Two=Yes).
When considering Step 2B of the Alice/Mayo test, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not amount to significantly more than the abstract idea.
More specifically, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using computing device (claims 1, 15), one or more processors (claims 1, 15), memory (claim 1), first trained machine learning model (claims 1, 8, 15, merely software used at an “apply it” level), first artificial neural network (claims 1, 8, 15, just like the machine learning model, merely software used at an “apply it” level); second trained machine learning model (claims 1, 8, 15, merely software used at an “apply it” level); second artificial neural network (claims 1, 8, 15, just like the machine learning model, merely software used at an “apply it” level); mobile device associated with a user (claims 1, 8, 15), user interface of the mobile device (claims 1, 8, 15). database (claims 1, 8, 15, not actively claimed as part of the invention, merely the passive target of operations), and one or more non-transitory computer-readable media (claim 15) to perform the claimed functions amounts to no more than mere instructions to apply the exception using a generic computer component.
“Generic computer implementation” is insufficient to transform a patent-ineligible abstract idea into a patent-eligible invention (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2352, 2357) and more generally, “simply appending conventional steps specified at a high level of generality” to an abstract idea does not make that idea patentable (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Mayo, 132 S. Ct. at 1300). Moreover, “the use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter (See FairWarning, 120 U.S.P.Q.2d. 1293, citing DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014)). As such, the additional elements of the claim do not add a meaningful limitation to the abstract idea because they would be generic computer functions in any computer implementation. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves any other technology. Their collective functions merely provide generic computer implementation.
The Examiner notes simply implementing an abstract concept on a computer, without meaningful limitations to that concept, does not transform a patent-ineligible claim into a patent- eligible one (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bancorp, 687 F.3d at 1280), limiting the application of an abstract idea to one field of use does not necessarily guard against preempting all uses of the abstract idea (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bilski, 130 S. Ct. at 3231), and further the prohibition against patenting an abstract principle “cannot be circumvented by attempting to limit the use of the [principle] to a particular technological environment” (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Flook, 437 U.S. at 584), and finally merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2358; Mayo, 132 S. Ct. at 1294; Bilski v. Kappos, 561 U.S. 593, 612 (2010); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat' l Ass' n, 776 F.3d 1343, 1348 (Fed. Cir. 2014); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014).
Applicant herein only requires a general purpose computer (see Applicant specification Paragraphs 0021-0026); therefore, there does not appear to be any alteration or modification to the generic activities indicated, and they are also therefore recognized as insignificant activity with respect to eligibility. Examiner notes that Paragraph 0023 of Applicant’s specification states “Memory 121 may store software for configuring computing device 101 into a special purpose computing device in order to perform one or more of the various functions discussed herein”, however nothing described in the specification would indicate that the recited computer components are anything other than general purpose.
The dependent claims 2-7, 9-14, and 16-20 appear to merely limit basing the storage of the association on a likelihood of reward usage, specifics of the final quantity of rewards, determining that the transaction is posted, indication of a refund, determining whether a transaction was conducted at a particular merchant, and displaying a reward on a point of sale system, and therefore only limit the application of the idea, and not add significantly more than the idea (i.e. “PEG” Step 2B=No).
The computing device (claims 1, 15), one or more processors (claims 1, 15), memory (claim 1), first trained machine learning model (claims 1, 8, 15, merely software used at an “apply it” level), first artificial neural network (claims 1, 8, 15, just like the machine learning model, merely software used at an “apply it” level); second trained machine learning model (claims 1, 8, 15, merely software used at an “apply it” level); second artificial neural network (claims 1, 8, 15, just like the machine learning model, merely software used at an “apply it” level); mobile device associated with a user (claims 1, 8, 15), user interface of the mobile device (claims 1, 8, 15). database (claims 1, 8, 15, not actively claimed as part of the invention, merely the passive target of operations), and one or more non-transitory computer-readable media (claim 15) are each functional generic computer components that perform the generic functions of generating, receiving, monitoring, determining, processing, providing, causing output, causing…to store, modifying, and further training, all common to electronics and computer systems.
Applicant's specification does not provide any indication that the computing device (claims 1, 15), one or more processors (claims 1, 15), memory (claim 1), first trained machine learning model (claims 1, 8, 15, merely software used at an “apply it” level), first artificial neural network (claims 1, 8, 15, just like the machine learning model, merely software used at an “apply it” level); second trained machine learning model (claims 1, 8, 15, merely software used at an “apply it” level); second artificial neural network (claims 1, 8, 15, just like the machine learning model, merely software used at an “apply it” level); mobile device associated with a user (claims 1, 8, 15), user interface of the mobile device (claims 1, 8, 15). database (claims 1, 8, 15, not actively claimed as part of the invention, merely the passive target of operations), and one or more non-transitory computer-readable media (claim 15) are anything other than generic, off-the-shelf computer components. Therefore, the claims do not amount to significantly more than the abstract idea (i.e. “PEG” Step 2B=No).
Thus, based on the detailed analysis above, claims 1-20 are not patent eligible.
Novel/Non-Obvious Subject Matter
Claims 1-20 as currently written are novel/non-obvious over prior art. However, the rejection under 35 U.S.C. 101 is currently pending and represent a barrier to allowability. Examiner notes that any amendments made to the claims in an attempt to correct pending rejections could drastically alter the claim scope and could open up the possibility of prior art being applied in a future action.
Posner (U.S. Pub No. 2010/0161399) teaches receiving an indication of an authorization of a transaction conducted by the user; based on the authorization of the transaction, processing transaction data corresponding to the transaction to identify a predicted quantity of rewards; determining, by comparing the transaction data to a history of financial transactions, a frequency that one or more second transactions similar to the transaction settle; causing, based on the frequency that the one or more second transactions similar to the transaction settle, output, on a mobile device associated with the user, of a notification indicating the predicted quantity of rewards; based on user input received in response to the notification, causing a database to store an association between an account of the user and the predicted quantity of rewards, wherein the predicted quantity of rewards are immediately usable by the user; and based on determining that a predetermined period of time has elapsed, modifying the association to indicate a final quantity of rewards. Posner, however, does not teach each and every limitation recited in the independent claim language.
Edwards (U.S. Pub No. 2022/0391905) teaches providing, as input to a trained machine learning model, at least a portion of the transaction data that indicates an authorized amount of the transaction and receiving, as output from the trained machine learning model, output indicating a predicted difference between the authorized amount of the transaction and a predicted posted amount of the transaction. However, Edwards does not cure all the deficiencies of Posner and the combination of Posner and Edwards do not teach each and every limitation recited in the independent claim language.
Trifiletti (U.S. Pub No. 2010/0082420) teaches receiving, from a user, a rewards notification threshold and causing, based on comparing the predicted amount of the transaction to the rewards notification threshold output, on a mobile device associated with the user, of a notification indicating the predicted quantity of rewards. However, Trifiletti does not cure all the deficiencies of Posner and Edwards, and the combination of Posner, Edwards, and Trifiletti do not teach each and every limitation recited in the independent claim language.
None of the prior art of record, alone or in combination, teaches each and every limitation of the claimed invention. Specifically, none of the applied references teaches 2 separate machine learning models used in combination, one being trained with correlations between historical transaction data and indications of whether each transaction of the historical transaction data was settled, and the other trained with correlations between historical transaction authorized amounts and final posted transaction amounts, wherein the first model outputs a likelihood that the transaction will settle and the second model outputs that a predicted posted amount of the transaction is lower than the authorized amount of the transaction. While such features on their own would not normally be an allowable feature (using multiple machine learning models is old and well known), it would simply not be obvious to apply another prior art reference to the other references already applied to arrive at the currently claimed invention and the order of steps currently taken by the currently claimed invention. There is no prior art that teaches each and every limitation of the invention as a whole in combination with one another. Therefore Examiner finds the independent claims to be novel/non-obvious over the prior art of record.
Response to Arguments
Applicant argues “Together, these steps define a technical solution for improving the accuracy and reliability of card-based transaction prediction systems operating on electronic payment networks. The dual- model architecture-where a first model predicts whether a transaction will settle and a second model predicts what the posted amount will be-combined with the feedback training mechanism that uses actual transaction outcomes to refine model parameters, represents a specific technical approach to overcoming the inherent uncertainty that exists during the delay between transaction authorization and posting”. However, the inherent uncertainty that exists during the delay between transaction authorization and posting is a business-centric problem, and not a problem that results in the improvement to the additional elements recited in the claim language. Likewise, the accuracy and reliability of card-based transaction prediction is also business-centric problem, and not one that results in the improvement to the additional elements recited in the claim language. Rather, the additional elements operate as intended. The improving of prediction accuracy occurs through training of machine learning models, which does not improve machine learning, but rather operates as machine learning is meant to operate.
Applicant argues “These limitations define a specific technical training architecture that produces a concrete technological output: a new, enhanced machine learning model to process and predict future card-based transaction data. The claims recite an improvement to the functioning of computer systems that process electronic payment transactions. The claimed improvement is not merely doing something faster on a computer. Rather, the claims recite a specific technical solution-using two cooperating machine learning models with distinct training data and outputs, combined with a feedback training mechanism-that enables a computing system to perform predictive operations that were not previously possible during the authorization-to-posting window. This is not generic application of machine learning. This is a specific technical system in which two distinct prediction models, each trained on different correlations in payment network data, cooperate to solve the technical problem of predicting transaction outcomes during the authorization-to-posting delay inherent in electronic payment systems, where the system continuously improves its own prediction accuracy through a feedback training mechanism”. However, the entire process of performing predictive operations during a specific window is part of the abstract idea. There is no improvement to the functioning of a computer system. The processing speed or memory of the computer is not changed. Rather, the computer system is merely being used to implement the abstract idea, and it is the abstract idea that is being improved.
Applicant cites paragraphs 0016-0018 of the instant specification for support with regard to limitations of current technology and argues “The claimed machine learning architecture directly addresses these technical limitations by predicting both the likelihood of settlement and the predicted posted amount, enabling the system to provision rewards during the authorization-to-posting window while managing the technical risk of transaction reversal. The practical application at least encompasses using two cooperating machine learning models with a feedback training mechanism to overcome the technical limitation of being unable to validate and process electronic payment transactions at an optimal time, thereby enabling the provisioning of rewards during the authorization-to-posting window before a card- based transaction is finalized”. However, the specification merely states that the improvement envisioned is merely meant to solve a delay in processing rewards data in order to provide instant reward gratification. The processing of rewards earlier than in past systems is still merely just a performance of the abstract idea at an earlier time than in past systems. In other words, any improvement outlined by the specification and the claims is merely an improvement to the abstract idea of rewards processing. In the SAP decision (See SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018)), the courts found that an improvement made to the abstract idea is not patent eligible. SAP v. Investpic: Page 2, line 22 through Page 3, line 13 - Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because there are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting; and Page 10, lines 18-24 - Even if a process of collecting and analyzing information is limited to particular content, or a particular source, that limitations does not make the collection and analysis other than abstract.
Applicant argues “This acknowledgment that the specific technical architecture is not found in the prior art strongly supports a finding that the claimed elements are not "well-understood, routine, [or] conventional" in the field”. However, a finding under 102 or 103 does not impact a finding under the analysis of 101. Many cases have been determined to be novel or non-obvious over prior art, but have failed the 101 analysis. The additional elements of the claimed invention have been analyzed separately from the abstract idea and have been found to fail in reciting significantly more than the abstract idea. Rather, the additional elements have been found to merely apply the abstract idea, and therefore are unable to overcome the 35 U.S.C. 101 rejection.
Conclusion
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/MICHAEL BEKERMAN/ Primary Examiner, Art Unit 3621
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