DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
2. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 June 25, 2026 has been entered. Claims 1, 3-4, 6-7, 10, 11-13 and 15-16 have been amended. Claims 1-18 are currently pending in this application.
Response to Arguments
3. The applicant's arguments filed on June 25, 2026 regarding claims 1-18 have been fully considered but are moot in view of the new ground(s) of rejection. The rejection has been revised and set forth below according to the claims.
Claim Rejections - 35 USC § 103
4. 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 of this title, 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.
5. Claims 1, 4, 5, 6, 7, 10, 13, 14, 15 and 16 are rejected under pre-AIA 35 U.S.C. 103 as being unpatentable over Karampatsis et al. (US 2025/0217701 A1), hereinafter “Karampatsis’701” in view of Karampatsis et al. (US 2026/0095381 A1), hereinafter “Karampatsis’381”.
Regarding claim 1, Karampatsis’701 discloses a method of using feedback information (Fig. 7, analytics in a wireless communications network), the method comprising:
requesting a machine learning (ML) model (Fig. 7, step 705, paragraphs [0073]-[0074], request for ML models);
receiving feedback information from a consumer provided with information generated through the ML model (Fig. 7, step725 [Wingdings font/0xE0] step730 [Wingdings font/0xE0] step735 [Wingdings font/0xE0] step740 [Wingdings font/0xE0] step745 [Wingdings font/0xE0] step750, paragraphs [0080]-[0084], feedback notification from a consumer provided with information generated through the ML model), wherein the feedback information is feedback regarding the information generated through the ML model from the consumer (Fig. 7, step725 [Wingdings font/0xE0] step730 [Wingdings font/0xE0] step735 [Wingdings font/0xE0] step740 [Wingdings font/0xE0] step745 [Wingdings font/0xE0] step750, paragraphs [0080]-[0084], analytics using provided ML model);
providing at least one of the feedback information or information on the accuracy (Fig. 7, paragraphs [0085], [0105]-[0106], report of the action in feedback notification),
wherein the information generated through the ML model provided to the consumer includes at least one of inference data or analytic information (paragraphs [0080]-[0081], analytics to the analytics consumer), and
wherein the feedback information includes an indication whether an action triggered by the information generated through the ML model (paragraphs [0069], [0070]-[0071], an indication that the action taken may significantly change the behaviour of an NF, a slice or the 5GC and/ 5GC and detail examples).
While Karampatsis’701 implicitly refers to “in response to the receiving of the feedback information, monitoring accuracy of the ML model”, Karampatsis’381 from the same or similar field of endeavor explicitly discloses receiving feedback information from a consumer provided with information generated through the ML model (Figs. 6-7, step 673 and/or step 773, paragraphs [0072]-[0073], [0087]-[0088], analytic feedback from analytics consumer), wherein the feedback information is feedback regarding the information generated through the ML model from the consumer (Figs. 6-7, step 673 and/or step 773, paragraphs [0072]-[0073], [0087]-[0088], feedback information from a consumer provided with information generated through the ML model);
in response to the receiving of the feedback information, monitoring accuracy of the ML model (Fig. 7, step 774, paragraphs [0066], [0069], [0100]-[0101], NWDAF AnLF 750 determines the accuracy of analytics in accordance with steps 674 to 680 described in connection with Fig. 6); and
providing at least one of the feedback information and or information on the accuracy (Figs. 6-7,paragraphs [0113]-[0114], information to determine ML model accuracy).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to provide “in response to the receiving of the feedback information, monitoring accuracy of the ML model” as taught by Karampatsis’381, in the system of Karampatsis’701, so that it would provide data analytics function implementing a rating accuracy of analytics in a wireless communication network (Karampatsis’381, paragraph [0001]).
Regarding claim 4, Karampatsis’701 discloses the providing comprises transmitting the at least one of the feedback information or the information on the accuracy in response to a request for subscription to accuracy monitoring of the ML model (Fig. 9, paragraphs [0105]-[0107], step 950 [Wingdings font/0xE0] step 960 [Wingdings font/0xE0] step 970, performance of each ML model supporting the same Analytic ID and identifies the performance of each ML model (e.g. confidence level of analytic output, variance between each analytic output, variance of each analytic output)
Regarding claim 5, Karampatsis’701 in view of Karampatsis’381 disclose the method according to claim 1.
Karampatsis’381 further discloses computing the accuracy based on the feedback information (Fig. 7, paragraphs [0066], [0069], [0100]-[0101], the accuracy of analytics in accordance with steps 674 to 680 described in connection with Fig. 6).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to provide “computing the accuracy based on the feedback information” as taught by Karampatsis’381, in the system of Karampatsis’701, so that it would provide data analytics function implementing a rating accuracy of analytics in a wireless communication network (Karampatsis’381, paragraph [0001]).
Regarding claim 6, Karampatsis’701 in view of Karampatsis’381 disclose the method according to claim 1.
Karampatsis’381 further discloses the at least one of the feedback information or the information on the accuracy is used for evaluation of the ML model (Fig. 7, paragraphs [0066], [0069], [0100]-[0101], the accuracy of analytics in accordance with steps 674 to 680 described in connection with Fig. 6).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to provide “the at least one of the feedback information or the information on the accuracy is used for evaluation of the ML model” as taught by Karampatsis’381, in the system of Karampatsis’701, so that it would provide data analytics function implementing a rating accuracy of analytics in a wireless communication network (Karampatsis’381, paragraph [0001]).
Regarding claim 7, Karampatsis’701 discloses receiving the ML model that is retrained or a newly selected ML model based on the at least one of the feedback information or the information on the accuracy (Fig. 9, paragraphs [0105]-[0107], retrained one or more of the ML models based on the performance feedback).
Regarding claim 10, the claim is rejected based on the same reasoning as presented in the rejection of claim 1.
Regarding claim 13, the claim is rejected based on the same reasoning as presented in the rejection of claim 4.
Regarding claim 14, the claim is rejected based on the same reasoning as presented in the rejection of claim 5.
Regarding claim 15, the claim is rejected based on the same reasoning as presented in the rejection of claim 6.
Regarding claim 16, the claim is rejected based on the same reasoning as presented in the rejection of claim 7.
6. Claims 2 and 11 are rejected under pre-AIA 35 U.S.C. 103 as being unpatentable over Karampatsis et al. (US 2025/0217701 A1), hereinafter “Karampatsis’701” in view of Karampatsis et al. (US 2026/0095381 A1), hereinafter “Karampatsis’381” in view of CHENG et al. (US 2025/0220462 A1), hereinafter “Cheng”.
Regarding claim 2, Karampatsis’701 in view of Karampatsis’381 disclose the method according to claim 1.
While Karampatsis’701 in view of Karampatsis’381 implicitly refer to “the consumer uses the ML model and has a capability of transmitting feedback information on analytics generated by the ML model, registering the consumer to a provider providing the ML model”, Cheng from the same or similar field of endeavor discloses the consumer uses the ML model and has a capability of transmitting feedback information on analytics generated by the ML model, registering the consumer to a provider providing the ML model (paragraphs [0250], [0257]-[0260], object information targeted by the target task, such as target of analytic reporting, used for indicating that the object of the data analytics is a particular terminal, a plurality of terminals or all terminals; model identifier information, such as a Model ID, used for indicating that the first data are targeted at a particular mode).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to provide “the consumer uses the ML model and has a capability of transmitting feedback information on analytics generated by the ML model, registering the consumer to a provider providing the ML model” as taught by Cheng, in the combined system of Karampatsis’701 and Karampatsis’381, so that it would provide data analytics result corresponding to the specific task based on the model performance of the artificial intelligence/machine learning model in an actual use stage (Cheng, paragraph [0005]).
Regarding claim 11, the claim is rejected based on the same reasoning as presented in the rejection of claim 2.
7. Claims 3 and 12 are rejected under pre-AIA 35 U.S.C. 103 as being unpatentable over Karampatsis et al. (US 2025/0217701 A1), hereinafter “Karampatsis’701” in view of Karampatsis et al. (US 2026/0095381 A1), hereinafter “Karampatsis’381” in view of CHENG et al. (US 2025/0220462 A1), hereinafter “Cheng” in view of PRAVINCHANDRA BHATT et al. (US 2024/0048988 A1), hereinafter “Pravinchandra”.
Regarding claim 3, Karampatsis’701 in view of Karampatsis’381 and Cheng disclose the method according to claim 2.
Neither Karampatsis’701, Karampatsis’381 nor Cheng explicitly discloses “a request for the registering comprises at least one of an identifier of the consumer provided with the ML model and an identifier of the ML model”.
However, Pravinchandra from the same or similar field of endeavor discloses a request for the registering comprises at least one of an identifier of the consumer provided with the ML model and an identifier of the ML model (Fig. 12, paragraphs [0160]-[0162], data notification response with “ML model ID accuracy, UE-IDs, data received from UE ID, UE cluster information” from the NF service consumer; NWDAF receives the data notification response with “ML model ID accuracy, UE-IDs, data received from UE ID, UE cluster information” from the NF service consumer).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to provide “a request for the registering comprises at least one of an identifier of the consumer provided with the ML model and an identifier of the ML model” as taught by Pravinchandra, in the combined system of Karampatsis’701, Karampatsis’381 and Cheng, so that it would provide improved robustness of artificial intelligence or machine learning capabilities against compromised input applied to be used for various network optimizations (Pravinchandra, paragraph [0003]).
Regarding claim 12, the claim is rejected based on the same reasoning as presented in the rejection of claim 3.
8. Claims 8 and 17 are rejected under pre-AIA 35 U.S.C. 103 as being unpatentable over Karampatsis et al. (US 2025/0217701 A1), hereinafter “Karampatsis’701” in view of Karampatsis et al. (US 2026/0095381 A1), hereinafter “Karampatsis’381” in view of Lee et al. (US 2022/0108214 A1), hereinafter “Lee’214”.
Regarding claim 8, Karampatsis’701 in view of Karampatsis’381 and Cheng disclose the method according to claim 1.
While Karampatsis’701 in view of Karampatsis’381 implicitly refer to “the feedback information is received via a network exposure function (NEF)”, Lee’214 from the same or similar field of endeavor discloses the feedback information is received via a network exposure function (NEF) (Fig. 1, paragraphs [0069]-[0070], [0078], data collection based on event subscriptions provided by network exposure function).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to provide “the feedback information is received via a network exposure function (NEF)” as taught by Lee’214, in the combined system of Karampatsis’701 and Karampatsis’381, so that it would provide improve accuracy of a network data analytics result relates to updating machine learning model (Lee’214, paragraph [0038]).
Regarding claim 17, the claim is rejected based on the same reasoning as presented in the rejection of claim 8.
9. Claims 9 and 18 are rejected under pre-AIA 35 U.S.C. 103 as being unpatentable over Karampatsis et al. (US 2025/0217701 A1), hereinafter “Karampatsis’701” in view of Karampatsis et al. (US 2026/0095381 A1), hereinafter “Karampatsis’381” in view of Lee et al. (US 2021/0144076 A1; as submitted by the applicant with IDS dated December 01, 2023), hereinafter “Lee’076”.
Regarding claim 9, Karampatsis’701 in view of Karampatsis’381 and Cheng disclose the method according to claim 1.
While Karampatsis’701 in view of Karampatsis’381 implicitly refer to “the feedback information comprises use case context”, Lee’076 from the same or similar field of endeavor discloses the feedback information comprises use case context (paragraphs [0063], [0065], case indication according to analytic information).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to provide “the feedback information comprises use case context” as taught by Lee’096, in the combined system of Karampatsis’701 and Karampatsis’381, so that it would provide various services and low-latency networks relate to optimization for network data analytic function device (Lee’096, paragraph [0002]).
Regarding claim 18, the claim is rejected based on the same reasoning as presented in the rejection of claim 9.
Conclusion
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to SITHU KO whose telephone number is 571-272-8647. The examiner can normally be reached on Mon-Friday 8:30am-5:00pmEST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Edan Orgad can be reached on 571-272-7884. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SITHU KO/Primary Examiner, Art Unit 2414
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