DETAILED ACTION
The following is a Final Office action. In response to Non-Final communications received 1/29/2026, Applicant, on 4/29/2026, amended Claims 1-2, 7-8, 11-18, and 20. Claims 1-20 are pending in this action, have been considered in full, and are rejected below.
Response to Arguments
Arguments regarding 35 USC §101 Alice – Applicant states the Desjardins Decision and then states that there is an improvement to a technical problem stating that this is an improvement to problems associated with processing large volumes of data to extract patterns and improving ML models for accuracy, by pointing to Applicant’s Specification in [0004] [0005] [0028] and [0035-36] stating that this improves the machine learning model. Examiner disagrees as the claims are not directed to an improvement in the artificial intelligence or in any additional element, combination, a technology, or technological field, but rather recites claims directed at computing a decay velocity and accumulated sum, which are not used in conjunction with any artificial intelligence, models, or algorithm other than utilizing a comparison. The machine learning model is claimed to be updated, but as claimed this has to do with a comparison with transactions which is new input, and thus this is utilization of current technologies such as AI models and algorithms to perform the abstract limitations of the Claims. Further, this has nothing to do with Desjardins other than a generic link to a machine learning model which is purported to have an improved accuracy. The claims as a whole do not improve any claimed addition element, such as by generally linking the claim to an updated machine learning model, computer system, storage devices, etc., and the whole of the rest, including the amended limitations, are part of the abstraction, as per the rejection below, as they merely are receiving, analyzing, and transmitting steps which are observations, evaluations, and judgments and also can be designated as a Certain Method of Organizing Human Activity. These are not practically integrated, as the claim limitations merely utilize current technologies, such as artificial intelligence, to perform the abstract limitations of the claims, similar to that of Alice, essentially “Applying It”. There is no improvement to any technology or any technological process, and any inventive concept would be contained wholly within the abstraction.
Applicant asserts the claims do not recite an abstract idea because there is use of a machine learning model using transaction data within one or more partitions and that the machine learning model is updated thus improving accuracy, stating that this is a technical improvement to data mapping, and again states that the claims are not directed to an abstraction. Examiner disagrees as this is a mere allegation of eligibility under 101 as Applicant has not stated why there is not a judicial exception present. There are two abstractions pointed out in the rejection below, that of a Mental Process and a Certain Method of Organizing Human Activity. The claims as a whole do not improve any claimed addition element, such as by generally linking the claim to an updated machine learning model, computer system, storage devices, etc., and the whole of the rest, including the amended limitations, are part of the abstraction, as per the rejection below, as they merely are receiving, analyzing, and transmitting steps which are observations, evaluations, and judgments and also can be designated as a Certain Method of Organizing Human Activity. These are not practically integrated, as the claim limitations merely utilize current technologies, such as artificial intelligence, to perform the abstract limitations of the claims, similar to that of Alice, essentially “Applying It”. There is no improvement to any technology or any technological process, and any inventive concept would be contained wholly within the abstraction.
Applicant asserts that the claims are drawn to a practical application by reciting the amended limitations of the claims, stating that this not a drafting process, and stating that the claims recite a meaningful limitation and recite a specific improvement in the field of electronic data processing. Applicant also states that the claims recite significantly more due to the combination of additional elements. Examiner disagrees as Applicant has not stated what is meaningful about any particular limitation, and further there is no improvement to any additional element, alone or in combination, and these are not practically integrated, as the claim limitations merely utilize current technologies, such as artificial intelligence and a computing system, to perform the abstract limitations of the claims, similar to that of Alice, essentially “Applying It”. There is no improvement to any technology or any technological process, and any inventive concept would be contained wholly within the abstraction.
Therefore, the arguments are non-persuasive, the Claims are ineligible as there is no inventive concept, and the rejection of the Claims and their dependents are maintained under 35 USC 101.
Arguments regarding 35 USC § 103 – The rejection is hereby removed for the reasons found in the “Allowable Subject Matter” section found below
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 4/29/2026 has been acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. The initialed and dated copy of Applicant’s IDS form 1449 is attached to the instant Office action.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1-20 are directed to “output the computed one or more decay velocities for the selected dimension key representing the one or more transactions as part of an update for the machine learning model such that the updated machine learning model has improved accuracy to detect anomalies when executed on subsequent transaction data.” This updating is not supported in the specification as to this process and how it would improve the accuracy. For instance, the Specification states:
“[0095] To facilitate this learning, the velocity analysis computing system 106 includes one or more databases (including transaction database 902) in which the data, including requests, responses, feature codes, evidence, outcomes, etc., is stored. This data becomes one or more input training sets used by the training set builder 918. Model outputs can be formatted for presentation or review as visual representations of recommendations, as text-based or natural language recommendations, and the like, for usability by a human reviewer (e.g., user) of the velocity analysis computing system 106. In exemplary embodiments, AI/ML model 916 may compare feedback, and may route a comparison result 934 generated by comparing recommendations 932 to the feedback to a model updater module 936 of the velocity analysis computing system 106. Model updater module 936 is configured to derive a correction signal 938 from comparison results 934 received for one or more recommendations and to provide correction signal 938 to model trainer module 930 to enable updating or “re-training” of the at least one machine learning model to improve performance. The retrained at least one machine learning model 928 may be periodically re-uploaded to AI/ML model 916.
[0096] The calculations resulting from velocity algorithm module 914 (and/or parameters of AI/ML model 916) may be output via an output module 940, which is configured to output the generated velocities for analyzing and/or categorizing transactions, and/or to further train an associated model (e.g., AI/ML model 916). Additionally, recommendations 932 from AI/ML model 916 may be output from output module 940 to user computer 224 so that a user of user computer 224 may review and implement any updates or improvements to aspects of velocity analysis computing system 106 based on an output of output module 940, in particular updates or improvements to AI/ML model 916 and/or velocity algorithm 914. Recommendations 932 (or other outputs from AI/ML model 916) may also be output to offline system 228 for evaluation in/by offline system 228 as described herein.”
Which is states how the updating and the training is performed but not that there is anything to do with the accuracy in the Specification, and earlier in the Specification it states:
“[0090] Model trainer module 930 is configured to compare, for each training data set 926, the at least one output of the model to the at least one result data field of the training data set 926, and apply a machine learning algorithm to adjust parameters of the model in order to reduce the difference or “error” between the at least one output and the corresponding at least one result data field. In this way, model trainer module 930 trains the machine learning model to accurately predict the value of the at least one result data field. In other words, model trainer module 930 cycles the one or more machine learning models through the training data sets 926, causing adjustments in the model parameters, until the error between the at least one output and the at least one result data field falls below a suitable threshold, and then uploads at least one trained machine learning model 928 to AI/ML model 916 for application, to generate recommendations 932. In exemplary embodiments, model trainer module 930 may be configured to simultaneously train multiple candidate machine learning models and to select the best performing candidate for each result data field, as measured by the “error” between the at least one output and the corresponding result data field, to upload to AI/ML model 916. This may be done in or in conjunction with offline system 228.”
About how the accuracy is determined, but as claimed this is different embodiments it is unsure how this would be connected as claimed and the model trainer module and how that is connected to the models are used here, but neither the Specification nor the Claims state what would be used to specifically update the machine learning model. To satisfy the written description requirement, a patent specification must describe the claimed invention in sufficient detail that a patent must describe the technology; the requirement serves both to satisfy the inventor’s obligation to disclose the technologic knowledge upon which the patent is based, and to demonstrate that the patentee was in possession of the invention that is claimed." Capon v. Eshhar, 418 F.3d 1349, 1357, 76 USPQ2d 1078, 1084 (Fed. Cir. 2005). The dependent Claims inherit the deficiencies of the independent claims and thus are similarly rejected.
Therefore, the claims and their dependent claims are rejected under 35 U.S.C. 112(a), written description, as being directed to non-statutory subject matter.
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.
Alice - Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 12, and 16 recite limitations to receive transaction data for one or more transactions of a cardholder initiated (Collecting Information, an Observation, a Mental Process; a Commercial Interaction, i.e. managing transactions; a Certain Method of Organizing Human Activity), process the transaction data for the one or more transactions for use with a velocity algorithm that is associated with a machine learning model by mapping the transaction data within the one or more partitions including (Analyzing Information, an Evaluation, a Mental Process; a Commercial Interaction, i.e. managing transactions; a Certain Method of Organizing Human Activity), assign an identification (ID) to each transaction of the one or more transactions (Analyzing Information, an Evaluation, a Mental Process; a Commercial Interaction, i.e. managing transactions; a Certain Method of Organizing Human Activity), generate a dataset based on the assigned IDs (Analyzing Information, an Evaluation, a Mental Process; a Commercial Interaction, i.e. managing transactions; a Certain Method of Organizing Human Activity), arrange the dataset into a plurality of subsets within the one or more partitions, wherein each subset of the plurality of subsets represents a range of the assigned IDs (Analyzing Information, an Evaluation, a Mental Process; a Commercial Interaction, i.e. managing transactions; a Certain Method of Organizing Human Activity), calculate a sum of a selected dimension key present in each partition of the one or more partitions (Analyzing Information, an Evaluation, a Mental Process; a Commercial Interaction, i.e. managing transactions; a Certain Method of Organizing Human Activity), calculate an accumulated sum by combining each sum of the selected dimension key in each partition (Analyzing Information, an Evaluation, a Mental Process; a Commercial Interaction, i.e. managing transactions; a Certain Method of Organizing Human Activity), sort the accumulated sum for the selected dimension key within one or more neighboring partitions that neighbor at least one partition of the one or more partitions (Analyzing the Information, an Evaluation, a Mental Process; a Commercial Interaction, i.e. managing transactions; a Certain Method of Organizing Human Activity), compute, via the velocity algorithm, one or more decay velocities of the accumulated sum for the selected dimension key (Analyzing Information, an Evaluation, a Mental Process; a Commercial Interaction, i.e. managing transactions; a Certain Method of Organizing Human Activity), and output the computed one or more decay velocities for the selected dimension key representing the one or more transactions as part of an update for the machine learning model such that the updated machine learning model has improved accuracy to detect anomalies when executed on subsequent transaction data (Transmitting the Analyzed Information, a Judgment, a Mental Process; a Commercial Interaction, i.e. managing transactions; a Certain Method of Organizing Human Activity), which under their broadest reasonable interpretation, covers performance of the limitation in the mind for the purposes of a Commercial Interaction, i.e. managing transaction information, but for the recitation of generic computer components. That is, other than reciting a computer system, one or more storage devices including one or more partitions defined therein, a computing device comprising at least one processor in communication with at least one memory device and the one or more storage devices, and a payment processing network, nothing in the claim element precludes the step from practically being performed or read into the mind for the purposes of a Commercial Interaction. For example, assigning an identification to each transaction of one or more transactions and generating a dataset based on the assigned ids encompasses a lemonade stand operator, taking transactions and giving receipts in order of the number of transactions, 1, 2, 3, … 6, 7… etc. and then writing them down, a dataset, which is an observation, evaluation, and judgment. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas, an observation, evaluation, and judgment. Further, as described above, the claims recite limitations for a Commercial Interaction, a “Certain Method of Organizing Human Activity”. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the above stated additional elements to perform the abstract limitations as above. The computer system, storage devices, computing device, memory device, processor, payment processing network, and medium are recited at a high-level of generality (i.e., as a generic software/module performing a generic computer function of storing, retrieving, sending, and processing data) such that they amount to no more than mere instructions to apply the exception using generic computer components. Even if taken as an additional element, the receiving and transmitting steps above are insignificant extra-solution activity as these are receiving, storing, and transmitting data as per the MPEP 2106.05(d). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered both individually and as an ordered combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional element being used to perform the abstract limitations stated above amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible. Applicant’s Specification states:
“[0059] Further, client sub-systems 202 and 204 may additionally communicate with interchange 104 using network 206. In more general terms, client sub-systems 202 and 204 could be any device capable of interconnecting to the Internet including a web-based (e.g., mobile) phone, PDA, smart devices, or any other web-based connectable equipment such as a POS terminal (e.g., an embodiment of transaction processing device 112 (shown in FIG. 1)).”
Which shows that any generic computer can be used to perform the abstract limitations, such as a laptop, phone, desktop, etc., and from this interpretation, one would reasonably deduce the aforementioned steps are all functions that can be done on generic components, and thus application of an abstract idea on a generic computer, as per the Alice decision and not requiring further analysis under Berkheimer, but for edification the Applicant’s specification has been used as above satisfying any such requirement. This is “Applying It” by utilizing current technologies. For the receiving and transmitting steps that were considered extra-solution activity in Step 2A above, if they were to be considered additional elements, they have been re-evaluated in Step 2B and determined to be well-understood, routine, conventional, activity in the field. The background does not provide any indication that the additional elements, such as the system, processor, memory, etc., nor the receiving or transmitting steps as above, are anything other than a generic, and the MPEP Section 2106.05(d) indicates that mere collection or receipt, storing, or transmission of data is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. The claim is not patent eligible.
Claims 2-11, 13-15, and 17-20 contain the identified abstract ideas, further narrowing them, and in the case of Claims 2, 7, 13, and 17 there is additionally recited “Mathematical Concepts/Formulas”, with the additional elements of distributed processors and computing devices which are all highly generalized as per Applicant’s Specification when considered as part of a practical application or under prong 2 of the Alice analysis of the MPEP, thus not integrated into a practical application, nor are they significantly more for the same reasons and rationale as above.
After considering all claim elements, both individually and in combination, Examiner has determined that the claims are directed to the above abstract ideas and do not amount to significantly more. Therefore, the claims and dependent claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank International, No. 13–298.
Allowable Subject Matter
Claims 1-20 have overcome the prior art and would be allowable if amended to overcome the 35 USC 101 rejections.
The closest prior art of record are Afzal (U.S. Publication No. 2023/013,7734), Allbright (U.S. Publication No. 2021/031,2450), and Hosp (U.S. Publication No. 2020/005,8079). Afzal, a system and method for improved detection of network attacks, teaches one or more storage devices including one or more partitions defined, a computing device comprising at least one processor in communication with at least one memory device and the one or more storage devices, to receive transaction data for one or more transactions of a cardholder initiated using a payment processing network, process the transaction data for the one or more transactions by mapping the transaction data within the one or more partitions including: assign an identification (ID) to each transaction of the one or more transactions, calculate an accumulated sum by combining each sum of the selected dimension key in each partition, compute one or more decay velocities of the accumulated sum for the selected dimension key, output the one or more decay velocities for the selected dimension key representing the one or more transactions, subsets of transactions which are where the transactions and their IDs originate from, a set of feature inputs which are used in identification of transactions with IDs, dimension keys of each partition, and calculation of sums which are used with these feature inputs and keys, but it does not explicitly state generation of a dataset and arranging one of these datasets, nor does it teach sorting the accumulated sum for a selected dimension key in the manner claimed. Allbright, a system and method for advanced velocity profile preparation and analysis, teaches to generate a dataset with a range, each subset of the plurality of subsets represents a range of the assigned IDs and these datasets include a range of transactions, which have ids, and to calculate a sum of velocities together to come up with final velocities, but not sorting the accumulated sum for a selected dimension key in the manner claimed. Hosp, a system and method for using machine learning and simulations for intelligent budgeting, teaches sorting of intelligent budgets, sorting using partitions, and use of machine learning, but does not teach sorting the accumulated sum for a selected dimension key in the manner, as claimed, along with the other limitations of the claims, and these are the reasons which adequately reflect the Examiner's opinion as to why Claims 1-20 are allowable over the prior art of record.
Conclusion
The prior art made of record is considered pertinent to applicant's disclosure.
US 20230137734 A1
Afzal; Sayed Amin
SYSTEMS AND METHODS FOR IMPROVED DETECTION OF NETWORK ATTACKS
US 20210312450 A1
Allbright; Joshua A. et al.
SYSTEMS AND METHODS FOR ADVANCED VELOCITY PROFILE PREPARATION AND ANALYSIS
US 20230289610 A1
Verma; Sangam et al.
ARTIFICIAL INTELLIGENCE BASED METHODS AND SYSTEMS FOR UNSUPERVISED REPRESENTATION LEARNING FOR BIPARTITE GRAPHS
US 20230083621 A1
Null; Bradley William et al.
ENTITY SCORING CALIBRATION
US 20210312453 A1
Allbright; Joshua A.
SYSTEMS AND METHODS FOR MESSAGE TRACKING USING REAL-TIME NORMALIZED SCORING
US 20200111109 A1
LEI; Ming et al.
Flexible Feature Regularization for Demand Model Generation
US 20200104771 A1
POPESCU; Catalin et al.
Optimized Selection of Demand Forecast Parameters
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 JOSEPH M WAESCO whose telephone number is (571)272-9913. The examiner can normally be reached on 8 AM - 5 PM M-F.
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.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, BETH BOSWELL can be reached on (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-1348.
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/JOSEPH M WAESCO/Primary Examiner, Art Unit 3683 7/16/2026
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