CTNF 18/784,301 CTNF 100625 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Information Disclosure Statement 06-52 AIA The information disclosure statement (IDS) submitted on 08/26/2024 was filed after the mailing date of the present application on 07/25/2024 . The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA Claim s 1-5, 8-15, and 20-24 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Jeon, U.S. Patent Application Publication No. 2024/0113794 (hereinafter Jeon) . Regarding Claim 1 , Jeon teaches a first apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor (“ As shown in FIG. 3, the UE 116 includes antenna(s) 305, a transceiver(s) 310, and a microphone 320. The UE 116 also includes a speaker 330, a processor 340, an input/output (I/O) interface (IF) 345, an input 350, a display 355, and a memory 360. The memory 360 includes an operating system (OS) 361 and one or more applications 362 ” – See [¶0060] and “ the processor 340 may execute processes for predicting future CSI in cellular systems ” – See [¶0064] and “ indicating to a UE one or multiple instances, including present and future instances, for which the CSI report is constructed ” – See [¶0096]), cause the first apparatus at least to: perform, during a first time period, first one or more measurements to obtain first measured information related to the first time period (“ The channel is measured using CSI reference resource at slot n ref ” – See [¶0090] and, as shown in Fig. 8, “ a UE utilizes a channel measured at the CSI reference resource at slot n ref and possibly a set of past channel measurements denoted {n ref –W m , ... , n ref } with measurement window duration denoted by Wm, ” – See [¶0106] i.e. , the first time period); determine, based on the first measured information, predicted information related to a second time period which is after the first time period (“ the CSI can be predicted for a future instance, e.g., n rep , based on the channel measurement at n ref ; ” – See [¶0090], e.g. , “[p] redicted future CSI instances are denoted by {n ref +o, ... , n ref +o+ W P }, where o is the time offset from the CSI reference resource in slot n ref and W P is the time window for prediction ” – See [¶0106] and Fig. 11 showing CSI measurement window W m and CSI prediction window W P ) perform, during the second time period, second one or more measurements to obtain second measured information related to the second time period (because “[t] he channel aging effect . . . channel tracking is important ” – See [¶0090] “ the network 130 can send CSI-RS at an instance, denoted by n ref_new which coincides with one of the instances for which the UE 116 predicted CSI previously, denoted by n ref_prev+o . This is one way that the UE 116 obtains knowledge on the ground truth CSI for which that it previously predicted ” and “ the performance monitoring report may indicate and/or include at least one of the predicted CSI, the ground-truth CSI, the performance index, and a timestamp ” – See [¶0161], i.e. , the UE performs measurement of the ground-truth CSI RS sent by the network at n ref_new ); and transmit, to a second apparatus, the second measured information and the predicted information (“ The network 130 may indicate the UE 116 to send the CSI report for the present instance with the CSI reference resource at n ref_new , for which the UE 116 reported the predicted CSI previously based on the CSI reference resource at n ref_prev , such that the comparison between the predicted CSI for n ref_new and the present ground truth CSI at n ref_new can be made at the network 130 ” – See [¶0162]; see also [¶0161] supra ). Therefore, Claim 1 is anticipated by Jeon. Regarding Claim 2 , dependent from Claim 1, further teaches the first apparatus of claim 1, wherein the first apparatus is further caused to: prior to performing the first one or more measurements, receive, from the second apparatus, configuration information for configuring the first apparatus to obtain the second measured information and the predicted information related to the second time period (“ The UE 116 can be indicated by the network 130 that certain predicted CSI for a future instance can be used for comparison with ground truth CSI at the future instance ,” e.g. , with “ periodic CSI, the UE 116 may be implicitly indicated from the network 130 via periodic CSI configuration and the configuration for future instances for prediction ” or with “ aperiodic CSI, the UE 116 may be indicated by the network 130 via PDCCH providing DCI triggering aperiodic CSI report that certain predicted CSI requested by the network 130 can be used for the purpose of comparison with ground truth CSI in the future. With such an indication, the UE 116 recognizes to store the predicted CSI for future use. For aperiodic CSI, the UE 116 may be indicated by the network 130 via PDCCH providing DCI triggering aperiodic CSI report one or multiple future instances that the network 130 will configure CSI reference resource. With such an indication, the UE 116 can derive instances coinciding between the instances for future CSI reference resources and the instances that the UE 116 is requested to send the predicted CSI report. Therefore, the UE 116 stores the predicted CSI for such coinciding instances for the purpose of comparison with ground truth CSI in the future ” – See [¶0166]). Therefore, Claim 2 is anticipated by Jeon. Regarding Claim 3 , dependent from Claim 2, further teaches the first apparatus is further caused to: transmit, to the second apparatus, acknowledgement information indicating that the first apparatus has been configured to obtain the second measured information and the predicted information related to the second time period (“ the received information related to monitoring the performance of the ML model for predicting CSI may include and/or indicate . . . an acknowledgement (ACK)/negative acknowledgement (NACK) and/or (ii) information related to a monitoring Periodicity ” – See [¶0163]; see e.g. , 3GPP TS 38.214 v17.6.0, “Technical Specification Group Radio Access Network; NR; Physical Layer Procedures for Data (Release 17)” (hereinafter 3GPP TS 38.214), included by reference – See [¶0036] stating, at page 131, that “ the UE would transmit a PUCCH with HARQ-ACK information in slot n corresponding to the PDSCH carrying the activation command described in clause 6.1.3.16 of [10, TS 38.321] ” whereby “[t] he activation command will contain one or more Reporting Settings where the associated CSI Resource Settings are configured, ” i.e. , the UE sends acknowledgement information indicating it has been configured; see also § 6.1.3.16, 3GPP TS 38.321 V17.5.0 (2023-06), “Technical Specification Group Radio Access Network; NR; Medium Access Control (MAC) protocol specification (Release 17)” (hereinafter 3GPP TS 38.321), also included by reference – See [¶0036] describing at page 176, “ the Semi-Persistent CSI report configuration within csi-ReportConfigToAddModList, as specified in TS 38.331 ”). Therefore, Claim 3 is anticipated by Jeon. Regarding Claim 4 , dependent from Claim 1, further teaches the first apparatus of claim 1, wherein the first apparatus is further caused to: receive, from the second apparatus, a request that the first apparatus transmits the second measured information and the predicted information to the second apparatus (“ the UE 116 may be indicated by the network 130 to report [a configured] metric value with and without the present CSI at n ref_new ” – See [¶0163] whereby “ the UE 116 may predict again the future instance n ref_prev+o , that it previously predicted using CSI reference resource at n ref_rev’ using newer CSI reference resource later in time, denoted by n ref_new' , which may not coincide with the predicted instance n ref_prev+o ” – See [¶0161]) wherein the transmitting of the second measured information and the predicted information is responsive to the request (“ the performance monitoring report may indicate and/or include at least one of the predicted CSI, the ground-truth CSI, the performance index, and a timestamp ” – See [¶0161]). Therefore, Claim 4 is anticipated by Jeon. Regarding Claim 5 , dependent from Claim 1, further teaches the first apparatus of claim 1, wherein the first apparatus is further caused to: during the second time period, inputting the first measured information into an artificial intelligence (AI)/machine learning (ML) model (“ the UE 116 may indicate that it supports an AI/ML-based CSI prediction method as in FIG. 8 ” – See [¶0146] including “ an indication on the maximum time span that the UE 116 can predict, i.e., how far in the future that the UE 116 can predict ” – See [¶0147]; and “ can be indicated by the network 130 the prediction window for which the UE 116 predicts future CSI . . . where WP is the prediction window duration and o is the prediction start offset from the CSI reference resource, ” – See [¶0114] i.e. , the second time period, and the “ UE utilizes a channel measured at the CSI reference resource at slot n ref , and possibly a set of past channel measurements ,” i.e. , the first measured information, with “ a CSI predictor is then utilized on the past channel measurements at the UE 116 for predicting CSI for future time instances ” – See [¶0106] and Fig. 8 at step 820). Therefore, Claim 5 is anticipated by Jeon. Regarding Claim 8 , dependent from Claim 1, Jeon further teaches the first apparatus of claim 1, wherein the predicted information comprises at least one predicted CSI report (“ the UE 116 then performs CSI prediction for future instances ” and “ the UE 116 then send the CSI report to the network 130 ” – See [¶0108] and Fig. 9 whereby “[o] nce the UE 116 predicts CSI for future instances, the information can be encoded into a CSI report and transmitted to the network 130 via various methods ” – See [¶0109]), and the at least one predicted CSI report is transmitted to the second apparatus within one of the following: a first reporting time period aligned with a plurality of predicted CSI reports (“ When CSI is reported for more than one instance, including the present and future instances, it can be compressed in time domain ” or “ the CSI report for multiple instances can be separately encoded and transmitted to the network 130 either in one report or multiple individual reports, ” – See [¶0109] i.e., reporting time period aligned with a plurality of predicted CSI reports as shown in Fig. 11 whereby the “ UE can be indicated by the network 130 the prediction window for which the UE 116 predicts future CSI ” – See [¶0114] or “ the network 130 can indicate a set of offset values, e.g., {o1, o2 , o3}, to the UE 116, and the UE 116 is to predict the CSI for {n ref +o1, n ref +o2 , n ref +o3 ” – See [¶0117] i.e. , first reporting time period is aligned with a plurality of predicted CSI reports.); or a second reporting time period longer than the first reporting time period (“ UE can be indicated by the network 130 the prediction start offset o, the prediction interval I, and the number of instances for prediction, ” e.g. , as shown in Fig. 12, wherein “ the start offset from CSI reference resource is set to 6 slots, the prediction interval is set to 2 slots, and the number of future CSI reports is set to 3 . . . to predict the CSI for a set of future instances of{n ref +6, n ref +8, n ref +10} ” – See [¶0116] i.e. , a second reporting time period longer than the first reporting time period). Therefore, Claim 8 is anticipated by Jeon. Regarding Claim 9 , dependent from Claim 1, further teaches the first apparatus of claim 1, wherein: the first measured information and the second measured information comprise measured CSI (“ A UE derives CSI from DL CSI-RS measurement ” – See [¶0087] e.g. , “[t] he channel is measured using CSI reference resource at slot n ref ” – See [¶0090] and “ the network 130 can send CSI-RS at an instance, denoted by n ref_new which coincides with one of the instances for which the UE 116 predicted CSI previously, denoted by n ref_prev +o. This is one way that the UE 116 obtains knowledge on the ground truth CSI for which that it previously predicted ” – See [¶0161] so a first and a second measurement are CSI-RS measurements); and the predicted information comprises predicted CSI (“ the UE 116 may predict again the future instance n ref_prev +o, that it previously predicted using CSI reference resource at n ref_rev' using newer CSI reference resource later in time, denoted by n ref_new' ” – See id .). Therefore, Claim 9 is anticipated by Jeon. Regarding Claim 10 , dependent from Claim 1, further teaches the first apparatus of claim 1, wherein: the first apparatus is a device under test (DUT) (“ The UE 116 can be indicated by the network 130 that certain predicted CSI for a future instance can be used for comparison with ground truth CSI at the future instance ” and “[t] he UE 116 derives instances coinciding between the instances for CSI reference resources and instances for CSI prediction. The UE 116 then stores the predicted CSI for such coinciding instances for the purpose of comparison with ground truth CSI which will become available in the future ” – See [¶0166], i.e., the UE is the DUT); and the second apparatus is test equipment (TE) (“ the comparison between the predicted CSI for n ref_new and the present ground truth CSI at n ref_new can be made at the network 130 ” – See [¶0162] i.e., the Network is the TE). Therefore, Claim 10 is anticipated by Jeon. Regarding Claim 11 , dependent from Claim 1, further teaches the first apparatus of claim 1, wherein the first apparatus is further caused to: determine, based on the first measured information and the AI/ML model, the predicted information related to the second time period. Regarding Claim 12 , Jeon teaches, in Fig. 2, a second apparatus comprising: at least one processor (“ The controller/processor 225 can include one or more processors or other processing devices that control the overall operation of the gNB 102 ” – See [¶0049]); and at least one memory storing instructions that, when executed by the at least one processor (“ The memory 230 is coupled to the controller/processor 225” – See [¶0052] “The controller/processor 225 is also capable of executing programs and other processes resident in the memory 230, such as to support prediction of future CSI in cellular systems. The controller/processor 225 can move data into or out of the memory 230 as required by an executing process ” – See [¶0050]), cause the second apparatus at least to: transmit, to a first apparatus, configuration information for configuring the first apparatus to obtain measured information and predicted information related to a time period (“ A UE can predict future CSI based on the measurement window indicated by the network 130 ” – See [¶0113] including “ the prediction window for which the UE 116 predicts future CSI ” – See [¶0114] and “ the UE 116 is then provided from the network 130 information related to CSI prediction model monitoring including performance index to monitor, triggering events to send a report and/or transmission of any assistance information to the network 130 ” – See [¶0143] and Fig. 11 wherein “[f] or aperiodic CSI report, all or part of information indicating future instances for prediction can be conveyed in the PDCCH providing downlink control information (DCI) triggering CSI report ” – See [¶0118]) wherein the predicted information is to be obtained based on an artificial intelligence (AI)/machine learning (ML) model (“ the UE 116 may indicate that it supports an AI/ML-based CSI prediction method as in FIG. 8 ” – See [¶0146]); receive, from the first apparatus, the measured information and the predicted information related to the time period (“ When a UE reports CSI for more than one instance, including the present and/or future instances, the CSI report for each instance can be self-contained or the CSI report for one instance can be relative to another instance ” – See [¶0120] and “ the performance monitoring report may indicate and/or include at least one of the predicted CSI, the ground-truth CSI, the performance index, and a timestamp ” – See [¶0161]); and determine performance information of the AI/ML model based on the measured information and the predicted information (“ The performance of the currently configured CSI prediction model may degrade over time as the UE's channel environment changes, ” – See [¶0132] e.g. , “[t] he network 130 may indicate the UE 116 to send the CSI report for the present instance with the CSI reference resource at n ref_new , for which the UE 116 predicted CSI previously based on the CSI reference resource at n ref_prev , such that the comparison between the predicted CSI for n ref_new and the present ground truth CSI at n ref_new can be made at the network 130 ” – See [¶0162]). Therefore, Claim 12 is anticipated by Jeon. Regarding Claim 13-14 , dependent from Claim 12, further teaches the second apparatus of claim 12 wherein the second apparatus is further caused to: perform the steps recited in Claims 3-4 using the same language, only from the perspective of the second apparatus. Because Claims 3-4 and 12 are anticipated by Jeon, Claims 13-14 are also anticipated by Jeon. Regarding Claim 15 , dependent from Claim 12, further teaches the second apparatus of claim 12, wherein the second apparatus is caused to determine the performance information by: comparing the predicted information to the measured information (“ The UE 116 can be indicated by the network 130 that certain predicted CSI for a future instance can be used for comparison with ground truth CSI at the future instance ” and “ the UE 116 can derive instances coinciding between the instances for future CSI reference resources and the instances that the UE 116 is requested to send the predicted CSI report. Therefore, the UE 116 stores the predicted CSI for such coinciding instances for the purpose of comparison with ground truth CSI in the future ” – See [¶0166] whereby “ the comparison between the predicted CSI [at one instance] and the present ground truth CSI [at that instance] can be made at the network 130 ” – See [¶0162]); and deriving, based on the comparison, a key performance indicator (KPI) of the AI/ML model (a KPI of the AI/ML model at the UE may be “ the accuracy of the CSI prediction in a certain measure such as in normalized mean squared error (NMSE), cosine similarity, etc., which may be provided separately for different channel environment, e.g., LOS/NLOS, UMa/UMi/InH/rural, different UE speed, etc ” – See [¶0150] devised using “ a metric, e.g., NMSE, cosine similarity, etc., for measuring the difference between the previously predicted CSI and the present ground truth CSI, and a method and/or parameters to perform averaging of such measurements ” – See [¶0168]). Therefore, Claim 15 is anticipated by Jeon. Regarding Claim 20 , dependent from Claim 12, further teaches the second apparatus of claim 12, wherein the predicted information comprises at least one predicted CSI report with the same limitations as recited in Claim 8 using the same language. Because Claims 8 and 12 are anticipated by Jeon, Claim 20 is anticipated by Jeon. Regarding Claim 21 , dependent from Claim 12, further teaches the second apparatus of claim 12, wherein: the measured information comprises measured CSI; the predicted information comprises predicted CSI (“ the network 130 can send CSI-RS at an instance, denoted by nref_new which coincides with one of the instances for which the UE 116 predicted CSI previously ” so “ the UE 116 obtains knowledge on the ground truth CSI for which that it previously predicted ” – See [¶0161] i.e. , both a measured CSI and a predicted CSI are reported by the UE; and the performance information comprises CSI prediction accuracy (“ the received information related to monitoring the performance of the ML model for predicting CSI may include and/or indicate (i) a performance index for measuring a performance of the ML model for CSI prediction including: a NMSE, metrics based on cosine similarity, including squared generalized cosine similarity (SGCS) ” – See [¶0163] whereby “ the performance monitoring report may indicate and/or include at least one of the predicted CSI, the ground-truth CSI, the performance index, and a timestamp ” – See [¶0161] and “ the accuracy of the CSI prediction [is] a certain measure such as in normalized mean squared error (NMSE), cosine similarity, etc .” – See [¶0150]]). Therefore, Claim 21 is anticipated by Jeon. Regarding Claim 22 , dependent from Claim 12, further teaches the second apparatus of claim 12, wherein the same limitations apply as recited in Claim 10 using the same language. Because Claims 10 and 12 are anticipated by Jeon, Claim 22 is also anticipated by Jeon. Regarding Claims 23-24 , each claim recites a method comprising the same steps as executed by the first apparatus of Claim 1 and the second apparatus of Claim 12, respectively, using the same language. Because each of Claims 1 and 12 is anticipated by Jeon, Claims 23-24 are anticipated by Jeon. In sum, Claims 1-5, 8-15, and 20-24 are rejected under 35 U.S.C. § 102(a)(2) as anticipated by Jeon . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA 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. 07-20-02-aia AIA 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. 07-22-aia AIA Claim s 6-7 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Jeon as applied to claim s 1 and 12 above, and further in view of Abebe et al., U.S. Patent Application Publication No. 2025/0047349 (hereinafter Abebe) . Regarding Claim 6 , dependent from Claim 1, Jeon further teaches the first apparatus of claim 1, wherein the first apparatus is further caused to perform the second one or more measurements by: measuring first one or more channel state information (CSI)-reference signals (RS) from the second apparatus, wherein the first one or more CSI-RSs are transmitted during the second time period (“ the network 130 can send CSI-RS at an instance, denoted by n ref_new which coincides with one of the instances for which the UE 116 predicted CSI previously, denoted by n ref_prev+o ” so that “ that the UE 116 obtains knowledge on the ground truth CSI for which that it previously predicted ,” – See [¶0161] i.e. , the UE measures a real CSI as the ground truth). While Jeon teaches that the CSI-RS is sent with non-zero power (“ For beamformed CSI-RS, beamforming operation, either cell-specific or UE-specific, is applied on a non-zero-power (NZP) CSI-RS resource (including multiple ports) ” – See [¶0080]), Jeon does not teach that the CSI-RS is at least one of (i) without noise and interference or (ii) with a power boost. Abebe teaches “ a method performed by a base station in a wireless communication system includes transmitting to a terminal, configuration information for performing performance monitoring and reporting the monitoring outcome of predictive CSI ” – See [¶0017] including “ information to activate/deactivate/switch/monitor artificial intelligence and machine learning (AIML) functionalities and models ” – See [¶0020]. Abebe further teaches the CSI-RS is at least one of (i) without noise and interference or (ii) with a power boost (“ the CSI-RS is the primary reference signal that is used by the UE to measure and report CSI ” and “[i] n MIMO systems ” is required “ that a signal from the BS is received at the UE with maximum possible received power and minimum possible interference ” – See [¶0078]). Thus, Jeon and Abebe each discloses a UE receiving one or more CSI-RSs from a base station to perform measurements and predict future instances based on an AI/ML model. A person of ordinary skill in the art before the effective filing date of the claimed invention would have understood that the CSI-RSs transmitted during the second time period for measuring the ground truth in Jeon could have been substituted by the CSI-RS sent by the base station with maximum power and minimum interference as taught in Abebe because both CSI-RSs are meant to help the UE to evaluate the efficiency of the AI/ML model. Furthermore, a person of ordinary skill in the art would have been able to carry out the substitution through techniques known in the art. Finally, the substitution achieves the predictable result of expanding Jeon teachings to MIMO systems, as taught in Abebe. Therefore, Claim 6 is obvious over Jeon in view of Abebe. Regarding Claim 7 , dependent from Claim 6, Jeon does not teach one or more CSI-RSs transmitted in parallel. Abebe teaches the method performed by a base station, wherein: the first one or more CSI-RSs are transmitted without noise and interference and are transmitted in parallel to second one or more CSI-RSs with noise and interference (“ The CSI resource configuration (602) may contain one or more CSI resource sets (604) for channel measurement (CMR) or inference measurement (IMR) ” – See [¶0080] and Fig. 6, whereby multiple “[a] ntenna ports (CSI-RS ports) are mapped to a CSI-RS with code-domain multiplexing (CDM) group 0, group 1 and group 2 ” in parallel on the same symbols, as shown in Fig. 5, e.g. , “ by mapping a CSI-RS port to one of the polarization of a subarray ” in Fig. 4 – See [¶0079] and it would be obvious for a person of ordinary skills in the art that CSI-RS for CMR would be sent with minimum interference and CSI-RS for IMR would be sent with noise and interference; see also Jeon:[¶0080] (“ For non-precoded (NP) CSI-RS, a cell-specific one-to-one mapping between CSI-RS port and transceiver unit (TXRU) is utilized, ” i.e. , different CSI-RSs can be sent in different frequency bands in FDD)); and resources for receiving the first one or more CSI-RSs are orthogonal to resources for receiving the second one or more CSI-RSs (“[t] he antenna ports that are mapped to the same CDM group can be orthogonalized in code-domain by employing orthogonal cover codes ” when “ each CDM group is mapped to 4 resource elements (REs) in OFDM time-frequency grid ”– See id . and Fig. 5). Therefore, Claim 7 is obvious over Jeon in view of Abebe. Regarding Claim 17 , dependent from Claim 12, Jeon further teaches the second apparatus of claim 12, wherein the second apparatus is further caused to determine the efficiency of the AI/ML model (“ there is a need to define a metric to measure the effectiveness of the currently used CSI prediction model, ” – See [¶0132]) and to provide model LCM to the UE (“ The UE 116 can be provided from the network 130 a new CSI prediction model, e.g., via model transfer, or a new model description ” – See [¶0172] and “ can be instructed by the network 130 to perform model retraining or finetuning ” – See [¶0173]). However, Jeon does not teach the second apparatus caused to determine at least one requirement for testing the AI/ML model; and determine the configuration information based on the at least one requirement. Abebe teaches at least one requirement for testing the AI/ML model (“ prediction method including the AI/ML model applied by the terminal may only be applicable to a certain set of applicable scenarios and configurations, such as the UE's speed, as shown below in Table 1 ” so that “[t] he AI/ML model for CSI prediction could be trained based on a training dataset associated with certain UE speed or certain range of UE speeds ” – See [¶0096] and Table 1 showing results of testing the model at different speeds); and determine the configuration information based on the at least one requirement (“ it may be beneficial for the UE to maintain multiple AI/ML models each applicable to a narrower range of scenarios, e.g., UE speeds ” and configure the UE to “ switch through these AI/ML models based on the current situation ,” i.e. , the present speed – See id .). Thus, Jeon and Abebe each discloses a UE receiving AI/ML model configuration based on model lifecycle management by the base station. A person of ordinary skill in the art before the effective filing date of the claimed invention would have understood that the testing of the CSI prediction AI/ML model(s) to measure efficiency based on UE speed requirement and to determine an appropriate configuration of the UE with one or mode models, as taught in Abebe, could have been combined with the model lifecycle management taught in Jeon because both are based on the model switch capability of the UE – See Jeon: [¶0154](“ the capability that the UE 116 can switch CSI prediction model among multiple models that the UE 116 supports ”). Furthermore, a person of ordinary skill in the art would have been able to carry out the combination through techniques known in the art. Finally, the combination achieves the predictable result of expanding AI/ML models used by the UE to a requirement related to UE’s speed, as taught by Abebe. Therefore, Claim 17 is obvious over Jeon in view of Abebe. Regarding Claim 18-19 , dependent from Claim 12, anticipated by Jeon, further teaches the second apparatus of claim 12, wherein the second apparatus is further caused to: transmit first one or more channel state information (CSI)-reference signals (RS) to the first apparatus with the same limitations as recited in Claims 6-7, respectively, using the same language. Because Claims 6-7 are obvious over Jeon in view of Abebe, Claims 18-19 are also obvious over Jeon in view of Abebe. In sum, Claims 6-7 and 17-19 are rejected under 35 U.S.C. § 103 as obvious over Jeon in view of Abebe . 07-22-aia AIA Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Jeon as applied to claim 15 above, and further in view of Hao et al., U.S. Patent Application Publication No. 2025/0173612 (hereinafter Hao) . Regarding Claim 16 , dependent from Claim 15, Jeon further teaches the second apparatus of claim 12, wherein the second apparatus is further caused to manage the lifecyle of the AI/ML model at the UE (“ supporting lifecycle management for CSI prediction can be performed . . . by the network 130 of FIG. 1 ” – See [¶0142] and Fig. 16 showing procedure 1600 for supporting lifecycle management). However, Jeon does not teach monitoring based on determining that the KPI is within a tolerance range to determine a test on the AI/ML model is passed or failed. Hao teaches a “ AI/ML functional framework 700 may be deployed in various RAN intelligence-based use cases. Such use cases may include CSI feedback enhancement, enhanced beam management (BM), positioning and location (Pos Loc) accuracy enhancement, and various other use cases ” – See [¶0123] and Fig. 7, whereby “ AI-based CSI feedback may replace the codebook by a CSI encoder 902 (at the UE) and a decoder 9004 at the network (e.g., gNB) ” – See [¶0137] and Fig. 9. Hao further teaches a base station (the second apparatus) based on determining that the KPI is within a tolerance range, determine a test on the AI/ML model is passed; or based on determining that the KPI is outside the tolerance range, determine the test on the AI/ML model is failed (“ As depicted, during time interval 802, the model output 804 closely tracks the actual values 806 (e.g., of a channel estimation) . . . During interval 802, there are two reporting events in which the device deploying the model reports the performance of the model, including the two out-of-distribution (OOD) events. Notably, the OOD events depict instances in which model output deviates significantly (e.g., based on a threshold) from the actual values 806 ” and “ Based on these OOD events, a network entity, such as the base station 102 described with respect to FIGS. 1-3, may determine to send a model update ” – See [¶0133] and Fig. 8; furthermore, the disclosure “ provide [s] various options regarding ML model performance monitoring and decision making. Aspects provide various options, including a first option involving network side (e.g., gNB-side) ML model performance monitoring ” – See [¶0140] whereby “ the gNB may request assistance information from the UE” and “may generate one or more key performance indicators (KPIs) and generate a monitoring performance report (e.g., including the KPIs) ” – See [¶0143] and Fig. 11, and “[i] f a condition is detected based on the monitoring, the gNB may decide to retrain or switch models, ” – See [¶0144] e.g. , “ the gNB may compare the CSI inference (predicted) results (i.e., CSI decoder output) to the ground-truth (provided in the assistance information). The comparison can be in terms of KPI (e.g., mean square error-MSE), spectral efficiency, throughput, or cosine-similarity ” – See [¶0149] or “ the gNB may use SRS measurements as ground-truth to determine an OOD/model failure ” – See [¶0150]). Thus, Jeon and Hao each discloses a base station monitoring performance of an AI/ML model used by a UE to predict future CSI feedback instances. A person of ordinary skill in the art before the effective filing date of the claimed invention would have understood that the UE reporting to the base station the ground truth CSI and the predicted CSI, as taught by Jeon, could have been combined with the ML model performance monitoring and decision making by the base station based on KPIs to determine model failure, as taught by Hao, because rely on the same type of KPIs for measuring AI/ML model efficiency for lifecycle management purposes. Furthermore, a person of ordinary skill in the art would have been able to carry out the combination through techniques known in the art. Finally, the combination achieves the predictable result of integrating AI/ML model lifecycle in the standards (O-RAN and 3GPP) aligned AI Functional Framework for RAN Intelligence, as taught by Hao. Therefore, Claim 16 is obvious over Jeon in view of Hao . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure : Sun, U.S. Patent Application Publication No. 2026/0025184 discloses a method and apparatus for monitoring CSI prediction performance, a terminal, and a network-side device using AI-based CSI prediction and determining the CSI prediction performance of the AI model; Gu, U.S. Patent Application Publication No. 20260019126 discloses method and apparatus for reporting ground truth in a group for enhance CSI feedback, predictive beam management, and/or positioning using AI/ML models; Abebe et al., U.S. Patent Application Publication No. 20250047331 teaches performance of AI/ML-based CSI prediction performed at the base station; Lo et al., U.S. Patent Application Publication No. 20230337036 teaches differential CSI prediction, using an ML model and trained model parameters updates; Echigo et al., WIPO Patent Application Publication No. WO2024261968A1 discloses AI-based CSI feedback; 3GPP TSG RAN WG1 #113, R1-2306235, Updates to 3GPP TR 38.843 V0.1.0 (2023-05), “Technical Specification Group Radio Access Networks; Study on Artificial Intelligence (AI)/Machine Learning (ML) for NR air interface (Release 18),” published May, 2023; 3GPP TSG RAN WG1 #113, Draft Report of 3GPP TSG RAN WG1 #113 v0.2.0, published June 1, 2023, §§ 9.1 &9.2, and documents referenced therein. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /L.G.G./ Examiner, Art Unit 2478 /JOSEPH E AVELLINO/ Supervisory Patent Examiner, Art Unit 2478 Application/Control Number: 18/784,301 Page 2 Art Unit: 2478 Application/Control Number: 18/784,301 Page 3 Art Unit: 2478 Application/Control Number: 18/784,301 Page 4 Art Unit: 2478 Application/Control Number: 18/784,301 Page 5 Art Unit: 2478 Application/Control Number: 18/784,301 Page 6 Art Unit: 2478 Application/Control Number: 18/784,301 Page 7 Art Unit: 2478 Application/Control Number: 18/784,301 Page 8 Art Unit: 2478 Application/Control Number: 18/784,301 Page 9 Art Unit: 2478 Application/Control Number: 18/784,301 Page 10 Art Unit: 2478 Application/Control Number: 18/784,301 Page 11 Art Unit: 2478 Application/Control Number: 18/784,301 Page 12 Art Unit: 2478 Application/Control Number: 18/784,301 Page 13 Art Unit: 2478 Application/Control Number: 18/784,301 Page 14 Art Unit: 2478 Application/Control Number: 18/784,301 Page 15 Art Unit: 2478 Application/Control Number: 18/784,301 Page 16 Art Unit: 2478 Application/Control Number: 18/784,301 Page 17 Art Unit: 2478 Application/Control Number: 18/784,301 Page 18 Art Unit: 2478 Application/Control Number: 18/784,301 Page 19 Art Unit: 2478 Application/Control Number: 18/784,301 Page 20 Art Unit: 2478 Application/Control Number: 18/784,301 Page 21 Art Unit: 2478 Application/Control Number: 18/784,301 Page 22 Art Unit: 2478
