DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
RCE Acknowledgement
Applicant’s Request for Continued Examination (RCE) dated 03/02/2026 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office Action has been withdrawn pursuant to 37 CFR 1.114, and the Applicant's RCE submission filed on 02 MARCH 2026 has been entered.
Status of Claims
Claims 7-20 are pending in this instant application per RCE claim amendments and remarks filed on 03/02/2026, wherein Claims 7-11 and 13-20 have been amended. Claims 7 and 14 are independent claims reciting method and system claims with claims 8-13 and 15-20 dependent on the independent claims respectively.
This Office Action is a non-final rejection in response to RCE claim amendments and remarks filed by the Applicant on 02 MARCH 2026 for its original application filed on 03 NOVEMBER 2023 that is titled: “Systems and Methods for Dynamically Updating Models using Machine Learning”.
Accordingly, amended Claims 7-20 are now being rejected herein.
Election/ Restriction Update
Claims 1-6 continue to be shown as withdrawn in RCE claims listing of 03/02/2026. Applicant is respectfully requested to cancel non-elected claims in response to this Office Action (OA). Claims 1-6 were withdrawn (in last Office Action) from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected invention, there being no allowable generic or linking claim. While Applicant had timely traversed the restriction requirement in the reply filed on 06/11/2025, Examiner provided a detailed response in the last Office Action of 12/02/2025; wherein the requirement was still deemed proper and therefore, Restriction was made FINAL.
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.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
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 of carrying out his invention.
Claims 7-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, 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 or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. As described above, the addition of “anomalous data” in claims is not supported by Specification, and no support to perform newly added amendments in claims, such as {“and identifying in real-time anomalous data included within the data messages;”} AND {“wherein the score represents a likelihood that the electronic data message includes anomalous data;”}. The Specification does not demonstrate that the Applicant has made an invention that achieves the claimed function because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention.
Appropriate correction is required.
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.
(NOTE: Latest RCE ‘amendments to the claims’ filed by the Applicant in the RCE on 03/02/2026 are shown as bold and underlined additions, and all deletions may not be shown, or may not be underlined when stricken through. Underlined amendments to the claims that are shown below are from previously submitted claim amendments by the Applicant.)
Claims 7-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more, wherein Claims 7 and 14 are independent system and method claims respectively.
Exemplary Analysis.
Claim 14: Ineligible.
The claim recites a series of steps. The claim is directed to a method reciting a series of steps, which is a statutory category of invention (Step 1--YES).
The claim is analyzed to determine whether it is directed to a judicial exception. The computer-implemented method claim recites the limitations of: detecting and preventing anomalous network events in a computer network, comprising: receiving, from a database, an initial dataset including historical data for a first time period; segmenting the initial dataset into a plurality of subsets, each subset including data that is associated with a second time period that is smaller than the first time period, each subset ordered in chronological order from an oldest data subset to a more recent data subset; assigning a weight factor to each of the plurality of subsets, each weight factor assigned based at least in part on an age of the associated subset with the oldest data subset being assigned a lesser weight factor than the most recent data subset; receiving in real-time the electronic data [[]] message associated with an electronic request being processed; analyzing in real-time the electronic data [[]] message; assigning a score to the electronic data [[]] message based on the analysis, wherein the score represents a likelihood that the electronic data message includes anomalous data; and generating in real-time, based at least in part on the score, a decision [[]] to [[]] decline the transaction request associated with the electronic data [[]] message including an indication that the electronic data [[]] message includes an anomalous data pattern. In other words, the claim describes a computer-implemented method for detecting and preventing fraudulent network events in a payment card network is provided (per para [0007] of Specification). These limitations, as drafted, are steps of a method that, under its broadest reasonable interpretation, covers performance of the limitations via a method of organizing human activity such as fundamental economic principles or practices (based on at least ‘payment card transaction request’ and as argued by Applicant in its RCE remarks), and/or commercial or legal interactions (based on at least ‘receiving dataset’, ‘segmenting dataset’ and ‘training a model’, etc. that are performing operations), and/or managing behavior or relationships or interactions between people (based at least on ‘assigning a score’ based on ‘a rules engine’), but for the recitation of generic processor and/or computer component/s such as the devices/ mobile devices. These limitations fall under the “certain methods of organizing human activity” group (Step 2A1--YES).
Next, the claim is analyzed to determine if it is integrated into a practical application. The claim recites additional elements of: a machine learning model; a model engine, a rules engine, a merchant computing device and a trained model, recited as in: incrementally training [[]] the machine learning model using [[]] each subset of the plurality of subsets in chronological order and using [[]] the assigned weighting for that subset, wherein the incremental, chronological training of the machine learning model with different weight factors improves the performance of the machine learning model in analyzing in real-time data messages processed over the computer network and identifying in real-time anomalous data included within the data messages; analyzing in real-time the electronic data [[]] message using the incrementally trained machine learning model; receiving, at a rules engine executed in real-time that is communicatively coupled to the model engine, the electronic data [[]] message and the corresponding score from the model engine.; and outputting the decision preventing the electronic [[]] data message from being further processed over the computer network in real-time, thereby improving an overall performance [[]] of the computer network [[]]. These additional elements are considered extra-solution activities. The processor/s and device/s in the steps, such as machine learning model, model engine, rules engine, and merchant computing device, are recited at a high level of generality, i.e., as generic processors performing generic computer/s functions of processing data (as described above). These generic processors are no more than mere instructions to apply the exception using generic processor/s and/or computing device/s. 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. Thus, the claim is directed to the abstract idea (Step 2A2--NO).
Next, the claim is analyzed to determine if there are additional elements in this claim that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract ideas (whether claim provides inventive concept). As discussed with respect to Step 2A2 above, the additional elements in the claim amount to no more than mere instructions to apply the exception using generic processor/s and/or computing device/s. The same analysis applies here in Step 2B, i.e., mere instructions to apply an exception using a generic processor/s and/or computing device/s over a network cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Because these additional elements were considered to be extra-solution activities in Step 2A, they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine and conventional in the field. The disclosure does not provide any indication that these processor/s and/ or computing device/s are anything other than generic processors and the Symantec, TLI, and OIP Techs. court decisions (MPEP 2106.05 (d) (II)) indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Also, paras [0063]-[0067] of the Applicant’s own Specification describe ---
{“[0063] In the example embodiment, fraud model engine 106 is configured to receive one or more payment card transaction requests 124 from one or more merchants 102, either directly from the merchants 102 or from the at least one merchant bank 112. In various embodiments, payment card transaction requests 124 are received by payment card interchange network 202 and forwarded to fraud model engine 106. Fraud model engine 106 is configured to analyze each of the received payment card transaction requests 124 on an individual basis (that is, without regard to characteristics of other incoming payment card transaction requests) for fraud, and to assign a fraud score to each of the payment card authorization requests 124. …….
…………………………………………………………………………………………………………………………………………………
[0064] In one example embodiment, fraud model engine 106 executes a fraud scoring model 126 to analyze and score payment card transaction requests 124. The resulting fraud score is indicative of a likelihood of fraud being associated with a respective payment card transaction requests 124. ……………………………………………………………………………………………………………………………..
[0065] In some embodiments, fraud model engine 106 includes or executes a plurality of machine learning algorithms, either separate from execution of fraud scoring model 126 or as part of fraud scoring model 126. In various embodiments, the machine learning algorithms may be selectable, either automatically or by an operator, and may include at least one of an Artificial Neural Network (ANN) machine learning algorithm and a Support Vector Machine (SVM) machine learning algorithm. Fraud model engine 106 may be configured to execute multiple machine learning algorithms singly or simultaneously in groups. ………………………………..
[0066] At least some scored payment card transaction requests 128 are transmitted to fraud rules engine 114 for further analysis. Fraud rules engine 114 applies one or more fraud rules to each scored payment card transaction request 128 to facilitate determining whether or not the transaction is likely fraudulent. For example, the fraud rules may determine whether or not a transaction should be identified as fraudulent based on one or more of the score assigned by fraud model engine 106, a dollar amount of the transaction, a location of the transaction (e.g., whether the transaction is a cross-border transaction), a merchant involved in the transaction, etc. …………………………………………………………………………………………………………………………………………….
[0067] Based on the analysis undertaken by fraud model engine 106 and fraud rules engine 114,fraud analysis computing system 100 generates an output 132 for each payment card transaction request 124. Output 132 may be, for example, a decision to approve or decline the transaction associated with payment card transaction request 124. In some embodiments, output 132 may include one or more scores (e.g., the fraud score assigned by fraud scoring model 126). Output 132 may be transmitted from fraud analysis computing system 100 (e.g., from fraud model engine 106 and/or fraud rules engine 114) to one or more of merchant 102,merchant bank 112, and issuer 104.”} ---
and indicate that the concept/s described by extra-solution additional elements is conventional. Accordingly, a conclusion that the aforementioned extra-solution additional elements are well-understood, routine and conventional activity is supported under Berkheimer options 2 and 3, respectively.
Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. When viewed either individually, or as an ordered combination, the additional elements do not amount to a claim as a whole that is significantly more than the abstract idea itself. Therefore, the claim does not amount to significantly more than the recited abstract idea (Step 2B--NO), and the claim is not patent eligible.
The analysis above applies to all statutory categories of the invention including independent system Claim 7, which perform the steps similar to those of independent method Claim 14. Furthermore, the limitations of dependent method Claims 15-20, further narrow the independent method Claim 14 with additional steps and limitations (e.g., wherein the fraud scoring model is a machine learning model; wherein the fraud scoring model is a gradient-boosted decision tree model; wherein training the fraud scoring model comprises updating probabilities associated with each leaf node of the fraud scoring model when training on each subset; wherein assigning a weight comprises assigning a lower weight to more recent subsets, such that the fraud scoring model trains less on more recent subsets; wherein the first time period is one year, and wherein each second time period is one month; and wherein the fraud scoring model is a supervised machine learning model; etc.), and do not resolve the issues raised in rejection of the independent method Claim 14. Similarly, dependent system Claims 8-13 also further narrow its independent system Claim 7, which are rejected as ineligible for patenting under 35 U.S.C. 101 based upon the same analysis.
Therefore, said Claims 7-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's remarks (pages 9-25) and claim amendments dated 02 MARCH 2026 with respect to the rejection of amended Claims 7-20 have been carefully considered, but they are not persuasive and do not put these amended claims in a condition ready for Allowance. Thus, the rejection of amended Claims 7-20, as described above, is being maintained herein under 35 USC 101 only with some modifications in this Office Action, with certain amended limitations being part of abstract idea and others being part of extra-solution activities as described above, in response to the Applicant’s latest claim amendments in the RCE of 03/02/2026.
In response to the Applicant’s arguments against the rejection under 35 USC 101, Examiner respectfully disagrees. Also, Examiner clarifies that the instant application is nothing more than an improvement of an abstract idea, wherein using technology/ computers to execute an abstract idea is at most an improvement to the abstract idea. Additionally, in response to Applicant’s arguments on page 22 of its Remarks -- {“and "The claim itself does not need to explicitly recite the improvement described in the specification." ”} -- Examiner notes that to evaluate an improvement to a computer or technical field, the specification must set forth an improvement in technology and the claim itself must reflect the disclosed improvement. See MPEP 2106.04(d)(1) and 2106.05(a).
With respect to Applicant's RCE arguments filed 03/02/2026 traversing 35 USC 101 rejection by claiming similarity to Example 47, Examiner respectfully disagrees. Examiner notes that Example 47, Claim 3 was found eligible not just because of adding limitations about “machine learning model” as have been added by the Applicant in its RCE claims listing of 03/02/2026, but it was found eligible based on limitations recited in steps (d)/(e)/(f) of Example 47, Claim 3 that recite an improvement in the technical field of network intrusion detection, and these steps recite ---
“(d) detecting a source address associated with one or more malicious network packets in real time;
(e) dropping the one or more malicious packets in real time; and
(f) blocking future traffic from the source address.”
Examiner notes that steps (d)/(e)/(f) provide for improved network security using the information from the detection to enhance security by taking proactive measures to remediate the danger by detecting the source address associated with the potentially malicious packets. Specifically, Claim 3 of Example 47 reflects the improvement in the limitations of steps (d)/(e)/(f); and Examiner notes that the instant application’s claims do not provide similar technical improvements as have been recited in Example 47, Claim 3). Lastly, Examiner notes that the instant application, as amended, recites {“wherein the incremental, chronological training of the machine learning model with different weight factors improves the performance of the machine learning model in analyzing in real-time data messages processed over the computer network”} that are based on mathematical calculations.
In additional response to the Applicant’s RCE arguments of 03/02/2026 against the rejection under 35 USC 101, Examiner respectfully disagrees with arguments that the instant application is similar to Ex Parte Desjardins argued as -- {“Thus, claim 1 explicitly recites an improvement in functioning of a computer or technical field including reduction in resource usage since less processor capacity and less memory capacity are being utilized, as Ex Parte Desjardins explicitly holds.”}. Examiner notes that in Ex Parte Desjardins, it was found --- {“The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification.”}. However, the instant application’s Specification does not recite nor support limitations of “reduced processor capacity” and/or “reduced memory capacity” as claimed by the Applicant by adding in claims as “less processor capacity” and “less memory capacity” -- but these are also not supported by Specification. It is noted by Examiner that the Applicant also see Response to Arguments para 18. above for no support in its Specification for 12/26 claim amendments with sub-phrases reciting “less processor capacity” and “less memory capacity”, and Specification has no support for the phrases from the Ex Parte Desjardins findings about “reduced storage”, “reduced system complexity” and “streamlining”, and the Specification does not support “system complexity” nor “complexity” nor “less storage”.
NOTE: Examiner notes that the previous Responses to Arguments from more than one previous Office Action/s are incorporated from the previous Office Actions by reference as recited in full here (for example, but not limited to, Examiner’s previous responses to Applicant arguments for prong one, prong two, etc.), but such previous Responses have not been retained herein for brevity.
Conclusion
The prior art made of record and not relied upon, listed in Form 892, that is considered pertinent to the Applicant's disclosure and review for not traversing already issued patents and/or claimed inventions by the claims of the current invention of the Applicant. Please note that Form 892 contains more references than those cited in the rejection above under 35 USC 103, which are relevant to this application and form a part of the body of prior art.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Sanjeev Malhotra whose telephone number is (571) 272-7292. The Examiner can normally be reached during Monday-Friday between 8:30-17:00 hours on a Flexible schedule.
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If attempts to reach the Examiner by telephone are unsuccessful, the examiner’s supervisor, Abhishek Vyas, can be reached on (571) 270-1836. The facsimile/fax phone number for the organization, where this application or proceeding is assigned, is 571-273-8300.
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Electronic Communications
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/S.M./
PSA Examiner, Art Unit 3691
sanjeev.malhotra@uspto.gov
/SANJEEV MALHOTRA/Examiner, Art Unit 3691
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