Prosecution Insights
Last updated: August 18, 2026
Application No. 18/082,618

REINFORCEMENT-LEARNING-AGENT-BASED GUI METRICS FOR MONITORING SYSTEM EFFECTIVENESS

Non-Final OA §101
Filed
Dec 16, 2022
Priority
Oct 25, 2022 — provisional 63/419,206
Examiner
KANG, IRENE S
Art Unit
3696
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
ORACLE INTERNATIONAL Corporation
OA Round
3 (Non-Final)
16%
Grant Probability
At Risk
3-4
OA Rounds
1y 2m
Est. Remaining
43%
With Interview

Examiner Intelligence

Grants only 16% of cases
16%
Career Allowance Rate
37 granted / 225 resolved
-35.6% vs TC avg
Strong +26% interview lift
Without
With
+26.2%
Interview Lift
resolved cases with interview
Typical timeline
4y 11m
Avg Prosecution
16 currently pending
Career history
243
Total Applications
across all art units

Statute-Specific Performance

§101
36.6%
-3.4% vs TC avg
§103
34.8%
-5.2% vs TC avg
§102
16.1%
-23.9% vs TC avg
§112
9.3%
-30.7% vs TC avg
Black line = Tech Center average estimate • Based on career data from 225 resolved cases

Office Action

§101
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 The following is a Non-Final Office Action in response to communications received April 30, 2026. Claims 1-20 are pending and examined. Response to Amendments and Arguments As to the rejection of Claims 1-20 under 35 U.S.C. § 101, Applicant’s arguments and amendments have been fully considered but are not persuasive. Applicant argues essentially that the instant claims are analogous to the claimed subject matter of Ex parte Desjardins et al., where the claim was found to be patent eligible. However, Desjardins recited subject matter that made improvements as to how the machine learning model itself operates. Unlike Desjardins, the instant claims do not improve on how the machine learning model itself operates but rather, the instant claims simply receive certain configuration data that indicates whether or not a certain payment transaction processor is capable of processing the payment transaction via one condition or another indicated by the configuration data and then inputting data into a selected artificial intelligence model. Examiner submits that testing an effectiveness of a transaction monitoring system, is an abstract idea, i.e., a certain method of organizing human activities such as in financial or commercial activities, not a technical problem. The monitoring system merely serves as the environment in which the abstract idea is applied to. Applicant also argues that the present claims as a whole integrate the judicial exception into a practical application of the exception to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. Examiner disagrees. The claims in the instant application include an abstract idea, and when considered as a whole, the claims (independent and dependent) do not integrate the exception into a practical application, and merely add the words “apply it” to the “the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). The additional elements do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. And simply relying on a computer to perform routine tasks or calculations more quickly or more accurately is insufficient to render a claim patent eligible. See Alice, 134 S. Ct. at 2359 (“use of a computer to create electronic records, track multiple transactions, and issue simultaneous instructions” is not an inventive concept); Bancorp Servs., L.L.C. v. Sun Life Assur. Co. of Can. (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (a computer “employed only for its most basic function . . . does not impose meaningful limits on the scope of those claims”); cf. DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258–59 (Fed. Cir. 2014) (finding a computer-implemented method patent eligible where the claims recite a specific manipulation of a general-purpose computer such that the claims do not rely on a “computer network operating in its normal, expected manner”). It is for these reasons that the present claims do not rise to the level of improvements to computer functionality or system operation as in McRO. The rejection is thereby maintained. As to the rejection of claims 1-20 under 35 U.S.C. § 103, Applicant's arguments and amendments have been fully considered and are persuasive. The rejection is thereby withdrawn. 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. (Step 1) The claims recite a method, system, and manufacture. For the purposes of this analysis, representative claim 1 is addressed. (Step 2A, prong 1) Abstract ideas are in bold below, and represents certain methods of organizing human activity, as a method of testing an effectiveness of a transaction monitoring system. Testing an effectiveness of a transaction monitoring system is akin to certain methods of organizing human activity. A computer-implemented method to test an effectiveness of a transaction monitoring system, the method comprising: executing a reinforcement learning agent to perform a sequence of test transactions, wherein the transaction monitoring system is configured to detect transactions that are suspicious based on satisfying a scenario that defines a suspicious activity, [[and]] wherein the reinforcement learning agent generates the sequence of test transactions to cumulatively transfer an amount without detection by the scenario, wherein the reinforcement learning agent selects each test transaction in the sequence according to a policy, and wherein the policy is updated, based on responses of the transaction monitoring system to prior test transactions in the sequence, to favor selecting subsequent transactions that do not violate the scenario while contributing to the cumulative transfer of the amount; recording the sequence of test transactions along with a set of responses made by the transaction monitoring system in response to each test transaction being performed, wherein the set of responses includes at least an alert status of detection by the scenario, and wherein the alert status indicates one of an alert for the suspicious activity is triggered or the alert for suspicious activity is not triggered; generating an alert-based metric that represents the effectiveness of the transaction monitoring system for resisting the suspicious activity based on identifying one or more alerts that are triggered among the alert statuses in the set of responses; automatically adjusting the scenario based at least in part on the alert-based metric; generating, for display in a graphical user interface, a visualization of the alert-based metric that represents the effectiveness of the transaction monitoring system for resisting the suspicious activity and an option to accept the adjusted scenario; and in response to selection of the option to accept, automatically deploying the adjusted scenario into the transaction monitoring system to adjust resistance to the suspicious activity. (Step 2A prong 2) The additional elements are considered as follows: “A computer-implemented method” This is merely “apply it” this sever is claimed at a high level of generality, it receives the information, performs the abstract idea, and outputs the results. “reinforcement learning agent” and “transaction monitoring system” These are described in Applicant’s Specification at ¶[0030] as “a computer-implemented system for autonomously selecting and performing test transactions in response to states of a transaction environment… To execute the reinforcement learning agent, a computer reads and implements instructions that cause the reinforcement learning agent to select and perform the test transactions in accordance with the policy. In one embodiment, the transaction system and the transaction monitoring system are an environment that is configured to simulate an actual transaction system and transaction monitoring system” These are merely “apply it” as these are claimed at a high level of generality, it receives the information, performs the abstract idea, and outputs the results. “non-transitory computer-readable medium having stored thereon computer-executable instructions that, when executed by a processor accessing memory of a computer” This is merely “apply it” the computer, processor, and memory are claimed at a high level of generality, they receive the information, perform the abstract idea, and output the results. (Step 2B) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration into a practical application, the additional elements amount to no more than mere instructions to apply the abstract idea of testing an effectiveness of a transaction monitoring system using generic computer components. The claim elements when considered separately and in an ordered combination, do not add significantly more than implementing the abstract idea of testing an effectiveness of a transaction monitoring system, over a generic computer network with generic computing elements, and generic hardware. Analysis of dependent claims 2-8, 10-14, and 16-20, recited additional details which only further narrow the abstract idea and do not add any additional features, alone or in combination, that would provide a practical application or provide significantly more For example, Claims 2-4 further narrows the limitation “generating the metric” of Claim 1. Claim 5 further narrows the limitations “recording the sequence of test transactions performed by the reinforcement learning agent” and “generating the metric’ of Claim 1. Claim 6 further narrows the limitations “recording the sequence of test transactions performed by the reinforcement learning agent” and “generating the metric” and “generating the visualization of the metric” of Claim 1. Claims 7 and 8 further narrow claim 1. Claims 10-14 and 16-20 similarly narrow the respective independent Claims 9 and 15 and similarly rejected under the same reasoning. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to IRENE S KANG whose telephone number is (571)270-3611. The examiner can normally be reached on Monday through Friday between M-F 10am-2pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matt Gart may be reached at (571)-273-3955. 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. /IRENE KANG/ Examiner, Art Unit 3695 7/27/2026 /MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696
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Prosecution Timeline

Dec 16, 2022
Application Filed
Mar 14, 2025
Non-Final Rejection mailed — §101
May 20, 2025
Interview Requested
Jun 16, 2025
Response Filed
Dec 30, 2025
Final Rejection mailed — §101
Apr 30, 2026
Request for Continued Examination
May 06, 2026
Response after Non-Final Action
Jul 29, 2026
Non-Final Rejection mailed — §101 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

3-4
Expected OA Rounds
16%
Grant Probability
43%
With Interview (+26.2%)
4y 11m (~1y 2m remaining)
Median Time to Grant
High
PTA Risk
Based on 225 resolved cases by this examiner. Grant probability derived from career allowance rate.

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