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 .
Claims Status
2. The response filed on March 04, 2026 has been entered and made of record.
3. Claims 1-8, 10-13 and 15-16 have been amended.
4. Claims 1-18 are currently pending.
Information Disclosure Statement
5. The Examiner has considered the reference(s) listed on the Information Disclosure Statement submitted on January 07, 2026.
Response to Arguments
6. The applicant's arguments filed on March 04, 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 § 112
7. 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.
8. 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.
9. There are two separate requirements under 35 U.S.C. § 112(b). MPEP § 2171. The first is subjective and requires that the claims must set forth the subject matter that the Applicants regard as their invention. Id. The second is objective and requires that the claims must particularly point out and distinctively define the metes and bounds of the subject matter that will be protected by the patent grant (i.e., whether the scope of the claim is clear to one of ordinary skill in the art). Id.
10. Claims 1 and 10 are 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 pre-AIA the applicant regards as the invention.
Regarding claim 1, the claim recites:
“providing at least one of the feedback information and information on the accuracy…” in line 9-10, and
“wherein the information provided to the consumer includes at least one of inference data or analytic information” in line 11.
It is unclear whether “the information provided to the consumer” in line 11 is referring back to “the feedback information” or “information on the accuracy” from lime 9-10.
Regarding claim 10, the claim is rejected based on the same reasoning as presented in the rejection of claim 1.
Appropriate corrections are requested. For the purpose of examinations, the examiner will interpret the claims as best understood.
Claim Rejections - 35 USC § 103
11. 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.
12. Claims 1, 4, 6, 9, 10, 13, 15 and 16 are rejected under pre-AIA 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2021/0144076 A1; as submitted by the applicant with IDS dated December 01, 2023), hereinafter “Lee’076” in view of CHONE et al. (US 2024/0428094 A1), hereinafter “Chone”.
Regarding claim 1, Lee’076 discloses a method of using feedback information (Fig.2, flowchart illustrating a method of optimizing for the network data analytics function device), the method comprising:
receiving feedback information of an information from a consumer provided with information generated through the ML model (Fig.2, paragraphs [0061], [0062], analytics information on the first network data from the network data analytics function device), wherein the feedback information is feedback regarding the information generated through the ML model from the consumer (Fig.2, paragraphs [0061], [0062], consumer network function device may provide feedback on the analytics information of the first network data to the network analytics function device); and
in response to the receiving of the feedback information (Fig.2, paragraphs [0067], [0070], evaluation value represents the degree of satisfaction with the analytics information), monitoring accuracy of the ML model (Fig.2, paragraphs [0067], [0070], in step (vi), the network data analytics function device evaluates the analytics information of the first network data based on the feedback obtained from the consumer network function device and the second network data collected in step (v)).
While Lee’076 implicitly refers to “requesting a machine learning (ML) model; and providing at least one of the feedback information and information on the accuracy, wherein the information provided to the consumer includes at least one of inference data or analytic information” (Fig.2, paragraphs [0056], [0057], [0078], the network data analytics function device 101 may generate an analytics model by itself based on model training (ex. Machine Learning) or may call an analytics model generated by another entity; accordingly, the network data analytics function device 101 may provide more appropriate analytics information for a specific use in the consumer network function device 102), Chone from the same or similar field of endeavor explicitly discloses requesting a machine learning (ML) model (Fig.3, A3-A4, paragraphs [0082], [0086], [0088], consumer NF sends a task request message to the AnLF to trigger the AnLF; AnLF sends a model request message to the MTLF); and
providing at least one of the feedback information and information on the accuracy (Fig. 3, A11-A13), wherein the information provided to the consumer includes at least one of inference data or analytic information (Fig. 3, paragraphs [0115]-[0116], [0122]-[0123], AnLF sends first information to the consumer NF to notify the consumer NF that the accuracy of the first model does not meet the accuracy requirement or has decreased).
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 “requesting a machine learning (ML) model; and providing at least one of the feedback information and information on the accuracy, wherein the information provided to the consumer includes at least one of inference data or analytic information” as taught by Chone, in the system of Lee’076, so that it would provide performing intelligent data analysis and generating data analytics results relate to a model accuracy determining method and apparatus, and a network-side device (Chone, paragraph [0002]).
Regarding claim 4, Lee’076 in view of Chone disclose the method according to claim 1.
Chone further discloses the providing comprises transmitting the at least one of the feedback information and the information on the accuracy in response to a request for subscription to accuracy monitoring of the ML model (Fig. 3, paragraphs [0115]-[0116], [0122]-[0123], consumer NF sends a task request message to the AnLF to trigger the AnLF; AnLF sends a model request message to the MTLF; AnLF sends first information to the consumer NF to notify the consumer NF that the accuracy of the first model does not meet the accuracy requirement or has decreased).
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 providing comprises transmitting the at least one of the feedback information and the information on the accuracy in response to a request for subscription to accuracy monitoring of the ML model” as taught by Chone, in the system of Lee’076, so that it would provide performing intelligent data analysis and generating data analytics results relate to a model accuracy determining method and apparatus, and a network-side device (Chone, paragraph [0002]).
Regarding claim 6, Lee’076 discloses at least one of the feedback information and the information on the accuracy is used for evaluation of the ML model (Fig.2, paragraphs [0067], [0070], in step (vi), the network data analytics function device evaluates the analytics information of the first network data based on the feedback obtained from the consumer network function device and the second network data collected in step (v)).
Regarding claim 9, Lee’076 discloses the feedback information comprises use case context (paragraphs [0063], [0065], case indication according to analytic information).
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 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.
13. Claims 2, 5, 7, 11, 14 and 16 are rejected under pre-AIA 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2021/0144076 A1; as submitted by the applicant with IDS dated December 01, 2023), hereinafter “Lee’076” in view of CHONE et al. (US 2024/0428094 A1), hereinafter “Chone” in view of CHENG et al. (US 2025/0220462 A1), hereinafter “Cheng”.
Regarding claim 2, Lee’076 in view of Chone disclose the method according to claim 1.
While Lee’076 in view of Chone 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 Lee’076 and Chone, 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 5, Lee’076 in view of Chone disclose the method according to claim 1.
While Lee’076 in view of Chone implicitly refer to “computing the accuracy based on the feedback information”, Cheng from the same or similar field of endeavor discloses computing the accuracy based on the feedback information (paragraphs [0078], [0245], [0281], [0292], model performance information feedback).
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 Cheng, in the combined system of Lee’076 and Chone, 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 7, Lee’076 in view of Chone disclose the method according to claim 1.
While Lee’076 in view of Chone implicitly refer to “receiving the ML model that is retrained or a newly selected ML model based on the at least one of the feedback information and the information on the accuracy”, Cheng from the same or similar field of endeavor discloses receiving the ML model that is retrained or a newly selected ML model based on the at least one of the feedback information and the information on the accuracy (paragraphs [0222]-[0224], second network side device may retrain the target model, and provide a retrained target model for the first network side device, to be put into actual use).
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 “receiving the ML model that is retrained or a newly selected ML model based on the at least one of the feedback information and the information on the accuracy” as taught by Cheng, in the combined system of Lee’076 and Chone, 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.
Regarding claim 14, the claim is rejected based on the same reasoning as presented in the rejection of claim 5.
Regarding claim 18, the claim is rejected based on the same reasoning as presented in the rejection of claim 9.
14. Claims 3 and 12 are rejected under pre-AIA 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2021/0144076 A1; as submitted by the applicant with IDS dated December 01, 2023), hereinafter “Lee’076” in view of CHONE et al. (US 2024/0428094 A1), hereinafter “Chone” 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, Lee’076 in view of Chone and Cheng disclose the method according to claim 2.
Neither Lee’076 nor Chone 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 Lee’076, Chone 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.
15. Claims 8 and 17 are rejected under pre-AIA 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2021/0144076 A1; as submitted by the applicant with IDS dated December 01, 2023), hereinafter “Lee’076” in view of CHONE et al. (US 2024/0428094 A1), hereinafter “Chone” in view of Lee et al. (US 2022/0108214 A1), hereinafter “Lee’214”.
Regarding claim 8, Lee’076 in view of Chone disclose the method according to claim 1.
While Lee’076 in view of Chone 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 Lee’076 and Chone, 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.
Conclusion
Applicant's amendment necessitated the new grounds 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 SITHU KO whose telephone number is 571-272-8647. The examiner can normally be reached on Monday-Friday 8:30am-5:00pmEST.
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/SITHU KO/ Primary Examiner, Art Unit 2414
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