DETAILED ACTION
The instant application having Application No. 18/138,457 filed on 04/24/2023 is presented for examination by the examiner.
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 .
Status of Claims
Claims 1, 3-4, 6, 14, 17, 19 and 20 are amended. Claims 21-23 are added. Claims 1-4, 6 and 14-23 are pending.
Response to Arguments
On page 25 of the Applicant’s remark (10/27/2025), Applicant further argues that the cited references do not disclose “obtain an action impact analysis for the at least one enforced action and the machine learning model; evaluate the action impact analysis”, as recited by claims 1, 4 and 6”. In response, Examiner respectfully disagrees.
As stated in NPL, “(Page 17, Section 6.28.1), Measuring the impact of the decision of a NF that uses predicted outputs of an Analytic ID. The impact can be calculated according to the change of relevant KPIs of the NF, after the enforcement of a decision that was made, based on a predicted output of an Analytic ID. For example, the Analytics ID performance information can be defined per Analytics ID from the NF who measures the impact of the decision of a NF that uses predicted outputs of the Analytic ID…. (Page 17, Option 1), Once an analytics consumer uses an Analytics ID, NWDAF (AnLF) requests the consumer on the feedback / evaluation of the analytics service (good or bad, experience level, success, or failure of prediction, with a possible cause)]”. As such, NPL discloses the limitation “obtain an action impact analysis for the at least one enforced action and the machine learning model; evaluate the action impact analysis”, as recited by claims 1, 4 and 6.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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, 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 6, and 14-23 are rejected under 35 U.S.C. 103 as being unpatentable over 3GPP TR 23.700-81 V0.2.0 (“3rd Generation Partnership Project; Technical Specification Group Services and System Aspects; Study of Enablers for Network Automation for 5G System (5GS); Phase 3 (Release 18)”; from Applicant’s IDS; hereinafter NPL) in view of Pateromichelakis et al. (WO 2023/104346 A1 hereinafter Pateromichelakis).
Regarding claim 1, NPL teaches “transmit, to an analytics producing entity, an indication to perform action impact analysis for a machine learning model;” as [(Pages 23-24, See Figure 6.4.2-1, Step 1), the consumer transmits to the NWDAF a Request Analytics (Analytic ID, Minimum accuracy)] “receive, from the analytics producing entity, a message comprising at least one of: analytics, a prediction, or a recommendation from the machine learning model;” [(Page 23-24, See Figure 6.4.2-1, Step 6), The AnLF NWDAF provides requested analytics to the consumer.] “select at least one action based, at least partially, on the received message;” [(Page 23-24, See Figure 6.4.2-1, Step 7), The consumer may determine an action based on the received analytics] “enforce the at least one selected action;” [(Page 23-24, See Figure 6.4.2-1, Step 7), The consumer may determine an action based on the received analytics. For example, if the analytics indicate a high load at a UPF function the consumer, i..e. SMF, may select a less loaded UPF.] “obtain an action impact analysis for the at least one enforced action and the machine learning model;” [(Page 17, Section 6.28.1), Measuring the impact of the decision of a NF that uses predicted outputs of an Analytic ID. The impact can be calculated according to the change of relevant KPIs of the NF, after the enforcement of a decision that was made, based on a predicted output of an Analytic ID. For example, the Analytics ID performance information can be defined per Analytics ID from the NF who measures the impact of the decision of a NF that uses predicted outputs of the Analytic ID] “evaluate the action impact analysis;” [(Page 17, Option 1), Once an analytics consumer uses an Analytics ID, NWDAF (AnLF) requests the consumer on the feedback / evaluation of the analytics service (good or bad, experience level, success, or failure of prediction, with a possible cause)] “determine whether to request a change regarding the machine learning model based, at least partially, on the evaluation of the action impact analysis” [(Page 110, Section 6.29.1), However, the accuracy of the trained ML model when used for analytics, e.g. accuracy level or confidence of the predictions using the trained ML model, may not meet the analytics requirement(s) of the NWDAF containing AnLF according to the analytics request(s)/subscription(s) from analytics consumer NF(s). Also the accuracy of the trained ML model may diminish, due to e.g. changes of network status and changes of UE communication patterns, etc…. The trained ML model needs to be updated by the NWDAF containing MTLF in order to guarantee or improve the correctness of analytics by the NWDAF containing AnLF. Hence, a solution of enhancing existing ML model provisioning procedure is proposed, so that the NWDAF containing MTLF can detect the degradation of an ML model, update the ML model towards the NWDAF containing AnLF accordingly and thus improve the correctness of NWDAF analytics].
However, NPL does not specifically disclose an apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed with the at least one processor, cause the apparatus at least to.…
In an analogous art, Pateromichelakis teaches “an apparatus comprising: at least one processor;” as [(Para. 0044), the processor 202] “and at least one memory;” [(Para. 0044), the memory 204] “storing instructions that, when executed with the at least one processor, cause the apparatus at least to:…” [(Para. 0044), T the processor 202 executes instructions stored in the memory 204 to perform the methods and routines described herein].
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the technique of NPL with the modified system of Pateromichelakis for effectively determining application data and/or analytics that may be useful for application service providers and/or verticals using the wireless communications networks for providing application service [Pateromichelakis: Para. 0002].
Regarding claim 2, the combination of NPL and Pateromichelakis, specifically NPL teaches “wherein the indication to perform action impact analysis for the machine learning model comprises at least one of: an indication of a start time for the action impact analysis, an indication of a duration for the action impact analysis, an indication of a time interval for the action impact analysis, an indication of a time frequency for the action impact analysis, an indication of a periodicity for the action impact analysis, an indication of a report type for the action impact analysis, an identifier of the machine learning model, an indication of a target aggregation for the action impact analysis, an indication of at least one network element to be monitored, an indication of a data flow to be monitored, an indication of a traffic flow to be monitored, an indication of a network area to be monitored, an indication of a cell to be monitored, an indication of a slice to be monitored, an indication of a set of indicators impacted by the at least one enforced action to be monitored, or an indication of one or more network functions to be monitored” as [(Page 33, See Figure 6.7.2.1-1, Step 7), The NWDAF sends through the Ntrlf_AnalyticsServiceConsumed service to the TRLF information about the Consumer ID, Analytics ID, information on the ML model used for producing the analytics (if any), its own NWDAF (instance or Set) ID and the token generated for the Analytics consumer. In this way, the TRLF can associate the rating from the Consumer to the analytics service provided by the NWDAF and, implicitly, to the ML model used to generate it in case the analytics service is based on an ML model].
Regarding claim 3, NPL teaches “cause the apparatus to: transmit, to the analytics producing entity, an indication of the at least one enforced action” as [(Page 23-24, See Figure 6.4.2-1, Step 8), Based on the feedback indication the consumer determines to report an action based on analytics received from an NWDAF… (Page 23-24, See Figure 6.4.2-1, Step 9), The consumer reports the action take to the MTLF].
However, NPL does not specifically disclose wherein the instructions, when executed with the at least one processor.
In an analogous art, Pateromichelakis teaches “wherein the instructions, when executed with the at least one processor” as [(Para. 0028), the instructions, which execute via the processor of the computer].
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the technique of NPL with the modified system of Pateromichelakis for effectively determining application data and/or analytics that may be useful for application service providers and/or verticals using the wireless communications networks for providing application service [Pateromichelakis: Para. 0002].
Regarding claim 4, the claim is interpreted and rejected for the same reason as set forth in claim 1.
Regarding claim 6, the claim is interpreted and rejected for the same reason as set forth in claim 1, including “a non-transitory computer-readable medium comprising instructions stored thereon which…” as [(Pateromichelakis: Para. 0019), The storage devices may be tangible, non-transitory, and/or non-transmission.].
Regarding claim 14, NPL teaches “cause the apparatus to: transmit, to the analytics producing entity, a request for the change regarding the machine learning model based, at least partially, on the evaluation of the action impact analysis” [(Page 110, Section 6.29.1), However, the accuracy of the trained ML model when used for analytics, e.g. accuracy level or confidence of the predictions using the trained ML model, may not meet the analytics requirement(s) of the NWDAF containing AnLF according to the analytics request(s)/subscription(s) from analytics consumer NF(s). Also the accuracy of the trained ML model may diminish, due to e.g. changes of network status and changes of UE communication patterns, etc…. The trained ML model needs to be updated by the NWDAF containing MTLF in order to guarantee or improve the correctness of analytics by the NWDAF containing AnLF. Hence, a solution of enhancing existing ML model provisioning procedure is proposed, so that the NWDAF containing MTLF can detect the degradation of an ML model, update the ML model towards the NWDAF containing AnLF accordingly and thus improve the correctness of NWDAF analytic] “comprising a determination that the at least one enforced action resulted in degradation of at least one monitored indicator impacted by the at least one enforced action” as [(Page 110, Section 6.29.1), However, the accuracy of the trained ML model when used for analytics, e.g. accuracy level or confidence of the predictions using the trained ML model, may not meet the analytics requirement(s) of the NWDAF containing AnLF according to the analytics request(s)/subscription(s) from analytics consumer NF(s). Also the accuracy of the trained ML model may diminish, due to e.g. changes of network status and changes of UE communication patterns, etc. This solution considers two aspects of the ML Model degradation problem: - Detection of the degradation of an ML model. -Triggering of a proper action in case any degradation in an ML model is observed.].
However, NPL does not specifically disclose wherein the instructions, when executed with the at least one processor.
In an analogous art, Pateromichelakis teaches “wherein the instructions, when executed with the at least one processor” as [(Para. 0028), the instructions, which execute via the processor of the computer].
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the technique of NPL with the modified system of Pateromichelakis for effectively determining application data and/or analytics that may be useful for application service providers and/or verticals using the wireless communications networks for providing application service [Pateromichelakis: Para. 0002].
Regarding claim 15, the claim is interpreted and rejected for the same reason as set forth in claim 2.
Regarding claim 16, the claim is interpreted and rejected for the same reason as set forth in claim 3.
Regarding claim 17, the claim is interpreted and rejected for the same reason as set forth in claim 14.
Regarding claim 18, the claim is interpreted and rejected for the same reason as set forth in claim 2.
Regarding claim 19, the claim is interpreted and rejected for the same reason as set forth in claim 3.
Regarding claim 20, the claim is interpreted and rejected for the same reason as set forth in claim 14.
Regarding claim 21, the combination of NPL and Pateromichelakis, specifically NPL teaches “wherein obtaining the action impact analysis comprises the instructions, when executed with the at least one processor, cause the apparatus to: receive the action impact analysis from the analytics producing entity” as [(Page 212, Option 2), NWDAF (AnLF) provides the analytics output to the analytics consumer].
Regarding claim 22, the combination of NPL and Pateromichelakis, specifically NPL teaches “wherein the action impact analysis comprises at least one of: an indication of whether the at least one enforced action results in degradation of at least one indicator, an indication that the at least one enforced action results in at least one positive impact, an indication that the at least one enforced action results in at least one negative impact, one or more indicators evaluated for the at least one enforced action, or at least one trend associated with the one or more indicators” as [(Page 212, Option 1), Once an analytics consumer uses an Analytics ID, NWDAF (AnLF) requests the consumer on the feedback / evaluation of the analytics service (good or bad, experience level, success, or failure of prediction, with a possible cause).].
Regarding claim 23, the combination of NPL and Pateromichelakis, specifically NPL teaches “wherein evaluating the action impact analysis comprises the instructions, when executed with the at least one processor, cause the apparatus to: determine whether an impact indicated with the obtained action impact analysis is a result of: the at least one enforced action, or the machine learning model” as [(Page 212, Section 6.28.1), Measuring the impact of the decision of a NF that uses predicted outputs of an Analytic ID. The impact can be calculated according to the change of relevant KPIs of the NF, after the enforcement of a decision that was made, based on a predicted output of an Analytic ID].
Conclusion
Applicant's amendment necessitated the new ground(s) 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 NATALI N PASCUAL PEGUERO whose telephone number is (571)272-4691. The examiner can normally be reached Monday-Friday 11AM-9PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ASAD M NAWAZ can be reached at (571)272-3988. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NATALI PASCUAL PEGUERO/Examiner, Art Unit 2463
/ASAD M NAWAZ/Supervisory Patent Examiner, Art Unit 2463