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 .
Response to Amendment
Applicant' s amendment and response filed 6/17/2026 has been entered and made record. This application contains 20 pending claims.
Claims 1, 11, and 20 have been amended.
Claim 16 has been cancelled.
Claim 21 has been added.
Response to Arguments
Applicant’s arguments filed 6/17/2026 regarding claims rejections under 35 U.S.C. 101 in claim 1-20 have been fully considered but they are not persuasive.
The applicant argues on pages 6-7 of the remark filed on 6/17/2026 that “… The Applicant respectfully disagrees that the examined claims were directed to non-statutory subject matter. In particular, the Office Action at pages 3- 4 asserted that certain features of the pending claims recited a "mathematical concept". The Applicant respectfully disagrees. … For example, "identifying a portion of the computer usage data that satisfies the condition of the specific issue" is simply not a recitation of any kind of mathematical relationship, mathematical formula or equation, or mathematical calculation. In addition, certain features were asserted to recite a "mental process". Again, the Applicant respectfully disagrees and submits that the identified features cannot be practically performed in the human mind.”
The Examiner respectfully disagrees applicant’s argument. The steps of “identifying a portion of the computer usage data that satisfies the condition of the specific issue”, and “validating the solution to the specific issue, wherein validating the solution comprises comparing the solution to a known solution” are a mathematical concept, therefore, they are considered to be an abstract idea. The step of “determining a prompt based on the portion of the computer usage data” a combination of a mathematical concept and a mental process, therefore, it is considered to be an abstract idea. A human mind can observe and evaluate of collected information of a computer usage data, and make determination, judgment and have opinion about appropriate message to relay based on the evaluation.
Thus, the claims are directed to an abstract idea.
Claims 11 and 20 recite subject matter that are similar to that of claim 1, and therefore, the claims are also patent ineligible.
Dependent claims 2-10, and 12-19 provide additional features/steps which are considered part of an expanded abstract idea of the independent claims, and do not integrate the abstract ideas into a practical application. Therefore, claims 2-10 and 12-19 are also patent ineligible.
Hence, the Examiner submits that the rejections of Claims 1-20 are proper.
Applicant’s arguments filed 6/17/2026 regarding claims rejections under 35 U.S.C. 103 in claim 1-20 have been fully considered and are persuasive. Newly discovered prior arts, Eberlein US 20210089384 and Bian CN 103761481A, will be used in combination with prior arts cited in the previous office action to reject the amended claim limitations.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15, and 17-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
As to claim 1, the claim recites “A method comprising:
receiving a specification of a condition to trigger detection of a specific issue of an information technology component;
collecting computer usage data via one or more computer agents on one or more clients;
identifying a portion of the computer usage data that satisfies the condition of the specific issue;
determining a prompt based on the portion of the computer usage data;
generating, using a generative machine learning model, a solution to the specific issue based on the prompt;
in response to generating the solution, validating the solution to the specific issue, wherein validating the solution comprises comparing the solution to a known solution; and
in response to validating the solution, deploying the solution to the specific issue to clients associated with the identified portion of the computer usage data.”
Under the Step 1 of the eligibility analysis, we determine whether the claim is directed to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process for claim 1).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the bold type portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim that covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental processes (concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions).
In claim 1, the steps of “identifying a portion of the computer usage data that satisfies the condition of the specific issue”, and
“validating the solution to the specific issue, wherein validating the solution comprises comparing the solution to a known solution” are a mathematical concept, therefore, they are considered to be an abstract idea.
The step of “determining a prompt based on the portion of the computer usage data” a combination of a mathematical concept and a mental process, therefore, it is considered to be an abstract idea.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The claim comprises the following additional elements:
receiving a specification of a condition to trigger detection of a specific issue of an information technology component; collecting computer usage data via one or more computer agents on one or more clients; generating, using a generative machine learning model, a solution to the specific issue based on the prompt; in response to generating the solution; in response to validating the solution, deploying the solution to the specific issue to clients associated with the identified portion of the computer usage data.
The additional element “receiving a specification of a condition to trigger detection of a specific issue of an information technology component”; and “generating, using a generative machine learning model, a solution to the specific issue based on the prompt”; “in response to generating the solution”; and “in response to validating the solution, deploying the solution to the specific issue to clients associated with the identified portion of the computer usage data” are not sufficient to integrate the abstract idea into a practical application because they only add insignificant extra-solution activities to the judicial exception. The additional element “collecting computer usage data via one or more computer agents on one or more clients” represents necessary data gathering and does not integrate the limitation into a practical application.
In conclusion, the above additional elements, considered individually and in combination with the other claims elements do not reflect an improvement to other technology or technical field, do not reflect improvements to the functioning of the computer itself, do not recite a particular machine, do not effect a transformation or reduction of a particular article to a different state or thing, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claim is directed to a judicial exception and require further analysis under the Step 2B.
The above claim, does not include additional elements that are sufficient to amount to significantly more than the judicial exception because they are generically recited and are well-understood/conventional in a relevant art as evidenced by the prior art of record (Step 2B analysis).
For example, collecting computer usage data via one or more computer agents on one or more clients is considered necessary data gathering. As recited in MPEP section 2106.05(g), necessary data gathering (i.e., collecting data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015).
For example, receiving a specification of a condition to trigger detection of a specific issue of an information technology component is disclosed by “Alcorn US 20210133054”, [0037], [0038], [0039]; and “Muller US 8793659B2”, Col. 1, Lines 55-61; Col. 2, Lines 4-10; Claim 5.
The claim, therefore, is not patent eligible.
Independent claims 11 and 20 recite subject matter that are similar or analogous to that of claim 1, and therefore, the claims are also patent ineligible.
With regards to the dependent claims, claims 2-10, 12-15, 17-19, and 21 provide additional features/steps which are considered part of an expanded abstract idea of the independent claims, and do not integrate the abstract ideas into a practical application.
The dependent claims are, therefore, also not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 7-13, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Alcorn et al. (US 20210133054, hereinafter Alcorn) in view of Wong et al. (US 20250130884, hereinafter Wong), and further in view of Eberlein et al. (US 20210089384, hereinafter Eberlein).
As to claims 1, 11, and 20, Alcorn teaches one or more processors (FIG. 1, CPU 22); and
a memory (FIG. 1, memory 24) coupled to the one or more processors, wherein the memory is configured to provide the one or more processors with instructions which when executed cause the one or more processors ([0005]) to:
receiving a specification of a condition to trigger detection of a specific issue of an information technology component ([0039] discloses the server 20 includes a central processing unit (CPU) 22 that executes a software application, and the host server 20 further includes an integrated management module 27 that runs the failure event log data collection agent 30. Should there be a system failure on the host server 20, the failure event log data collection agent 30 will detect the system failure , select and collect certain first failure data from the log files);
collecting computer usage data via one or more computer agents on one or more clients ([0005] discloses in response to receiving the request for failure event log data, a processor performs operations of transferring the event log data (i.e., computer usage data - emphasis added by Examiner) in order of descending priority until the plurality of log files have been transferred);
identifying a portion of the computer usage data that satisfies the condition of the specific issue ([0021] discloses “Data from the plurality of log files that is to be
included in the failure event log data may be prioritized so that data with the highest priority or importance toward determining the cause of the system failure (i.e., data with the highest priority or importance are the computer usage data that satisfies the condition of the specific issue - emphasis added by Examiner)”).
Alcorn does not explicitly teach determining a prompt based on the portion of the computer usage data; and generating, using a generative machine learning model, a solution to the specific issue based on the prompt.
Wong teaches determining a prompt based on the portion of the computer usage data; and generating, using a generative machine learning model, a solution to the specific issue based on the prompt ([0027] and [0028] disclose prompt generation system 106 generates a prompt 164, and the prompt can be a single, complex prompt which is provided to root cause processing system 108 (which may be a trained AI model or generative AI model), where the prompt 164 prompts system 108 to identify the top ranked incident (i.e., the portion of the computer usage data - emphasis added by Examiner) as the root cause of the incidents identified in the prompt as well as mitigation steps that can be used to remedy or mitigate the root cause incident (i.e., a solution to the specific issue based on the prompt - emphasis added by Examiner)).
It would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to incorporate Wong into Alcorn for the purpose of generating a prompt to an artificial intelligence (AI) model based on ranked and related incidents in order to identify root cause incident (top ranked incident) and
providing mitigation steps corresponding to the root cause incident. This combination would improve in accurately analyzing the logged incident so that an effective respond for mitigating or remedying the incident can be provided.
The combination of Alcorn and Wong does not explicitly teach in response to generating the solution, validating the solution to the specific issue, wherein validating the solution comprises comparing the solution to a known solution.
Eberlein teaches in response to generating the solution, validating the solution to the specific issue, wherein validating the solution comprises comparing the solution to a known solution ([0006] discloses “an incident report including a description of an issue of a process and a context of the issue are received from a monitoring system. Features associated with the issue based on the context of the issue are retrieved . The features are processed to extract a set of solutions that were executed to resolve associated issues. The set of solutions is processed to generate a solution for the issue. It is determined whether an accuracy of the solution exceeds a solution implementation threshold, and in response to determining that the accuracy of the solution exceeds the solution implementation threshold, the solution to resolve the issue is implemented.”); and
in response to validating the solution, deploying the solution to the specific issue to clients associated with the identified portion of the computer usage data ([0025] and [0074] disclose retrieve action data (i.e., the retrieve action data would include identified portion of the computer usage data - emphasis added by Examiner) and implement actions associated with the issues to be resolved; and processing the set of solutions to generate a solution for the issue, determining whether an accuracy of the solution with a solution implementation threshold, and in response to determining that the accuracy of the solution exceeds the solution implementation threshold, implementing the solution to resolve the issue).
It would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to incorporate Eberlein into Alcorn in view of Wong for the purpose of providing information about processes, issues, and solutions to support issue resolution automation, thereby reducing errors, as well as reducing an
amount of maintenance such as human input. This combination would improve in efficiently generating a solution for the issue by determining the accuracy of the solution that exceeds the solution implementation threshold so that the solution to resolve the issue can be implemented.
As to claims 2 and 12, the combination of Alcorn, Wong, and Eberlein teaches the claimed limitations as discussed in claims 1 and 11, respectively.
Alcorn does not explicitly teach receiving an indication that the solution to the specific issue characterized by the condition is to be generated.
Wong teaches receiving an indication that the solution to the specific issue characterized by the condition is to be generated dynamically using the generative machine learning model ([0005] and [0028] disclose a prompt is generated to an artificial intelligence (AI) model or a generative AI model based on the ranked, related incidents and the AI model or generative AI model returns a response that identifies a root cause incident (top ranked incident) and mitigation steps corresponding to the root cause incident).
It would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to incorporate Wong into Alcorn in view of Eberlein for the purpose of generating a prompt to an artificial intelligence (AI) model based on ranked and related incidents in order to identify root cause incident (top ranked incident) and providing mitigation steps corresponding to the root cause incident. This combination would improve in accurately analyzing the logged incident so that an effective respond for mitigating or remedying the incident can be provided.
As to claims 3 and 13, the combination of Alcorn, Wong, and Eberlein teaches the claimed limitations as discussed in claims 1 and 11, respectively.
Alcorn teaches wherein the condition to trigger the detection of the specific issue is associated with a specific geographic region or a specific application name ([0023] and [0034] disclose a network–related error, error logs created by the networking subsystem or errors collected from the networking subsystem (i.e., a network–related error and/or errors collected from the networking subsystem are the specific issue is associated with a specific application name, and a processor performs operations detection of the errors or issues - emphasis added by Examiner) may be given a high priority for inclusion and transmission as part of the failure event log data (FELD) while error logs related to other hardware and software subsystems (such as Operating System-level events) would be given a lower priority for inclusion).
As to claims 7 and 17, the combination of Alcorn, Wong, and Eberlein teaches the claimed limitations as discussed in claims 1 and 11, respectively.
Alcorn teaches wherein the specific issue of the information technology component is associated with an application ([0015] and [0023] disclose a user may interact with the failure event log data collection agent of the host directly via a browser or Secure Shell (SSH) tool, or via an Application Programming Interface (API); and a software error includes an application crashing).
As to claim 8, the combination of Alcorn, Wong, and Eberlein teaches the claimed limitations as discussed in claim 7.
Alcorn teaches wherein the application is a web-based application ([0015] discloses a user may interact with the failure event log data collection agent of the host directly via a browser or Secure Shell (SSH) tool, or via an Application Programming Interface (API)).
As to claims 9 and 18, the combination of Alcorn, Wong, and Eberlein teaches the claimed limitations as discussed in claims 1 and 11, respectively.
Alcorn does not teach wherein the generative machine learning model is accessed via an application programming interface.
Wong teaches wherein the generative machine learning model is accessed via an application programming interface (FIG. 1 shows that a user can access an AI system 105 or a generative machine learning model via an application programming interface of Incident management system 102; [0028]).
It would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to incorporate Wong into Alcorn in view of Eberlein for the purpose of generating a prompt to an artificial intelligence (AI) model based on ranked and related incidents in order to identify root cause incident (top ranked incident) and providing mitigation steps corresponding to the root cause incident. This combination would improve in accurately analyzing the logged incident so that an effective respond for mitigating or remedying the incident can be provided.
As to claims 10 and 19, the combination of Alcorn, Wong, and Eberlein teaches the claimed limitations as discussed in claims 1 and 11, respectively.
Alcorn teaches wherein the memory is further configured to provide the one or more processors with instructions which when executed cause the one or more processors to store ([0039] discloses the server 20 includes a central processing unit (CPU) 22 that executes a software application that is stored, and during operation of the host server 20, if there is a system failure on the host server 20, the failure event log data collection agent 30 will detect the system failure, select and collect certain first failure data from the log files, and store the selected data on a designated data storage device).
Alcorn does not explicitly teach store the solution to the specific issue in a known solutions repository.
Wong teaches store the solution to the specific issue in a known solutions repository ([0050] discloses the prompt generation system 106 identifies an incident in the example data store that is similar, and include, in the prompt, the incident extracted from the example data store along with the corresponding mitigation steps as an (i.e., store the solution to the specific issue in a known solutions repository - emphasis added by Examiner) example for the model 108; [0067]).
It would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to incorporate Wong into Alcorn in view of Eberlein for the purpose of generating a prompt to an artificial intelligence (AI) model based on ranked and related incidents in order to identify root cause incident (top ranked incident) and providing mitigation steps corresponding to the root cause incident. This combination would improve in accurately analyzing the logged incident so that an effective respond for mitigating or remedying the incident can be provided.
Claims 4-6, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Alcorn, Wang, and Eberlein, in view of Hong (US 20200104392, hereinafter Hong).
As to claims 4 and 14, the combination of Alcorn and Wong teaches the claimed limitations as discussed in claims 1 and 11, respectively.
The combination of Alcorn and Wong does not explicitly teach wherein the collected computer usage data includes at least one of the following: a response time, a session time, a page load time, or a last access time.
Hong teaches wherein the collected computer usage data includes at least one of the following: a response time ([0046] discloses a data center (e.g., 22A) may estimate when resources may become overutilized based on metrics associated with client instance performance, such as response time of the virtual server 24A to user requests), a session time, a page load time, or a last access time.
It would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to incorporate Hong into Alcorn in view of Wong and Eberlein for the purpose of analyzing the client instance performance trends and adjusting thresholds used to send resource utilization alerts based on analyzing the client instance performance trends. This combination would improve in effectively utilizing resources for efficient computing.
As to claims 5 and 15, the combination of Alcorn, Wong, and Eberlein teaches the claimed limitations as discussed in claims 1 and 11, respectively.
The combination of Alcorn and Wong does not explicitly teach wherein the collected computer usage data includes at least one of the following: processor activity data, processor performance data, memory usage data, storage usage data, pending update data, application log data, application crash report data, or network activity data.
Hong teaches wherein the collected computer usage data includes at least one of the following: processor activity data, processor performance data, memory usage data, storage usage data, pending update data, application log data, application crash report data, or network activity data ([0003] and [0031] disclose a cloud computing infrastructure allows users, such as individuals and/or enterprises, to access a shared pool of computing resources, such as servers, storage devices, networks, applications, and/or other computing based services. The data center may classify, group, and/or pattern match the performance data based on historical trends to characterize the performance data as indicative of certain circumstances, such as whether resources are likely to become insufficient to handle upcoming utilization (i.e., resources utilization includes storage usage data, memory usage data, processor activity data, and processor performance data - emphasis added by Examiner)).
It would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to incorporate Hong into Alcorn in view of Wong and Eberlein for the purpose of analyzing the client instance performance trends and adjusting thresholds used to send resource utilization alerts based on analyzing the client instance performance trends. This combination would improve in effectively utilizing resources for efficient computing.
As to claim 6, the combination of Alcorn, Wong, and Eberlein teaches the claimed limitations as discussed in claim 1.
The combination of Alcorn and Wong does not explicitly teach wherein the collected computer usage data includes a list of current running processes or a memory usage of one or more processes.
Hong teaches wherein the collected computer usage data includes a list of current running processes or a memory usage of one or more processes ([0003] and [0031] disclose a cloud computing infrastructure allows users, such as individuals and/or enterprises, to access a shared pool of computing resources, such as servers, storage devices, networks, applications, and/or other computing based services. The data center may classify, group, and/or pattern match the performance data based on historical trends to characterize the performance data as indicative of certain circumstances, such as whether resources are likely to become insufficient to handle upcoming utilization (i.e., the usage data includes a memory usage of one or more processes or a list of current running processes - emphasis added by Examiner)).
It would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to incorporate Hong into Alcorn in view of Wong and Eberlein for the purpose of analyzing the client instance performance trends and adjusting thresholds used to send resource utilization alerts based on analyzing the client instance performance trends. This combination would improve in effectively utilizing resources for efficient computing.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Alcorn, Wang, and Eberlein, in view of Bian et al. (CN 103761481A, hereinafter Bian).
As to claim 21, the combination of Alcorn, Wong, and Eberlein teaches the claimed limitations as discussed in claim 1.
The combination of Alcorn and Wong does not explicitly teach searching a known solutions repository for an existing validated solution for the specific issue, wherein the prompt is determined in response to no existing validated solution being identified by the search.
Bian teaches searching a known solutions repository for an existing validated solution for the specific issue, wherein the prompt is determined in response to no existing validated solution being identified by the search ([0054] discloses the static features of the malicious code sample are used to identify whether the malicious code sample is malicious code. If the static features cannot identify whether the malicious code sample is malicious code (i.e., no existing validated solution being identified by the search - emphasis added by Examiner), then the dynamic behavior features of the malicious code sample are used for identification. If the dynamic behavior feature library determines that the obtained malicious code sample is not malicious code, then a false alarm feedback is given to indicate that the malicious code sample is not malicious code, thereby achieving accurate identification of malicious code
samples and reducing the false alarm rate of malicious code.”).
It would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to incorporate Bian into Alcorn in view of Wong and Eberlein for the purpose of automatically processing malicious code samples that overcomes or at least partially solves problems of malicious code that can embed instructions into other code while concealing itself, thereby compromising the integrity of data on the infected computer and running intrusive programs. This combination would improve in accurately identifying the malicious code sample as malicious code so that corresponding removal or recovery measures can be developed for malwares such as computer viruses, worms, Trojan horses, botnets, spyware, backdoors, and rootkits.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this 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 filed 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAL CE MANG whose telephone number is (571)272-0370. The examiner can normally be reached Monday to Friday- 8:30-12:00, 1:00-5:30 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine T Rastovski can be reached at (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LAL CE MANG/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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