DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
Claims 1 and 22 recite “specific to the current incident” and “to economize computing resources”. These limitations are intended results because the claim contain no steps or structure to implement the limitations. Therefore, the limitations will not be given patentable weight.
Claims 1 and 22 recite “a learning operation that limits model updates to parameters relevant to the matched historical incident”. The limitation can be interpreted as (i) limiting model parameter updates to model parameters relevant to the matched historical incident or (ii) updating the model based only on parameters relevant to the matched historical incident. Interpretation (i) is not supported, but interpretation (ii) is supported. Therefore, for examination purposes, the limitation will be interpreted as interpretation (ii).
Claim Objections
Claim(s) 1 objected to because of the following informalities:
Claim 1 recites “querying the trained predictive model subsequent to the training for return of descriptive text based data describing the current incident, the descriptive text-based data being adapted from the historical administrator-entered labels to incorporate identifiers of the current incident”. The examiner believes this is a typo. A suggested amendment to the claim is “querying the trained predictive model subsequent to the training for return of descriptive text based data describing the current incident, the descriptive text[[-]]_based data being adapted from [[the]] historical administrator-entered labels to incorporate identifiers of the current incident”. For examination purposes, the claim will be interpreted as suggested.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-7, 21-25 rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exceptions without significantly more.
Claim(s) 1-7, 21-25 recite(s) methods. Therefore, claim(s) 1-7, 21-25 fall(s) within a statutory category.
Claim 1 recites abstract ideas.
evaluating alert data received from one or more computer environment in reference to a criterion, the alert data specifying alert type, alert location, and timestamp values, including alert types selected from the group consisting of loss-of-signal, link aggregation (LAG) failure, excessive onboarding activity; detecting that a current incident has occurred based on the criterion being satisfied; performing similarity analysis between the current incident and one or more historical incident; identifying, from the similarity analysis, a match between the current incident and the one or more historical incident; responsively to the identifying of the match corresponds to data analysis steps recited at a high level of generality such that they could practically be performed in the human mind, which are mental processes. The broadest reasonable interpretation of the limitation in light of the specification encompasses automating a manual process of analyzing human-readable alerts ([0041]-[0046], [0050], [0085]-[0086]).
Claim 1 does not recite additional limitations that integrate the judicial exceptions into practical application.
A computer implemented method comprising amounts to mere instructions to implement the abstract ideas on a computer, which is mere instructions to apply an exception. See MPEP 2106.05(f).
dynamically training a predictive model instance…, for production of a trained predictive model with use of dataset data of the one or more historical incident and historical text based data describing the one or more historical incident, wherein the training is performed using a learning operation that limits model updates to parameters relevant to the matched historical incident; querying the trained predictive model subsequent to the training for return of descriptive text based data describing the current incident, the descriptive text-based data being adapted from the historical administrator-entered labels to incorporate identifiers of the current incident amounts to merely using a computer in its ordinary capacity, which is mere instructions to apply an exception. See MPEP 2106.05(f).
specific to the current incident; to economize computing resources amount to an attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, which is mere instructions to apply an exception. See MPEP 2106.05(f).
presenting user prompting data for remediation of the current incident, wherein the prompting data includes the descriptive text based data describing the current incident, together with alert dataset details and text-based descriptions of historical remediations performed with respect to the matched historical incident, the remediations being selected from the group consisting of software version upgrades, soft resets, hard resets, failover to backup systems, replacement hardware action, wherein the prompting data is presented via a human-computer interface that enables administrator interaction with one or more candidate remediation actions associated with the match amounts to mere data output, which is insignificant extra-solution activity. See MPEP 2106.05(g).
delivering executable code associated with the one or more candidate remediation actions to the administrator user, wherein the administrator user is enabled to initiate execution of the executable code such that the current incident is remediated through automated or administrator-assisted performance of the remediation actions in the monitored computer environment does not apply or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim as a whole is more than a drafting effort designed to monopolize the judicial exception. The well-understood, routine, and conventional nature of this limitation is demonstrated by
Non-Patent Literature Automated Program Repair (“Goues”): automated program repair involves providing patches for bugs for developers to select and apply (Pg. 57-58)
Non-Patent Literature A Review on Code Generation with LLMs: Application and Evaluation (“Wang”): using LLMs for automated program repair (Pg. 286-287)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions because the additional elements amount to mere instructions to apply an exception, insignificant extra-solution activity, and well-understood, routine, and conventional activity. See MPEP 2106.05(I)(A).
Claim 21 recites abstract ideas.
evaluating alert data received from one or more computer environment in reference to a criterion; detecting that a current incident has occurred based on the criterion being satisfied; performing similarity analysis between the current incident and one or more historical incident; identifying, from the similarity analysis, a match between the current incident and the one or more historical incident; responsively to the identifying of the match corresponds to data analysis steps recited at a high level of generality such that they could practically be performed in the human mind, which are mental processes. The broadest reasonable interpretation of the limitation in light of the specification encompasses automating a manual process of analyzing human-readable alerts ([0041]-[0046], [0050], [0085]-[0086]).
The claim does not recite additional limitations that integrate the judicial exceptions into practical application.
A computer implemented method amounts to mere instructions to implement the abstract ideas on a computer, which is mere instructions to apply an exception. See MPEP 2106.05(f).
training a predictive model for production of a trained predictive model with use of dataset data of the one or more historical incident and historical text based data describing the one or more historical incident, wherein the historical text based data has been defined by an administrative user; querying the trained predictive model subsequent to the training for return of descriptive text based data describing the current incident amounts to merely using a computer in its ordinary capacity, which is mere instructions to apply an exception. See MPEP 2106.05(f).
presenting user prompting data for remediation of the current incident, wherein the prompting data includes the descriptive text based data describing the current incident amounts to mere data output, which is insignificant extra-solution activity. See MPEP 2106.05(g).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions because the additional elements amount to mere instructions to apply an exception and insignificant extra-solution activity. See MPEP 2106.05(I)(A).
Claim 22 recites abstract ideas.
evaluating alert data received from one or more computer environment in reference to a criterion; detecting that a current incident has occurred based on the criterion being satisfied; performing similarity analysis between the current incident and one or more historical incident; identifying, from the similarity analysis, a match between the current incident and the one or more historical incident; responsively to the identifying of the match corresponds to data analysis steps recited at a high level of generality such that they could practically be performed in the human mind, which are mental processes. The broadest reasonable interpretation of the limitation in light of the specification encompasses automating a manual process of analyzing human-readable alerts ([0041]-[0046], [0050], [0085]-[0086]).
Claim 22 does not recite additional limitations that integrate the judicial exceptions into practical application.
A computer implemented method comprising amounts to mere instructions to implement the abstract ideas on a computer, which is mere instructions to apply an exception. See MPEP 2106.05(f).
dynamically training a predictive model instance…, for production of a trained predictive model with use of dataset data of the one or more historical incident and historical text based data describing the one or more historical incident, wherein the training is performed using a learning operation that limits model updates to parameters relevant to the matched historical incident; querying the trained predictive model subsequent to the training for return of descriptive text based data describing the current incident amounts to merely using a computer in its ordinary capacity, which is mere instructions to apply an exception. See MPEP 2106.05(f).
specific to the current incident; to economize computing resources amount to an attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, which is mere instructions to apply an exception. See MPEP 2106.05(f).
presenting user prompting data for remediation of the current incident, wherein the prompting data includes the descriptive text based data describing the current incident, wherein the prompting data is presented via a human-computer interface that enables administrator interaction with one or more candidate remediation actions associated with the match amounts to mere data output, which is insignificant extra-solution activity. See MPEP 2106.05(g).
delivering executable code associated with the one or more candidate remediation actions to the administrator user, wherein the administrator user is enabled to initiate execution of the executable code such that the current incident is remediated does not apply or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim as a whole is more than a drafting effort designed to monopolize the judicial exception. The well-understood, routine, and conventional nature of this limitation is demonstrated by
Non-Patent Literature Automated Program Repair (“Goues”): automated program repair involves providing patches for bugs to developers for validation (Pg. 57-58)
Non-Patent Literature A Review on Code Generation with LLMs: Application and Evaluation (“Wang”): using LLMs for automated program repair (Pg. 286-287)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions because the additional elements amount to mere instructions to apply an exception, insignificant extra-solution activity, and well-understood, routine, and conventional activity. See MPEP 2106.05(I)(A).
Claim 2 refines recited abstract ideas.
wherein performing similarity analysis includes performing clustering analysis further describes details of data analysis steps recited at a high level of generality such that they could practically be performed in the human mind, which are mental processes. The broadest reasonable interpretation of the limitation in light of the specification encompasses automating a manual process of analyzing human-readable alerts ([0041]-[0046], [0050], [0085]-[0086]).
The claim does not contain additional limitations that integrate the judicial exceptions into practical application and does not contain additional limitations that are sufficient to amount to significantly more than the judicial exceptions.
Claim 3 does not recite additional limitations that integrate the judicial exceptions into practical application.
wherein the prompting data includes alert dataset data and text based data describing remediations performed with respect to the one or more historical incident amounts to mere data output, which is insignificant extra-solution activity. See MPEP 2106.05(g).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions because the additional elements amount to insignificant extra-solution activity. See MPEP 2106.05(I)(A).
Claim 4 further recites abstract ideas.
responsively to a determination that there is no match between the historical incident and a prior historical incident, the prior historical incident preceding the historical incident corresponds to data analysis steps recited at a high level of generality such that they could practically be performed in the human mind, which are mental processes. The broadest reasonable interpretation of the limitation in light of the specification encompasses automating a manual process of analyzing human-readable alerts ([0041]-[0046], [0050], [0085]-[0086]).
Claim 4 does not recite additional limitations that integrate the judicial exceptions into practical application.
wherein the historical text based data describing the one or more historical incident has been entered by the administrator user amounts to mere data gathering, which is insignificant extra-solution activity. See MPEP 2106.05(g).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions because the additional elements amount to insignificant extra-solution activity. See MPEP 2106.05(I)(A).
Claim 5 does not recite additional limitations that integrate the judicial exceptions into practical application.
wherein the method includes transmitting executable code for remediation of the current incident in dependence on the identifying the match between the current incident and the one or more historical incident does not apply or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim as a whole is more than a drafting effort designed to monopolize the judicial exception. The well-understood, routine, and conventional nature of this limitation is demonstrated by
Non-Patent Literature Automated Program Repair (“Goues”): automated program repair involves providing patches for bugs to developers for validation (Pg. 58)
Non-Patent Literature A Review on Code Generation with LLMs: Application and Evaluation (“Wang”): using LLMs for automated program repair (Pg. 286-287)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions because the additional elements amount to well-understood, routine, and conventional activity. See MPEP 2106.05(I)(A).
Claim 6 does not recite additional limitations that integrate the judicial exceptions into practical application.
wherein the predictive model is a pre-trained large language model (LLM) amounts to merely using a computer in its ordinary capacity, which is mere instructions to apply an exception. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions because the additional elements amount to mere instructions to apply an exception. See MPEP 2106.05(I)(A).
Claim 7 does not recite additional limitations that integrate the judicial exceptions into practical application.
wherein the presenting user prompting data for remediation of the current incident includes presenting text based data describing historical remediations performed with respect to the one or more historical incident amounts to mere data output, which is insignificant extra-solution activity. See MPEP 2106.05(g).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions because the additional elements amount to insignificant extra-solution activity. See MPEP 2106.05(I)(A).
Claim 23 refines recited abstract ideas.
wherein the alert data comprises an incident dataset including a plurality of alert datasets specifying alert type, alert location, and timestamp values, and wherein the current incident is determined based on satisfaction of a criterion involving at least a specified alert type persisting for a threshold duration or a combination of alert types further describe details of data analysis steps recited at a high level of generality such that they could practically be performed in the human mind, which are mental processes. The broadest reasonable interpretation of the limitation in light of the specification encompasses automating a manual process of analyzing human-readable alerts ([0041]-[0046], [0050], [0085]-[0086]).
The claim does not contain additional limitations that integrate the judicial exceptions into practical application and does not contain additional limitations that are sufficient to amount to significantly more than the judicial exceptions.
Claim 24 refines recited abstract ideas.
wherein performing similarity analysis includes clustering analysis or shape analysis of the current incident dataset against historical incident datasets, and wherein a match is identified when the current incident dataset falls within a cluster or within a threshold Euclidean distance of a historical incident dataset further describes details of data analysis steps recited at a high level of generality such that they could practically be performed in the human mind, which are mental processes. The broadest reasonable interpretation of the limitation in light of the specification encompasses automating a manual process of analyzing human-readable alerts ([0041]-[0046], [0050], [0085]-[0086]).
The claim does not contain additional limitations that integrate the judicial exceptions into practical application and does not contain additional limitations that are sufficient to amount to significantly more than the judicial exceptions.
Claim 25 does not recite additional limitations that integrate the judicial exceptions into practical application.
wherein the user prompting data for remediation includes text-based descriptions of remediations performed in respect to a historical incident matching the current incident, the remediations being selected from the group consisting of software version upgrades, soft resets, hard resets, failover to backup systems, and replacement hardware actions amounts to mere data output, which is insignificant extra-solution activity. See MPEP 2106.05(g).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exceptions because the additional elements amount to insignificant extra-solution activity. See MPEP 2106.05(I)(A).
For at least the reasons provided above, claim(s) 1-7, 21-25 are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 21 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US Patent Application Publication No. 20250130884 (“Wong”).
Regarding claim 21, Wong teaches
A computer implemented method comprising: ([0056]: computer implemented method)
evaluating alert data received from one or more computer environment in reference to a criterion; (Fig. 1, 2, [0021], [0032]: evaluate an incident record from a help desk for incident data).
The presence of incident data corresponds to the criterion.
detecting that a current incident has occurred based on the criterion being satisfied; ([0032]: detect the current incident in the incident data in the incident record)
performing similarity analysis between the current incident and one or more historical incident; ([0023], [0033], [0034]: similarity analysis between the current incident and historical incidents)
identifying, from the similarity analysis, a match between the current incident and the one or more historical incident; ([0023], [0034], [0036]: identify historical incidents related to the current incident)
responsively to the identifying of the match, training a predictive model for production of a trained predictive model with use of dataset data of the one or more historical incident and historical text based data describing the one or more historical incident, ([0021], [0028], [0034], [0036], [0049], [0050]: providing matched historical incidents and text data describing the incidents with a prompt to an LLM. Based on the prompt, the LLM determines a root cause and mitigation of the current incident) wherein the historical text based data has been defined by an administrative user; ([0021], [0023]: the text data of historical incident records are defined by help desk workers)
As noted by Non-Patent Literature Few-shot training LLMs for project-specific code-summarization (“Ahmed”), few-shot learning for LLMs involves providing examples with the prompt to the LLM so that the LLM can learn from the examples and generate a response to the prompt (1: Introduction) based on the examples. As further noted by Ahmed, few-shot learning for LLMs is a type of training that does not require any weight adjustment of the LLM (1: Introduction). Wong discloses providing example incidents that match the current incident with the prompt to the LLM to determine a root cause and mitigation for the current incident. Therefore, Wong describes all the steps of few-shot learning for an LLM, and teaches the training.
querying the trained predictive model subsequent to the training for return of descriptive text based data describing the current incident; and ([0028], [0034], [0036], [0049], [0050]: prompt the LLM to generate a root cause and mitigation for the current incident)
As noted in Ahmed, the LLM trains on the provided examples, and then responds to the prompt. Wong discloses providing example incidents that match the current incident with the prompt to the LLM to determine a root cause and mitigation for the current incident. Therefore, Wong teaches the querying subsequent to the training.
presenting user prompting data for remediation of the current incident, wherein the prompting data includes the descriptive text based data describing the current incident. ([0036], [0053]: present to a user the LLM’s response containing the root cause and mitigation)
Allowable Subject Matter
Claims 1-7, 22-25 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101 set forth in this Office action.
Claim 26 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
None of the prior art of record, either alone or when combined, teaches or suggests detecting that a current incident has occurred based on the criterion being satisfied; performing similarity analysis between the current incident and one or more historical incident; identifying, from the similarity analysis, a match between the current incident and the one or more historical incident; responsively to the identifying of the match, dynamically training a predictive model instance specific to the current incident, for production of a trained predictive model with use of dataset data of the one or more historical incident and historical text based data describing the one or more historical incident, wherein the historical text based data has been defined by an administrative user; wherein the training is performed using a learning operation that limits model updates to parameters relevant to the matched historical incident to economize computing resources; querying the trained predictive model subsequent to the training for return of descriptive text based data describing the current incident in claim(s) 1, 22.
Response to Arguments
Applicant's arguments filed 09/30/2025, with respect to the 101 rejections of claims 1-7, 21-25 have been fully considered but they are not persuasive.
On pg. 9-10, Applicant argues:
“Applicant respectfully submits that claim 1 is patent-eligible under 35 U.S.C. 101. The Office has cited MPEPG 2106.05(f)(3), which addresses the particularity or generality of the application of a judicial exception. However, claim 1 is not drafted at a level of generality that merely states "apply it," nor does it monopolize every mode of accomplishing a result. Instead, claim 1 confines any alleged abstract idea to a specific, practical implementation that meaningfully improves computer technology.
Claim 1 does not simply recite "detecting incidents" or "generating predictions" at a high level. Rather, it recites:
evaluating alert data in reference to a criterion;
detecting a current incident based on that criterion;
performing similarity analysis with historical incidents;
dynamically training a predictive model instance specific to the current incident; and
limiting model updates to parameters relevant to the matched historical incident to
economize computing resources.
These operations are not field-agnostic or result-oriented statements; they define a particular
machine learning training strategy, tightly integrated with incident-specific historical text data provided by an administrator, and they expressly economize computing resources. This is a meaningful limitation that confines the alleged exception to a practical application within the field of computer incident remediation.”
The Examiner respectfully disagrees. The claim does not describe how the training is specific to the current incident or economizes computing resources. The claim only describe how the training is specific to the matched historical incidents. Merely training a model on a dataset amounts to merely using a computer in its ordinary capacity. The intended results of training specific to the current incident and economizing computing resources amounts to an attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result. These limitations do not integrate the judicial exceptions into practical application or amount to significantly more than the judicial exceptions.
On pg. 10, Applicant argues:
“Further, claim 1 recites querying the trained predictive model for descriptive text output and presenting prompting data for remediation via a human-computer interface that allows an administrator to interact with candidate remediation actions and initiate executable remediation code. These limitations do not preempt the broad idea of "remediating incidents" or "generating predictions." Instead, they require a concrete sequence of technical operations: real-time model training, resource- conscious parameter updating, descriptive output generation aligned with administrator-defined text, and integration into an interface that delivers executable remediation code.”
The Examiner respectfully disagrees. Querying the trained predictive model for descriptive text output amounts to merely using a computer in its ordinary capacity, which is mere instructions to apply an exception. Presenting prompting data for remediation via a human-computer interface amounts to mere data output, which is insignificant extra-solution activity. Doing this to allow an administrator to interact with candidate remediation actions and initiate executable remediation code is well-understood, routine, and conventional. These limitations do not integrate the judicial exceptions into practical application or amount to significantly more than the judicial exceptions. Real-time model training and resource-conscious parameter updating are not reflected in the claim.
On pg. 10-11, Applicant argues:
“This level of specificity parallels the claims upheld in BASCOM (827 F.3d at 1350-51) referenced in MPEP 2106(f)(3), where the combination of filtering at a specific location with account-level customization was found to be a meaningful limitation confining the abstract idea. Here, the particular configuration of dynamic training, parameter-limited updating, descriptive text generation, and remediation code delivery likewise confines any alleged judicial exception to a specific technological implementation that improves how computer systems detect, explain, and remediate incidents.
BASCOM is a Federal Circuit decision expressly highlighted in MPEP 2106.05(f), the very section relied upon by the Examiner. In BASCOM, the court found eligibility because the ordered combination of known elements-placing a filtering tool at a specific location remote from end users, combined with account-level customization-confined the abstract idea of content filtering to a particular, practical application. The present claims are directly analogous. Here, the inventive significance lies in the ordered combination and placement of otherwise familiar operations: performing similarity analysis against historical incidents, dynamically training a predictive model instance specific to the current incident, limiting parameter updates to economize computing resources, generating descriptive output aligned with administrator-defined historical text, and delivering executable remediation code through a human-computer interface.
As in BASCOM, this sequence is not a generic application of an abstract idea; it is a non- conventional arrangement that improves computer functionality. The specific placement of dynamic training only after a similarity match, and the constraint of updates to parameters relevant to that match, yields concrete technical benefits in reduced compute, lower latency, and resource economy. The customization tied to administrator-defined labels parallels BASCOM's account-level customization, confining the system's operation to descriptors authored by identified administrators and thereby tailoring outputs to that controlled source. The integration of descriptive text generation and executable remediation further demonstrates a non-generic orchestration that enhances both transparency and speed of system operation.
Taken together, these ordered limitations represent the type of meaningful combination
recognized in BASCOM as supplying an inventive concept. They confine any alleged exception to a particular technological implementation, avoid preemption of the field, and improve the functioning of the computer system itself. Accordingly, consistent with BASCOM and MPEP 2106.05(f), claim 1 integrates any alleged judicial exception into a specific practical application, and is patent-eligible under 35 U.S.C. 101.
Accordingly, under MPEP 2106.05(f)(3), claim 1 integrates any alleged judicial exception into a particular, practical application, and therefore is patent-eligible.”
The Examiner respectfully disagrees. The claims do not improve how computer systems detect, explain, and remediate incidents. Reduced compute, lower latency, resource economy, and enhanced transparency and speed of system operation are not reflected in the claim. As shown in the specification, [0041]-[0046], [0050], [0085]-[0086], the detecting and explaining incidents in the claims encompass merely automating a manual process of analyzing human-readable alerts. Descriptive text of the remediation is merely output, and executable remediation code is merely delivered in a well-understood, routine, and conventional nature. As a whole, the claim amounts to using a computing system to automate the manual process of analyzing incident remediation and output results of the analysis, which does not supply an inventive concept. Any improvements improve the abstract ideas, rather than improving the computing system itself, which does not integrate the judicial exception into a practical application.
On pg. 11-12, Applicant argues:
Applicant further submits that claim 1 is patent-eligible in view of the recent memorandum
issued by Director Kathi Vidal (Kim Memo, August 2024), which expressly cautions Examiners against misapplication of the "apply it" shorthand. The Kim Memo explains that examiners must not oversimplify claims into a mere abstract idea and then conclude ineligibility by asserting that the claim only "applies" the idea. Instead, examiners must conduct a full Step 2A analysis, focusing on whether the claims are directed to a judicial exception per se, or whether they instead recite a specific practical application that improves technology.
Here, the Office's reliance on MPEP @ 2106.05(f)(3) effectively reduces the claim to the discredited "apply it" framework. The rejection treats the invention as if it were nothing more than "detecting incidents and applying prediction," ignoring the express claim limitations requiring (i) dynamic training of a predictive model instance specific to a current incident, (ii) limiting parameter updates to economize computing resources, (iii) querying the trained model to generate administrator- aligned descriptive output, and (iv) presenting executable remediation code via a human-computer interface. These are not generic "apply it" recitations-they are concrete technical operations rooted in the specification's disclosed improvements to latency, resource consumption, and transparency.
As the Kim Memo makes clear, dismissing such improvements under an "apply it" label is legal error. The claim does not monopolize a result across all fields, but confines the alleged judicial exception to a particular practical application in the technical field of computer incident management. By expressly improving the functioning of predictive model training and human-computer interaction in real-time remediation workflows, the claim falls squarely within the type of technological improvements that the Memo identifies as patent-eligible.
Accordingly, the Office's application of @ 2106.05(f)(3) is inconsistent with the Kim Memo's guidance and must be withdrawn. Claim 1, properly analyzed, is directed to a specific asserted improvement in computer technology and is patent-eligible under @ 101.
Applicant respectfully maintains that prior claim 1 is patent-eligible under 35 U.S.C. @ 101. The claim without amendment recites a specific sequence of technical operations for detecting incidents in monitored environments, training an incident-specific predictive model, querying that model for descriptive text output, and presenting prompting data to guide remediation. These features already integrate any alleged abstract idea into a concrete technological application that improves the functioning of a computer system, consistent with Enfjsh, McRO, Core Wireless, and the August 2024 Kim Memo's directive against misapplying the "apply it" shorthand.
The Examiner respectfully disagrees. The cited limitations have been analyzed in the above rejection and do not reflect the improvements to latency, resource consumption, transparency in computing systems or improvements of functioning of predictive model training and human-computer interaction in real-time remediation workflows. The only improvements reflected in the claims are improvements to abstract ideas.
On pg. 12-13, Applicant argues:
“Nevertheless, in the interest of expediting prosecution and clarifying the scope of the invention, Applicant has amended claim 1 to expressly recite:
Concrete incident types: "alert types selected from the group consisting of loss-of-signal, link aggregation (LAG) failure, and excessive onboarding activity," which ties the claim to specific, non-generic alert conditions described in the specification (see, e.g., [0043]-[0044], [0075]- [0087]).
Defined incident characterization: "the descriptive text-based data being adapted from the historical administrator-entered labels to incorporate identifiers of the current incident," anchoring the output to administrator-provided labels such as "Fiber Cut between RCH and NY" or "Security attack on ABC server."
Concrete remediation actions: "remediations being selected from the group consisting of software version upgrades, soft resets, hard resets, failover to backup systems, [and] replacement hardware action," which are technical operations performed within the computing environment (see [0082]-[0083]).
These amendments do not add new subject matter or concede ineligibility, but instead sharpen the claims by expressly incorporating technical detail that was already disclosed in the specification and recognized as improvements to computer technology. In doing so, the amendments further distinguish the claims from those that merely "state a result" or "apply it" at a high level of generality under MPEP 2106.05(f)(3).
By reciting specific incident types and remediation implementations, amended claim 1
removes any doubt that the claimed method is rooted in a particular practical application, confined to the technical domain of computer incident management. These clarifications therefore expedite allowance by providing the Examiner with concrete eligibility grounds fully consistent with the Kim Memo's instruction to avoid oversimplification.”
The Examiner respectfully disagrees. The claim merely lists multiple types of incidents and remediations, but does not reflect how the listed remediations are tied to the incident. Therefore, the listed incidents amount to further details of the abstract ideas and the listed remediations amount to mere data output. The incident characterization merely provides more detail of the data being provided to the model, amounting to using a computer in its ordinary capacity. These limitations do not improve computer technology or integrate the judicial exceptions into practical application.
On pg. 13-14, Applicant argues:
“Applicant respectfully submits that claim 21 is patent-eligible. The Office's citation to MPEP @ 2106.05(f)(3) is misplaced, because claim 21 is not drafted at a level of abstraction that merely says "apply it." Rather, claim 21 recites a sequence of concrete operations that confine any alleged judicial exception to a particular, practical application in the technological field of incident management.
The claim requires: (i) evaluating alert data against a criterion; (ii) detecting a current incident based on that criterion; (iii) performing similarity analysis against historical incidents; (iv) training a predictive model using dataset data and administrator-defined historical text data; (v) querying that trained model for descriptive text output of the current incident; and (vi) presenting prompting data including the descriptive text for remediation. These recitations do not broadly monopolize "incident detection" or "machine learning," but instead integrate machine learning into a specific, administrator-anchored, incident-driven workflow that produces descriptive, actionable outputs tied to historical similarity data.
This architecture provides technical improvements, including: (1) confining training to
administrator-defined historical text data to increase fidelity; (2) enabling targeted retraining in real time to reduce latency and computational cost; and (3) generating descriptive remediation-aligned outputs that directly enhance human-computer interaction. These features parallel the "particularized application" that BASCOM identified as meaningful limitations under 2106.05(f)(3).
Moreover, under the Kim Memo (August 2024), the Office is cautioned against misapplying the "apply it" shorthand by oversimplifying claims into a bare idea and then dismissing them as ineligible. That error is precisely what has occurred here. Claim 21 does not recite a desired result in the abstract; it recites the how: training based on administrator-defined incident text, querying the trained model for descriptive data, and presenting prompting data through a human-computer interface to guide remediation. These are implementation-specific improvements to computer functionality that cannot be collapsed into "apply prediction to incidents."
Accordingly, claim 21 integrates any alleged exception into a concrete technological application that improves the functioning of computer systems in real-time incident remediation. In line with MPEP @ 2106.05(f)(3), BASCOM, and the Kim Memo, claim 21 is patent-eligible under @ 101.”
The Examiner respectfully disagrees. The claim is not tied to incident management and instead is tied to incident analysis, which according the specification, [0085]-[0086], can be manually performed by humans. The recited improvements are not reflected in the claims. Training on administrator-defined historical text data amounts to merely using a computer in its ordinary capacity. Retraining is not reflected in the claims. Generating text outputs describing remediation amounts to mere data output. Instead, the claims reflect improvements to the abstract ideas.
On pg. 13-14, Applicant argues:
“For expedited identification of eligible subject matter, Applicant now adds new claims 23-26. Each of these claims incorporates concrete technical features expressly disclosed in the specification and further illustrates why the invention is not directed to an abstract idea, but rather to a particular, practical application in computer incident management.
New claim 23 specifies that the incident is defined by an incident dataset including multiple alert datasets with explicit fields such as type, location, and timestamp, and that incidents are determined based on structured criteria such as persistence or combinations of alerts. This recitation ties the invention to technical data structures and detection logic that improve system performance, which is a hallmark of eligibility.
New claim 24 recites that similarity analysis is performed using clustering or shape analysis with thresholds such as Euclidean distance. These are not field-agnostic generalities, but specific algorithmic implementations that improve accuracy and efficiency of computer-based matching, consistent with cases like McRO.
New claim 25 recites that the user prompting data includes text-based descriptions of
remediations previously performed in respect to matching historical incidents, with remediations expressly limited to technical actions such as software upgrades, resets, failover, and replacement hardware. This confines the claim to a concrete application with direct impact on computer system operation, well beyond any "apply it" abstraction.”
The Examiner respectfully disagrees. Describing the type of alerts amounts to describing the type of data being analyzed using abstract ideas, which does not reflect improving system performance. Describing the algorithms used for the abstract ideas further describes details of the abstract ideas, which merely improves abstract ideas. Describing the remediations outputted amounts to mere data output. These limitations are not integrated into practical application.
Applicant's arguments filed 09/30/2025, with respect to the prior art rejection of claim 21 have been fully considered but they are not persuasive.
On pg. 18, Applicant argues:
“Claim 21 is rejected over Wong. Applicant respectfully traverses. On current review of Wong, the Examiner's position rests on equating Wong's disclosure of incident records and prompts with the claimed operations of dynamically training a predictive model using administrator-defined historical text data and querying that model for descriptive text. This interpretation reads into Wong what is not actually taught.
Claim 21 requires that, once a match is identified between a current incident and one or more historical incidents, the system performs dynamic training of a predictive model instance using both dataset data of the historical incident and administrator-defined historical text data. Wong, on current review, does not disclose such training. At most, Wong describes a "prompt generation system" that packages incident context and passes it to another system for processing. This is a query-time operation, not a training operation. Prompting an existing model with examples is not equivalent to modifying model parameters with training data, which is what the claim requires.”
The Examiner respectfully disagrees. As noted by Non-Patent Literature Few-shot training LLMs for project-specific code-summarization (“Ahmed”), few-shot learning for LLMs involves providing examples with the prompt to the LLM so that the LLM can learn from the examples and generate a response to the prompt (1: Introduction). As further noted by Ahmed, few-shot learning for LLMs is a type of training that does not require any weight adjustment of the LLM (1: Introduction). Wong discloses providing example incidents that match the current incident with the prompt to the LLM to determine a root cause incident and mitigation steps for the current incident. Claim 21 does not require modifying model parameters with training data. Therefore, Wong describes all the steps of few-shot learning for an LLM, and teaches the training in claim 21.
On pg. 18, Applicant argues:
“Claim 21 further requires that the historical text-based data used in training has been defined by an administrative user. Wong, on current review, does not disclose administrator-authored incident descriptors being recorded for use in training datasets. The passages cited by the Examiner merely describe that a user, such as a help desk worker, may generate an incident record. That is not the same as the structured administrator-entered labels called for in the claim, which are expressly used to guide training and later descriptive output.”
The Examiner respectfully disagrees. A help desk worker that generates text for an incident record corresponds to an administrative user that defines historical text data for an incident. The claim does not require structured labels.
On pg. 18, Applicant argues:
“The claim also requires querying the trained predictive model to return descriptive text describing the current incident. Wong, on current review, discloses returning identifiers and references to existing mitigation steps, but it does not generate new descriptive text adapted from administrator-defined historical labels. The claimed system uses the administrator-defined incident text to produce descriptive narrative output that reflects the current incident. Wong only provides pointers to preexisting records.
Finally, the claim requires presenting user prompting data for remediation that includes this descriptive text about the current incident. Wong, on current review, outputs a set of identifiers or locations of mitigation steps, which is not equivalent to descriptive text output about the incident itself.”
The Examiner respectfully disagrees. The claim does not require new descriptive text or narrative output, and only requires descriptive text. Identifying the root cause and mitigation for a current incident from an LLM encompasses types of descriptive text of the current incident.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALBERT LI whose telephone number is (571)272-5721. The examiner can normally be reached M-F 7:00AM-3:00PM PT.
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, Bryce Bonzo can be reached at (571)272-3655. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/A.L./Examiner, Art Unit 2113
/MARC DUNCAN/Primary Examiner, Art Unit 2113