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 .
DETAILED ACTION
This action is in response to papers filed on 6/16/2026.
Claims 1, 10, and 19 have been amended.
Claims 7, 9, 16, and 18 have been cancelled.
Claims 21-24 have been added.
Claims 1-6, 8, 10-15, 17, 19, and 20-24 are pending.
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 1-6, 8, 10-15, 17, 19, and 20-24 are rejected under 35 U.S.C. because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claims are directed to a process (method as introduced in Claim 1), system (claim 19), and/or non-transitory computer readable storage medium (claim 10), thus Claims 1-6, 8, 10-15, 17, 19, and 20-24 fall within one of the four statutory categories. See MPEP 2106.03.
Step 2A, Prong 1:
The claimed invention recites an abstract idea according to MPEP §2106.04. The independent claims which recite the following claim limitations as an abstract idea, are underlined below.
Claims 1 recites:
encoding, by the server computer system, signal data for each of a plurality of computing systems into a plurality of sets of input signals, the signal data generated using computing system requests processed by the server computer system for each of the computing systems;
generating an individual fraud score for each computing system1 by:
inputting, into a first machine learning model, a first set of input signals of the plurality of sets of input signals to generate a first individual fraud score based on the first set of input signals, wherein the first set of input signals comprises structured data generated and encoded for the computing system;
inputting, into a second machine learning model, a second set of input signals of the plurality of sets of input signals to generate a second individual fraud score based on the second set of input signals, wherein the second set of input signals comprises unstructured data generated and encoded for the computing system;
inputting, into a third machine learning model, a third set of input signals of the plurality of sets of input signals, to generate a third individual fraud score based on the third set of input signals, wherein the third set of input signals comprises time-based data generated and encoded for the computing system; and
determining the individual fraud score for the computing system1 based on the generated first individual fraud score, the generated second individual fraud score, and the generated third individual fraud score;
clustering, by the server computer system, the computing systems1 into one or more computing system cohort clusters based on data values indicative of characteristics of each computing system1;
generating, at the server computer system, for each of the one or more computing system cohort clusters, a corresponding cluster fraud score indicative of computing systems1 of the computing system cohort clusters being associated with fraudulent activities , the corresponding cluster fraud score being generated based at least in part on individual fraud scores of each computing system1 in the computing system cohort cluster; and
in accordance with a determination that the corresponding a cluster fraud score for at least one of the one or more computing system1 cohort clusters reaches a cohort cluster fraud threshold, by the server computer system, initiating one or more remediative actions to prevent completion of at least one computing system request from the at least one computing system cohort cluster, the one or more remediative actions including at least one of:
blocking at least one of the computing system requests for each computing system in the at least one computing system2 cohort cluster;
suspending accounts associated with each computing system2 in the at least one computing system cohort cluster for a period of time; or
deactivating accounts associated with each computing system2 in the at least one computing system cohort cluster;
Claims 10 recites:
encoding, by the server computer system, signal data for each of a plurality of computing systems into a plurality of sets of input signals, the signal data generated using computing system requests processed by the server computer system for each of the computing systems;
generating an individual fraud score for each computing system1 by:
inputting, into a first machine learning model, a first set of input signals of the plurality of sets of input signals to generate a first individual fraud score based on the first set of input signals, wherein the first set of input signals comprises structured data generated and encoded for the computing system;
inputting, into a second machine learning model, a second set of input signals of the plurality of sets of input signals to generate a second individual fraud score based on the second set of input signals, wherein the second set of input signals comprises unstructured data generated and encoded for the computing system;
inputting, into a third machine learning model, a third set of input signals of the plurality of sets of input signals, to generate a third individual fraud score based on the third set of input signals, wherein the third set of input signals comprises time-based data generated and encoded for the computing system; and
determining the individual fraud score for the computing system1 based on the generated first individual fraud score, the generated second individual fraud score, and the generated third individual fraud score;
clustering, by the server computer system, the computing systems1 into one or more computing system cohort clusters based on data values indicative of characteristics of each computing system1;
generating, at the server computer system, for each of the one or more computing system cohort clusters, a corresponding cluster fraud score indicative of computing systems1 of the computing system cohort clusters being associated with fraudulent activities , the corresponding cluster fraud score being generated based at least in part on individual fraud scores of each computing system1 in the computing system cohort cluster, wherein generating the corresponding cluster fraud score is performed asynchronously and separately from generating the individual fraud scores; and
in accordance with a determination that the corresponding a cluster fraud score for at least one of the one or more computing system1 cohort clusters reaches a cohort cluster fraud threshold, by the server computer system, initiating one or more remediative actions to prevent completion of at least one computing system request from the at least one computing system cohort cluster, the one or more remediative actions including at least one of:
blocking at least one of the computing system requests for each computing system in the at least one computing system2 cohort cluster;
suspending accounts associated with each computing system2 in the at least one computing system cohort cluster for a period of time; or
deactivating accounts associated with each computing system2 in the at least one computing system cohort cluster;
Claims 19 recites:
encoding, by the server computer system, signal data for each of a plurality of computing systems into a plurality of sets of input signals, the signal data generated using computing system requests processed by the server computer system for each of the computing systems;
generating an individual fraud score for each computing system1 by:
inputting, into a first machine learning model, a first set of input signals of the plurality of sets of input signals to generate a first individual fraud score based on the first set of input signals, wherein the first set of input signals comprises structured data generated and encoded for the computing system;
inputting, into a second machine learning model, a second set of input signals of the plurality of sets of input signals to generate a second individual fraud score based on the second set of input signals, wherein the second set of input signals comprises unstructured data generated and encoded for the computing system;
inputting, into a third machine learning model, a third set of input signals of the plurality of sets of input signals, to generate a third individual fraud score based on the third set of input signals, wherein the third set of input signals comprises time-based data generated and encoded for the computing system; and
determining the individual fraud score for the computing system1 based on the generated first individual fraud score, the generated second individual fraud score, and the generated third individual fraud score;
clustering, by the server computer system, the computing systems1 into multiple computing system cohort clusters based on data values indicative of characteristics of each computing system1;
generating, at the server computer system, for each of the multiple computing system cohort clusters, a corresponding cluster fraud score indicative of computing systems1 of each computing system cohort clusters being associated with fraudulent activities , the corresponding cluster fraud score being generated based at least in part on individual fraud scores of each computing system1 in the corresponding computing system cohort cluster; and
in accordance with a determination that the corresponding a cluster fraud score for each computing system1 cohort cluster reaches a cohort cluster fraud threshold, by the server computer system, initiating one or more remediative actions to prevent completion of at least one computing system request from the at least one computing system cohort cluster, the one or more remediative actions including at least one of:
blocking at least one of the computing system requests for each computing system in the at least one computing system2 cohort cluster;
suspending accounts associated with each computing system2 in the at least one computing system cohort cluster for a period of time; or
deactivating accounts associated with each computing system2 in the at least one computing system cohort cluster,
wherein initiating the one or more remediative actions includes initiating a remediative action against the computing system for each cluster of the multiple clusters that the computing system belongs to.
1 The “computing systems” referenced in this limitation refers to the use of data collected
about the computing systems to cluster them into cohort clusters and does not positively recite or
use the actual computer systems. This does not pertain to any recitations of “computing system” that are not underlined.
2 Similar to Note #1, the “computing systems” referenced in this limitation refers to a description of the accounts being acted upon and does not use the actual computer systems for any activities.
The underlined claim limitations as emphasized above, as drafted, recite a process that, under its broadest reasonable interpretation performance of commercial or legal interactions in the form of determining fraudulent behavior or risk in business relations and activities. Other than reciting a computer implementation, nothing in the claim elements precludes the step from encompassing the performance of commercial or legal interactions which represents the abstract idea of certain methods of organizing human activity. But for the recitation of generic implementation of computer system components, the claimed invention merely recites a process for clustering and comparing collected data in order to predict fraud and provide remediative actions.
Step 2A, Prong 2:
This judicial exception is not integrated into a practical application. In particular, the claims recite additional elements such as:
a server computer system to perform the data encoding, data receiving (requests, signals), and data processing (clustering, generating scores) steps;
a server computing system, with one or more processors coupled with memory configured to perform operations of the claims; and/or
a memory and/or non-transitory computer readable storage medium storing instructions for performing the claimed steps.
In particular, the additional elements cited above beyond the abstract idea are recited at a high-level of generality and simply equivalent to a generic recitation and basic functionality that amount to no more than mere instructions to apply the judicial exception using generic computer technology components.
Accordingly, since the specification describes the additional elements in general terms, without describing the particulars, the additional elements may be broadly but reasonably construed as generic computing components being used to perform the judicial exception (see specification at [0024], systems using generic, general-purpose technology). Furthermore, the machine learning models (including first, second, and third machine learning model) used to perform the recited steps is recited at a high-level of generality and is only nominally and generically recited as a tool for performing these steps. These claimed additional elements merely recite the words “apply it" (or an equivalent) with the judicial exception, or merely include instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
Additionally, it is noted that the “input signals” and “encoding” (such as encoding the signal data into a plurality of sets of input signals), under broadest reasonable interpretation, merely recite steps/elements used for transmitting data and/or processing data for transmission that can be performed using general-purpose and/or generic technology (see at least [0020], [0024]; [0054], and [0057] in Applicant’s specification).
Additionally, it is noted that the “computing systems”3 are systems outside of the claimed invention and are not a positively recited component of the system/method. The server computer system (or computer processing system), uses data about the computing systems based on data from the computing systems (such as requests) to generate signal data, cluster, determine scores, etc., for the computing systems. However, any activities implied to be performed by the computing system (such as generating and/or transmitting requests) are not part of the claimed invention and its processes. For future reference, it is also noted that as currently written, even if these computing system activities were positively claimed they would merely represent generic computer functions, such as generating and transmitting requests.
3 Referring to the individual computing systems, such as the merchant systems that are clustered and scored. This is not a reference to the server computing system that performs the clustering, scoring, etc. (that is addressed above). Nor does it refer to the overall “computer processing system” in the preamble of Claim 10, which performs the processing steps using the server computing system (that is addressed above).
Thus, the additional claim elements are not indicative of integration into a practical application, because the claims do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e)). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea and the claims are directed to an abstract idea.
Step 2B:
The claims do not include additional elements, individually or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept at Step 2B. Thus, the claim is not patent eligible.
Dependent Claims:
Claims 2-6, 8, 11-15, 17, and 20-24 recite further elements related to the analysis, scoring, and message improvement steps of the parent claims. These activities fail to differentiate the claims from the related activities in the parent claims and fail to provide any material to render the claimed invention to be significantly more than the identified abstract ideas.
Claims 2 and 11 recite “wherein the first machine learning model comprises an ensemble of machine learning models, the ensemble of machine learning models comprising a gradient boosting machine learning model and a Neural Network machine learning model, and wherein the clustering is performed using density-based spatial clustering or KMeans clustering”. The mere recitation of an ensemble machine learning model (ML) or specific types of ML (or specific analysis techniques) does not integrate the abstract idea into a practical application or provide an inventive concept.
Claims 3 and 12 recite “wherein the first set of input signals comprises structure data input signals and unstructured data input signals, and wherein the second set of input signals further comprises computing system characteristics signal data indicative of computing system activities at the server computer system” which narrows how the abstract idea may be performed but does not make the claim any less abstract. Additionally, as explained in the rejections of the parent claims (provided above), the input signals merely recite steps/elements used for transmitting data and/or processing data for transmission that can be performed using general-purpose and/or generic technology (see at least [0020], [0024]; [0054], and [0057] in Applicant’s specification). Reciting specific types of data or specific types of input signals does not integrate the abstract idea into a practical application or provide an inventive concept.
Claims 4 and 13 recite “wherein the computing system characteristics signal data comprises a volume of requests over a period of time, an aggregate amount of requests of all server computer system over the period of time, an average volume of requests over the period of time, a computing system location, a total number of fraud detections by the first machine learning model over a period of time, or a combination thereof” which narrows how the abstract idea may be performed but does not make the claim any less abstract.
Claims 5, 14, and 20 recite “generating a first cluster fraud score for a first computing system cluster into which the computing system was clustered by: generating a first score indicative of a probability of fraud of the first computing system cluster, the first score generated from the total number of computing systems within the first cluster for which fraud has been detected during a request divided by a total number of computing systems with the first cluster, generating a second score indicative of an average of fraud scores associated with each computing system within the first cluster, generating a third score indictive of an average of maximum fraud scores determined for each computing system within the first cluster, determining the first cluster fraud score based on a combination of the first score, the second score, and the third score” which narrows how the abstract idea may be performed but does not make the claim any less abstract.
Claims 6 and 15 recite “wherein determining the first cluster fraud score based on a combination of the first score, the second score, and the third score comprises determining the first cluster fraud score based on an average of the first score, the second score, and the third score” which narrows how the abstract idea may be performed but does not make the claim any less abstract.
Claims 8 and 17 recite “wherein the clustering is performed on a periodic basis” which narrows how the abstract idea may be performed but does not make the claim any less abstract.
Claim 21 recites “wherein generating the corresponding cluster fraud score is performed asynchronously and separately from generating the individual fraud scores” which narrows how the abstract idea may be performed but does not make the claim any less abstract.
Claim 22 recites “wherein clustering the computing systems includes placing a computing system in multiple clusters” which narrows how the abstract idea may be performed but does not make the claim any less abstract.
Claim 23 recites “wherein initiating the one or more remediative actions includes initiating a remediative action against the computing system for each cluster of the multiple clusters that the computing system belongs to” which narrows how the abstract idea may be performed but does not make the claim any less abstract.
Claim 24 recites “wherein initiating the one or more remediative actions includes initiating a most severe remediative action against the computing system among the remediative actions for each cluster of the multiple clusters that the computing system belongs to” which narrows how the abstract idea may be performed but does not make the claim any less abstract.
The claims do not provide any new additional limitations or meaningful limits beyond abstract idea that are not addressed above in the independent claims therefore, they do not integrate the abstract idea into a practical application nor do they provide significantly more to the abstract idea. Thus, after considering all claim elements, both individually and as a whole, it has been determined that the claims do not integrate the judicial exception into a practical application or provide an inventive concept. Therefore, Claims 2-6, 8, 11-15, 17, and 20-24 are ineligible.
Additional Prior Art Identified but not Relied Upon
No prior art references were identified, alone or in combination, that teach(es) the claimed invention using the particular method/system as recited in the independent claims. The closest prior art identified includes:
Martins (Pub. No. US 2024/0013220 A1), which discloses encoding, by the server computer system, the signal data for each of a plurality of computing systems into a plurality of sets of input signals, the signal data generated using computing system requests processed by the server computer system for each of the computing systems; generating an individual fraud score for each computing system by: inputting, into a first machine learning model, a first set of input signals of the plurality of input signals to generate a first individual fraud score based on the first set of input signals, wherein the first set of input signals comprises structured data generated and encoded for the computing system; clustering, by the server computer system, the computing systems into one or more computing system cohort clusters based on data values indicative of characteristics of each computing system; one or more remediative actions including at least one of: blocking at least one of the computing system; suspending accounts associated with each computing system in the at least one computing system cohort cluster for a period of time; or deactivating accounts associated with each computing system in the at least one computing system cohort cluster (see at least [0053]; [0056]; [0058]-[0060]); [0035], “…the organization system 108 may include and/or be associated with a bank and/or a financial institution that facilitates transactions between a user and a merchant.”, demonstrates a “commerce platform system” and server computing platform for processing transaction between a merchant and a customer; [0021]; [0044]; [0045], collects transaction data generated from transactions (“accessing signal data” represents the collection input data, see specification at [0068] and Fig. 7), transactions represent requests (see also [0055]); [0027], merchant represents a merchant computing system; [0033], a predictive machine learning model is used to classify merchants based on fraud probabilities; [0062]; [0080], discloses fraudulent merchant determinations for merchants such as high-risk, low-risk, fraudulent, or not fraudulent based on numerical values compared to thresholds, this ratio of fraud transactions represents a numerical score; [0063], includes transaction data such as all transactions and transactions reported as fraudulent (structured data); [0043], includes data indicative of a location of a merchant (unstructured data), merchants can be clustered based on a second data input (input signals), such as, but not limited to, location (see also [0041]; [0054]; etc.) and common customers (see [0056]); [0055], provides example of clustering a merchant with similar merchants if a previous record does not exist; [0062], the model is trained on aggregated historical data that identifies similarities between fraudulent businesses; [0053]; [0056]; [0058]-[0060], merchants are clustered based on similarities with other merchants, merchants can be clustered based on a second data input (input signals), such as, but not limited to, location (see also [0041]; [0054]; etc.) and common customers (see [0056]); [0055], provides example of clustering a merchant with similar merchants if a previous record does not exist; [0062], the model is trained on aggregated historical data that identifies similarities between fraudulent businesses; [0045], “Such flags may include labeling a merchant as a high-risk merchant and/or blocking such a high-risk merchant when a transaction is detected”; [0073], “In this regard, the defensive mechanism may include blocking the pending transaction…”; [0074], “…place the merchant on a probationary category where the merchant needs to exhibit a sufficient number of non-fraudulent transactions.”, “Moreover, the application server 120 may also indicate that the identified merchant may be blocked from any further transactions for a predetermined period of time (e.g., 24 hours, 48 hours, 72 hours, etc.).”)
Wadhwa et al. (Pub. No. US 2022/0020026 A1), which discloses generating, for each of the computing system cohort clusters, a corresponding cluster fraud score indicative of computing systems of the cohort cluster being associated with fraudulent activities, based at least in part on see at least [0074]-[0076]; [0101], cluster fraud score are provided for clusters of users, both nodes (users) and clusters fraud score being updated when a change occurs showing the relationship between node scores and cluster scores; [0031], a cluster is identified as suspicious based on fraudulent activity of nodes, providing further evidence that cluster fraud scores are base do the individual fraud scores of the nodes; [0033]; [0100]-[0102], identifies and compares additional entities related to the cluster entities/nodes (in a manner similar to Martins) and when the probability for the nodes (and thus the cluster) passes a threshold, remediative steps are taken (such as alerting the issuer, etc.)).
Patten, Jr. et al. (Pub. No. US 2021/0241279 A1), which discloses a machine learning model comprising an ensemble of machine learning models, including a gradient boosting, XGBoost, KMeans clustering, etc. (see at least [0045]).
However, none of the prior art, alone or in combination, disclose or teach all of the claim limitations, including in accordance with a determination that the corresponding cluster fraud score for at least one of the one or more cohort clusters reaches a cohort cluster fraud threshold, initiating one or more remediative actions…for each computing system in the at least one computing system cohort cluster.
Additional references include:
Anderson et al. (Pub. No. US 2017/0278085 A1). Discloses fraud detection for a payment system, including cohort clusters and the ability to perform remediative actions (see at least [0043]; [0047]), but does not disclose the particular method for clustering and scoring computing systems recited in the instant claims or that the fraud protection operations are performed on each computing system in the cohort cluster if at least one reaches a threshold.
Formsma et al. (Pub. No. US 2019/0236608 A1). Discloses generating fraud clusters to generate a predictive fraud score using machine learning models (see at least Abstract; [0002]-[00027]; [0042]-[0051]; Claim 1; Claim 20).
McEachern et al. (Pub. No. US 2019/0066248 A1). Discloses “…wherein the user potential fraud risk score is a combination of individual scores for a plurality of risk categories…” (see at least Claim 5)
Siroshi et al. (Patent No. US 6,226,408 B1). Discloses merchant groups being categorized and rated for fraud (see at least Column 12, line 58-Column 13, line 34)
Li et al. (WO 2020046577 A1). Discloses the use of patterns among merchants to identify fraudulent activity including use of AI engines and suspending accounts (see at least Abstract; [0046]; [0047]).
Liu et al. (Pub. No. US 2021/0168166 A1). Discloses ensemble machine learning models, including gradient boosting machines, neural networks, and KMeans clustering (see at least [0045]).
Response to Arguments
Applicant’s arguments filed 6/16/2026, in regards to the rejection of claims under 35 U.S.C. §101, have been fully considered but they are not persuasive.
Applicant argues that [t]he claims provide a specific technical claim architecture that is used to cluster computer systems based on prior behaviors to enable a redundant check on fraud detection when one or more of the "fraud models does not indicate a specific merchant as being fraudulent." As written, the claim merely enters data into three generic machine learning models and then creates an individual fraud score based on the three outputs. There is no specific algorithm or technique to demonstrate how the three scores are combined to generate the individual fraud score. There is no indication in the claims that any score is based on past behavior, that any scores fail to indicate that a specific merchant is fraudulent, or that a redundancy is needed or desired when a merchant is not found fraudulent.
Although the use of individual fraud scores and cluster fraud scores could essentially act as redundancies, there is no indication in the claim language that this is how it is used, therefore it is not clear that any technical claim architecture would be specific to this application of the scores (since there is no indication that the cluster fraud score is used for identifying fraudulent merchants in response to the individual fraud scores failing to do so). As written the claims merely provide both types of scores. Applicant’s remarks appear to be reading much narrower material into the claims than what is actually recited.
Applicant’s remarks regarding a practical application (Step 2A – Prong 2 section) fail for similar reasons. The alleged practical application is not clearly identified for the same reasons as provided above. The claims do not clearly provide the described failure to indicate fraudulent merchants and subsequent use of cluster scores for redundancy checking. Even if they did, it is not clear that this is performed in any technical manner that would be significantly more than the abstract ideas, since the steps for determining the scores (both individual and cluster) are part of the abstract idea. Using the cluster score to redundancy check the previous scores or basing scores on past behavior does not clearly indicate a practical application, improvement to the computer, etc.
Regarding a technical solution to a technical problem, applicant refers to the technical problem being associated with clustering computer system based on prior behaviors (which is not part of the claims), and then discusses that the solution will “enable” the redundancy, etc. However, Applicant does not discuss or demonstrate what the clustering problems are, explain how they are overcome, and/or explain how this specifically leads to enabling the redundancy, etc. For example, what problems existed regarding clustering, why were prior systems unable to cluster (or what deficiencies existed in their clustering techniques), how do the instant claims solve these deficiencies, how does this solution provide redundancy checking, why were prior systems unable to provide redundancy techniques in their scoring, etc.
Applicant argues that the claim does not intend to tie-up or preempt all uses of the abstract idea (page 22). However, Applicant is reminded that preemption is not a standalone test for eligibility. Questions of preemption are inherent in and resolved by the two-part framework from Alice Corp. and Mayo, as explained by the federal circuit in OIP and Sequenom. Additionally, while a preemptive claim may be ineligible, the absence of complete preemption does not demonstrate that a claim is eligible.
Applicant asserts that the claims operate in a non-conventional and non-generic way, however, Applicant does not explain how/why the claims would operate in a non-conventional and non-generic way and merely recites the same material related to failure to identify fraudulent merchants and using the cluster as a redundancy. IT is not clear how this is performed in a non-conventional and non-generic way.
Applicant asserts that the claimed combination of additional elements present a specific and discrete implementation in a manner similar to Bascom. However, Applicant fails to provide any companions, analysis, etc. to demonstrate how/why the claim features are comparable to the findings of Bascom and merely asserts that they are.
In regards to Claims 21-24, the newly provided limitations do not clearly indicate specific technical features that improve the operation of the computing system. For example, it is not clear how performing the cluster fraud score asynchronously and separately from the individual fraud score improves operating of the computer.
Additionally, placing computing systems in multiple clusters and initiating remediate actions against each cluster represent steps in the abstract ideas (scoring and performing the remediate actions on each cluster would be performed in the same manner and merely repeated for each cluster). It is not clear how this provides real-time preventative fraud detection in a significant technical manner. Although it could possibly be used for those purposes, there is no technical detail to indicate that it would be for those purposes a manner that improves the operating of the computer. Again, Applicant appears to be reading material into the claims that is not recited, and the specification does not include sufficient detail to support the alleged problems solutions, improvements, etc.
Conclusion
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 SHAUN D SENSENIG whose telephone number is (571)270-5393. The examiner can normally be reached M-F: 10:00am-4:00pm.
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, Lynda Jasmin can be reached on 571-272-6872. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. 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.
/S.D.S/Examiner, Art Unit 3629 July 10, 2026
/ANDREW B WHITAKER/Primary Examiner, Art Unit 3629
Prosecution Insights