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 .
Acknowledgement
The Applicant’s Arguments/Remarks dated 07/07/2026 are acknowledged and have been considered.
Status of Claims
Claims 1-3, 5, 9-11, 13-15, 17-21 and 23 are amended.
Claims 1-3, 5-11, 13-15 and 17-23 are pending.
Response to Arguments
The Applicant has amended the claim 1 to recite,
“1. A computer-implemented method for generating a real-time embedding prediction for a real-time transaction for use by a downstream machine- learning model in a network environment comprising an issuer server and a payment server, the method comprising:
periodically generating, at predefined time intervals, by a server system and using a static embedding model associated with the server system, a static embedding based, at least in part, on a set of velocity features derived from a historical transaction dataset;
storing the static embedding in a database associated with the server system for reuse across a plurality of real-time transactions occurring during a predefined time duration;
receiving, from the issuer server or the payment server, by the server system, an embedding generation request for a real-time transaction between a cardholder and a merchant;
executing, by the server system in response to the embedding generation request, an embedding generation method that generates a real- time embedding prediction for the real-time transaction without analyzing the historical transaction dataset used to generate the static embedding and without generating an actual real-time embedding for the real-time transaction, the embedding generation method comprising:
accessing, from the database and by the server system in response to the embedding generation request, the static embedding generated most recently by the static embedding model, the set of velocity features used to generate the static embedding, and a set of real-time velocity features associated with the real-time transaction, wherein the set of real-time velocity features are generated based, at least in part, on one or more real-time transaction features extracted from the embedding generation request for the real-time transaction;
computing, by the server system, a difference between the set of real-time velocity features and the set of velocity features, the difference representing a change in each individual velocity feature from the set of velocity features at an instance when the real-time transaction takes place;
generating, by a prediction model associated with the server system, the real-time embedding prediction for the real-time transaction based, at least in part, on the static embedding, the set of velocity features, and the computed difference; and
supplying the real-time embedding prediction to a fraud-detection machine-learning model configured to generate, based at least in part on the real-time embedding prediction, a fraud-detection output for the real-time transaction.”
It should be noted that “for generating a real-time embedding prediction for a real-time transaction for use by a downstream machine- learning model” has limited patentable weight being in the preamble [MPEP 2111.02(II)] as well as a recitation of intended use. It is also the case that “for reuse across a plurality of real-time transactions occurring during a predefined time duration”, is a recitation of the intended use of the claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use, then it meets the claim.
In regards to, “supplying the real-time embedding prediction to a fraud-detection machine learning model to generate,…, a fraud-detection output for the real-time transaction” does not provide a an improvement to the computer itself (e.g., issuer server or payment server) or the technology, but uses the embedding prediction as input a convention mathematical the machine learning model to generate a fraud-detection output similar to collecting information, analyzing that data and displaying certain results of the collection and analysis.
It is maintained from the previous office action that the embedded models (i.e., MLMs) are considered abstract in that they are mathematical multi-dimensional numeric factors that generally represent abstract contextual semantic concepts. Also, the wherein the additional elements that use the models, increase the accuracy of, or decrease latency of determining fraudulent transactions, it is considered as an improvement to the judicial exception [i.e., detecting a fraudulent transaction]. Thus, it is maintained that an improvement to the judicial exception (i.e., an abstract idea) is still abstract. [see also OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359 (Fed. Cir. 2015)( relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible-quoting Alice, 134 S.Ct. at 2359 (“use of a computer to create electronic records, track multiple transactions, and issue simultaneous instructions” is not an inventive concept) or see also Ultramercial, 772 F.3d at 716 (“[T]he steps of consulting and updating an activity log represent insignificant ‘data-gathering steps,’ ․ and thus add nothing of practical significance to the underlying abstract idea.”)];
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 1-3, 5-11, 13-15 and 17-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In the instant case, claim 1 is directed to a computer-implemented transaction method and claim 13 is directed to a system for predicting embedding transaction between a cardholder and a merchant.
The amended claim language is directed to "transaction fraud detection" which under the broadest reasonable interpretation, can be grouped under certain methods of organizing human activity because the claims are related to fundamental economic principles and practices involving mitigating risk (e.g., lessening the risk of dishonest transactions between the cardholder and the merchant), as well as collecting information (i.e., "receiving a generation request and accessing a static embedding, a set of velocity features, etc.,), analyzing (i.e., "computing a difference between the set of real-time velocity features and the set of velocity features) and displaying results of the collection and analysis (i.e., "generating a real-time embedding prediction detecting that the real-time transaction is fraudulent based at least on the real-time embedding prediction"). Under the broadest reasonable interpretation, transactions may represent legal interactions, including agreements in the form of contracts and sales activities. In this case the claims also describe extrapolation using types of data (i.e., "accessing,..., a static embedding, a set of velocity features, and a set of real-time velocity features. ; computing,... a difference between the set of real-time velocity features and the set of velocity features; and generating,.., a real- time bedding prediction for the real-time transaction based, at least in part, on static embedding,...) to manage interactions (i.e., transactions) between the cardholder and the merchant. 13. Claim 1 recites, " transaction fraud detection comprising: receiving, embedding generation request for a real-time transaction between a cardholder and a merchant; accessing, in response to the embedding generation request, a static embedding, a set of velocity features, and a set of real-time velocity features... the static embedding, being based, at least in part on the set of velocity features; computing,.. a difference between the set of real-time velocity features and the set of velocity features and the set of velocity features; and generating,... a real-time embedding prediction for the real-time transaction based, at least in part, on the static embedding. ; and detecting,.. that the transaction between the cardholder and the merchant is fraudulent based at least in part on the real-time bedding prediction. 1
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A (See 2019 Revised Patent Subject Matter Eligibility Guidance), the additional elements of the claim such as "server system", "communication interface", "a memory" an "a processor" represent the use of a computer as a tool to perform an abstract idea and/or does no more than generally link the abstract idea to a particular field of use. Therefore, the additional elements do not integrate the abstract idea into a practical application as they do no more than represent a computer performing functions that correspond to predicting embedding transaction between a cardholder and a merchant. 5. When analyzed under step 2B (See 2019 Revised Patent Subject Matter Eligibility Guidance), the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception itself. Viewed as a whole, the combination of elements recited in the claims merely describe the concept of predicting embedding transaction between a cardholder and a merchant using computer technology (e.g. server system. Therefore, the use of these additional elements does no more than employ a computer as a tool to automate and/or implement the abstract idea, which cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). 15. Hence, claim 1 is not patent eligible. Independent claims 9, 13 are rejected for similar reasons. 16. Claims 2-8, 10-12 and 14-18 are rejected as providing features which further describe the function of "predicting embedding transaction between a cardholder and a merchant " without providing additional elements that adding significantly more.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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 DANIEL S FELTEN whose telephone number is (571)272-6742. The examiner can normally be reached Flex.
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DANIEL S. FELTEN
Examiner
Art Unit 3692
/DANIEL S FELTEN/Primary Examiner, Art Unit 3692
