CTFR 18/481,788 CTFR 82841 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. Response to Amendment Applicant's amendment filed on 04/21/2026 has been entered. Claims 1, 8-9, 11, 15, 18-19 and 23 have been amended. No claims have been added. Claims 7 and 17 have been cancelled. Claims 1-6, 8-16 and 19-27 are still pending in this application, with claims 1, 15, 26 and 27, being independent. Response to Arguments Applicant’s arguments with respect to claim(s) 1-6, 8-16 and 19-27 have been considered but are moot based on new grounds of rejections. 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. 07-20-02-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. 07-21-aia AIA Claim (s) 1-4, 8-9, 11, 14-16, 19, 21-22 and 25-27 are rejected under 35 U.S.C. 103 as being unpatentable over Tang et al. (US 2024/0106508; hereinafter Tang) and Chai et al. (US 2024/0187283; hereinafter Chai) . Regarding claim 1, Tang shows an apparatus (Figure 6 shows network device/TRP performing in-part the method of Figure 15.) configured for wireless communications, comprising: one or more memories (Figure 6 shows memory 456); and one or more processors coupled to the one or more memories (Figure 6 shows processor 454 coupled to memory 456.), the one or more processors being configured to cause the apparatus to: obtain, from a user equipment (UE), one or more parameters of a UE receiver (Figure 15; Par. 0221; during the AI/ML training phase 1402, the training data is determined and includes the one or more MCS parameters predicted by the trained ML module at the TRP 452.) wherein the one or more parameters comprise a receiver index indicating a receiver architecture of the UE receiver (Par. 0134; MCS parameters includes a TB-level modulation order and a TB-level coding rate, subband-level modulation order(s) and a TB-level coding rate, or subband-level modulation order(s) and subband-level coding rate(s). Each of the above parameters indicate a receiver configuration/architecture of the UE.); determine one or more channel characteristics of a communication channel between the apparatus and the UE based on a measurement of a signal received from the UE (Figure 15; Par. 0153, 0224; TRP 452 obtains uplink channel state information based on the UL reference signal received from UE 402 at 1430. The uplink channel state information includes any information that characterizes an uplink communication channel between the UE 402 and the TRP 452. The uplink channel state information UL includes information estimated based on an uplink reference signal.); estimate a response of the UE receiver communicating on the communication channel having the one or more channel characteristics (Figure 15; Par. 0153, 0224; TRP 452 obtains uplink channel state information based on the UL reference signal received from UE 402 at 1430. The uplink channel state information includes any information that characterizes an uplink communication channel between the UE 402 and the TRP 452. The uplink channel state information UL includes information estimated based on an uplink reference signal.) based on a digital representation of the UE receiver, wherein the digital representation of the UE receiver is based on the one or more parameters of the UE receiver (Figure 15; Par. 0223-0224; noted UL reference signal, where the uplink channel state information is obtained, is communicated based on parameters determined in the training phase 1402. Training phase 1402 is performed by machine learning (ML) modules (i.e. digital representation) of TRP 452 and UE 402.); determine, based on the estimated response, at least one parameter for communication on the communication channel with the UE (Figure 15; Par. 0224; At 1436, the ML module at TRP 452 is used to obtain one or more MCS parameters to use for a scheduled downlink transmission to UE 402.); send, to the UE, an indication of the at least one parameter (Figure 15; Par. 0224; scheduling information, for the scheduled downlink transmission, sent to the UE wherein the scheduling information includes one or more MCS parameters.); and communicate with the UE in accordance with the at least one parameter (Figure 15; Par. 0224; scheduled downlink transmission uses the obtained one or more MCS parameters.). Tang shows all of the elements except wherein the receiver architecture comprises one or more signal decoding operations including one or more of: a Fast Fourier Transform (FFT) type, a channel estimation type, or a channel equalization type . However, the above-mentioned claim limitations are well-established in the art as evidenced by Chai. Specifically, Chai shows wherein the receiver architecture comprises one or more signal decoding operations including one or more of : a Fast Fourier Transform (FFT) type, a channel estimation type (Examiner elects this claim limitation for prosecution. Figure 4; Par. 0116; a method for performing channel estimation by the network device based on the second reference signal to obtain the third channel state matrix is not limited, and may be a conventional channel estimation algorithm, for example, a minimum mean square error estimation (MMSE) algorithm, or may be a neural network-based channel estimation algorithm.) , or a channel equalization type . In view of the above, having the system of Tang, then given the well-established teaching of Chai, it would have been obvious before the effective filing date of the claimed invention to modify the system of Tang as taught by Chai, in order to provide motivation to improve channel information feedback accuracy while reducing channel information feedback overheads (Par. 0006 of Chai). Regarding claim 2, modified Tang wherein the digital representation comprises a machine learning model (Tang: Figure 15; Par. 0223; the trained ML modules of UE 402 and TRP 452 are used to predict optimal MCS parameters for downlink communication between TRP 452 and UE 402 in the normal operations phase 1404.) configured to estimate the response of the UE receiver (Tang: Figure 15; Par. 0153, 0224; TRP 452 obtains uplink channel state information based on the UL reference signal received from UE 402 at 1430. The uplink channel state information includes any information that characterizes an uplink communication channel between the UE 402 and the TRP 452. The uplink channel state information UL includes information estimated based on an uplink reference signal.), and wherein the one or more parameters comprise one or more coefficients for the machine learning model (Tang: Figure 15; Par. 0221; during the AI/ML training phase 1402, the training data is determined and includes the one or more MCS parameters predicted by the trained ML module at the TRP 452. Training data is also provided to ML module of UE 402.). Regarding claim 3, modified Tang shows wherein, to estimate the response of the UE receiver, the one or more processors are configured to further cause the apparatus to: provide, to the machine learning model, input comprising the one or more channel characteristics (Tang: Figure 15; Par. 0224; At 1436, the ML module at TRP 452 is used to obtain, based on using the channel state information obtained at 1434 as an input to the trained ML module, one or more MCS parameters to use for a scheduled downlink transmission to UE 402.); and obtain, from the machine learning model, output comprising the estimated response (Tang: Figure 15; Par. 0224; At 1436, the ML module at TRP 452 is used to obtain as output, based on using the channel state information obtained at 1434 as an input to the trained ML module, one or more MCS parameters to use for a scheduled downlink transmission to UE 402. Regarding claim 4, modified Tang shows wherein the estimated response comprises an indication of mutual information decoded via the digital representation of the UE receiver (Tang: Figure 14; Par. 0232; in the normal operation phase 1404, compressed DL channel state information z received by TRP 452 at 1439 is successfully decoded to obtain reconstructed DL channel state information H′, which is then used as an input of the ML module of TRP 452 at 1436.). Regarding claim 8, modified Tang shows wherein the receiver architecture comprises a demodulator type (Tang: Par. 0134; MCS parameters includes a TB-level modulation order and a TB-level coding rate, subband-level modulation order(s) and a TB-level coding rate, or subband-level modulation order(s) and subband-level coding rate(s). Each of the above parameters indicate a receiver configuration/architecture of the UE.). Regarding claim 9, modified Tang shows wherein the channel estimation type comprises one or more of: frequency domain minimum mean square error (MMSE)-based channel estimation, or time domain MMSE-based channel estimation (Chai: Figure 4; Par. 0115-0116; a method for performing channel estimation by the network device based on the second reference signal to obtain the third channel state matrix is not limited, and may be a conventional channel estimation algorithm, for example, a minimum mean square error estimation (MMSE) algorithm, or may be a neural network-based channel estimation algorithm.). Regarding claim 11, modified Tang shows wherein the one or more parameters further comprise a number of receive antennas of the UE (Tang: Par. 0134; rank indicator is also determined as part of the channel state information.). Regarding claim 14, modified Tang shows wherein the at least one parameter comprises: a modulation and coding scheme (MCS) (Tang: Par. 0134; noted MCS.), a precoding matrix indicator (PMI) (Tang: Par. 0134; noted PMI), a rank indicator (RI) (Tang: Par. 0134; noted RI.), or a combination thereof. Regarding claim 15, Tang shows an apparatus (Figure 6 shows a UE performing in-part the method of Figure 15.) configured for wireless communications, comprising: one or more memories (Figure 6 shows memory 208); and one or more processors coupled to the one or more memories (Figure 6 shows processor 210 coupled to memory 208.), the one or more processors being configured to cause the apparatus to: send one or more parameters of a user equipment (UE) receiver (Figure 15; Par. 0153, 0224; UL reference signal transmitted from UE 402 at 1430 is used to obtain uplink channel state information. The uplink channel state information includes any information that characterizes an uplink communication channel between the UE 402 and the TRP 452.), the one or more parameters indicating a digital representation of the UE receiver used to estimate a response of the UE receiver communicating on a communication channel having one or more channel characteristics (Figure 15; Par. 0223-0224; noted UL reference signal, where the uplink channel state information is obtained, is communicated based on parameters determined in the training phase 1402. Training phase 1402 is performed by machine learning (ML) modules (i.e. digital representation) of TRP 452 and UE 402.) wherein the one or more parameters comprise a receiver index indicating a receiver architecture of the UE receiver (Par. 0134; MCS parameters includes a TB-level modulation order and a TB-level coding rate, subband-level modulation order(s) and a TB-level coding rate, or subband-level modulation order(s) and subband-level coding rate(s). Each of the above parameters indicate a receiver configuration/architecture of the UE.); receive at least one parameter for communicating on the communication channel (Figure 15; Par. 0224; UE receives scheduling information for the scheduled downlink transmission, wherein the scheduling information includes one or more MCS parameters.), the at least one parameter based at least in part on the one or parameters and the one or more channel characteristics of the communication channel (Figure 15; Par. 0224; uplink channel state information is used to obtain one or more MCS parameters to use for a scheduled downlink transmission to UE 402.); and communicate on the communication channel in accordance with the at least one parameter (Figure 15; Par. 0224; scheduled downlink transmission uses the obtained one or more MCS parameters.). Tang shows all of the elements except wherein the receiver architecture comprises one or more signal decoding operations including one or more of: a Fast Fourier Transform (FFT) type, a channel estimation type, or a channel equalization type . However, the above-mentioned claim limitations are well-established in the art as evidenced by Chai. Specifically, Chai shows wherein the receiver architecture comprises one or more signal decoding operations including one or more of : a Fast Fourier Transform (FFT) type, a channel estimation type (Examiner elects this claim limitation for prosecution. Figure 4; Par. 0116; a method for performing channel estimation by the network device based on the second reference signal to obtain the third channel state matrix is not limited, and may be a conventional channel estimation algorithm, for example, a minimum mean square error estimation (MMSE) algorithm, or may be a neural network-based channel estimation algorithm.) , or a channel equalization type . In view of the above, having the system of Tang, then given the well-established teaching of Chai, it would have been obvious before the effective filing date of the claimed invention to modify the system of Tang as taught by Chai, in order to provide motivation to improve channel information feedback accuracy while reducing channel information feedback overheads (Par. 0006 of Chai). Regarding claims 16, 18, 19, 21 and 25, these claims are rejected based on the same reasoning as presented in the rejection of claims 2, 8, 9, 11 and 14, respectively. Regarding claim 22, modified Tang shows wherein the one or more processors are further configured to cause the apparatus to send a reference signal on the communication channel (Tang: Figure 15 shows UE 402 transmitting an uplink reference signal, i.e. SRS, to the TRP 452.). Regarding claim 26, Tang shows a method (Figure 6 shows network device/TRP performing in-part the method of Figure 15.) for wireless communications by an apparatus, comprising: obtaining, from a user equipment (UE), one or more parameters of a UE receiver (Figure 15; Par. 0221; during the AI/ML training phase 1402, the training data is determined and includes the one or more MCS parameters predicted by the trained ML module at the TRP 452.) wherein the one or more parameters comprise a receiver index indicating a receiver architecture of the UE receiver (Par. 0134; MCS parameters includes a TB-level modulation order and a TB-level coding rate, subband-level modulation order(s) and a TB-level coding rate, or subband-level modulation order(s) and subband-level coding rate(s). Each of the above parameters indicate a receiver configuration/architecture of the UE.); determining one or more channel characteristics of a communication channel between the apparatus and the UE based on a measurement of a signal received from the UE (Figure 15; Par. 0153, 0224; TRP 452 obtains uplink channel state information based on the UL reference signal received from UE 402 at 1430. The uplink channel state information includes any information that characterizes an uplink communication channel between the UE 402 and the TRP 452. The uplink channel state information UL includes information estimated based on an uplink reference signal.); estimating a response of the UE receiver communicating on the communication channel having the one or more channel characteristics (Figure 15; Par. 0153, 0224; TRP 452 obtains uplink channel state information based on the UL reference signal received from UE 402 at 1430. The uplink channel state information includes any information that characterizes an uplink communication channel between the UE 402 and the TRP 452. The uplink channel state information UL includes information estimated based on an uplink reference signal.) based on a digital representation of the UE receiver, wherein the digital representation of the UE receiver is based on the one or more parameters of the UE receiver (Figure 15; Par. 0223-0224; noted UL reference signal, where the uplink channel state information is obtained, is communicated based on parameters determined in the training phase 1402. Training phase 1402 is performed by machine learning (ML) modules (i.e. digital representation) of TRP 452 and UE 402.); determining, based on the estimated response, at least one parameter for communication on the communication channel with the UE (Figure 15; Par. 0224; At 1436, the ML module at TRP 452 is used to obtain one or more MCS parameters to use for a scheduled downlink transmission to UE 402.); sending, to the UE, an indication of the at least one parameter (Figure 15; Par. 0224; scheduling information, for the scheduled downlink transmission, sent to the UE wherein the scheduling information includes one or more MCS parameters.); and communicating with the UE in accordance with the at least one parameter (Figure 15; Par. 0224; scheduled downlink transmission uses the obtained one or more MCS parameters.). Tang shows all of the elements except wherein the receiver architecture comprises one or more signal decoding operations including one or more of: a Fast Fourier Transform (FFT) type, a channel estimation type, or a channel equalization type . However, the above-mentioned claim limitations are well-established in the art as evidenced by Chai. Specifically, Chai shows wherein the receiver architecture comprises one or more signal decoding operations including one or more of : a Fast Fourier Transform (FFT) type, a channel estimation type (Examiner elects this claim limitation for prosecution. Figure 4; Par. 0116; a method for performing channel estimation by the network device based on the second reference signal to obtain the third channel state matrix is not limited, and may be a conventional channel estimation algorithm, for example, a minimum mean square error estimation (MMSE) algorithm, or may be a neural network-based channel estimation algorithm.) , or a channel equalization type . In view of the above, having the system of Tang, then given the well-established teaching of Chai, it would have been obvious before the effective filing date of the claimed invention to modify the system of Tang as taught by Chai, in order to provide motivation to improve channel information feedback accuracy while reducing channel information feedback overheads (Par. 0006 of Chai). Regarding claim 27, Tang shows a method for wireless communications by an apparatus comprising: sending one or more parameters of a user equipment (UE) receiver (Figure 15; Par. 0153, 0224; UL reference signal transmitted from UE 402 at 1430 is used to obtain uplink channel state information. The uplink channel state information includes any information that characterizes an uplink communication channel between the UE 402 and the TRP 452.), the one or more parameters indicating a digital representation of the UE receiver used to estimate a response of the UE receiver communicating on a communication channel having one or more channel characteristics (Figure 15; Par. 0223-0224; noted UL reference signal, where the uplink channel state information is obtained, is communicated based on parameters determined in the training phase 1402. Training phase 1402 is performed by machine learning (ML) modules (i.e. digital representation) of TRP 452 and UE 402.) wherein the one or more parameters comprise a receiver index indicating a receiver architecture of the UE receiver (Par. 0134; MCS parameters includes a TB-level modulation order and a TB-level coding rate, subband-level modulation order(s) and a TB-level coding rate, or subband-level modulation order(s) and subband-level coding rate(s). Each of the above parameters indicate a receiver configuration/architecture of the UE.); receiving at least one parameter for communicating on the communication channel (Figure 15; Par. 0224; UE receives scheduling information for the scheduled downlink transmission, wherein the scheduling information includes one or more MCS parameters.), the at least one parameter based at least in part on the one or parameters and the one or more channel characteristics of the communication channel (Figure 15; Par. 0224; uplink channel state information is used to obtain one or more MCS parameters to use for a scheduled downlink transmission to UE 402.); and communicating on the communication channel in accordance with the at least one parameter (Figure 15; Par. 0224; scheduled downlink transmission uses the obtained one or more MCS parameters.). Tang shows all of the elements except wherein the receiver architecture comprises one or more signal decoding operations including one or more of: a Fast Fourier Transform (FFT) type, a channel estimation type, or a channel equalization type . However, the above-mentioned claim limitations are well-established in the art as evidenced by Chai. Specifically, Chai shows wherein the receiver architecture comprises one or more signal decoding operations including one or more of : a Fast Fourier Transform (FFT) type, a channel estimation type (Examiner elects this claim limitation for prosecution. Figure 4; Par. 0116; a method for performing channel estimation by the network device based on the second reference signal to obtain the third channel state matrix is not limited, and may be a conventional channel estimation algorithm, for example, a minimum mean square error estimation (MMSE) algorithm, or may be a neural network-based channel estimation algorithm.) , or a channel equalization type . In view of the above, having the system of Tang, then given the well-established teaching of Chai, it would have been obvious before the effective filing date of the claimed invention to modify the system of Tang as taught by Chai, in order to provide motivation to improve channel information feedback accuracy while reducing channel information feedback overheads (Par. 0006 of Chai) . 07-21-aia AIA Claim (s) 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Tang in view of Chai and Li et al. (US 2025/0220471; hereinafter Li) . Regarding claim 5, modified Tang shows wherein, to estimate the response of the UE receiver, the one or more processors are configured to further cause the apparatus to: provide, to the machine learning model, input comprising one or more combinations of inputs, each of the one or more combinations of inputs comprising the one or more channel characteristics and one or more of a respective modulation and coding scheme (MCS) (Tang: Figure 15; Par. 0224; At 1436, the ML module at TRP 452 is used to obtain, based on using the channel state information obtained at 1434 as an input to the trained ML module, one or more MCS parameters to use for a scheduled downlink transmission to UE 402 (e.g., for a scheduled downlink transmission at time slot n1+m).), a respective precoding matrix indicator (PMI), or a respective rank indicator (RI); and for each of the one or more combinations of inputs, obtain, from the machine learning model, output comprising an indication (Tang: Figure 15; Par. 0224; At 1436, the ML module at TRP 452 is used to obtain, based on using the channel state information obtained at 1434 as an input to the trained ML module, one or more MCS parameters to use for a scheduled downlink transmission to UE 402 (e.g., for a scheduled downlink transmission at time slot n1+m).). Tang shows all of the elements as discussed above. Tang does not specifically show obtaining an output comprising an indication of whether the respective combination of inputs passes a cyclic redundancy check (CRC) as the estimated response. However, the above-mentioned claim limitations are well-established in the art as evidenced by Li. Specifically, Li shows obtaining an output comprising an indication of whether the respective combination of inputs passes a cyclic redundancy check (CRC) as the estimated response (Figure 13; Par. 0172; the network node determines degradation information based on the at least one ML-model output and the communication information associated with communication performance of the UE. The degradation information is determined by performing analyzing an uplink control information (UCI) false detection probability, based on a cyclic redundancy check (CRC) length for a first report payload.). In view of the above, having the system of Tang, then given the well-established teaching of Li, it would have been obvious before the effective filing date of the claimed invention to modify the system of Tang as taught by Li, in order to provide motivation for improving machine learning (ML) model performance by detecting ML model performance degradation and analyzing causes for the degradation (Par. 0002 of Li). for each of the one or more combinations of inputs, obtain, from the machine learning model, output comprising an indication of whether the respective combination of inputs passes a cyclic redundancy check (CRC) as the estimated response (Li: Figure 13; Par. 0172; the network node determines degradation information based on the at least one ML-model output and the communication information associated with communication performance of the UE. The degradation information is determined by performing analyzing an uplink control information (UCI) false detection probability, based on a cyclic redundancy check (CRC) length for a first report payload.). Regarding claim 6, modified Tang shows wherein the at least one parameter comprises the one or more of the respective MCS (Tang: Figure 15; Par. 0224; At 1436, the ML module at TRP 452 is used to obtain, based on using the channel state information obtained at 1434 as an input to the trained ML module, one or more MCS parameters to use for a scheduled downlink transmission to UE 402 (e.g., for a scheduled downlink transmission at time slot n1+m).), the respective PMI, or the respective RI of one of the one or more combinations of inputs that would pass the CRC . 07-21-aia AIA Claim (s) 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Tang in view of Chai and O’Shea et al. (US 2020/0343985; hereinafter O’Shea) . Regarding claim 10, modified Tang shows all of the elements except wherein the demodulator type comprises one or more of: a minimum mean square error (MMSE)-based demodulator, or a maximum likelihood-based demodulator. However, the above-mentioned claim limitations are well-established in the art as evidenced by O’Shea. Specifically, O’Shea shows wherein the demodulator type comprises one or more of: a minimum mean square error (MMSE)-based demodulator, or a maximum likelihood-based demodulator (Par. 0083; training may include using other estimation or equalization approaches. For example, linear MMSE, max likelihood, successive interference cancellation (SIC), or other suitable approaches can be used to produce estimates of the channel response.) In view of the above, having the system of Tang, then given the well-established teaching of O’Shea, it would have been obvious before the effective filing date of the claimed invention to modify the system of Tang as taught by O’Shea, in order to provide motivation for an optimization approach with different free parameters, lower bit error rate performance, improved error vector magnitude, frame error rate, enhance bitrates, among other improvements, can be attained over a given communications channel (Par. 0004 of O’Shea). Regarding claim 20, this claim is rejected based on the same reasoning as presented in the rejection of claim 10 . 07-21-aia AIA Claim (s) 12 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Tang in view of Chai and Wei et al. (US 2025/0374155; hereinafter Wei) . Regarding claim 12, modified Tang shows all of the elements including obtaining the one or more parameters, as discussed above. Modified Tang does not specifically show obtaining the one or more parameters prior to radio resource control (RRC) connection establishment between the apparatus and the UE. However, the above-mentioned claim limitations are well-established in the art as evidenced by Wei. Specifically, Wei shows obtaining the one or more parameters prior to radio resource control (RRC) connection establishment between the apparatus and the UE (Par. 0183; an evaluation or estimation of the loss function value corresponding to the outcome selected by the model at step S808 may be done at step S814, and the model used in the determination at step S808 may be updated at step S816, based on the input parameter values determined at step S804 and the determined (e.g. estimated) loss function. Step S816 is performed prior to establishing an RRC connection in the repeat step of S802.). In view of the above, having the system of Tang, then given the well-established teaching of Wei, it would have been obvious before the effective filing date of the claimed invention to modify the system of Tang as taught by Wei, in order to provide motivation to maintain (or minimize any degradation of) the ability to transmit and receive data (Par. 0011 of Wei). Regarding claim 23, this claim is rejected based on the same reasoning as presented in the rejection of claim 12 . 07-21-aia AIA Claim (s) 13 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Tang in view of Chai and Wang et al. (US 2020/0351697; hereinafter Wang) . Regarding claim 13, modified Tang shows all of the elements, as discussed above. Modified Tang does not specifically show sending an indication to the UE to reduce a rate of communicating channel state feedback (CSF) in response to obtaining the one or more parameters. However, the above-mentioned claim limitations are well-established in the art as evidenced by Wang. Specifically, Wang shows sending an indication to the UE to reduce a rate of communicating channel state feedback (CSF) in response to obtaining the one or more parameters (Figure 6; Par. 0051; while in battery saving mode, CSI reference signals can continue to be sent, but the number of CSI reference signals can be reduced, for example, by not transmitting any aperiodic CSI reference signals, and correspondingly, by not providing an aperiodic CSI report.). In view of the above, having the system of Tang, then given the well-established teaching of Wang, it would have been obvious before the effective filing date of the claimed invention to modify the system of Tang as taught by Wang, in order to provide motivation to use of antenna schemes that reduce the number of antennas being used and also reduce the number of transmissions, which can reduce power consumption when the battery is already low on charge. (Par. 0011 of Wang). Regarding claim 24, this claim is rejected based on the same reasoning as presented in the rejection of claim 13 . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20260121893 A1 AI/ML-BASED JOINT DENOISING AND COMPRESSION OF CSI FEEDBACK US 20260100744 A1 COMMUNICATION METHOD AND COMMUNICATION APPARATUS US 20260012378 A1 CHANNEL ESTIMATION USING ARTIFICIAL INTELLIGENCE US 20250374192 A1 COMMUNICATION CONTROL METHOD AND USER EQUIPMENT US 20240220582 A1 DETERMINING A VALUE FOR A DIGITAL SIGNAL PROCESSING COMPONENT BASED ON INPUT DATA CORRESPONDING TO CLASSES US 20240187283 A1 CHANNEL INFORMATION FEEDBACK METHOD, CHANNEL INFORMATION RECOVERY METHOD, AND APPARATUS Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to REDENTOR M PASIA whose telephone number is (571)272-9745. The examiner can normally be reached Mondays-Thursdays - 5am-245pm and Fridays 5am-330pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Un Cho can be reached at (571)272-7919. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /REDENTOR PASIA/Primary Examiner, Art Unit 2413 Application/Control Number: 18/481,788 Page 2 Art Unit: 2413 Application/Control Number: 18/481,788 Page 3 Art Unit: 2413 Application/Control Number: 18/481,788 Page 4 Art Unit: 2413 Application/Control Number: 18/481,788 Page 5 Art Unit: 2413 Application/Control Number: 18/481,788 Page 6 Art Unit: 2413 Application/Control Number: 18/481,788 Page 7 Art Unit: 2413 Application/Control Number: 18/481,788 Page 8 Art Unit: 2413 Application/Control Number: 18/481,788 Page 9 Art Unit: 2413 Application/Control Number: 18/481,788 Page 10 Art Unit: 2413 Application/Control Number: 18/481,788 Page 11 Art Unit: 2413 Application/Control Number: 18/481,788 Page 12 Art Unit: 2413 Application/Control Number: 18/481,788 Page 13 Art Unit: 2413 Application/Control Number: 18/481,788 Page 14 Art Unit: 2413 Application/Control Number: 18/481,788 Page 15 Art Unit: 2413 Application/Control Number: 18/481,788 Page 16 Art Unit: 2413 Application/Control Number: 18/481,788 Page 17 Art Unit: 2413 Application/Control Number: 18/481,788 Page 18 Art Unit: 2413 Application/Control Number: 18/481,788 Page 19 Art Unit: 2413 Application/Control Number: 18/481,788 Page 20 Art Unit: 2413 Application/Control Number: 18/481,788 Page 21 Art Unit: 2413 Application/Control Number: 18/481,788 Page 22 Art Unit: 2413
Prosecution Insights