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 .
2. The following office action is a Final Office Action in response to the communications received on 06/30/2026.
Claims 1, 4, 5, 8, 11, 15-18 and 20 have been amended; claims 2, 6, 9 are canceled. Therefore, claims 1, 3-5, 7, 8 and 10-21 are currently pending in this application.
Response to Amendment
3 The amendment to claim 1 is sufficient to overcome the rejection set forth in the previous office action under section §112(b). Accordingly, the Office withdraws the above rejection.
Claim Rejections - 35 USC § 101
4. Non-Statutory (Directed to a Judicial Exception without an Inventive Concept/Significantly More).
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, 3-5, 7, 8 and 10-21 are rejected under 35 U.S.C.101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The current claims fall within one of the four statutory categories of invention (MPEP 2106.03).
Step 2A [Wingdings font/0xE0] Prong-One:
The claim(s) recite a judicial exception, namely an abstract idea, as shown below:
Considering each of claims 1, 15 and 20 as representative claims, the following limitations recite an abstract idea:
— regarding claim 1, the following claimed limitations recite an abstract idea:
[collect] input data;
determine an identity of a user based on the input data;
generate, based on the identity of the user, a competency baseline using historical data associated with the user, the competency baseline based at least partly on amount of user experience;
[collect] intervention data;
detect an intervention event in connection with user activity of the user involving cash management from intervention data;
determine the intervention event is a new action based on the competency baseline [by]:
comparing a number of previous occurrences of the user activity being performed with an intervention threshold; and
determine the intervention invent is the new action when the number is less than the intervention threshold;
prevent performance of new action;
process one or more cash management [tasks] using a first set of funds, the one or more cash management [tasks] are excluded from accounting as a site transaction and a transaction report;
allow performance of new action.
— regarding claim 15, the following claimed limitations recite an abstract idea:
determine an identity of a user;
generate, based on the identity of the user, a competency baseline using historical data associated with the user, the competency baseline based at least partly on an amount of user experience;
[collect] an intervention data;
detect an intervention event in connection with user activity of the user involving cash management from intervention data;
determine the intervention event is a new action based on the competency baseline [by]:
comparing a number of previous occurrences of the user activity being performed with an intervention threshold; and
determining the intervention event is the new action based on when the number is less than a threshold;
when the intervention event is determined to be the new action, prevent performance of the new action;
process one or more cash management [tasks], the one or more cash management tasks are excluded from accounting as a site transaction;
allow performance of the new action.
— regarding claim 20, the following claimed limitations recite an abstract idea:
determine an identity of [a] user;
determine an authentication level for the user based on the identity of the user;
generate, based on the identity of the user, a competency baseline using historical data associated with the user, the competency baseline based at least partly on an amount of user experience;
[collect] intervention data;
detect an intervention event in connection with user activity of the user involving cash management;
determine the intervention event is a new action based on the competency baseline [by]:
comparing a number of previous occurrences of the user activity being performed with an intervention threshold;
determine the intervention event is the new action when the number is less than the intervention threshold,
in response to determining the intervention event is the new action, prevent performance of the new action;
process one or more cash management [tasks] using a first set of funds, the one or more cash management [tasks] are excluded from accounting as a site transaction and a transaction report;
allow performance of the new action.
Thus, the limitations identified above recite an abstract idea since the limitations correspond to certain methods of organizing human activity or mental processes, which are part of the enumerated groupings of abstract ideas identified according to the current eligibility standard (see MPEP 2106.04(a)).
For instance, the current claims correspond to managing personal behavior, such as teaching. It is worth noting—per the original disclosure—that: (i) “intervention data” corresponds to the user’s activity, (ii) “intervention event” corresponds to an error or an insufficiency determined regarding the user’s activity, and (iii) the “one or more cash management operations”, which are being processed “using a first set of funds”, are training materials; and accordingly, based on the evaluation of one or more parameters relevant to the user (e.g., data that indicates: the user’s identity, the user’s competency baseline, and/or the user’s actions etc.), the user is presented with the training materials above when a cash management task, which the user is attempting to perform, is determined to be a new task for the user (also see the specification: [0005]; [0017]; [0026], [0027], [0043], etc.).
The observation above confirms that the current claims recite an abstract idea; such as, the subgrouping managing personal behavior (e.g., teaching), under the group certain methods of organizing human activity.
Similarly, given one or more of the current claimed limitations that recite the process of: determining an identity of a user based on input data; determining an intervention event is a new action based on a competency baseline; comparing a number of previous occurrences of the user activity being performed with an intervention threshold; determining the intervention event is a new action when the number is less than the intervention threshold, etc., the current claims also recite a mental processe; such as an evaluation, a judgment and/or an observation process, etc.
Step 2A [Wingdings font/0xE0] Prong-Two:
The claim(s) recite additional elements, wherein one or more computing systems (e.g., an intervention computing system, an authentication systems), including a cash management system that comprises one or more sensors (one or more of a microphone, an imager, and a touchscreen sensor), which is utilized to facilitate the recited functions/steps regarding one or more of the following: generating a trained neural network (e.g., generating, by an intervention computing system, a learned neural network using at least one of user inefficiency data, error data, or transaction data); receiving user input (e.g., “receiving. by an authentication system, input data from an input system”); determining a user’s identify and subsequently generating a competency baseline (e.g., “determining, by the authentication system, an identity of a user based on the input data; generating, based on the identity of the user, a competency baseline using historical data associated with the user, the competency baseline based at least partly on an amount of user experience”); capturing data (e.g., capturing intervention data using one or more sensors of an interactive interface computing system . . . the one or more sensors including a microphone, an imager, and a touchscreen sensor); transmitting data (e.g., “transmitting the intervention data to the intervention system”); detecting an intervention event (e.g., “detecting, by the intervention computing system using the learned neural network while the cash management system is operating in a first mode, an intervention event in connection with user activity of the user involving the cash management system from the intervention data in real time); determining the state of the detected intervention event (e.g., “determining the intervention event is a new action based on the competency baseline, the determining including: comparing a number of previous occurrences of the user activity being performed with an intervention threshold; and determining the intervention event is the new action when the number is less than the intervention threshold); generating/transmitting a first command to cash management system (e.g., “generating, in real-time, a first command by the intervention computing system in response to determining the intervention event is the new action using the intervention data captured in real-time”; transmitting the first command to the cash management system to cause the cash management system to transition from the first mode to a second to prevent performance of the new action ); performing cash management tasks (e.g., “processing, at the cash management system, one or more cash management operations using first set of funds, the one or more cash management operations are excluded from accounting as a site transaction and a transaction report for the cash management system, while the cash management system is in the second mode”); generating and transmitting a second command to the cash management system (e.g., “generating a second command by the intervention computing system; transmitting the second command to the cash management system to cause the cash management system to transition to the first mode and allow performance of the new action”), etc.
However, the claimed additional elements fail to integrate the abstract idea into a patent-eligible practical application since the additional elements are utilized merely as a tool to facilitate the abstract idea. Thus, when each claim is considered as a whole, the additional elements fail to impose meaningful limits on practicing the abstract idea. Although each of the claims recites at least one sensor that can be a touchscreen, a microphone or an imager, the sensor(s) is used merely for data gathering purpose; and therefore, this corresponds to insignificant extra-solution activity.
Thus, when each of the claims is considered as a whole, none of the claims provides an improvement over the relevant existing technology.
The observations above confirm that the claims are indeed directed to an abstract idea.
Step 2B
Accordingly, when the claim(s) is considered as a whole (i.e., considering all claim elements both individually and in combination), the claimed additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to “significantly more” than the abstract idea itself (also see MPEP 2106). The claimed additional elements are directed to conventional computer elements, which are serving merely to perform conventional computer functions.
Accordingly, when each of the current claims is considered as a whole (e.g., see the discussion under Prong Two above regarding such consideration of the claim as a whole), none of the claims recites an element—or a combination of elements—directed to an inventive concept.
In addition, per the original disclosure, the current claimed invention is directed to a conventional and generic arrangement of the additional elements. For instance, the disclosure describes a computer system (FIG 5, label “500”), which is utilized to implement the various systems and methods of the current invention; and wherein this
computer system encompasses a conventional computer, which includes commercially available conventional devices—such as, personal computers, laptops, mobile phones, tablets, etc. (see specification: [0056] to [0059]). Thus, the claimed additional elements are directed to a well-understood, routine, conventional activity in the art.
Note also that the use of one or more well-known algorithms, such as a trained neural network, to detect—from activity data collected regarding a user—one or more issues or errors (i.e., an intervention event); and subsequently generating one or more pertinent training materials to the user, etc., is directed to a well-understood, routine, conventional activity in the art (e.g., see US 2010/0299314; US 2006/0256953, etc.).
The observation above confirms that the current claimed invention fails to amount to “significantly more” than an abstract idea.
It is worth noting that the above analysis already encompasses each of the current dependent claims (i.e., claims 3-5, 7, 8, 10-14, 16-19 and 21). Particularly, each of the dependent claims also fails to amount to “significantly more” than the abstract idea since each dependent claim is directed to a further abstract idea, and/or a further conventional computer element/function utilized to facilitate the abstract idea.
Thus, none of the current claims, when considered as a whole, implements an element—or a combination of elements—directed to an inventive concept.
► Applicant’s arguments directed to section §101 have been fully considered (the arguments filed on 06/30/2026). However, the arguments are not persuasive at least for the following reasons:
Firstly, while attempting to disregard the basic fact from the claims, Applicant is asserting that “[t]he Office characterizes the claims at a high level as allegedly presenting training materials. Applicant respectfully submits that the claimed subject matter does not involve presenting any training materials. Instead, the claims are directed to cash management system mode-control and transaction isolation in response to detection of certain events. The Office's characterization of the claims is not consistent with Office guidance and the MPEP” (emphasis added). Applicant has also attempted to summarize the limitations that current claim is reciting in an attempt to substantiate the above assertion, “[t]he claims recite a concrete control architecture for materially changing how a cash-management device operates in real time . . . the cash-management system, in response to that command, is transitioned out of the transactional mode in which operations are processed using funds excluded from site-transaction accounting and reporting. A second command is generated and used to control the cash-management system to return to transactional operation” (emphasis added).
However, Applicant appears to fail to notice that the claimed method as a whole is directed to a process for improving the user’s skill—such as, training the user how to correctly perform a transaction task when using a cash management system (e.g., an ATM or a self-checkout kiosk, etc.). Thus, when considering Prong One of Step 2A, it is worth nothing that a human—such as a teacher—can train the user how to perform a given transaction. For instance, based on evaluating the user’s history (e.g., data recorded in a diary, which shows the number of times the user performed one or more transaction tasks, etc.), the teacher easily recognizes whether the current transaction task, which the user is attempting to accomplish, is new to the user or not. Accordingly, once the teacher realizes that the current transaction is new to the user, the teacher provides proper training to the user before allowing the user to proceed with the transaction. Of course, once the user completes the training, the user can perform the transaction, etc.
The test above confirms that the claims indeed recite an abstract idea. When considering Prong Two of Step 2A, it is evident that the claimed method implements a computer-based system that involves one or more units. However, when each claim is considered as a whole, the above system is used—merely as a tool—to facilitate the delivery of training to the user. For instance, the system implements a neural network, which is trained based on data elements that signify the skill level of the user (e.g., user inefficiency data, error data, transaction data, etc.). Accordingly, as the user is using the cash management system, the system first determines the identity of the user based on the user’s input; and subsequently, it determines—based on stored historical data—the user’s competency baseline, which indicates the user’s experience level. Thus, as the user is attempting the transaction task, the system captures and analyzes the user’s activity in order to determine—based on the competency baseline above—whether the task that the user is attempting to perform is a new task for the user (i.e., “comparing a number of previous occurrences of the user activity being performed with an intervention threshold; and determining the intervention event is the new action when the number is less than the intervention threshold”). Of course, once it determines that the transaction task is new to the user, it switches to a training mode (the so-called “second mode”) in order to provide the user with training regarding the transaction task (i.e., “processing, at the cash management system, one or more cash management operations using a first set of funds, the one or more cash management operations are excluded from accounting as a site transaction and a transaction report for the cash management system, while
the cash management system is in the second mode”). Once the above training is concluded, it switches back to its initial/transaction mode (the so-called “first mode”), so that the user proceeds with the transaction.
The observation above once again confirms that there is no technological improvement with respect to any one or more features of the claimed (and the disclosed) implementation—i.e., no technological improvement with respect to: (a) the neural network (or machine-learning) training scheme, (b) the user authentication scheme, (c) the sensor or the detection scheme, etc. Instead, the clamed (and the disclosed) system is being used—merely as a tool—to facilitate the delivery of training to the user, so that the user understands how to perform the transaction correctly.
Consequently, Applicant’s conclusory assertion, “[t]he claims are not directed to teaching a user how to use a machine more skillfully. Instead, the claimed subject matter prevents misuse of the cash management system by automatically transitioning, in real time, into a materially different operational state, with different accounting treatment, based on live sensor-derived data and user-specific history” (emphasis added), is not persuasive. In fact, besides contradicting the current claims, Applicant also appears to contradict the crux of the disclosed system/method. For instance, the specification states that “[a]spects of the present disclosure relate to systems and method for improving an operation of a cash management system by one or more users and more particularly to a guided help and training artificial intelligence (Al) that detects user competencies through user interaction with the cash management system, adjusts a behavior of the cash management system to improve user competency, provides automatic intervention for the user, and/or reports user competency levels to a training administrator or vendor with recommendations for supplemental training” ([0002], emphasis added). In this regard, Applicant appears to contradict the above fundamental fact.
Although the level of details and/or format of the guidance may differ from one system to another, it is worth noting that existing cash management systems (e.g., ATMs, self-checkout kiosks, etc.) automatically provide guidance to the user (e.g., text, audio, and/or image prompts, etc.) when they detect—based on captured errors—that the user is incorrectly performing one or more transaction tasks. Of course, due to the errors, the user is essentially prevented from performing the transaction until the user is able to correctly perform the required steps by following the guidance. Accordingly, when applying Applicant’s theory to such existing systems, one can easily conclude that existing cash management systems already adapt their behavior in order to (a) improve the user’s competency, and/or (b) prevent the user from misusing the system, etc. Nevertheless, regardless of the theoretical conclusions being made, the fact remains the same. In particular, neither the current claims nor the original disclosure implements an element—or a combination of elements—that is directed to a technological improvement. Consequently, Applicant’s assumption regarding the alleged “concrete control architecture”, which is assumed to “materially change[] how a cash-management device operates in real time”, has nothing to do with technological improvement.
Applicant further asserts, “the rejection overgeneralizes the claims detached from the recited claim language. The claims are not directed to teaching, managing personal behavior, or a mental evaluation of user skill. Rather, the claims use real-time sensor-derived intervention data and a learned neural network to detect a qualifying intervention event, then generate commands that cause a material change to how a cash management system operates by transitioning it out of transactional mode, thereby preventing performance of a new action in the transactional environment, processing operations excluded from site-transaction accounting and reporting, and later returning the machine to transactional operation. The asserted ‘mental process’ characterization is incorrect because the claimed machine-state control cannot practically be performed in the human mind, and any alleged abstract idea is integrated into a practical application through a technological control loop that materially changes operation of a cash management machine” (emphasis added).
However, besides mischaracterizing the Office’s analysis, Applicant also appears to fail to properly apply the inquiry under Prong One. For instance, unlike Applicant’s assertion, the Office’s analysis is based on the features that the claims are positively reciting; and therefore, Applicant’s conclusory assertion, “the rejection overgeneralizes the claims detached from the recited claim language”, is inaccurate. In fact, Applicant’s failure to quote any specific section from the office action confirms the inaccuracy of Applicant’s conclusory assertion. Moreover, Applicant appears to fail to address the core issue regarding eligibility. In particular, except for the attempt made to summarize the claimed components and/or their purposes (e.g., the use of sensors and neural networks to detect intervention event; the transition of the cash management system to a training mode; performing operations while in training mode; switching back to a transaction mode, etc.), Applicant fails to demonstrate whether any one—or a combination—of the elements is providing a technological improvement. Instead, while incorrectly blending the inquiries of Prong One and Prong Two, Applicant concludes that “the claimed machine-state control cannot practically be performed in the human mind” (emphasis added). However, unlike Applicant’s assertion above, the eligibility test regarding mental process (Prong One) does not consider any of the claimed computer elements, which are part of the additional elements (Prong Two). Instead, while excluding the claimed computer elements, Prong One requires one to identify only the limitations that recite the abstract idea (e.g., see MPEP 2106.07(a)). Thus, when the eligibility test is correctly applied, it is evident that a human—such as a teacher—can mentally (and/or using a pen and paper) perform the limitations that recite the abstract idea (e.g., determining an identity of a user based on input data; determining an intervention event is a new action based on a competency baseline; comparing a number of previous occurrences of the user activity being performed with an intervention threshold, etc.). In contrast, while erroneously relying on the additional elements, Applicant is attempting to challenge the Office’s findings presented under Prong One. Consequently, Applicant’s arguments are not persuasive. Of course, the same is true regarding Applicant’s conclusory assertion, “any alleged abstract idea is integrated into a practical application through a technological control loop that materially changes operation of a cash management machine” (emphasis added). In particular, Applicant fails to demonstrate whether the alleged “technological control loop”, which is assumed to “materially change[] operation of a cash management machine”, is an advance over the relevant existing technology. Moreover, unlike Applicant’s assumption, the operation of the cash management system does not “materially change” since it is still a cash management system and it is operating in the same fashion (e.g., collecting input data from the user; analyzing the user’s input data using an algorithm; generating/presenting one or more pertinent results, etc.). Of course, given the lack of technological improvement over the relevant existing technology, none of the current claims—when considered as a whole—integrates the abstract idea into a patent-eligible practical application (i.e., none of the current claims implements an element—or a combination of elements—that provides a technological improvement over the relevant existing technology). Consequently, Applicant’s arguments are not persuasive.
Applicant has also attempted to identify some sections from the specification (e.g., [0052], [0053], etc.), which supposedly demonstrate the technological improvement that Applicant is alleging. Applicant asserts, “[t]he application's technical field is ‘systems and methods for improving an operation of a cash management system,’ and specifically describes automated control that ‘adjusts a behavior of the cash management system,’ provides ‘automatic intervention,’ and controls access and operation of the system based on detected events. The specification states that the intervention system provides a system for detecting intervention events and ’controlling access to the cash management system 102.’ . . . The specification further describes the use of intervention data and a neural network to control the system in real time” (emphasis added).
However, Applicant appears to cherry-pick some lines out of context in an attempt to substantiate the alleged technological improvement. For instance, the first line, “systems and methods for improving an operation of a cash management system”, which Applicant identified from the “TECHNICAL FIELD” section of the specification (¶[0002] of the specification), is incomplete. For instance, the first two lines of the actual paragraph state, “[a]spects of the present disclosure relate to systems and method for improving an operation of a cash management system by one or more users” (emphasis added). Similarly, the last four lines of the actual paragraph state, “. . . the cash management system, adjusts a behavior of the cash management system to improve user competency, provides automatic intervention for the user, and/or reports user competency levels to a training administrator or vendor with recommendations for supplemental training” (emphasis added).
Accordingly, it is evident from the actual paragraph or description in the specification that no technological improvement is implemented with respect to the cash management system itself (e.g., no improvement with respect to the user detection scheme; no improvement with respect the user verification or authentication scheme, etc.). Instead, it is providing training to the user in in order to improve the user’s competency level. In fact, none of the paragraphs in the original specification, including [0052] and [0053], demonstrates any technological improvement. Consequently, Applicant’s arguments are not persuasive.
Secondly, regarding Prong One, Applicant is asserting that “[t]he Office's Prong One analysis is inconsistent with the claim language and omits material language from the limitations. The claims require real-time acquisition of sensor data while a cash management system is operating in transactional mode, learned neural-network detection of an intervention event from that data, generation of a first command, and a resulting transition of the cash management system out of transactional mode so that operations are processed without being accounted for as site transactions. Those relationships among sensor input, neural-network detection, machine-state control, and accounting treatment cannot practically be performed in the human mind . . . Applicant has clarified the claimed subject matter involving use of a detected intervention event to control operation of a particular cash management machine and materially alter how that machine processes and records operations . . .” (emphasis added).
However, quite similar to the point made above, here also Applicant once again fails to properly apply the inquiry under Prong One. It is once again worth noting that Prong One does not require any of the computer elements to be considered. Instead, it requires one to identify only the limitations that recite the abstract idea (e.g., see MPEP 2106.07(a)). In contrast, while emphasizing the computer elements, Applicant is repeatedly attempting to challenge the Office’s finding under Prong One. Consequently, Applicant’s arguments are not persuasive.
Similarly, regarding Prong Two, Applicant argues, “[t]he claims recite controlling the machine's operational state and the accounting treatment of subsequent operations. The claims use real-time sensor input and trained-model event detection to drive a specific machine-control sequence in a specific device environment . . . The specification identifies technical improvements in cash-management-system operation by controlling material operations of the system to prevent misuse of the system. The claims reflect that disclosed improvement by reciting the real-time sensor capture, neural-network event detection, generation of the first and second commands, transition between operating modes, and exclusion of operations from site accounting and reporting based on the operating mode” (emphasis added).
However, here also Applicant appears to be repeating the same arguments that are already addressed above. In particular, Applicant is summarizing the components of the system and/or their function. However, such summary does not necessarily demonstrate a technological improvement over the relevant existing technology. In particular, as already pointed out above, existing cash management systems (e.g., ATM, self-checkout kiosks, etc.) typically present guidance to the user when the user makes an error during a transaction. Of course, such guidance is itself a form of training to improve the user’s competency. Nevertheless, even assuming arguendo that the training material, which the claimed (and disclosed) method is providing to the user, is different from the prior art, this still does not necessarily mean a technological improvement. Instead, it is representing a new abstract idea. This is once again because a claim for a new abstract idea is still an abstract idea. See Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016). Consequently, Applicant’s arguments are not persuasive.
Applicant also fails to properly construe the Office’s previous remark. Applicant asserts, “[t]he Office argues that the system pauses the ordinary transaction path and thus could be seen as ‘inefficient’ from one perspective because the user spends more time on the device. Office Action, at pp. 14-15. This position by the Office concedes that the claimed subject matter transforms how the system operates and results in an improved output by the system by preventing lower quality inputs. It is immaterial whether this improvement is viewed as more efficient. Subject matter eligibility does not ask whether the resulting machine behavior is more efficient. It asks whether the claim is directed to a technological improvement or merely to an abstract idea. Moreover, the claimed improvement effectively increases efficiency by preventing bad inputs that would later need correction” (emphasis added).
However, the Office’s analysis is pointing out a fact that a skilled person readily recognizes. In particular, if a given system is providing detailed training to a user, as opposed to a quick guidance intended to resolve the detected error, the system may still be considered inefficient. Again, this is a fact that a PHOSITA readily recognizes regardless of Applicant’s speculation. Moreover, the process of detecting an error or a new task that the user is performing, and subsequently providing a relevant training to the user based on such detection, does not even remotely suggest a transformation process that signifies a technological improvement. Consequently, Applicant’s theory in this regard also fails to demonstrate whether the claimed/disclosed implementation is providing a technological improvement.
Moreover, again unlike Applicant’s theory, even basic common sense dictates that inefficiency of a system is one or the fundamental attributes that indicates a lack of technological improvement. In fact, several court decisions, such as Enfish, consider efficiency of a system as one of the factors that indicate technological improvement.
Consequently, Applicant’s attempt to simply dismiss the consideration of efficiency during the eligibility analysis does contradict the basics of the eligibility standard.
Thirdly, Applicant has also attempted to rely one Claim 3 of Example 47 to substantiate the alleged technological improvement. Applicant asserts, “the trained neural network likewise does not merely identify a condition and display information. The detected new-action intervention event is used to generate a command that transforms the operating state of a particular system . . . a technical improvement that prevents misuse of a system by automatically controlling a cash-management machine to operate in a different operating mode, with different permissions and different accounting consequences” (emphasis added).
However, here also except for simply summarizing the components of the system and/or its operation, Applicant fails to address the crux of the inquiry under Prong Two. In particular, Applicant fails demonstrate a technological improvement (if any) that any of the current claims is implementing. Otherwise, simply summarizing the components and/or the operations of the system, and then declaring the system as a technological improvement, does not satisfy the inquiry under Prong Two. In contrast, Claim 3 of Example 47 provides a technological improvement; namely, an improvement in computer-network security (improvement in the technical field of network intrusion detection); wherein the claimed method not only drops detected potentially malicious packets in real-time, but also blocks future network traffic from the source address that is associated with the potentially malicious packets. Thus, unlike Applicant’s current theory, the implementation of Claim 3 of Example 47 has nothing to do with evaluating attributes related to the user—such as weaknesses or competency issues related to the user. Instead, Claim 3 of Example 47, is resolving technical problems specific to computer network. Thus, it quite clear—at least to a PHOSITA—that Claim 3 of Example 47 is not even remotely relevant to any of the current claims. Consequently, Applicant’s arguments are not persuasive.
Regarding Step 2B, Applicant asserts, “[t]he Office states, at a high level, that it is conventional to use machine-learning algorithms to analyze data and produce pertinent results, and that generating training materials based on activity data is routine . . . The Office itself indicates that the prior art does not teach or suggest the current claims as a whole . . . the absence of a showing that this specific ordered arrangement was well-understood, routine, and conventional underscores that the rejection's Step 2B rationale is too generalized. The issue is not whether neural networks were known, or whether instructional content was known. The issue is whether this claimed arrangement, which uses learned-model event detection to automatically control a state transition and to segregate machine operations from site accounting, is merely conventional. The Office has failed to show that it is” (emphasis added).
However, here also Applicant appears to fail to properly construe the Office’s analysis. For instance, unlike Applicant’s theory, the Office’s analysis under Step 2B is based on the consideration of the claim as a whole. Thus, Applicant’s assertion regarding the Offices analysis—namely, the alleged “high level” analysis in the office action, and/or the alleged “too generalized” rationale in the office action, etc., are not valid.
Similarly, again unlike Applicant’s theory, the analysis under Step 2B has nothing to do with evaluating whether neural networks are well-known, or whether instructional content is well-known. Instead, as quite clear from the Office’s analysis, the inquiry under Step 2B is evaluating whether the claimed combined arrangement of the additional elements is beyond the conventional technology. Thus, while considering the claim as a whole, the inquiry under Step 2B is evaluating the combined arrangement, but not the individual elements. Consequently, Applicant’s arguments are not persuasive since Applicant appears to fail to properly construe the Office’s analysis under Step 2B.
Note also that the current claims overcome the prior art merely due to the new abstract idea; and thus, any novelty and/or non-obviousness of the claims does not necessarily signify a technological improvement; see MPEP 2106.06 (I) (emphasis added),
Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 . . . As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the §101 categories of possibly patentable subject matter." Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016) . . . See also Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a claim for a new abstract idea is still an abstract idea. The search for a §101 inventive concept is thus distinct from demonstrating §102 novelty.") . . . the search for an inventive concept is different from an obviousness analysis under 35 U.S.C. 103 . . . patentability of the claimed invention under 35 U.S.C.102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C.101.
Thus, at least for the reasons above, the Office concludes that none of the current clams—when considered as a whole—implements an inventive concept that amounts to “significantly more” than an abstract idea.
Prior Art.
5. Considering each of claims 1, 15 and 20 as a whole (including the respective dependent claims), the prior art does not teach or suggest the current claims (regarding the state of the prior art, see the office-action dated 02/13/2025).
Conclusion
Applicant’s amendment necessitated the new grounds of rejection presented in this final office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filled within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUK A GEBREMICHAEL whose telephone number is (571) 270-3079. The examiner can normally be reached from 7:00 AM - 3:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PETER VASAT can be reached on (571) 270-7625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRUK A GEBREMICHAEL/Primary Examiner, Art Unit 3715
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