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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 4/24/26. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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) 1, 8, 15 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Balmakhtar et al. (US Publication 2025/0126522) .
The applied reference has a common assignee with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2). This rejection under 35 U.S.C. 102(a)(2) might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C. 102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B) if the same invention is not being claimed; or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed in the reference and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement.
Regarding claims 1 8, and 15, Balmakhtar teaches an apparatus and a method comprising:
generating feature vectors based on Key Performance Indicator (KPI) values associated with network conditions for an access network, wherein the access network comprises a network slice; (i.e. fig. 2 shows KPIs for a network slice may be retrieved, the key performance indicators comprising wireless network conditions (element 201)see paragraph 35) (See Also; the KPI values may be utilized to generate feature vectors; see paragraph 60, 79)
providing the feature vectors to a machine learning model trained to output values corresponding to slice parameters associated with the network slice; (i.e. fig. 2 shows a prediction of network conditions may be made for the wireless network slice based upon the KPI information (element 202); see paragraph 35) (see Also; the feature vectors based upon network KPI values may be provided to a machine learning model in order to predict network conditions of the network slice; see paragraph 60, 79)
configuring the slice parameters of the network slice using the values output by the machine learning model; and serving a wireless user device over the network slice. (i.e. fig. 2 shows parameters belonging to the network slice may be adjusted or updated based upon the predicted information in order to serve a user or particular application on that slice (elements 203, 204); see paragraphs 35) (See Also; based upon the output prediction from the machine learning model may update network parameters for the slice based upon the predictive output in order to support a particular user or application; see paragraphs 61, 61 and 79, 80)
Claim(s) 1 - 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Bogineni et al. (US Publication 2024/0129198).
Regarding claims 1 8, and 15, Bogineni teaches an apparatus and a method comprising: (i.e. fig. 4 shows an network apparatus comprising a processor, memory and transceiver for executing programmed instructions; see paragraph 68)
generating feature vectors based on Key Performance Indicator (KPI) values associated with network conditions for an access network, wherein the access network comprises a network slice; (i.e. fig. 5 shows a network performance apparatus may determine analytics and state information comprised of information received about a network slice, the information may include performance metrics about a current network slice (element 525); see paragraphs 84) (see Also; performance metrics may be comprised of KPIs; see paragraph 43)
providing the feature vectors to a machine learning model trained to output values corresponding to slice parameters associated with the network slice; (i.e. fig. 5 shows the analytics and state information (feature vector) may be provided to an artificial intelligence/ machine learning system within the apparatus to determine expected performance information (element 525); see paragraphs 84)
configuring the slice parameters of the network slice using the values output by the machine learning model; and serving a wireless user device over the network slice. (i.e. fig. 5 shows the expected performance information may be utilized in determining whether performance of the network slice is satisfied based on the result, a modification of the network slice may be performed and the wireless device / app may communicate with the slice (elements 530, 535, 540, 545 ); see paragraphs 85 - 87)
Regarding claims 2 9, and 16, Bogineni teaches the method of claim 1 further comprising obtaining the KPI values associated with the network conditions for the access network via a measurement report generated by the wireless user device that characterizes radio conditions for the access network. (i.e. fig. 3d shows the network performance device (127) comprising AI/ML components (205), may obtain the performance metrics measured from an end user device (130) that is accessing the slice; see paragraph 51, 52) (see Also; performance metrics may be comprised of KPIs; see paragraph 43)
Regarding claims 3, 10, and 16, Bogineni teaches the method of claim 1 further comprising obtaining the KPI values associated with the network conditions for the access network by generating loading data that characterizes cell loading on the access network. (i.e. the performance metrics or determining / predicting network conditions include may number of connected users and traffic volume; see paragraphs 41, 42) ) (see Also; performance metrics may be comprised of KPIs; see paragraph 43)
Regarding claims 4, 11, and 17, Bogineni teaches the method of claim 1 wherein: the machine learning output comprises a pre-configuration grant parameter for the network slice; and configuring the slice parameters of the network slice using the values output by the machine learning model comprises configuring a pre-configuration grant parameter of the network slice using the values output by the machine learning model. (i.e. the AI/ML uses as input analytics and state information that are formed by network performance values (KPI or other parameters) for a network slice and utilizing the predictive output to configure network slice in anticipation of the user/app needs on the slice; see paragraphs 45 – 48, 55, 65 - 67)
Regarding claims 5, 12, and 18 Bogineni teaches the method of claim 1 wherein: the machine learning output comprises a pre-scheduling parameter for the network slice; and configuring the slice parameters of the network slice using the values output by the machine learning model comprises configuring a pre-scheduling parameter for the network slice using the values output by the machine learning model. (i.e. the AI/ML uses as input analytics and state information that are formed by network performance values (KPI or other parameters) for a network slice and utilizing the predictive output to configure network slice in anticipation of the user/app needs on the slice; see paragraphs 45 – 48, 55, 65 - 67)
Regarding claims 6, 13, and 19 Bogineni teaches the method of claim 1 wherein: the machine learning output comprises a relative priority scheduling parameter for the network slice; and configuring the slice parameters of the network slice using the values output by the machine learning model comprises configuring an existing relative priority scheduling parameter for the network slice using the values output by the machine learning model. (i.e. the AI/ML uses as input analytics and state information that are formed by network performance values (KPI or other parameters) for a network slice and utilizing the predictive output to configure network slice in anticipation of the user/app needs on the slice; see paragraphs 45 – 48, 55, 65 - 67)
Regarding claims 7, 14, and 20 Bogineni teaches the method of claim 1 wherein: the machine learning output comprises the slice parameters and a recommendation to create a new network slice; configuring the slice parameters of the network slice using the values output by the machine learning model comprises generating a request to create the new network slice using the slice parameters obtained in the machine learning output; and serving the wireless user device over the network slice comprises serving the wireless user device over the new network slice. (i.e. the AI/ML uses as input analytics and state information that are formed by network performance values (KPI or other parameters) for a network slice and utilizing the predictive output to configure a new network slice in anticipation of the user/app needs on the slice; see paragraphs 13 – 15, 45 – 48, 65 - 67)
Conclusion
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ROBERT J. LOPATA
Primary Examiner
Art Unit 2471
/ROBERT J LOPATA/
May 6, 2026Primary Examiner, Art Unit 2471
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