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 Objections
Claim 3 is objected to because of the following informalities:
Regarding claim 3, lines 6-7, the term “the respective candidate modifications” should be changed to “the set of respective candidate modifications”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 11 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 11, lines 1-2, the term “the deep neural network labels states” has no antecedent basis.
Allowable Subject Matter
Claims 1-10, 12-20 are allowed. The prior art of record fails to disclose the limitations for predicting a network deficiency by applying a machine learning model trained on a log from a monitoring tool that monitors a resource within a cloud computing platform on which is executing at least part of a cellular telecommunication network core that is configured within a managed container-orchestration system as specified by a file chart generated by a cloud native computing package manager; and modifying how the cellular telecommunication network core is configured within the managed container-orchestration system such that the predicted network deficiency is at least partially prevented by modifying the file chart according to a recommendation of the machine learning model and deploying the modified file chart; wherein the machine learning model is configured to predict, as output, a future state of the cellular telecommunication network core based on a candidate modification to the cellular telecommunication network core, as recited in claim 1 and similarly recited in claims 16, 17.
Claim 11 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Alon et al. (Pub No.: 2024/0406058) discloses a network monitor may execute, or communicate with, one or more stored machine learning models that are trained to predict a failure probability for one or more ports and/or links within a network fabric. Systems and methods may monitor a set of ports and/or links to generate predictions for failure probabilities using a first trained model and low frequency telemetry data. For a subset of ports and/or links with failure probabilities exceeding a first threshold, high speed telemetry data may be used by a second trained model to generate predictions for failure probabilities for the subset of ports. Suspicious ports may then be isolated and undergo various remediation and/or monitoring actions prior to de-isolating the isolated ports.
Yip et al. (Pub No.: 2025/0280305) discloses a 5G or 6G communication system for supporting higher data rates. According to various embodiments of the present disclosure, a method performed by a user equipment (UE) in a wireless communication system, the method comprising: receiving, from a first network entity, information regarding at least one artificial intelligence (AI) model; determining an AI model based on the information regarding at least one AI model; determining whether to use the AI model for an AI split inference service; requesting, to the first network entity, the AI split inference service; establishing an AI model deliver pipeline for the AI model; and establishing a media deliver pipeline for delivering media data used in the AI model.
Shi et al. (Pub No.: 2020/0053155) discloses systems and methods are provided for recognition of an application in communication traffic flow in a network using an artificial intelligence (AI) based hierarchical service awareness engine. A decode equivalent class (DEC) can be used to provide information on the application. A DEC corresponds to a class of traffic that is mapped to an artificial intelligence (AI) model associated with parameters related to the class of traffic. DEC information can be fed to an AI model set and an inference model can be derived from a AI model of the AI model set corresponding to a DEC. The inference model can be provided to a gateway of the network to recognize a specific application of a service in communication flows. In various embodiments, in training the AI models, the gateway can provide DEC information for the AI model set from classifying flows of data traffic received from the network into DECs.
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/KAN YUEN/Primary Examiner, Art Unit 2464