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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Claims have priority date of foreign filing 04/03/2023 and 11/30/2023.
Response to Amendment
Office action in response to amendment entered 6/11/2026. Claims are not amended except for a minor correction, claims 16-19 are added; claims 1-19 remain pending in this application.
Response to Arguments
Applicant's arguments filed 6/11/2026 have been fully considered but they are not persuasive.
Applicant submits that Eklof in view of Zhang does not disclose or suggest a source node configured to "transmit... to a target NG-RAN node... an Xn handover request... compris[ing] a list of Artificial Intelligence or Machine Learning (AI/ML) use cases configured at the source NG-RAN node," as recited in claim 1. Examiner respectfully disagrees.
Applicant argues that “Zhang's request message does not include any list of AI/ML use cases” but only various measurements. What exactly is an “AI/ML use case”? The term is not defined and from its context appears to loosely reference AI models and measurements (See spec ¶13-16). Yet Applicant asserts that Zhang’s disclosed set measurements are different from the list of AI/ML use cases. This is not found persuasive. The content of Zhang’s request message in ¶87 shows it sends “measurement ID” as well as “report characteristics” – where the bitmap report characteristics is equivalent of the list as claimed as (See ¶87 Table “First Bit = PRB Periodic, Second Bit = TNL Capacity Ind Periodic, Third Bit = Composite Available Capacity Periodic, Fourth Bit = Number of Active UEs, Fifth Bit = RRC connections. Sixth Bit = Avg. Cell Throughput Seventh Bit = Predicted avg. throughput over Interval X Eighth Bit = Confidence level of predicted information Ninth Bit = Avg. time for UE to connect to cell 10.sup.th Bit = Quantized Histogram of AI/model error’). This discloses not only measurements but and ID and characteristics of those measurements as well as a specific histogram of AI/model errors. These features, independently and in combination could be said to teach an AI/ML “use case” especially since “use case” is not a well defined term in the art nor is it defined in a limited way in the specification.
Applicant adds claims 16-17 to try to define the term. Those claims state “the AI/ML use cases configured at the source NG-RAN node are different network operations that utilize corresponding measurement” however this is also problematic because the term “network operations” is itself not defined or appearing in the specification. For these reasons claims 16-17 are also rejected below for the same reasons and claim 1.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 16-17 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Applicant adds new claims 16-17 to recite “the AI/ML use cases configured at the source NG-RAN node are different network operations that utilize corresponding measurement” however this is also problematic because the term “network operations” is not defined or appearing in the specification, thus applicant has not shown that the feature was in the inventor’s grasp when filing. Further applicant has not cited any part of the specification in support of this amendment.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-3, 5-10, 12-17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over US 20220022121 A1 Eklöf; Cecilia et al. in view of US 20230138190 A1 Zhang; Yi et al.
Claims 1 and 8:
A system comprising: a source Next Generation-Radio Access Network (NG-RAN) node (Eklöf System of Figs. 10-12, Source node 1020) (204) configured to:
transmit over an inter NG-RAN node interface to a target NG-RAN node (206), an Xn handover request to handover (Eklöf Figs. 10-12 CHO Request e.g. ¶207 “The source node sends, to each of one or more target node candidates, a request to accept the conditional mobility operation related to the UE”) a User Equipment (UE) (202) (Eklöf Figs. 10-12 UE 1010) from the source NG-RAN node (204) (Eklöf Figs 10-12 Source node 1020) to the target NG-RAN node (206) (Eklöf Figs 10-12 Target node 1030),
wherein the Xn handover request comprises first measurement information configured at the source NG-RAN node (204) for the UE (202) (Eklöf Figs. 10-12 CHO Request e.g. ¶207 “The request can include the UE's current measConfig.”);
in response to the Xn handover request, receive from the target NG-RAN node (206), a Xn handover response comprising second measurement information to be configured for the UE (202) at the target NG-RAN node (206) (Eklöf Figs. 10-12 CHO Request Response e.g. ¶207 “The response can include the measConfig* that the target node has determined to be needed during execution of the mobility operation, i.e., after the UE has detected the condition triggering the mobility operation. The configuration measConfig* can be a full configuration or a delta configuration with respect to measConfig”), based on first measurement information received by the target NG- RAN node (206) from the source NG-RAN node (204) (¶207 “The configuration measConfig* can be a full configuration or a delta configuration with respect to measConfig,” thus based on source measConfg; ¶20 delta signaling explained ;
determine measurement reconfiguration data (319) for the UE (202), based on second measurement information received from the target NG-RAN node (206), until the UE (202) is handed over to the target NG-RAN node (206) (Eklöf Figs. 10-12 ¶208 “The source node then determines the measConfig′ needed so that the UE can detect and/or monitor the condition triggering the mobility operation. . . the source node determines measConfig′ after receiving measConfig* from the target node.”); and
transmit in a Radio Resource Control (RRC) reconfiguration message, the measurement reconfiguration data (319), to the UE (202), for dynamically updating measurements according to the list of AI/ML use cases supported by the target NG- RAN node (206) for the UE (202), based on the measurement reconfiguration data (319) (Eklöf Figs. 10-12 ¶209 “..the source node sends the UE a conditional mobility command that includes a first indication of the mobility operation to be performed, a second indication of the triggering condition, the measConfig′ related to detecting the triggering condition, and the measConfig* related to execution of the mobility operation with the target node..” where disclosed structure meets intended function of ‘for dynamically updating measurements according to the list of AI/ML use cases’ at the UE).
Eklöf does not explicitly disclose wherein the Xn handover request comprises a list of Artificial Intelligence or Machine Learning (AI/ML) use cases configured at the source NG-RAN node (204) for the UE (202) and a corresponding first measurement information associated with the list of AI/ML use cases;
wherein the Xn handover response comprising an active list of AI/ML use cases to be configured for the UE (202) at the target NG-RAN node (206) and a corresponding second measurement information associated with the active list of AI/ML use cases.
determine measurement reconfiguration data (319) for the UE (202), based on the active list of AI/ML use cases received from the target NG-RAN node (206).
Zhang is in the same field of endeavor, and figure 3 reproduced for convenience below.
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1 Zhang Figure 3
Zhang teaches wherein the Xn handover request (Zhang Figs. 3-5 Resource Status Request from Node 1 to Node 2; See also ¶93 new handover request) comprises a list of Artificial Intelligence or Machine Learning (AI/ML) use cases configured at the source NG-RAN node (204) for the UE (202) (¶61 “Energy efficiency” KPI “applied to AI/ML based energy saving use case”; ¶79 “The procedures shown in FIGS. 3-5 may also be applied to other use cases, like load balancing and mobility optimization” ¶87 message contents “measurement ID” and “report characteristics” fields) and a corresponding first measurement information associated with the list of AI/ML use cases (Zhang e.g. ¶75 “the cell may advertise the impact of changing the power state on the KPIs for each energy state (e.g. current/predicted cell capacity, current/predicted avg. cell throughput, current/predicted resource availability, current/predicted # of UEs able to be handled, current/predicted average time for a UE to connect to the cell from idle state, current CQI information, current mobility information of UEs, predicted UE latency and predicted UE throughput, etc.)”;
wherein the Xn handover response (Zhang Figs. 3-5 Resource Status Update / Response from Node 2 to Node 1; See also ¶93 new handover request acknowledgement) comprising an active list of AI/ML use cases to be configured for the UE (202) at the target NG-RAN node (206) and a corresponding second measurement information associated with the active list of AI/ML use cases (Zhang e.g ¶80 “feedback from the target NG-RAN node to the source NG-RAN node after handover occurs due to power saving or mobility optimization. The feedback may be used for further ML model training. . . . Similarly, the target NG-RAN may provide periodical feedback to the source NG-RAN to ensure the handover strategy (e.g., how many UEs are to be handed over, to which node the UE is to be handed over, which UE are to be handed over, etc.) is correctly selected by the source NG-RAN node” ¶95 “The feedback may also include error statistics as observed in the different AI/ML models. The statistics may include the CDF/histogram of the observed error in the AI/ML models. The feedback can be used for further training of the model, either locally at the NG-RAN or at the OAM to better predict the success/failure of the ML model output.”; ¶79 other use cases thus ‘list’ taught).
determine measurement reconfiguration data (319) for the UE (202), based on the active list of AI/ML use cases received from the target NG-RAN node (206) (Zhang Figs. 3-5, 3. RRC Reconfig send to UE).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the invention of Eklof to include the noted teachings of Zhang in order to provide information exchange to enable system energy savings AI/ML model work well, which is an important consideration for 5G and beyond 5G network deployment. Zhang ¶71
Claims 2 and 9:
The combination teaches The serving NG-RAN node of claim 1, wherein the active list of AI/ML use cases comprises at least one of the one or more AI/ML use cases selected by the target NG- RAN node (206) from the list of AI/ML use cases provided by the source NG-RAN node (204) to the target NG-RAN node (206) for the UE (202) and one or more additional AI/ML use cases needed by the target NG-RAN node (206) (Zhang ¶95 “The feedback may also include error statistics as observed in the different AI/ML models. The statistics may include the CDF/histogram of the observed error in the AI/ML models. The feedback can be used for further training of the model, either locally at the NG-RAN or at the OAM to better predict the success/failure of the ML model output.”; ¶79 other use cases thus obvious can include additional use cases needed by Target).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the combination include the noted teachings of Zhang in order to provide information exchange to enable system energy savings AI/ML model work well, which is an important consideration for 5G and beyond 5G network deployment. Zhang ¶71
Claims 3 and 10:
The combination teaches The source NG-RAN node (204) of claim 1, wherein the measurement reconfiguration data (319) comprises at least one of a set of measurements for AI/ML use cases to be de-configured from the list of AI/ML use cases configured at the source NG-RAN node (204), and a set of additional measurements for the new AI/ML use cases to be configured at the target NG-RAN node (206) for the UE (202) (where Eklöf Figs. 10-12 ¶209 “..the source node sends the UE a conditional mobility command that includes a first indication of the mobility operation to be performed, a second indication of the triggering condition, the measConfig′ related to detecting the triggering condition, and the measConfig* related to execution of the mobility operation with the target node..” meets intended function of configure/deconfigure at the UE, as the UE and operations at the UE are not bearing on the patentability of the source node invention).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the combination include the noted teachings of Zhang in order to provide information exchange to enable system energy savings AI/ML model work well, which is an important consideration for 5G and beyond 5G network deployment. Zhang ¶71
Claims 5 and 12:
The combination teaches The source NG-RAN node (204) of claim 1, wherein the first measurement information comprises measurement parameters of the UE (202) associated with the list of AI/ML use cases configured at the source NG-RAN node (204) (See as cited rejection of claim 1 supra).
Claims 6 and 13:
The source NG-RAN node (204) of claim 1, wherein the second measurement information comprises measurement parameters of the UE (202) associated with the active list of AI/ML use cases received from the target NG-RAN node (206) (See as cited rejection of claim 1 supra).
Claims 7 and 14:
The combination teaches The source NG-RAN node (204) of claim 1, further configured to:
prior to the handover of the UE (202), receive a Xn setup request from the target NG-RAN node (206), wherein the Xn setup request comprises information indicating a list of AI/ML use cases supported by the target NG-RAN node (206), and interoperability information of AI/ML models of the AI/ML use cases supported by the target NG-RAN node (206); and
transmit a Xn setup response to the target NG-RAN node (206) (Zhang Figs. 3-5 Resource Status Request from Node 1 to Node 2; See also ¶93 new handover request, wherein the Xn setup response comprises information indicating a list of AI/ML use cases supported by the source NG-RAN node (204) (¶79 “The procedures shown in FIGS. 3-5 may also be applied to other use cases, like load balancing and mobility optimization” thus presence of KPIs indicates use cases are supported), and interoperability information of AI/ML models of the AI/ML use cases supported by the source NG-RAN node (204)) comprises a list of Artificial Intelligence or Machine Learning (AI/ML) use cases configured at the source NG-RAN node (204) for the UE (202) (¶61 “Energy efficiency” KPI “applied to AI/ML based energy saving use case”; ¶79 “The procedures shown in FIGS. 3-5 may also be applied to other use cases, like load balancing and mobility optimization” teaching “interoperability information” as broadly claimed).
Claims 16-17
The combination teaches “The source NG-RAN node of claim 1, wherein the AI/ML use cases configured at the source NG-RAN node are different network operations that utilize corresponding measurements, configured for the UE, together with an NG-RAN node AI/ML model(s).” and “ The source NG-RAN node of claim 1, wherein the AI/ML use cases to be configured at the target NR-RAN node are different network operations that utilize corresponding measurements, to be configured for the UE, together with an NG-RAN node AI/ML model(s)” because the term “network operations” is itself not defined or appearing in the specification. For these reasons claims 16-17 are also rejected for the same reasons as claim 1 which teaches measurements and identification of AI/ML models in Zhang’s request message (Zhang ¶87).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the invention of Eklof to include the noted teachings of Zhang in order to provide information exchange to enable system energy savings AI/ML model work well, which is an important consideration for 5G and beyond 5G network deployment. Zhang ¶71
Claim 19
The combination teaches wherein the active list comprises at least one of one or more AI/ML use cases from the list of Artificial Intelligence or Machine Learning (AI/ML) use cases configured at the source NG-RAN node (204) for the UE (202) ((Zhang ¶61 “Energy efficiency” KPI “applied to AI/ML based energy saving use case”; ¶79 “The procedures shown in FIGS. 3-5 may also be applied to other use cases, like load balancing and mobility optimization” ¶87 message contents “measurement ID” and “report characteristics” fields))
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the invention of Eklof to include the noted teachings of Zhang in order to provide information exchange to enable system energy savings AI/ML model work well, which is an important consideration for 5G and beyond 5G network deployment. Zhang ¶71
Pertinent Prior Art(s)
The prior art made of record though not relied upon in the current rejection is considered pertinent to applicant's disclosure:
US 20220217597 A1 ISHII; Atsushi
[0011] In one example, an access node of a radio access network (RAN), the access node comprising: processor circuitry configured to generate at least one reconfiguration message comprising a measurement object, a trigger configuration for a conditional handover and an identity of a candidate target cell; transmitter circuitry configured to transmit the at least one reconfiguration message; wherein: the trigger configuration does not include a measurement report configuration.
[0012] In one example, a method for an access node of a radio access network (RAN), the method comprising: generating at least one reconfiguration message comprising a measurement object, a trigger configuration for a conditional handover and an identity of a candidate target cell, and; transmitting the at least one reconfiguration message; wherein: the trigger configuration does not include a measurement report configuration.
NEC, AIML Model Transfer Requirements during Handover, R2-2300253, 3GPP TSG-RAN WG2 Meeting #121, Athens, Greece, 16 February 2023
ERICSSON et al., (TP for AI/ML BLCR to TS38.423) Procedures for exchanging AI/ML-related information, R3-225509, 3GPP TSG-RAN WG3 Meeting #l l 7bis-e, E-meeting, 27 September 2022
NOKIA et al., (TP for TR 37.817) Discussion on AI/ML Energy Saving, Load Balancing and Mobility Optimization Use Cases, R3-220632, 3GPP TSG-RAN WG3 Meeting #14bis-e, E-meeting, 07 January 2022
US 2023-0014613 Al (SAMSUNG ELECTRONICS CO., LTD.) 19 January 2023
US 2022-0386196 Al (SAMSUNG ELECTRONICS CO., LTD.) 01 December 2022
Allowable Subject Matter
Claims 4 and 11 and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Claims 4 and 11 recite, in the language of claim 4, The source NG-RAN node (204) of claim 3, further configured to determine the measurements for the set of AI/ML use cases to be de-configured. And further recite identifying the AI/ML use cases other than the one or more AI/ML use cases selected by the target NG- RAN node (206) from the list of AI/ML use cases configured at the source NG RAN node. That limitation is not taught by or obvious in view of the cited prior art above.
Claim 18 recites, The source NG-RAN node of claim 1, further configured to determine measurements for at least one AI/ML use case to be de-configured by identifying the at least one AI/ML use case other than one or more AI/ML use cases selected by the target NG-RAN node (206) from the list of AI/ML use cases configured at the source NG RAN node. That limitation is not taught by or obvious in view of the cited prior art above.
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 extension fee 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 date of this final action.
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/UMAIR AHSAN/ Primary Examiner, Art Unit 2647
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