Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office action is in response to Applicant’s amendment submitted on May 5, 2026.
Claims 1-20 are pending in the application.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on May 5, 2026 has been entered.
Response to Arguments/Remarks
Claim Rejections - 35 USC § 103
Claims 1-3, 5, 7, 10, 12-15, and 18 were rejected under 35 U.S.C. 103 as being unpatentable over Patel et al. US Patent Publication No. 2023/0054815 in view of Betge-Brezetz et al. US Patent Publication No. 2003/0221005 and Patil et al. US Patent Publication No. 2020/0366563.
The amendments to claims 1, 12, and 18 have overcome rejections. Accordingly, the rejections have been withdrawn.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Applicant has not pointed out support the limitations (MPEP 714.02 The prompt development of a clear issue requires that the replies of the applicant meet the objections to and rejections of the claims. Applicant should also specifically point out the support for any amendments made to the disclosure. Also see MPEP § 2163.06.).
Claim 1 has been amended to recite the limitations,
comparing at least a portion of the plurality of second processed results to actions taken in response to the second network monitoring data, wherein the actions taken include generating a ticket, logging an event, or executing a predetermined network reporting or configuration action, to determine an error indicator, and wherein the error indicator corresponds to a mismatch between a predicted prioritization of the second network monitoring data according to the plurality of second processed results and actual results including the actions taken;
comparing the error indicator to an error threshold; and
responsive to the error indicator exceeding the error threshold, re-training the first trained model based on at least the second network monitoring data and the actions taken to obtain a second trained model.
Upon review of the specification, the limitations are not supported by the specification. Applicant’s specification states in part,
[0053] In at least some embodiments, the trained model evaluator can receive predicted severity and/or predictive reactions to alarm data. The predictions can be compared with actual results, e.g., actions taken, which can be determined to be correct, e.g., upon manual inspection and/or further analysis and/or test. To the extent that the predictions are determined to be inaccurate, the trained model evaluator can identify and/or otherwise initiate further adjustments to the trained model based upon the model's predictions made to the live data, to obtain an ongoing training.
[0071] According to the example NSA predictive analysis process 270, an operational requirement of a network is identified at 271. In at least some embodiments, the operational requirement relates to an NSA objective. In at least some embodiments, the example NSA predictive analysis process 270 can generate model training data at 272. According to the illustrative example, generation of the model training data can include one or more of obtaining network monitoring data at 273, processing monitoring data at 274 according to the identified operational requirement to obtain processed monitoring data and generating training data at 275 based on the processed monitoring data.
[0072] According to a training process, modeled results may be compared at 277 with training data generated at 272. In at least some embodiments, an error indicator can be determined based on the comparison. The error value can be compared to a threshold error value at 278. For example, it is understood that there is some value of error that is small enough, i.e., below the threshold, such that the trained model may be validated and/or otherwise identified as being suitable for deployment. To the extent it is determined at 278 that the error exceeds the error threshold, the process continues to train and/or retrain the model at 276, determine an updated modeling error indicator at 277 and again comparing the updated modeling error to the threshold at 278.
Applicant’s specification, on paragraph [0053], discloses comparing predicted severity with the actual results and determining that the predictions are inaccurate, which in part corresponds to the limitations, “comparing at least a portion of the plurality of second processed results to actions taken in response to the second network monitoring data” and “wherein the error indicator corresponds to a mismatch between a predicted prioritization.” Paragraph [0053] does not disclose “comparing the error indicator to an error threshold; and responsive to the error indicator exceeding the error threshold, re-training the first trained model based on at least the second network monitoring data and the actions taken to obtain a second trained model.”
Paragraphs [0071] and [0072] discloses comparing model results with training data to determine an error indicator/value, and determining that the error exceeds an error threshold, which may correspond to comparing an “error indicator to an error threshold; and responsive to the error indicator exceeding the error threshold…” However, the comparing is a comparing of the model results with training data, which can include one or more of obtaining network monitoring data (according to [0071]). The comparing is not “comparing at least a portion of the plurality of second processed results to actions in response to the second network monitoring data, wherein the actions taken include generating a ticket, logging an event, or executing a predetermined network reporting or configuration action.”
Claims 12 and 18 are rejected for the same reasons as claim 1.
Examiner’s Note
Singh et al. US Patent Publication No. 2025/0317517 discloses re-training a trained model based on action(s) taken in response to output of the trained model (para. [0036] reviewer may provide feedback 180 including, but not limited to, whether the identified issue 168 is correct/incorrect and/or changes/adjustments to the identified issue 168. feedback 180 may be fed back to the ML model 178 to retrain the ML model 178 and improve identification of subsequent issues 168).
Erlingsson et al. US Patent No. 12,621,324 discloses re-training a trained model based on action(s) taken in response to output of the trained model (col. 96, lines 27-50. user feedback may include an indication of why the user agrees or disagrees with the alert. risk score or severity being too high or too low. the alert needing to be suppressed. col. 97, lines 7-42. one or more parameters for generating the alert to be modified are parameters used in generating instances of the alert across multiple customers. including models for identifying particular events that cause alerts to be generated, models for evaluating the risk or severity of particular events).
Li et al. US Patent Publication No. 2021/0099336 discloses re-training a trained model based on determining correctness of the model (para. [0097] if being correctly determined by the classification model, the root cause alarm event is marked as 1; if being incorrectly determined by the classification model, the root cause alarm event is marked as 0. first column in Table 3 includes feedback labels, and the generated Table 3 is updated to a database to be a training alarm event set of the classification model).
Christodorescu et al. US Patent Publication No. 2018/0198812 discloses comparing an error indicator to an error threshold and responsive to the error indicator exceeding the error threshold, re-training the first trained model (para. [0084] in response to determining that the calculated error rate exceeds an error threshold…, the processor may retrain the behavior classifier model).
Yaron et al. US Patent Publication No. 2025/0173434 discloses using a trained model to output prioritized results and performing actions corresponding to the prioritized results (para. [0006] determining a priority for the plurality of cybersecurity alerts based on outputs of the prioritization model; and performing a plurality of remediation actions based on the determined priority).
The prior art of record does not teach, individually or in combination, the invention in whole as claimed according to claims 1, 12, and 18.
Conclusion
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/JOSHUA JOO/Primary Examiner, Art Unit 2445
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