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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 20 January 2026 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 18 November 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
Applicant’s response, filed 20 January 2026, has been entered and carefully considered.
Claims 21, 28, 32 and 37 are amended.
Claims 21-40 are currently pending.
The outstanding rejections of Claims 21-40 under 35 U.S.C. 103 are withdrawn in light of Applicant’s amendment to Claims 21 and 32.
Response to Arguments
Applicant’s arguments with respect to claims 21 and 32 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 21-29 and 32-38 are rejected under 35 U.S.C. 103 as being unpatentable over Dao et al (WIPO Publication 2020/069662), hereinafter Dao ‘662, in view of Dao et al (United States Pre-Grant Publication 2019/0253917), hereinafter Dao ‘917, Sharma et al (United States Pre-Grant Publication 2024/0064105), hereinafter Sharma.
Regarding Claim 21, Dao ‘662 discloses a network apparatus comprising:
at least one memory (Figure 9, 920 and paragraph 0251);
and at least one processor coupled with the at least one memory (Figure 9, 910 and paragraph 0251) and configured to cause the network apparatus to:
receive a Quality of Service ("QoS") parameter for at least one QoS flow corresponding to at least one user equipment ("UE") (paragraph 0120 -- may send a Network QoS Information request message to the NWDAF 105. The request message may include one or more of PDU Session information, Application QoS Level (s) , PQCNC, and UE Travel Information. The PDU Session Information may include one or more of UE ID, S-NSSAI, DNN, RAT Type (e.g. 4G (e.g. LTE eNB) , 5G (R) AN node (NR gNB) ) Access Type (e.g. 3GPP, non-3GPP) , PDU Session Type (e.g. IPv4, IPv6, Ethernet, unstructured PDU session) , current QoS setting in RAN (e.g. from RAN QoS Profiles, 5QI, of PDU Session for UL and DL) , and mode of redundant packet transmission in the (R) AN (the UE is connected to two (R) AN nodes simultaneously for packet duplication transmission) , and/or CN UP path duplication (there are two separate UP paths over N3/N9 interfaces between the UPF (s) and (R) AN node (s) ) and/or UE duplication (e.g. two UEs in one mobile device to support data transmission for 1 application));
obtain a model related to data analytics outputting associated with the at least one UE, or at least one serving base station, or both (paragraph 00121 – the NWDAF receives the Network QoS information request, and accesses a critical sub-segment(s) of the road segment, where one important QoS parameter may drop below a critical threshold where the associated probability (expected network condition) is equal or higher than the pre-determined probability threshold; parameters include guaranteed flow bit rate and packet error rate are QoS parameters supported by the mobile network (or a radio node), as described in paragraph 0042);
determine an expected QoS profile pattern for a first time interval according to the Al model, wherein the expected QoS profile pattern comprises a plurality of QoS profiles for the at least one QoS flow during the first time interval (paragraph 00121, the NWDAF determines a time period that critical road sub-segment may happen according to UE travel information and other statistical network QoS information configured in a potential QoS change notification configuration (PQCNC); paragraph 00132, wherein the Network QoS Information response comprises time periods that road sub-segments may or may not be traversed);
and transmit an indication of the expected QoS profile pattern to at least one network node associated with the QoS flow (paragraph 0122 and Figure 4, wherein the network QoS information response is sent to the UE via the SMF, AMF and RAN).
However, Dao ‘662 does not disclose wherein the QoS parameter comprises a QoS flow ID and a prioritized list of QoS profiles, the list comprising an original QoS profile and at least one alternative QoS profile. In an analogous art, Dao ‘917 discloses this. Specifically, Dao ‘917 discloses an SMF providing one or more QoS profiles to a RAN node, where the profiles include parameters such as a QoS Identifier and an Allocation and Retention Priority (paragraph 0101). Thus, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine Dao ‘662 and Dao ‘917. One would have been motivated to do so in order to improve reporting of events in which QoS parameters of delay critical GBR QoS flows are violated (refer to paragraph 0005 of Dao ‘917).
However, the aforementioned references do not disclose an Artificial Intelligence (“AI”) model. In an analogous art, Sharma discloses this (paragraph 0145, wherein the NWDAF is implemented using AI to predict congestion periods). Thus, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine Dao ‘662 with Sharma. One would have been motivated to do so in order to manage network exposure function traffic congestion in 5G networks (paragraphs 0002 and 0014-0016 of Sharma).
Claim 32 is directed to a method comprising the steps performed in the claimed apparatus of Claim 21. Therefore Claim 32 is rejected using the same rationale as presented for Claim 21.
Regarding Claims 22 and 33, Dao ‘662 further discloses the first time interval comprises a second time interval and a third time interval, wherein the expected QoS profile pattern comprises a sequence of QoS profiles of the plurality of QoS profiles for the at least one QoS flow, the sequence comprising a first QoS profile of the plurality of QoS profiles for the second time interval and a second QoS profile of the plurality of QoS profiles for the third time interval, the second QoS profile different than the first QoS profile (paragraph 00132, wherein the Network QoS Information response comprises time periods (i.e., multiple time intervals) that road sub-segments may or may not be traversed, as well as QoS profiles for the time periods (e.g., that the road sub-segment will be in low QoS status)).
Regarding Claims 23 and 34, Dao further discloses the expected QoS profile pattern indicates whether the current QoS profile is expected to downgrade to a different QoS profile of the plurality of QoS profiles for at least one of the first, the second, or the third time intervals, or for the geographical area, or both (paragraph 00132, wherein the Network QoS Information response comprises time periods (i.e., multiple time intervals) that road sub-segments may or may not be traversed, as well as QoS profiles for the time periods (e.g., that the road sub-segment will be in low QoS status)), thereby meeting the claimed alternative limitation.
Regarding Claims 24 and 35, Dao ‘662 further discloses the at least one processor is configured to cause the network apparatus to receive, from the at least one serving base station, a request to determine the expected QoS profile pattern (paragraphs 0041 and 00113; where in the UE (transmitting via the RAN) requests network QoS information; paragraph 00121, the NWDAF determines a time period that critical road sub-segment may happen according to UE travel information and other statistical network QoS information configured in a potential QoS change notification configuration (PQCNC)), and wherein to transmit the indication of the expected QoS profile pattern, at least one processor is configured to cause the network apparatus to transmit to the at least one serving base station (paragraph 0041, wherein the QoS information is provided to/from the UE via the radio access node (RAN)).
Regarding Claims 25 and 36, Dao ‘662 further discloses wherein to transmit the indication of the expected QoS profile pattern, the at least one processor is configured to cause the network apparatus to send a predictive QoS report to the at least one serving base station (paragraph 0041, wherein the QoS information is provided from the UE via the radio access node (RAN)), and wherein the predictive QoS report includes the expected QoS profile pattern and at least one of the following: an area and time of validity; an enforcement flag indicating whether the expected QoS profile pattern is to be enforced; an upgrade or downgrade indication for each QoS transition in the expected QoS profile pattern; (paragraph 0049, wherein locations/road segments where the UE may experience lower QoS than a QoS threshold are provided; paragraph 00132, wherein the Network QoS Information response comprises time periods (i.e., multiple time intervals) that road sub-segments may or may not be traversed, as well as QoS profiles for the time periods (e.g., that the road sub-segment will be in low QoS status)), therefore meeting the claimed alternative limitation.
Regarding Claim 26, Dao ‘662 discloses the at least one serving base station determines whether the expected QoS profile pattern requires QoS profile remapping or RAN-level adaptation (paragraph 0058, the QoS notification control message from the RAN indicates new values of supported QoS parameters).
Regarding Claim 27, Dao ‘662 discloses wherein the at least one serving base station transmits to a core network function in response to determining that expected QoS profile pattern requires QoS profile remapping, wherein the transmission to the core network function includes a QoS flow indicator and indicates at least one alternative QoS profile of the plurality of QoS profiles (paragraph 0058, the QoS notification control message from the RAN indicates new values of supported QoS parameters).
Regarding Claims 28 and 37, Dao ‘662 discloses the QoS parameter comprises at least one of the following: a session ID, and a UE ID; a geographical area; a time validity; a hysteresis threshold; a network slice identifier; or a combination thereof (paragraph 0047, wherein network slice information (e.g., S-NSSAI) and network slice instance identifier (NSI ID) are provided).
Regarding Claims 29 and 38, Dao ‘662 further discloses the at least one processor is configured to cause the network apparatus to receive, from the at least one serving base station, a parameter for a network slice (paragraph 0047, wherein network slice information (e.g., S-NSSAI) and network slice instance identifier (NSI ID) are provided), meeting the claimed alternative limitation.
Claims 30, 31, 39, and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Dao ‘662 in view of Dao ‘917 and Sharma, as applied to claims 21 and 32 above, and further in view of Kuai (United States Pre-Grant Publication 2022/0150130 A1).
Regarding Claims 30 and 39, the combination of Dao ‘662, Dao ‘917 and Sharma discloses all of the limitations of Claims 21 and 32, as described above. However, the aforementioned references do not disclose the AI model is a trained AI model comprising at least one of: an expected RAN resource condition for a future duration; an expected wireless backhaul resource condition for the future duration; an expected UE mobility pattern; expected UE trajectories for all the UEs in a service area; an expected channel quality fluctuation in an expected route of the UE; an expected performance metric for one or more selected UEs in the service area; or a combination thereof. In an analogous art, Kuai discloses this (paragraphs 0081-0082, wherein network performance indicators (i.e., expected performance metrics for one more selected UEs in the service area) are utilized to train the NWDAF mean opinion score (MOS) ML model in paragraphs 0111-0114). Thus, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine Dao ‘662 / Dao ‘917 / Sharma with Kuai. One would have been motivated to do so in order to improve accuracy of quality of service MOS scores or target services in 5G networks (paragraph 0008 of Kuai).
Regarding Claims 31 and 40, the combination of Dao ‘662, Sharma, and Kuai discloses the limitations of Claims 30 and 39, as described above. However, Dao’ 662, Dao ‘917 and Sharma do not disclose wherein to determine the expected QoS profile pattern from the AI model for the first time interval, the at least one processor is configured to cause the network apparatus to map a set of expected conditions to the plurality of QoS profiles, wherein the set of expected conditions are predicted by the trained AI model. In an analogous art, Kuai discloses this (paragraphs 0051 (including Table 1) and 0052, which describes separate resource types, priority levels, and network parameters to define end-to-end network quality of service levels, thereby allowing the MOS model of specified services to be obtained). Thus, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine Dao ‘662 / Dao ‘917 / Sharma with Kuai. One would have been motivated to do so in order to improve accuracy of quality of service MOS scores or target services in 5G networks (paragraph 0008 of Kuai).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
NWDAF: Automating the 5G network with machine learning and data analytics is directed to predictive QoS in V2X deployments.
SG Automotive Association; Working Group System Architecture and Solution Development; SGS Enhancements for Providing Predictive QoS in C-V2X discloses making UE-based QoS Predictions (section 5.1.4).
3GPP TS 23.288 V16.4.0 (3rd Generation Partnership Project; Technical Specification Group Services and System Aspects; Architecture enhancements for 5G System (5GS) to support network data analytics services (Release 16)) discloses NWDAF-assisted UE behavioral analytics (clause 6.7.4.4.1) and QoS sustainability analysis (clause 6.9).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW W. CHRISS whose telephone number is (571)272-1774. The examiner can normally be reached Monday-Friday, 8am-4pm ET.
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/ANDREW W CHRISS/Primary Examiner, Art Unit 2472