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 9/9/2026.
Claims 1, 10, and 19 have been amended.
Claims 7-9, 16-18, and 21 have been cancelled.
Claims 21-24 have been added.
Claims 1-6, 10-15, 19, 20, and 22-24 are pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 9/9/2026 has been entered.
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, 10-15, 19, 20, and 22-24 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, 10-15, 19, 20, and 22-24 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.
Claim 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, wherein the individual fraud score for each computing system is generated in line with a computing system request as the computing system request is processed by the server computer system and before completion of the computing system request;
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, the clustering being performed periodically and asynchronously with the computing system requests;
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,
wherein the at least one computing system cohort cluster includes a first computing system for which the individual fraud score does not reach an individual fraud threshold, and the one or more remediative actions are initiated against the first computing system in accordance with the determination that the corresponding cluster fraud score reaches the cohort cluster fraud threshold1.
Claim 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, wherein the individual fraud score for each computing system is generated in line with a computing system request as the computing system request is processed by the server computer system and before completion of the computing system request;
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, the clustering being performed periodically and asynchronously with the computing system requests;
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,
wherein the at least one computing system cohort cluster includes a first computing system for which the individual fraud score does not reach an individual fraud threshold, and the one or more remediative actions are initiated against the first computing system in accordance with the determination that the corresponding cluster fraud score reaches the cohort cluster fraud threshold1.
Claim 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, wherein the individual fraud score for each computing system is generated in line with a computing system request as the computing system request is processed by the server computer system and before completion of the computing system request;
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, the clustering being performed periodically and asynchronously with the computing system requests;
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, 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 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 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, wherein the at least one computing system cohort cluster includes a first computing system for which the individual fraud score does not reach an individual fraud threshold, and the one or more remediative actions are initiated against the first computing system in accordance with the determination that the corresponding cluster fraud score reaches the cohort cluster fraud threshold1.
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. For example, a user can (1) apply a number of models (set of steps for making a determination, as the ML are recited broadly with no technical detail as to how they process the data or generate output) to determine a score, (2) periodically cluster the data pertaining to the computing systems based on characteristics, (3) generate cluster scores based on the clusters and individual scores of members of the cluster, (4) compare the cluster fraud score (and/or individual fraud score) to a threshold, and (5) perform remediation action based on the threshold comparisons. These activities are merely automated using generic computers (see below). A user could perform the scoring steps separately and asynchronously of each other, without altering the claimed invention.
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, 11-15, 20, and 22-24 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.
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, 11-15, 20, and 22-24 are ineligible.
Additional Prior Art Identified but not Relied Upon
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]).
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), Wadhwa et al. (Pub. No. US 2022/0020026 A1), and Patten, Jr. et al. (Pub. No. US 2021/0241279 A1), as applied to the claims rejections in previous office actions.
Response to Arguments
Applicant’s arguments filed 9/9/2026, in regard to the rejection of claims under 35 U.S.C. §101, have been fully considered but they are not persuasive.
Applicant argues that the claims now include the previously argued redundancy and that the cluster score detection can identify fraudulent activity that the individual scores fail to identify and that the second tier (being interpreted as the clustering and cluster scoring steps) contrasts with the real-time models (being interpreted as the individual scoring and three machine learning-models steps) by collecting data over time.
Although the claims do include these two types of scoring, they are not clearly claimed in a way that defines the stated two-tier redundancy system. For example, in contrasts to Applicant’s remarks, the claims do not identify that the first score (individual score) “failed” and therefore the second score (cluster score) will be implemented (redundancy). The individual score is determined and then used for generating the cluster fraud score. Specifying that the clustering and cluster fraud scores are performed “asynchronously and separately from generating the individual fraud scores” does not clearly provide this “tier” or “redundancy”, since the individual fraud score (IFS) can be determined, then subsequently used for the clustering and cluster fraud score (CFS). For example, the system determines the IFS when the request is being performed (before completion of request). It then uses that IFS to determine the clustering and CFS, which when performed after the IFS determination would be an “asynchronously and separate” process (under broadest reasonable interpretation, the clustering could be performed at any time, including any time after the request/IFS is completed). It is also noted that being performed periodically does not necessarily mean that it uses data “gathered over time”, merely that it is performed periodically, which under broadest reasonable interpretation could include merely performing the clustering periodically. As a non-limiting example, the claims can be interpreted as periodically performing the clustering to update the CFSs based on new or updated requests/IFSs, not that it uses a collection of data gathered over time.
Additionally, the final limitation (wherein clause added at the end of the independent claims in the amended claim set), merely uses both fraud scores to determine a response, such as which systems to target for remediation rather than all. It does not clearly recite any type of hierarchy or tier relationship between the two (such as one being used when another one fails).
Applicant argues “claims require a specific ordered arrangement in which a first, request-synchronous scoring tier acts on encoded structured, unstructured, and time-based signal sets before a request completes, and a second, request-asynchronous cluster-scoring tier acts on cluster membership to reach computing systems the first tier does not flag”. Similar to the response above, the claims do not clearly identify the alleged two-tier approach. For example, there is no clear indication that the second (CFS) tier acts on the data when the first tier (IFS) fails to flag a merchant (a second, request-asynchronous cluster-scoring tier acts on cluster membership to reach computing systems the first tier does not flag). Although the claims include the two methods of scoring merchants, it does not demonstrate the alleged two-tier arrangement, in which one scoring method is performed in response to the failure of another scoring method.
Applicant is also reminded that the fact patterns and rationales for determining novelty under 35 U.S.C. §102/103 and patentability under 35 U.S.C. §101 are different and being allowable under one statute does not necessarily mean that the claims are ultimately allowable under the other statute.
The above issues were also addressed in the previous office action and can be consulted for additional details (excerpts provided below for reference). Applicant’s amendments do not clarify these features of the claimed invention (redundancy, using o second score when a first score fails, etc.). The addition of the “asynchronous and separate” does not provide a clear and specific order of operations that would provide any tier/redundancy aspects.
Excerpts: 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.
Conclusion
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 September 19, 2026
/LYNDA JASMIN/Supervisory Patent Examiner, Art Unit 3629
