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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/18/2024 and 05/11/2026 are 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 § 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.
In 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 factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-3, 6-7, 10-13, 16-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over KHOSHNEVISAN et al. (2025/0047460), KHOSHNEVISAN hereinafter, in view of Lo et al. (2024/0088968, as submitted in IDS), Lo hereinafter.
Re. claims 1, and 11, KHOSHNEVISAN teaches a method (Fig. 3A-3B & Fig. 5 & ¶0085-¶0086/¶0088) implemented in a wireless transmit/receive unit (WTRU) (Fig. 2, 220/Fig. 3A, UE 320), and a wireless transmit/receive unit (WTRU) (Fig. 2, 220/Fig. 3A, UE 320) comprising: a processor (Fig. 2, 280) configured to: receive network assistance information (Fig. 3A-3B & Fig. 5 & ¶0086 - The Tx/Rx calibration phase 330 includes a third operation 336 in which the assisting node 310 transmits a reference signal, such as a CSI-RS, to the UE 320, which enables the UE 320 to estimate the effective downlink channel. For example, the CSI-RS may be received by the UE 320, and may be represented as Z=Φ.sub.URH.sub.DLΦ.sub.BT+N, where Φ.sub.UR is a receive-side mismatch at the UE 320, H.sub.DL is the downlink (or sidelink) channel over which the CSI-RS is transmitted, @BT is a transmit-side mismatch at the assisting node 310, and N is additive noise. At this point, the UE 320 has access to both the estimate of the effective uplink channel (or transmission channel) and the effective downlink channel (or reception channel). Fig. 3 & ¶0088 - in a first operation 352, the assisting node 310 may transmit a CSI-RS or other reference signal to the UE 320. Also, see steps 510/520 in Fig.5); determine a downlink channel state information (CSI) based one on more downlink reference signals (RSs) (Fig. 3A-3B & Fig. 5 & ¶0086 - in a fourth operation 338, the UE 320 can calculate the imbalance matrix Φ.sub.U=Φ.sub.UTΦ.sub.UR.sup.−1 based on the received version of the CSI-RS, Z, and the effective uplink channel that is indicated in the assistance information provided by the assisting node 310. Fig. 3A-3B & Fig. 5 & ¶0088 – In a second operation 354, the UE 320 may estimate a downlink channel from the assisting node 310 based on the received reference signal, and may estimate an effective uplink channel to the assisting node 310 based on the estimate of the downlink channel from the received reference signal and the imbalance matrix that was obtained during the Tx/Rx calibration phase 330/340. The UE 320 may then determine a precoding for a transmission (for example, a PUSCH, PSSCH, or SRS) based on the estimate of the downlink channel from the received reference signal and the imbalance matrix that was obtained during the Tx/Rx calibration phase 330/340. Also, see steps 530 in Fig.5); determine a value associated with an uplink/downlink (UL/DL) channel relation based on the network assistance information and measurements performed on the one or more downlink RSs (Fig. 3A-3B & Fig. 5 & ¶0085 - the UE 320 to obtain a Tx/Rx imbalance matrix that contains the one or more Tx/Rx imbalance values…..a dimension of the Tx/Rx imbalance matrix (or the number of values included in the Tx/Rx imbalance matrix) may correspond to the number of antennas at the UE 320, and each Tx/Rx imbalance value may be a complex value that includes a gain imbalance component and a phase imbalance component. For example, in FIG. 3A, example 300A depicts a first approach that includes a Tx/Rx mismatch calibration phase 330 in which the UE 320 calculates the Tx/RX imbalance matrix Φ.sub.U=Φ.sub.UTΦ.sub.UR.sup.−1 using assistance information provided by the assisting node 310. Fig. 3A-3B & Fig. 5 & ¶0086 - in a fourth operation 338, the UE 320 can calculate the imbalance matrix Φ.sub.U=Φ.sub.UTΦ.sub.UR.sup.−1 based on the received version of the CSI-RS, Z, and the effective uplink channel that is indicated in the assistance information provided by the assisting node 310. Fig. 3 & ¶0088 – In a second operation 354, the UE 320 may estimate a downlink channel from the assisting node 310 based on the received reference signal, and may estimate an effective uplink channel to the assisting node 310 based on the estimate of the downlink channel from the received reference signal and the imbalance matrix that was obtained during the Tx/Rx calibration phase 330/340. The UE 320 may then determine a precoding for a transmission (for example, a PUSCH, PSSCH, or SRS) based on the estimate of the downlink channel from the received reference signal and the imbalance matrix that was obtained during the Tx/Rx calibration phase 330/340. Also, see ¶0089. Also, see steps 520/530 in Fig.);
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Yet, KHOSHNEVISAN does not expressly teach send a CSI feedback report, wherein the CSI feedback report is based on the measurements performed on the one or more downlink RSs and/or the value of the measured statistics that quantify the UL/DL channel relation.
However, in the analogous art, Lo explicitly discloses send a CSI feedback report, wherein the CSI feedback report is based on the measurements performed on the one or more downlink RSs (Fig. 1-32 &¶0005 - user equipment (UE) is provided. … The processor is configured to measure the CSI-RSs; determine, based on the configuration information, the measured CSI-RSs, and information related to localized observations of channel statistics in one or more domains, configuration parameters in the one or more domains for the CSI reports; and determine, based on the configuration parameters in the one or more domains, a CSI report. The transceiver is configured to transmit the CSI report. Fig. 1-32 & ¶0007 - a method performed by a UE is provided. The method includes transmitting capability information indicating a capability of the UE to support a ML based parameter configuration associated with CSI reports; …The method further includes measuring the CSI-RSs; determining, based on the configuration information, the measured CSI-RSs,…...determining, based on the configuration parameters in the one or more domains, a CSI report; and transmitting the CSI report. Also, see step 2308/2312 in Fig. 23, step 2408/2412 in Fig. 24, step 3012/3016 in Fig. 30. Examiner interprets that one of the claimed features to be mapped because of the presence of “and/Or” in the limitation) and/or the value of the measured statistics that quantify the UL/DL channel relation.
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Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine KHOSHNEVISAN’s invention of user equipment (UE) transmit/receive (Tx/Rx) calibration in time division duplexing (TDD) using assistance information in a wireless communication system to include Lo’s invention of method and apparatus for support of machine learning (ML) or artificial intelligence (AI) assisted channel state information (CSI) feedback in a (5G)/new radio (NR) wireless communication system, because it provides an efficient mechanism in reducing overhead of DL <downlink> CSI feedback significantly by using pre-defined codebooks (e.g., Type I and Type II) for compressing DL CSI before it is reported to a serving base station, in turns, improves overall user throughput for a user equipment (UE) operating in the (5G)/new radio (NR) wireless communication system. (¶0078-¶0079, Lo)
Re. claims 2 and 12, KHOSHNEVISAN and Lo teach claims 1 and 11.
KHOSHNEVISAN further teaches wherein the associated value that quantifies UL/DL channel relation indicates a relation between an uplink channel and a downlink channel associated with the one or more downlink RSs. (Fig.2/Fig. 3A-3B & Fig. 5 & ¶0071 - when there is channel reciprocity between the uplink and the downlink, the network node 210 may derive the particular beam to communicate with the UE 220 (for example, on both the uplink and downlink) based on uplink measurements of one or more uplink reference signals, such as an SRS, transmitted by the UE 220. Fig.2/Fig. 3A-3B & Fig. 5 & ¶0079 - uplink and downlink channels are reciprocal (H.sub.DL=H.sub.UL.sup.T), the network node can then obtain a precoding for one or more downlink transmissions (for example, a PDSCH transmission or a CSI-RS transmission) based on H.sub.UL. Similarly, in an uplink direction, the UE may receive a CSI-RS from the network node, which the UE may use to obtain H.sub.DL. Assuming that the uplink and downlink channels are reciprocal (H.sub.UL=H.sub.DL.sup.T), the UE can then obtain a precoding for one or more uplink transmissions (for example, a PUSCH transmission or an SRS transmission) based on H.sub.DL. Fig. 3A-3B & Fig. 5 & ¶0085 - the UE 320 to obtain a Tx/Rx imbalance matrix that contains the one or more Tx/Rx imbalance values…..a dimension of the Tx/Rx imbalance matrix (or the number of values included in the Tx/Rx imbalance matrix) may correspond to the number of antennas at the UE 320, and each Tx/Rx imbalance value may be a complex value that includes a gain imbalance component and a phase imbalance component. For example, in FIG. 3A, example 300A depicts a first approach that includes a Tx/Rx mismatch calibration phase 330 in which the UE 320 calculates the Tx/RX imbalance matrix Φ.sub.U=Φ.sub.UTΦ.sub.UR.sup.−1 using assistance information provided by the assisting node 310. Fig. 3A-3B & Fig. 5 & ¶0086 - in a fourth operation 338, the UE 320 can calculate the imbalance matrix Φ.sub.U=Φ.sub.UTΦ.sub.UR.sup.−1 based on the received version of the CSI-RS, Z, and the effective uplink channel that is indicated in the assistance information provided by the assisting node 310. See steps 338 & 354 in Fig. 3A).
Re. claims 3 and 13, KHOSHNEVISAN and Lo teach claims 1 and 11.
Yet, KHOSHNEVISAN does not expressly teach wherein the processor is configured to adjust one or more parameters used to generate the CSI feedback report based on a comparison between the associated value that quantifies the UL/DL channel relation and one or more thresholds.
However, in the analogous art, Lo explicitly discloses wherein the processor is configured to adjust one or more parameters used to generate the CSI feedback report based on a comparison between the associated value that quantifies the UL/DL channel relation and one or more thresholds. (Fig. 1-32 & ¶0203 - a BS can include a resource utilization threshold in configuration information for a UE. If a UE determines that the resource utilization is equal or higher than the resource utilization threshold, then the UE may use a conventional method to determine the CSI report to not exacerbate its resource utilization (as using an ML-based method may be resource-intensive). On the other hand, if the UE determines that the resource utilization is lower than the resource utilization threshold, then the UE may use an ML-based method to determine the CSI report. Fig. 1-32 & ¶0204 - a BS can configure a UE to select either an AI/ML-based CSI feedback/report method from among several candidate AI/ML-based CSI feedback/report methods (where each method corresponds to a certain resource utilization level) or a conventional CSI feedback/report method. A BS can also configure a UE to report its current resource utilization along with an indication of its selected method. Fig. 1-32 & ¶0207 - BS can configure a UE with threshold values for several DL channel statistics; a UE can then apply either an AI/ML-based CSI feedback/report method or a conventional CSI feedback/report method, depending on its measured DL channel statistics relative to the configured thresholds (e.g., if the coherence bandwidth of the DL channel lies below the corresponding configured threshold, then a UE can apply a conventional CSI feedback/report method). Fig. 1-32 & ¶0209 - a BS can configure a UE with a set of reference channels (e.g., each element of that set can be an N-by-K complex matrix, where N corresponds to the number of antennas, and K corresponds to the number of SBs), a similarity metric (e.g., NMSE), and a similarity threshold. A UE can then compute the similarity between its measured DL channel and each of the elements in the set of reference channels; if any of these similarity values exceeds the similarity threshold, then a UE can apply an AI/ML-based CSI feedback/report method (otherwise, a UE can apply a conventional CSI feedback/report method). A BS can also configure a UE to report its computed similarity values along with the CSI feedback/report method that it has selected.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine KHOSHNEVISAN’s invention of user equipment (UE) transmit/receive (Tx/Rx) calibration in time division duplexing (TDD) using assistance information in a wireless communication system to include Lo’s invention of method and apparatus for support of machine learning (ML) or artificial intelligence (AI) assisted channel state information (CSI) feedback in a (5G)/new radio (NR) wireless communication system, because it provides an efficient mechanism in reducing overhead of DL <downlink> CSI feedback significantly by using pre-defined codebooks (e.g., Type I and Type II) for compressing DL CSI before it is reported to a serving base station, in turns, improves overall user throughput for a user equipment (UE) operating in the (5G)/new radio (NR) wireless communication system. (¶0078-¶0079, Lo)
Re. claims 6 and 16, KHOSHNEVISAN and Lo teach claims 1 and 11.
KHOSHNEVISAN further teaches wherein the CSI feedback report may include the associated value that quantifies the UL/DL channel relation. (Fig.2/Fig. 3A-3B & Fig. 5 & ¶0101 - process 500 includes estimating the one or more Tx/Rx imbalance values associated with the assisting node in accordance with the received version of the CSI-RS and the assistance information received from the assisting node, and transmitting, to the assisting node, feedback that indicates the one or more estimated Tx/Rx imbalance values associated with the assisting node.)
Re. claims 7 and 17, KHOSHNEVISAN and Lo teach claims 1 and 11.
Yet, KHOSHNEVISAN does not expressly teach wherein the processor is configured to generate the CSI feedback report via an artificial intelligence/machine learning (AI/ML) model.
However, in the analogous art, Lo explicitly discloses wherein the processor is configured to generate the CSI feedback report via an artificial intelligence/machine learning (AI/ML) model. (Fig. 1-32 & ¶0043 - FIG. 30 illustrates an example method for operations at a UE to support a BS-initiated or BS-controlled fallback from an AI/ML-based multi-time CSI feedback/report method to an AI/ML-based CSI feedback/report method. Fig. 1-32 & ¶0377 - At operation 3012, the UE sends CSI reports to the BS that have been generated by an AI/ML-based multi-time feedback/report method.).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine KHOSHNEVISAN’s invention of user equipment (UE) transmit/receive (Tx/Rx) calibration in time division duplexing (TDD) using assistance information in a wireless communication system to include Lo’s invention of method and apparatus for support of machine learning (ML) or artificial intelligence (AI) assisted channel state information (CSI) feedback in a (5G)/new radio (NR) wireless communication system, because it provides an efficient mechanism in reducing overhead of DL <downlink> CSI feedback significantly by using pre-defined codebooks (e.g., Type I and Type II) for compressing DL CSI before it is reported to a serving base station, in turns, improves overall user throughput for a user equipment (UE) operating in the (5G)/new radio (NR) wireless communication system. (¶0078-¶0079, Lo)
Re. claims 10 and 20, KHOSHNEVISAN and Lo teach claims 1 and 11.
KHOSHNEVISAN further teaches wherein the downlink CSI comprises an indication of one or more of (i) a downlink CSI estimate (Fig.2/Fig. 3A-3B & Fig. 5 & ¶0004 - UE may receive a CSI-RS from the network node, which may be used to estimate the downlink channel. Examiner interprets that one of the claimed features to be mapped because of the presence of “one or more “and “Or” in the limitation.), (ii) a rank associated with the downlink CSI estimate, (iii) one or more eigenvectors associated with the downlink CSI estimate, (iv) a channel quality indicator (CQI), or (v) a complete DL channel.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over KHOSHNEVISAN, in view of Lo, further in view of Madadi et al. (2022/0338189), Madadi hereinafter.
Re. claims 4 and 14, KHOSHNEVISAN and Lo teach claims 1 and 11.
Yet, KHOSHNEVISAN and Lo do not expressly teach wherein the processor is configured to determine a CSI compression ratio for the CSI feedback report based on the associated value that quantifies the UL/DL channel relation.
However, in the analogous art, Madadi explicitly discloses wherein the processor is configured to determine a CSI compression ratio for the CSI feedback report based on the associated value that quantifies the UL/DL channel relation. (Fig. 1-15 & ¶0011 - the configurations include information to configure the AI-CFI < artificial intelligence channel feature information>, wherein the information is one of: a quantization method to be used to quantize an output of the ML model, a number of quantization bits to be used, a compression ratio from original CSI to the AI-CFI, or a total number of CSI feedback bits. Fig. 1-15 & ¶0108 - the size of the AI-CFI configured refers to the number of bits used in the transmission of the AI-CFI, i.e., overhead associated with CSI feedback. In other embodiment, the size of the AI-CFI can refer to the compression ratio, i.e., the ratio of the feedback bits of original CSI to AI-CFI. Part of or all of the CSI report configuration information is set in the RRC IE CSI-ReportConfig. Fig. 1-15 & ¶0142 - Reconstruction using UL partial reciprocity: When using AI/ML based CSI feedback mechanism, base station reconstructs the channel matrix (CSI) from the extracted feature (AI-CFI) it received from the UE. In this embodiment, additional supporting information like UL partial reciprocity information available at the base station is used to enhance the reconstruction as illustrated in FIG. 14.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine KHOSHNEVISAN’s invention of user equipment (UE) transmit/receive (Tx/Rx) calibration in time division duplexing (TDD) using assistance information in a wireless communication system and Lo’s invention of method and apparatus for support of machine learning (ML) or artificial intelligence (AI) assisted channel state information (CSI) feedback in a (5G)/new radio (NR) wireless communication system to include Madadi’s invention of method and apparatus for supporting of machine learning or artificial intelligence techniques for channel state information (CSI) feedback in FDD <Frequency Division Duplex > MIMO < multiple-input multiple-output> systems in a wireless communication network, because it provides an efficient mechanism in compressing (reducing) CSI overhead while maintaining large MU-MIMO performance gain for a gNodeB (gNB) and a user equipment (UE) operating in the wireless communication network. (¶0066-¶0068, Madadi)
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over KHOSHNEVISAN, in view of Lo, further in view of Jeon et al. (2024/0097764), Jeon hereinafter.
Re. claims 5 and 15, KHOSHNEVISAN and Lo teach claims 1 and 11.
Yet, KHOSHNEVISAN and Lo do not expressly teach wherein the processor is configured to determine an amount of CSI feedback payload for the CSI feedback report based on the associated value that quantifies the UL/DL channel relation.
However, in the analogous art, Jeon explicitly discloses wherein the processor is configured to determine an amount of CSI feedback payload for the CSI feedback report based on the associated value that quantifies the UL/DL channel relation. (Fig. 6-9 & ¶0073 - For multiple user (MU)-MIMO, in particular, the availability of accurate CSI is essential in order to guarantee high MU performance. For time division duplex (TDD) systems, the CSI can be acquired using the SRS transmission relying on the channel reciprocity. For frequency division duplexing (FDD) systems, on the other hand, it can be acquired using the CSI-RS transmission from gNB, CSI acquisition, and feedback from UE. … NR system has been designed to be more MU-centric from its first release with high resolution Type-II codebook in addition to low resolution Type-I codebook. Fig. 6-9 & ¶0075 - It is expected that AI/ML methods will be applied for various cellular system air interface designs including CSI compression/recovery, future CSI prediction, learning-based channel estimation, channel coding, and modulation, just to name a few. Fig. 6-9 & ¶0130 - the information related to processing the ML model output indicates at least one of parameters related to a CSI payload size including a size of an output vector of the first ML model and parameters related to quantizing the output vector of the first ML model including a quantization method and quantization granularity. Fig. 6-9 & ¶0131 - The UE then receives the CSI-RS s (930). For example, in 930, the UE receives the CSI-RS based on the received information related to reception of CSI-RS s on the cell. The UE then determines a CSI report using the first ML model (940). For example, in 940, the UE determines the CSI report based on the information related to configuring a first ML model for determining the CSI, the information related to processing a ML model output, and the reception of the CSI-RS s. The UE then transmits a channel with the CSI report (950)).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine KHOSHNEVISAN’s invention of user equipment (UE) transmit/receive (Tx/Rx) calibration in time division duplexing (TDD) using assistance information in a wireless communication system and Lo’s invention of method and apparatus for support of machine learning (ML) or artificial intelligence (AI) assisted channel state information (CSI) feedback in a (5G)/new radio (NR) wireless communication system to include Jeon’s invention of method and apparatus for channel state information (CSI) feedback in cellular systems, because it provides an efficient mechanism in improving system performance such as throughput, spectral efficiency, and latency by using exploiting artificial intelligence (AI)/machine learning (ML)-for channel state information (CSI) feedback in the cellular systems. (¶0075-¶0076, Jeon)
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over KHOSHNEVISAN, in view of Lo, further in view of Liu et al. (2026/0205185), Liu hereinafter.
Re. claims 8 and 18, KHOSHNEVISAN and Lo teach claims 1 and 11.
Yet, KHOSHNEVISAN and Lo do not expressly teach wherein the one or more pilots associated with uplink information provide an indication of a downlink channel, and wherein the one or more pilots are selected by a network and the configuration information indicates how the WTRU is to use the one or more pilots.
However, in the analogous art, Liu explicitly discloses wherein the one or more pilots associated with uplink information provide an indication of a downlink channel, and wherein the one or more pilots are selected by a network and the configuration information indicates how the WTRU is to use the one or more pilots. (Fig. 2A- Fig. 13B & ¶0142 - The terminal may receive the first downlink measurement pilot and send the first uplink measurement pilot in a first measurement pilot periodicity, and receive the second downlink measurement pilot and send the second uplink measurement pilot in a second measurement pilot periodicity. Because the terminal sends a low-compression-ratio codeword in the second measurement pilot periodicity, and the second measurement pilot periodicity is greater than the first measurement pilot periodicity, a high-compression-ratio codeword may be sent for a short time, and the low-compression-ratio codeword may be sent for a long time, to reduce CSI feedback overheads. In an optional embodiment, the second measurement pilot periodicity may be less than preset duration, and the first measurement pilot periodicity is greater than or equal to the preset duration. The second measurement pilot periodicity, the first measurement pilot periodicity, and the preset duration may be set based on an actual situation. Fig. 2A- Fig. 13B & ¶0145 - After receiving a first uplink measurement pilot sent by the terminal, a signal processing unit of the access network device determines a first uplink channel vector based on the first uplink measurement pilot, inputs the first uplink channel vector into the second feature extraction network, outputs an uplink channel feature by using the second feature extraction network, after inputting the uplink channel feature into the common information network, outputs CSI common information in the first time period by using the common information network based on the codeword feature and the uplink channel feature, and after inputting the CSI common information into the channel reconstruction network, estimates a downlink channel vector in the first time period by using the channel reconstruction network.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine KHOSHNEVISAN’s invention of user equipment (UE) transmit/receive (Tx/Rx) calibration in time division duplexing (TDD) using assistance information in a wireless communication system and Lo’s invention of method and apparatus for support of machine learning (ML) or artificial intelligence (AI) assisted channel state information (CSI) feedback in a (5G)/new radio (NR) wireless communication system to include Liu’s invention of method and apparatus for channel state information feedback in a wireless communication system, because it provides an efficient mechanism in implementing CSI feedback by using contrastive learning network, resulting in a reduced signaling overhead associated with an increased quantity of antenna deployed in the wireless communication system. (¶0002-¶0006, Liu)
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over KHOSHNEVISAN, in view of Lo, further in view of Niu et al. (2026/0088879), Niu hereinafter.
Re. claims 9 and 19, KHOSHNEVISAN and Lo teach claims 1 and 11.
Yet, KHOSHNEVISAN and Lo do not expressly teach wherein the network assistance information comprises one or more CSI feedback components associated with an uplink channel, a compressed representation of a CSI estimate associated with the uplink channel, and one or more parameters associated with the uplink channel.
However, in the analogous art, Niu explicitly discloses wherein the network assistance information comprises one or more CSI feedback components associated with an uplink channel, a compressed representation of a CSI estimate associated with the uplink channel, and one or more parameters associated with the uplink channel. (Fig. 2 & ¶0029 - As shown in FIG. 2 as 212, the UE 202 may perform CSI-RS measurements for monitoring performance of the AI model-based CSI feedback (or CSI compression feedback) using the network configuration on assisted information for performance monitoring and an AI model identified using an AI model ID. .. The CSI feedback may indicate whether the AI model meets requirements or performs satisfactorily/unsatisfactorily as described herein based on the UAI configuration or the decoder output received from the network. Fig. 2 & ¶0030 - At 214, based on the CSI feedback or monitored performance of the AI model-based CSI compression feedback (or CSI feedback), which identifies whether the AI model performs satisfactorily or unsatisfactorily, the UE may send its preference using a UAI message or a UL MAC control element (MAC CE)).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filling date of the claimed invention to combine KHOSHNEVISAN’s invention of user equipment (UE) transmit/receive (Tx/Rx) calibration in time division duplexing (TDD) using assistance information in a wireless communication system and Lo’s invention of method and apparatus for support of machine learning (ML) or artificial intelligence (AI) assisted channel state information (CSI) feedback in a (5G)/new radio (NR) wireless communication system to include Niu’s invention of method and apparatus for performance monitoring for artificial intelligence (AI) model-based channel state information (CSI) feedback in a wireless communication system, because it provides an efficient and flexible mechanism for performance monitoring of artificial intelligence (AI) model-based channel state information (CSI) feedback and deactivating AI model-based CSI feedback in case when CSI report does not meet a specific criterion, resulting in reducing overhead, improves accuracy, and improves channel prediction for the CSI report for wireless communication between a serving base station and a user equipment (UE) operating in the wireless communication system. (¶0014, Niu)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
3GPP TSG RAN WG1 #116; R1-2400723; Source: Samsung; Title: Discussion for further study on AI/ML-based CSI compression; Athens, Greece, February 26th– March 1st, 2024. See §2-§3.
3GPP TSG-RAN WG1 Meeting #116; Tdoc R1-2400166; Source: Ericsson; Title: AI/ML for CSI compression; Athens, Greece, February 26th – March 1st, 2024. See §2-§5
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/MOHAMMED S CHOWDHURY/Primary Examiner, Art Unit 2467
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