DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
2. Claims 1, 3-11 and 13-22 are currently pending. Claims 1, 5, 8, 11, 15, 18 and 20 have been amended. Claims 1, 3-11 and 13-22 have been rejected.
Status of the Application
3. Claims 1, 3-11 and 13-22 are currently pending and have been examined in this application. This communication is the first action on the merits.
Response to Amendments
4. Applicant’s amendment filed on 02/19/2026 necessitated new grounds of rejection in this office action.
Continued Examination under 37 CFR 1.114
5. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/26/2026 has been entered.
Priority
6. The Examiner has noted the Applicants claiming Continuation in Part (CIP) of Case #18/474,492 filed on 09/26/2023. Therefore, the earliest effective filing date examined for this application is of 09/26/2023.
Response to Arguments
7. Applicant’s arguments, see pages 1-2 filed on 02/19/2026, with respect to the 35 U.S.C. § 112 (b) Claim Rejections for Claims 11, 13-19 and 22 have been fully considered and are found to be persuasive. Therefore, the 35 U.S.C. § 112 (b) Claim Rejections for Claims 11, 13-19 and 22 are withdrawn.
8. Applicant’s arguments, see pages 8-12 filed on 02/19/2026, with respect to the 35 U.S.C. § 103 Claim Rejections for Claims 1, 3-11 and 13-22 have been fully considered and are found to be persuasive. Therefore, the 35 U.S.C. § 103 Claim Rejections for Claims 1, 3-11 and 13-22 have been withdrawn. See Examining Claims with Respect to Prior Art Section shown below.
Response to 35 U.S.C. § 101 Arguments
9. Applicant’s 35 U.S.C. § 101 arguments, filed with respect to Claims 1, 3-11 and 13-22 have been fully considered, but they are found not persuasive (see Applicant Remarks, Pages 2-7, dated 02/19/2026). Examiner respectfully disagrees.
Argument #1:
(A). Applicant argues that Claims 1, 3-11 and 13-22 do not recite an abstract idea, law of nature of natural phenomenon under revised step 2a prong one of the 35 U.S.C § 101 analysis (see Applicant Remarks, Pages 3-5, dated 02/19/2026). Examiner respectfully disagrees.
Specifically, Applicant argues that amended Independent Claims 1, 11 and 20 do not recite an abstract idea under 35 U.S.C. § 101 step 2a prong 1 because by using a plurality of machine learning models to monitor events, where each model is trained to monitor events that involve a different combination of the subsystems, it is possible to have relatively small models that “allow for quick if not real-time monitoring” which is a specific technical improvement to the underlying computer (see Applicant Remarks, Page 4, dated 02/19/2026). Examiner respectfully disagrees.
In response to Applicant’s Remarks for step 2a prong 1, Examiner notes that the claims recite an abstract idea notwithstanding the additional limitations directed to event managers and machine learning models. Applicant’s amendments merely append computing components and analytical techniques to an otherwise abstract workflow for monitoring operational activity and initiating corrective responses (e.g., root cause analysis for diagnosing and correcting errors or failures) in a banking enterprise environment. Applicant argues that the claims are not directed to an abstract idea because the claims recite “a plurality of machine learning models” trained for different subsystem combinations and because the claims employ dedicated event managers. However, these limitations do not remove the claims from the realm of abstract information processing.
The focus of Independent Claims 1, 11 and 20 remains: receiving requests, monitoring events, analyzing events for failures, generating trigger events and transmitting remediation response. These activities amount to collecting information, analyzing information, and initiating responsive action based upon the analysis. Such activity falls squarely within the categories of “Mental Processes” and “Certain Methods of Organizing Human Activities” identified in the 2019 PEG.
Moreover, the claims merely automate operational oversight activities that could conceptually be performed by human administrators monitoring system logs and dispatching corrective instructions. Although the claims invoke machine learning models and event managers, the claims do not recite any specific improvement to machine-learning technology itself, nor do they recite any specific algorithmic advancement in event-processing architecture.
With respect to the argument that amended Independent Claims 1, 11 and 20 cannot be performed/executed by the human mind”, Examiner refers Applicant to MPEP § 2106.04 (a) (2) (III) (C): “Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer").” “For instance, the Examiner has reviewed Applicant’s Specification and determined that the claimed invention is described as concepts that are performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer (e.g., see Applicant’s Specification ¶ [0034-0035]: “User devices 12 can include, but are not limited to, a personal computer, a laptop computer, a tablet computer, a notebook computer, a hand-held computer, a personal digital assistant, a portable navigation device, a mobile phone, a wearable device, a gaming device, an embedded device, a smart phone, a virtual reality device, an augmented reality device, third party portals, an automated teller machine (ATM), and any additional or alternate computing device, and may be operable to transmit and receive data across communication network 14.”) or 2) in a computer environment (e.g., see Applicant’s Specification ¶ [0030]: “The computing environment 10 can include one or more user devices 12 (shown as user devices 12 a, 12 b . . . 12 n), an enterprise platform 16, an intermediary 20, a cooperating entity 22, and a communications network 14 connecting one or more components of the computing environment 10.”), or 3) is merely using a computer as a tool to perform these concepts.” Also, Examiner refers Applicant to MPEP § 2106.04 (a) III (B): “The use of a physical aid (e.g., pencil and paper or a slide rule) to help perform a mental step (e.g., such as “obtaining first/second site data from the second site APIs”) does not negate the mental nature of the limitation, but simply accounts for variations in memory capacity from one person to another. The use of "physical aids" in implementing the abstract mental process, does not preclude the claim from reciting an abstract idea. See MPEP § 2106.04(a) III C. Thus, Examiner maintains that the claims still recite a mental process.
Examiner refers Applicant to MPEP § 2106.04 (a) (2) II which states that: “the sub-groupings encompass both activity of a single person and activity that involves multiple people, and thus, certain activity between a person and a computer may fall within the "Certain Methods of Organizing Human Activities" groupings. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings”. The steps of Independent Claims 1, 11 and 20 recites abstract ideas under step 2a prong 1 of the 35 U.S.C. § 101 analysis because it consists of limitations that describe mental processes and certain methods of organizing human activities. Under 2025 Federal Circuit standards (e.g., Recentive Analytics v. Fox Corp. (Fed. Cir. 2025)), if the claim merely applies generic machine learning to a data environment without a specific improvement to the model itself, it remains "directed to" an abstract idea.
Applicant’s reliance upon Applicant’s Specification ¶ [0045] is unpersuasive. The assertion that smaller models permit “quick if not real-time monitoring” merely describes an expected benefit arising from dividing a problem into smaller analytical tasks. The claims do not recite a particular machine learning architecture, a novel training methodology, a specific feature-extraction technique, a particular inference mechanism, or any concrete technical mechanism that improves computer performance. Instead, the claims merely state the desired functional result that different models monitor different subsystem combinations. Such result-oriented functional language does not transform the abstract idea into a technological invention.
Similarly, Applicant’s reliance upon Applicant’s Specification ¶ [0051] is insufficient. The use of separate event managers for notification events and action events merely reflects logical categorization and administrative partitioning of tasks. Separating functions into different software modules is a longstanding and conventional software-engineering practice. The claims do not recite any technological implementation details explaining how the event managers improve computer operation at a technical level.
The asserted benefits of “simplified updating and maintenance” are themselves generic software-development advantages that naturally arise whenever software functionality is modularized. Courts have repeatedly held that generic improvements in maintainability, organization, or workflow management do not constitute technological improvements sufficient to avoid abstraction. Further, the dependent limitations concerning restricted access to the action event manager merely recite access-control policies and administrative permissions. Controlling access rights for different users is a known security practice and does not alter the abstract character of the claims.
Accordingly, the claims continue to recite the abstract idea of monitoring operational events, detecting failures using analytical models, and initiating responsive remedial actions within an organizational system environment. In conclusion, these claims are "directed to" an abstract idea because it describes functional steps that can be performed mentally or as part of a human-managed business process. The focus is on the administrative goal (remediation automation) rather than a specific technical improvement to how computers handle information or network interactions. Examiner maintains that Claims 1, 3-11 and 13-22 are directed to abstract ideas under “Mental Processes” or “Certain Methods of Organizing Human Activities” Groupings under 35 U.S.C. § 101 Step 2A Prong 1.
Argument #2:
(B). Applicant argues that Claims 1, 3-11 and 13-22 recite additional elements that integrate the judicial exception into a practical application under revised step 2a prong two of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 5-6, dated 02/19/2026). Examiner respectfully disagrees.
Specifically, Applicant argues that from a computing perspective, using a plurality of machine learning models to monitor events, where each model is trained to monitor events that involve a different combination of the subsystems, improves the functionality of the associated system in general under 35 U.S.C. § 101 step 2a prong 2 (see Applicant Remarks Page 6, dated 02/19/2026). Examiner respectfully disagrees.
In response to Applicant’s Remarks for step 2a prong 2, Examiner notes even assuming the claims recite an abstract idea, the additional claim elements do not integrate the judicial exception into a practical application.
Applicant argues that the claims improve system functionality because of multiple machine-learning models allegedly permit faster monitoring and separate event managers allegedly simplify maintenance and updating. However, the claims do not recite a technological improvement to computer functionality itself. Rather, the claims merely invoke generic computing components as tools to implement the abstract monitoring and remediation process.
The claims do not recite an improved processor operation, an improved memory utilization, an improved networking protocols, an improved database structures, an improved event-transmission mechanisms, an improved machine-learning computation techniques, or any other concrete technological improvement to computer functionality. Instead, the claims merely describe the desired operational outcome that monitoring becomes “quick” and maintenance becomes “simplified.”
The Federal Circuit has repeatedly explained that merely improving the speed or efficiency of an abstract process through automation does not constitute a practical application where the improvement derives from applying generic computing technology to the abstract process itself. Applicant’s argument regarding “small models” is likewise insufficient because the claims do not recite any specific technical implementation producing the alleged performance improvement. The claims merely recite the abstract organizational concept of assigning different models to different subsystem combinations. Such segmentation of analytical tasks reflects data organization and problem decomposition, not a technological improvement to machine-learning functionality itself.
Moreover, the event subsystem limitations do not integrate the alleged exception into a practical application. The recitation of: a dedicated notification event manager, and a dedicated action event manager merely reflects functional segregation of software responsibilities. The claims do not recite any specialized architecture, communication protocol, synchronization mechanism, or low-level event-processing improvement that would improve the functioning of the computer itself.
The claims also fail to impose any meaningful technological limitation on the alleged abstract idea because the claims remain drafted at a high level of functional abstraction. The claims broadly preempt the concept of using multiple analytical models to monitor subsystem interactions and generate remediation events through categorized event handlers. Moreover, with respect to Independent Claims 1, 11 and 20, certain/particular limitations shown recite (1) mere data gathering (e.g., “receive a request to perform an action, wherein completion of the action involves at least one subsystem of a plurality of subsystems comprising employee subsystems and customer subsystems”) (2) mere data outputting/displaying (e.g., “transmit an event, based on the request, to the at least one subsystem, wherein the event subsystem comprises a dedicated notification event manager for managing notification events and a dedicated action event manager for managing action events” & “transmit a remediation event for consumption by the at least one subsystem, wherein the remediation event is one of (i) a notification event handled by the dedicated notification event manager and (ii) an action event handled by the dedicated action event manager”) in which each of these claim limitations reflects mere insignificant extra-solution activities (see MPEP § 2106.05 (g)).
Independent Claims 1, 11 and 20: With respect to reliance on (e.g., “the at least one of a plurality of subsystems” & “event subsystem” & “device” & “at least one subsystem” & “employee subsystems” & “customer subsystems” & “automated remediation platform” & “a plurality of subsystems” & “computer readable medium” & “enterprise system” & “machine learning models”) as additional elements shown in Independent Claims 1, 11 and 20 when considered individually and as an ordered combination (as a whole) in view of these claim limitations, these additional elements do not provide limitations that are indicative of integration into a practical application under step 2a prong 2 due to: (1) the claims as a whole are limited to a particular field of use or technological environment for generating a trigger event for consumption by the event subsystem and transmitting a remediation event for consumption by the at least one of a plurality of subsystems in a banking or financial institution business enterprise environment (see MPEP § 2106.05 (h)) or (2) recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)).
According to USPTO guidance, simply reciting that a computer performs an abstract idea (e.g., monitoring for failures) is not an integration into a practical application; it is merely the automated version of a mental process. Lack of a Specific Technical Solution: These claims recite "machine learning models trained to detect failures" as a "black box". It does not describe a specific technical architecture or a novel mathematical algorithm that improves how the computer itself operates (e.g., reducing memory usage or increasing processing speed). Insignificant Extra-Solution Activity: The steps "generate a trigger event" and "transmit a remediation event" are considered "insignificant extra-solution activities". These are standard follow-up actions once a failure is detected and do not impose a "meaningful limit" on the judicial exception. General Technological Environment: Merely limiting the abstract idea to a specific field, such as an "enterprise system" with "employee and customer subsystems," is a "field of use" restriction. Such limitations do not transform the abstract idea into a patent-eligible practical application. Because the additional elements (processor, memory, event subsystems) are used in their standard, generic capacity to carry out the abstract idea of "automated remediation," they do not provide a technical solution to a technical problem. Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception. The claims merely use computing infrastructure as a drafting effort designed to monopolize the abstract idea itself rather than integrating the idea into a practical technological application. Therefore, at step 2a prong 2, Claims 1, 3-11 and 13-22 are directed to the abstract idea and do not recite additional elements that integrate into a practical application. Examiner maintains that the claims are still patent ineligible under step 2a prong 2 of the 35 U.S.C. § 101 analysis.
Argument #3:
(C). Applicant argues that Claims 1, 3-11 and 13-22 recite additional elements that amount to significantly more than the recited judicial exceptions under revised step 2B of the 35 U.S.C. § 101 analysis (see Applicant Remarks, Pages 6-7, dated 02/19/2026). Examiner respectfully disagrees.
In response, Examiner refers Applicant to Examiner’s 35 U.S.C. 101 analysis section (e.g., Claim Rejections - 35 U.S.C. § 101 section shown below) shown for step 2B particularly for Independent Claims 1, 11 and 20. The claims do not recite additional elements that amount to significantly more than the recited judicial exceptions, because they are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exceptions. The limitations are directed to limitations referenced in MPEP § 2106.05I.A. that are not enough to qualify as significantly more when recited in these claims with the abstract idea which include: (1) adding the words “apply it” (or an equivalent) with the judicial exception, (2) or mere instructions to implement an abstract idea on a computer and providing the results to the user on a computer, and (3) generally linking the use of the judicial exception to a particular technological environment or field of use.
The claims also fail to recite an inventive concept sufficient to amount to significantly more than the abstract idea itself. Applicant argues that the inventive concept resides in: a plurality of machine-learning models trained for different subsystem combinations; and dedicated notification and action event managers. However, these additional elements merely represent conventional computing practices implemented in their ordinary capacities. Partitioning analytical models according to data source, subsystem, or application context represents a routine design choice commonly employed in distributed monitoring systems and machine-learning deployments.
For instance, under 35 U.S.C. § 101, the provided claim steps are patent ineligible because they are "directed to" an abstract idea and lack an "inventive concept" to transform them into a patent-eligible application. This analysis focuses on Step 2B of the Alice/Mayo framework, as updated by 2025 USPTO guidance and Federal Circuit precedent. Step 2B determines whether the claim recites an "inventive concept"—additional elements that amount to significantly more than the judicial exception. The provided steps fail this test for several reasons: Application of Generic Machine Learning: The steps "store a plurality of machine learning models" and "using the plurality of machine learning models, monitor events" describe the application of established ML techniques. In Recentive Analytics, Inc. v. Fox Corp. (Fed. Cir. 2025), the court held that patents merely applying generic machine learning to new data environments—without disclosing specific technical improvements to the models themselves—are ineligible.
Applicant’s assertion that Nowak allegedly fails to disclose the exact claimed arrangement is not dispositive under §101. Eligibility under §101 is distinct from novelty and non-obviousness under §§102 and 103. Examiner respectively disagrees. Examiner submits that the question of novelty and non-obviousness evidence (application of prior art) is not relevant to the question of determining whether the claims as constructed contain an inventive concept. Lastly, Examiner cites the case of (Two-Way Media v. Comcast, (Fed. Cir. 2017)) and the District Court from this case concluded that “the proffered materials are irrelevant to the § 101 motion for judgment on the pleadings. None of the proffered materials addresses a § 101 challenge to claims of the asserted patents. The novelty and non-obviousness of the claims under §§ 102 and 103 does not bear on whether the claims are directed to patent-eligible subject matter under § 101. . . . Because the proffered materials are irrelevant to the instant§ 101 issue, I have not considered them.” The appeal to Federal Circuit Court affirmed the District Court’s ruling that “eligibility and novelty are separate inquiries”. Examiner refers Applicant to BSG Tech LLC v. Buyseasons Inc. decision (Aug. 15, 2018) court case noting that: “But the relevant inquiry is not whether the claimed invention as a whole is unconventional or non-routine. At Step two, we “search for an ‘inventive concept’… that is sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept] itself.” Alice, 134 S. Ct. at 2355 (internal quotation marks omitted) (quoting Mayo, 566 U.S. at 72-73). But this simply restates what we have already determined is an abstract idea. At Alice step two, it is irrelevant whether considering historical usage information while inputting data may have been non-routine or unconventional as a factual matter. As a matter of law, narrowing or reformulating an abstract idea does not add “significantly more” to it. See SAP Am., Inc. v. InvestPic, LLC. No. 2017-2081, slip op. at 14 (Fed. Cir. 2018). Therefore, Applicant’s suggestion that a specific limitation (or the claimed invention as a whole) must be shown to be well-understood, routine and conventional to support the conclusion of subject matter ineligibility is not persuasive.
The claims merely recite generic functional results: storing models, monitoring events, detecting failures, generating trigger events, and transmitting remediation events. The claims do not recite any unconventional technical implementation for performing these functions. Likewise, the recited event managers do not provide significantly more. Separating notification processing from action processing merely reflects conventional software modularization and workflow partitioning. Such architectural decomposition into dedicated handlers or managers is a basic software-engineering technique routinely used in event-driven systems. Applicant’s reliance on simplified maintenance and controlled access likewise fails to establish an inventive concept because: maintainability benefits naturally arise from modular software design; access restrictions are routine security practices; and neither feature reflects a non-conventional technological implementation. Further, when considered as an ordered combination, the claims merely implement the abstract idea using generic computer functionality performing conventional operations in their expected manner: receiving data, analyzing data, categorizing events, applying machine-learning analysis, and issuing responsive actions. The ordered combination therefore does not transform the abstract idea into patent-eligible subject matter
Moreover, with respect to Independent Claims 1, 11 and 20, certain/particular limitations shown recite (1) mere data gathering (e.g., “receive a request to perform an action, wherein completion of the action involves at least one subsystem of a plurality of subsystems comprising employee subsystems and customer subsystems”) (2) mere data outputting/displaying (e.g., “transmit an event, based on the request, to the at least one subsystem” & “transmit a remediation event for consumption by the at least one subsystem”) in which each of these claim limitations reflects mere insignificant extra-solution activities (see MPEP § 2106.05 (g)). Furthermore, these certain/particular limitations claim limitations as demonstrated above for Independent Claims 1, 11 and 20 reflect Well-Understood, Routine and Conventional Activities (WURC) under MPEP § 2106.05 (d) ii: See Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec,838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359,1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Moreover, with respect to Independent Claims 1, 11 and 20, certain/particular limitations shown recite storing data such as (e.g., “store a plurality of machine learning models trained to detect failures, each machine learning model having been trained to monitor events that involve a different combination of the plurality of subsystems”) reflect Well-Understood, Routine and Conventional Activities (WURC) under MPEP § 2106.05 (d) ii: See Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc.,793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115USPQ2d at 1092-93.
Under Step 2B, Claims 1, 3-11 and 13-22 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Examiner maintains that the claims are still patent ineligible under step 2B of the 35 U.S.C. § 101 analysis. According the rejection under 35 U.S.C. § 101 is maintained.
Claim Rejections - 35 USC § 101
10. 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.
11. Claims 1, 3-11 and 13-22 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1, 3-11 and 13-22 are focused to a statutory category namely, a “device” or a “system” (Claims 1, 3-10 and 21), a “method” or a “process” (Claims 11, 13-19 and 22) and a “non-transitory computer-readable medium” or an “article of manufacture” (Claim 20).
Step 2A Prong One: Independent Claims 1, 11 and 20 recites limitations that set forth the abstract idea(s), namely (see in bold except where strikethrough):
“” (see Independent Claim 1);
“” (see Independent Claim 1);
“for at least in part automating remediation in an enterprise , ” (see Independent Claim 20);
“receive a request to perform an action, wherein completion of the action involves comprising employee and customer ” (see Independent Claims 1, 11 and 20);
“transmit an event, based on the request, via an event , wherein the event comprises a dedicated notification event manager for managing notification events and a dedicated action event manager for managing action events” (see Independent Claims 1, 11 and 20);
“at an automated remediation ” (see Independent Claims 1, 11 and 20);
“store a plurality of models trained to detect failures, each model having been trained to monitor events that involve a different combination ” (see Independent Claims 1, 11 and 20);
“using the plurality of models, monitor events sent from tto the event to detect a failure” (see Independent Claims 1, 11 and 20);
“in response to detecting the failure, generate a trigger event for consumption by the event ” (see Independent Claims 1, 11 and 20);
“in response to receiving the trigger event at the event , transmit a remediation event for consumption , wherein the remediation event is one of (i) a notification event handled by the dedicated notification event manager; and (ii) an action event handled by the dedicated action event manager” (see Independent Claims 1, 11 and 20).
Here, for Independent Claims 1, 11 and 20, Examiner points out that the claim limitations of Independent Claims 1, 11 and 20 that the core abstract ideas of these claims are monitoring and responding to events to detect and remediate failures within a business system. This is characterized as an abstract idea because it describes a high-level functional goal—collecting data, analyzing it for specific patterns (failures), and initiating a response—without reciting a specific technical improvement to the underlying computer or machine learning architecture. This falls into the categories of Certain Methods of Organizing Human Activities and Mental Processes. For instance, the step of "receive a request to perform an action": The general concept of receiving information or a command is a fundamental human mental process. The step of "transmit an event, based on the request, to the at least one subsystem via an event subsystem": Communicating or passing a message is a basic mental process and communication method. Additionally, the step of "in response to detecting the failure, generate a trigger event..."pertains to generating an alert or a subsequent action based on an observation or conclusion (detection) is a human decision-making and judgment process and the step of "in response to receiving the trigger event... transmit a remediation event...": pertains to responding to an event with a specific action is a process of decision and communication.
Therefore, these abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Certain Methods of Organizing Human Activities” which pertains to (1) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions) or (2) fundamental economic principles or practices (including mitigating risk) or (3) commercial interactions (including business relations).
Additionally, or alternatively, these abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Mental Processes” which pertains to (4) concepts performed in the human mind (including observations or evaluations or judgments) or (5) using pen and paper as a physical aid, which in order to help perform these mental steps does not negate the mental nature of these limitations. The use of "physical aids" in implementing the abstract mental process, does not preclude the claim from reciting an abstract idea. See MPEP § 2106.04(a) III C.
That is, other than reciting (e.g., “the at least one of a plurality of subsystems” & “event subsystem” & “device” & “at least one subsystem” & “employee subsystems” & “customer subsystems” & “automated remediation platform” & “a plurality of subsystems” & “computer readable medium” & “enterprise system” & “a processor” & “a memory”, etc…), nothing in the claim elements precludes the steps from being performed as “Certain Methods of Organizing Human Activities” which pertains to (1) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions) or (2) fundamental economic principles or practices (including mitigating risk) or (3) commercial interactions (including business relations) and additionally or alternatively as “Mental Processes” which pertains to (4) concepts performed in the human mind (including observations or evaluations or judgments) or (5) using pen and paper as a physical aid.
Therefore, at step 2a prong 1, Yes, Claims 1, 3-11 and 13-22 recites an abstract idea. We proceed onto analyzing the claims at step 2a prong 2.
Step 2A Prong Two: With respect to Step 2A Prong Two of the eligibility inquiry (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claim 1 recites additional elements directed to: (e.g., “a processor” & “a memory”). These additional elements have been considered individually and in combination, but fail to integrate the abstract idea into a practical application because they amount to using computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP § 2106.05(f) and MPEP § 2106.05(h).
Independent Claims 1, 11 and 20: With respect to reliance on (e.g., “the at least one of a plurality of subsystems” & “event subsystem” & “device” & “at least one subsystem” & “employee subsystems” & “customer subsystems” & “automated remediation platform” & “a plurality of subsystems” & “computer readable medium” & “enterprise system” & “machine learning models”) as additional elements shown in Independent Claims 1, 11 and 20 when considered individually and as an ordered combination (as a whole) in view of these claim limitations, these additional elements do not provide limitations that are indicative of integration into a practical application under step 2a prong 2 due to: (1) the claims as a whole are limited to a particular field of use or technological environment for generating a trigger event for consumption by the event subsystem and transmitting a remediation event for consumption by the at least one of a plurality of subsystems in a banking or financial institution business enterprise environment (see MPEP § 2106.05 (h)) or (2) recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)).
Moreover, with respect to Independent Claims 1, 11 and 20, certain/particular limitations shown recite (1) mere data gathering (e.g., “receive a request to perform an action, wherein completion of the action involves at least one subsystem of a plurality of subsystems comprising employee subsystems and customer subsystems”) (2) mere data outputting/displaying (e.g., “transmit an event, based on the request, to the at least one subsystem” & “transmit a remediation event for consumption by the at least one subsystem”) in which each of these claim limitations reflects mere insignificant extra-solution activities (see MPEP § 2106.05 (g)). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception. Therefore, at step 2a prong 2, Claims 1, 3-11 and 13-22 are directed to the abstract idea and do not recite additional elements that integrate into a practical application.
Step 2B: (As explained in MPEP § 2106.05), it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Independent Claim 1 recites additional elements directed to: (e.g., “a processor” & “a memory”). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (computing environment) and does not amount to significantly more than the abstract idea itself. Notably, Applicant’s Specification suggests that the claimed invention relies on nothing more than a computer executing the instructions to implement the invention (see at least Applicant’s Specification ¶ [0107]: “The user device 12 includes a display module 164 for rendering GUIs and other visual outputs on a display device such as a display screen, and an input module 1866 for processing user or other inputs received at the user device 12, .g., via a touchscreen, input button, transceiver, microphone, keyboard.”)
Independent Claims 1, 11 and 20: With respect to reliance on (e.g., “the at least one of a plurality of subsystems” & “event subsystem” & “device” & “at least one subsystem” & “employee subsystems” & “customer subsystems” & “automated remediation platform” & “a plurality of subsystems” & “computer readable medium” & “enterprise system” & “machine learning models”) as additional elements shown in Independent Claims 1, 11 and 20 when considered individually and as an ordered combination (as a whole) in view of these claim limitations, these additional elements do not recite additional elements that amount to significantly more than the recited judicial exceptions under step 2B due to: (1) the claims as a whole are limited to a particular field of use or technological environment for generating a trigger event for consumption by the event subsystem and transmitting a remediation event for consumption by the at least one of a plurality of subsystems in a banking or financial institution business enterprise environment (see MPEP § 2106.05 (h)) or (2) recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)).
Moreover, with respect to Independent Claims 1, 11 and 20, certain/particular limitations shown recite (1) mere data gathering (e.g., “receive a request to perform an action, wherein completion of the action involves at least one subsystem of a plurality of subsystems comprising employee subsystems and customer subsystems”) (2) mere data outputting/displaying (e.g., “transmit an event, based on the request, to the at least one subsystem” & “transmit a remediation event for consumption by the at least one subsystem”) in which each of these claim limitations reflects mere insignificant extra-solution activities (see MPEP § 2106.05 (g)). Furthermore, these certain/particular limitations claim limitations as demonstrated above for Independent Claims 1, 11 and 20 reflect Well-Understood, Routine and Conventional Activities (WURC) under MPEP § 2106.05 (d) ii: See Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec,838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359,1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Moreover, with respect to Independent Claims 1, 11 and 20, certain/particular limitations shown recite storing data such as (e.g., “store a plurality of machine learning models trained to detect failures, each machine learning model having been trained to monitor events that involve a different combination of the plurality of subsystems”) reflect Well-Understood, Routine and Conventional Activities (WURC) under MPEP § 2106.05 (d) ii: See Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc.,793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115USPQ2d at 1092-93.
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent Claims 3-10, 13-19 and 21-22 recite additional elements directed to: (e.g. “enterprise account subsystem” (Dependent Claims 4 and 14) & “wealth management” (Dependent Claims 4 and 14) & “payments” (Dependent Claims 4 and 14) & “an employee subsystem” (Dependent Claims 5 and 15) & “automated event manager” (Dependent Claims 8 and 18 ) & “notification manager” (Dependent Claims 8 and 18) & “new machine learning model” (Dependent Claim 10) & “the plurality of machine learning models” (Dependent Claims 21-22)), which in conjunction with the limitations recite the same abstract idea(s) as shown in Independent Claims 1, 11 and 20 along with further steps/details that reflect “Certain Methods of Organizing Human Activities” which pertains to (1) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions) or (2) fundamental economic principles or practices (including mitigating risk) or (3) commercial interactions (including business relations) and additionally or alternatively as “Mental Processes” which pertains to (4) concepts performed in the human mind (including observations or evaluations or judgments) or (5) using pen and paper as a physical aid.
Dependent Claims 3, 6-7, 9, 13, 16-17 and 19 further narrow the abstract ideas, and are therefore still ineligible for the reasons previously provided in Steps 2A Prong 2 and Step 2B for Independent Claims 1, 11 and 20. Dependent Claims 4-5, 8, 10, 14-15, 18 and 21-22: With respect to reliance on (e.g. “enterprise account subsystem” (Dependent Claims 4 and 14) & “wealth management” (Dependent Claims 4 and 14) & “payments” (Dependent Claims 4 and 14) & “an employee subsystem” (Dependent Claims 5 and 15) & “automated event manager” (Dependent Claims 8 and 18 ) & “notification manager” (Dependent Claims 8 and 18) & “new machine learning model” (Dependent Claim 10) & “the plurality of machine learning models” (Dependent Claims 21-22)) as additional elements shown in Dependent Claims 4-5, 8, 10, 14-15, 18 and 21-22 when considered both individually and as an ordered combination (as a whole) with these recited claim limitations, these additional elements do not provide limitations that are indicative of integration into a practical application under step 2a prong 2 and also do not amount to significantly more than the judicial exceptions under step 2B due to the following: (1) the claims as a whole are limited to a particular field of use or technological environment for generating a trigger event for consumption by the event subsystem and transmitting a remediation event for consumption by the at least one of a plurality of subsystems in a banking or financial institution business enterprise environment (see MPEP § 2106.05 (h)) or (2) recites mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions by providing the results to the user on a computer (see MPEP § 2106.05 (f)).
The additional element of “machine learning models” for these claims does not amount to significantly more than the judicial exception under step 2B due to being expressly recognized as known in the art. See for example., US PG Pub (US 2023/0126193 A1) hereinafter Nowak, et. al. Nowak at ¶ [0053]: This user confirmation and/or user override of remediation action assignment may be feedback data to the machine learning model data store 311 and/or new incident data 301. Data maintained in the new incident data 301 and utilized by the machine learning model 331 described herein may be updated to account for the confirmation data 361. Such an update may include creating, in the database maintaining new incident data, a new database entry comprising the assigned remediation actions and the one or more other assets of the entity that have not been subjected to the specific incident. Nowak at ¶ [0007]: Generally, enable predicting similarities, in incident data, of incidents that, for a first asset of the entity, were reviewed and had remediation actions assigned to them. The remediation actions may be assigned to one or more second assets of the entity. Nowak at ¶ [0028]: Conventional systems are susceptible to failure or repetition of occurrence of a previous incident—for example, an incident that may occur similarly for another entity resource under a similar situation as an incident that had remediation actions assigned to mitigate reoccurrence of that incident may lead to wasted time and resources to address the occurrence of an incident. As such, these conventional techniques leave entities exposed to the possibility of a constant reoccurrence of the incident on the operation of the entity. By providing improved assignment techniques—for example, based on predicting the likely remediation actions to assign to mitigate occurrence of an incident—a proper remediation action assignment can be more accurately determined.
The ordered combination of elements in the Dependent Claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself. Therefore, under Step 2B, Claims 1, 3-11 and 13-22 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 1, 3-11 and 13-22 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Examining Claims with Respect to Prior Art
12. Applicant’s arguments, see pages 8-12 filed on 02/19/2026, with respect to the 35 U.S.C. § 103 Claim Rejections for Claims 1, 3-11 and 13-22 have been fully considered and are found to be persuasive. Therefore, Claims 1, 3-11 and 13-22 overcomes the prior art only. Please note that the following issues still remain: (1) 35 U.S.C. § 101 Claim Rejections for Claims 1, 3-11 and 13-22.
Regarding Independent Claims 1, 11 and 20, there is no disclosure in the existing prior art or any new art that either teaches and/or discloses the sequence operation of features either individually or in combination relating to:
“store a plurality of machine learning models trained to detect failures, each machine learning model having been trained to monitor events that involve a different combination of the plurality of subsystems” (see Independent Claims 1, 11 and 20);
“using the plurality of machine learning models, monitor events sent from the at least one of a plurality of subsystems to the event subsystem to detect a failure” (see Independent Claims 1, 11 and 20);
“in response to detecting the failure, generate a trigger event for consumption by the event subsystem” (see Independent Claims 1, 11 and 20);
“in response to receiving the trigger event at the event subsystem, transmit a remediation event for consumption by the at least one subsystem, wherein the remediation event is one of (i) a notification event handled by the dedicated notification event manager; and (ii) an action event handled by the dedicated action event manager” (see Independent Claims 1, 11 and 20).
The closest prior arts are as follows:
#1) US PG Pub (US 2019/0347627 A1) – “Systems and Methods for Tracking Enterprise Events Using Hybrid Public-Private Blockchain Ledgers”, hereinafter Lin, et. al.
#2) US PG Pub (US 2023/0230063 A1) – “System and Method for Self-Correcting Errors in Data Resource Transfers”, hereinafter Meenavalli, et. al.
#3) US PG Pub (US 2022/0253866 A1) – “Systems and Methods for Monitoring Services Using Smart Contracts”, hereinafter Tedesco, et. al.
Regarding Lin reference, Lin device for at least in part automating remediation in an enterprise system teaches the following:
- receive a request to perform an action (see at least Lin: Fig. 5 & ¶ [0033] & ¶ [0112] & ¶ [0131]. Lin notes that user 110's tracked assets (e.g., units of virtual currency specified within one of more blocks of the hybrid public-private ledger) may represent a triggering event that causes system 140 to initiate a recovery protocol to generate a transaction request to recover the value of the stolen assets (e.g., to transfer the stolen assets back to user 110). User 110 may represent a triggering event that causes system 140 to initiate a series of transaction to distribute of at least a portion of the tracked assets (e.g., through corresponding transaction requests consistent with the disclosed embodiments) to one or more additional owners identified by user 110 and specified within corresponding ones of the identified rules. See also Lin at ¶ [0112]: May perform operations that automatically create a request for a new transaction that returns the stolen Bitcoins™ to user 110. See also Lin at ¶ [0131]: Accounting ledger 500 may store data records indicative of transactions involving user 108, and may include a data record 510 corresponding to user 108's request to schedule an appointment with the loan officer at the physical branch of the financial institution (i.e., “Transaction No. 1” in FIG. 5). Further, in one aspect, data record 502 also identifies a time stamp of user 108's request (e.g., 7:01 p.m. on Nov. 10, 2014), a source of the request (e.g., web-based interface “EasyWeb™”), a target of the request (e.g., the loan officer), and a lead value associated with the request (e.g., $500,000). See also Lin at Fig. 5.), wherein completion the action (see at least Lin: ¶ [0034] & ¶ [0104-0105] & ¶ [0177].) involves at least one subsystem of a plurality of subsystems comprising employee subsystems and customer systems (see at least Lin: ¶ [0034] & ¶ [0104-0105] & ¶ [0177]. Lin teaches that user 110 may specify, as input to the web page or GUI presented by client device 104, one or more individuals that would receive portions of the tracked assets upon completion of one or more tasks and/or in the event of user 110's accidental death. See also Lin at ¶ [0104-0105]: Lin notes that if system 140 were to determine that no modification to the rules engine and/or the list of triggering events is warranted (e.g., step 418; NO), exemplary process 400 may pass forward to step 426, and exemplary process 400 is complete. See also Lin at ¶ [0125]: Further, prior to departing the branch, the customer may inquire about the financial institution's wealth management and commercial banking services. For example, the customer may consider transferring management of a portion of an investment portfolio (e.g., valued at $400,000) to the wealth management unit of the financial institution. See also Lin at Fig. 5.), the plurality of subsystems comprising employee and customer subsystems (see at least Lin: ¶ [0026] & ¶ [0041] & ¶ [0138]. Lin notes that system 140 may be associated with a business entity 150 (e.g., a financial institution) that provides financial accounts, financial services transactions, and investment services one or more users (e.g., customers of the business entity 150). See also Lin at Fig. 1 noting one or more of peer systems 160. See also Lin at ¶ [0041]: Data repository 144 may store customer data that uniquely identifies customers of a financial institution associated with system 140. By way of example, a customer of the financial institution (e.g., users 108, 110, and/or 112) may access a web page associated with system 140 (e.g., through a web server executed by a corresponding front end), and may register for digital banking services and provide data, which may be linked to corresponding ones of users 108, 110, and/or 112). See also Lin at ¶ [0138]: A target line-of-business, data identifying a system or device that captured and transmitted the transaction data (e.g., a MAC address, IP Address, etc.), and employee of the source line-of-business that initiated the particular transaction, an activity associated the transaction (e.g., an acquisition of a mortgage, an opening of a credit line, etc.). See also Lin at Figs. 1-3.)
- transmit an event, based on the request, to the at least one subsystem via an event subsystem (see at least Lin: ¶ [0112] & ¶ [0116] & ¶ [0123-0127]. Lin notes that the police e-crime unit may notify the rules authority of the theft of user 110's Bitcoins™ and destination address associated with the malicious entity (e.g., through a message transmitted to system 140 and received, e.g., in step 408). System 140 may determine that the theft of the Bitcoins™ represents a triggering event included within the generated list (e.g., step 410; YES), and may perform operations that automatically create a request for a new transaction that returns the stolen Bitcoins™ to user 110. System 140 may also perform operations that regenerate a pair of private and public blockchain keys for user 110, which system 140 may transmit to user 110 through any of the secure non-accessible processes outlined above (e.g., in steps 412, 414, and 416). See also Lin at ¶ [0116]: Lin teaches that client devices 102, 104, and/or 106 may execute stored software applications (e.g., mobile applications provided by the rules authority), which may cause client devices 102, 104, and/or 106 to transmit data identifying transactions involving held assets to one or more computer systems across network 120 (e.g., one or more of peer systems 160). See also Lin at Figs. 5-6.)
However, neither Lin, et. al and the other prior art of record do not reach or render obvious the sequence of limitations directed to:
“store a plurality of machine learning models trained to detect failures, each machine learning model having been trained to monitor events that involve a different combination of the plurality of subsystems” (see Independent Claims 1, 11 and 20);
“using the plurality of machine learning models, monitor events sent from the at least one of a plurality of subsystems to the event subsystem to detect a failure” (see Independent Claims 1, 11 and 20);
“in response to detecting the failure, generate a trigger event for consumption by the event subsystem” (see Independent Claims 1, 11 and 20);
“in response to receiving the trigger event at the event subsystem, transmit a remediation event for consumption by the at least one subsystem, wherein the remediation event is one of (i) a notification event handled by the dedicated notification event manager; and (ii) an action event handled by the dedicated action event manager” (see Independent Claims 1, 11 and 20).
Regarding Meenavalli reference, Meenavalli device for at least in part automating remediation in an enterprise system teaches the following:
- at with an automated remediation platform (see at least Meenavalli: Fig. 4 & ¶ [0049]. Meenavalli teaches that developed models are used by the machine learning engine 261 for intelligent diagnosis 410 as new transaction data is provided via the production error dataset 408. The machine learning engine 404 will select a rule based on its training in order to attempt to automatically resolve the error, as indicated by rule selection 412.);
- store a plurality of machine learning models (see at least Meenavalli: ¶ [0034-0036] & ¶ [0048-0049]. Meenavalli notes that the machine learning dataset(s) 262 may also contain data relating to user activity or device information, which may be stored in a user account managed by the managing entity system. The machine learning engine 261 may be a single-layer recurrent neural network (RNN) which utilizes sequential models to achieve results in audio and textual domains. See also Meenavalli at ¶ [0034]: The machine learning engine 261, as well as store the files in a catalog of data files in the data repository 256 or database 300 (e.g., files may be catalogued according to any metadata characteristic, including descriptive characteristics such as source, identity, content, data field types, or the like, or including data characteristics such as file type, size, encryption type, obfuscation, access rights, or the like). The machine learning engine 261 and machine learning dataset(s) 262 may store instructions and/or data that cause or enable the resource transfer system 200 to generate, based on received information, new output in the form of prediction, current status, analysis, or the like of one or more communications, network activity data streams, or data field patterns. See also Meenavalli at ¶ [0048-0049]: The training dataset 402 is utilized by a training and development environment 420 in order to develop, train, and test models for intelligent self-correction of resource transfer errors.) trained to detect failures (see at least Meenavalli: ¶ [0034] & ¶ [0048] & ¶ [0061] & Fig. 7. Meenavalli notes that the machine learning engine 261 and machine learning dataset(s) 262 may store instructions and/or data that cause or enable the resource transfer system 200 to determine recommended actions for resolution of resource transfer failure or partial failure, determine access limitations or authorization privileges, or determine prophylactic actions to be taken to benefit one or more specific users or systems for their protection or privacy. See also Meenavalli at ¶ [0048]: The training dataset 402 may be considered at least a subset of machine learning dataset(s) 262. The training dataset 402 may contain a multitude of data regarding transactions which succeeded or failed for different reasons, as well as the steps necessary to resolve such errors. Over time, the machine learning engine 261 is trained during model development 404 in order to intelligently recognize and group similar transactions, contextualize situational data, and recommend solutions on an issue-by-issue basis for future failed transactions or authorizations, resulting in an intelligent recommendation model. See also Meenavalli at Fig. 7 & ¶ [0061]: As shown in block 720, the process continues by utilizing the data set for iteratively training a model using a machine learning process, as described with regard to the machine learning engine 261 of the resource transfer system 200. This results in an intelligent recommendation model which can receive data from the production error dataset 408 in order to analyze real-time payment failures.), each machine learning model having been trained to monitor events that involve a different combination of the plurality of subsystems (see at least Meenavalli: ¶ [0036-0037] & ¶ [0055-0056] & Figs. 1-4.).
However, neither Meenavalli, et. al and the other prior art of record do not reach or render obvious the sequence of limitations directed to:
“store a plurality of machine learning models trained to detect failures, each machine learning model having been trained to monitor events that involve a different combination of the plurality of subsystems” (see Independent Claims 1, 11 and 20);
“using the plurality of machine learning models, monitor events sent from the at least one of a plurality of subsystems to the event subsystem to detect a failure” (see Independent Claims 1, 11 and 20);
“in response to detecting the failure, generate a trigger event for consumption by the event subsystem” (see Independent Claims 1, 11 and 20);
“in response to receiving the trigger event at the event subsystem, transmit a remediation event for consumption by the at least one subsystem, wherein the remediation event is one of (i) a notification event handled by the dedicated notification event manager; and (ii) an action event handled by the dedicated action event manager” (see Independent Claims 1, 11 and 20).
Regarding Tedesco reference, Tedesco device for at least in part automating remediation in an enterprise system teaches the following:
- using the plurality of machine learning models (see at least Tedesco: ¶ [0043-0044]. Tedesco notes that during the training mode, the pattern recognizer may previously identified service deficiencies and applied remedial actions to train one or more ML models. Once the one or more ML models are trained, the pattern recognizer may enter processing mode, where input data (e.g., service performance status reports) is compared against the trained ML models in the pattern recognizer.), monitor events sent from the at least one subsystem to the event subsystem (see at least Tedesco: Figs. 1-5 & ¶ [0055-0056] & ¶ [0058]. Tedesco notes that the service performance data 514 may be captured and transmitted back to the blockchain from consumer 504. In examples, each hardware unit in the service system may be configured with a monitoring software that transmits the data back to the blockchain. In particular, the service performance data 514 may be transmitted first to a DeFi application via DeFi API 508, where the application receives the service performance data, analyzes the service performance data, and writes the service performance data to the blockchain 506 at 516. In the example where a service deficiency is identified and causes a smart contract trigger, one result of the rule trigger may be to transfer funds from the service provider to the consumer, which is illustrated at 520 in FIG. 5, where the service provider transmits funds to consumer 504. The funds may be in the form of dollars transferred from one checking account to another, or in some examples, the funds may be in the form of cryptocurrency that is transferred from one wallet to another wallet. In either scenario, the transfer of assets is recorded to the blockchain 506.) to detect a failure (see at least Tedesco: ¶ [0017] & ¶ [0042] & ¶ [0051-0054]. Tedesco notes that the monitoring of the services can comprise monitoring usage by a consumer, as well as the performance of the service. By monitoring the performance of the service, the service-provider may be able to better assess when a potential technological issue is present and how to remedy it. See at least Tedesco at ¶ [0042]: The pattern recognizer within service performance module 325 may be configured to identify and extract certain features from the identified service deficiency, such as duration of the deficiency, which piece of hardware in the system is at issue, any error statements, etc. See at least Tedesco at ¶ [0051]: Here, the service performance data may be analyzed to check if there are any deficiencies. For example, if an error message is included in the service performance data, then the error message will be noted at step 408, which may prompt a triggering event (in step 410). See at least Tedesco at ¶ [0054]: At step 414, a service deficiency is identified in the service performance data from step 406 and analyzed in step 408. For instance, an error message may be flagged in step 408, and the system may conclude this is associated with a service deficiency in step 414. Once the service performance deficiency is identified in step 414, the system may apply at least one machine-learning model to the service performance deficiency. See also Tedesco at Figs. 1-5.);
- in response to detecting the failure (see at least Tedesco: ¶ [0042] & ¶ [0051-0054] & Fig. 4.), generate a trigger event for consumption by the event subsystem (see at least Tedesco: ¶ [0042] & ¶ [0051]. Tedesco teaches that the pattern recognizer within service performance module 325 may be configured to identify and extract certain features from the identified service deficiency, such as duration of the deficiency, which piece of hardware in the system is at issue, any error statements, etc. See also Tedesco at ¶ [0051]: “Here, the service performance data may be analyzed to check if there are any deficiencies. For example, if an error message is included in the service performance data, then the error message will be noted at step 408, which may prompt a triggering event (in step 410)”. See also Tedesco at ¶ [0054]: For instance, an error message may be flagged in step 408, and the system may conclude this is associated with a service deficiency in step 414. Once the service performance deficiency is identified in step 414, the system may apply at least one machine-learning model to the service performance deficiency. See also Tedesco at Figs. 1-3.);
- in response to receiving the trigger event at the event subsystem (see at least Tedesco: ¶ [0051-0054] & Fig. 4. Tedesco notes that if an error message is included in the service performance data, then the error message will be noted at step 408, which may prompt a triggering event (in step 410). After the service performance data is analyzed at 408, a trigger event may be received by the system at step 410. The trigger event may be due to a comparison between the service performance indicators and smart contract rules. For example, if the SLA smart contract specified that the consumer would pay $X per month for access to 50 television channels, but the service performance indicators only show availability of 45 television channels for a certain period of time, the SLA smart contract may have an automatic trigger for reimbursing the consumer (e.g., transfer 10% of $X back to consumer because 10% of channels were unavailable).), transmit a remediation event for consumption by the at least one subsystem (see at least Tedesco: ¶ [0054] & Figs. 4-5. Tedesco notes that steps 414 and 416 are optional steps and are implicated when service deficiencies are detected. At step 414, a service deficiency is identified in the service performance data from step 406 and analyzed in step 408. For instance, an error message may be flagged in step 408, and the system may conclude this is associated with a service deficiency in step 414. Once the service performance deficiency is identified in step 414, the system may apply at least one machine-learning model to the service performance deficiency. The output of this analysis may be a suggested remedial action that is provided at step 416. The suggested remedial action may be supplied back to the service provider to consider and then permit to be applied to the system, or, in some examples, the remedial action may be suggested by the system and then automatically applied without further input by the service provider. See also Tedesco at Fig. 5 step 524: “Provide remediation suggestion for the service.”).
However, neither Tedesco, et. al and the other prior art of record do not reach or render obvious the sequence of limitations directed to:
“store a plurality of machine learning models trained to detect failures, each machine learning model having been trained to monitor events that involve a different combination of the plurality of subsystems” (see Independent Claims 1, 11 and 20);
“using the plurality of machine learning models, monitor events sent from the at least one of a plurality of subsystems to the event subsystem to detect a failure” (see Independent Claims 1, 11 and 20);
“in response to detecting the failure, generate a trigger event for consumption by the event subsystem” (see Independent Claims 1, 11 and 20);
“in response to receiving the trigger event at the event subsystem, transmit a remediation event for consumption by the at least one subsystem, wherein the remediation event is one of (i) a notification event handled by the dedicated notification event manager; and (ii) an action event handled by the dedicated action event manager” (see Independent Claims 1, 11 and 20).
Therefore, when taken as a whole, the claims are not rendered obvious as the available prior art does not suggest or otherwise render obvious the noted features nor do the available art suggest or otherwise render obvious further modification of the evidence at hand. Such modification would require substantial reconstruction relying solely on improper hindsight bias, and thus would not be obvious.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DERICK HOLZMACHER whose telephone number is (571) 270-7853. The examiner can normally be reached on Monday-Friday 9:00 AM – 6:30 PM EST.
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, Brian Epstein can be reached on 571-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-270-8853.
Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/DERICK J HOLZMACHER/ Patent Examiner, Art Unit 3625A
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625
Prosecution Insights